dog_app (1).html

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<html>
<head><meta charset="utf-8" />

<title>dog_app</title>

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  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.fa-pull-left {
  float: left;
}
.fa-pull-right {
  float: right;
}
.fa.fa-pull-left {
  margin-right: .3em;
}
.fa.fa-pull-right {
  margin-left: .3em;
}
/* Deprecated as of 4.4.0 */
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
.fa-pulse {
  -webkit-animation: fa-spin 1s infinite steps(8);
  animation: fa-spin 1s infinite steps(8);
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)";
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)";
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook-f:before,
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-feed:before,
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before,
.fa-gratipay:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper-pp:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-resistance:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-y-combinator-square:before,
.fa-yc-square:before,
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
.fa-buysellads:before {
  content: "\f20d";
}
.fa-connectdevelop:before {
  content: "\f20e";
}
.fa-dashcube:before {
  content: "\f210";
}
.fa-forumbee:before {
  content: "\f211";
}
.fa-leanpub:before {
  content: "\f212";
}
.fa-sellsy:before {
  content: "\f213";
}
.fa-shirtsinbulk:before {
  content: "\f214";
}
.fa-simplybuilt:before {
  content: "\f215";
}
.fa-skyatlas:before {
  content: "\f216";
}
.fa-cart-plus:before {
  content: "\f217";
}
.fa-cart-arrow-down:before {
  content: "\f218";
}
.fa-diamond:before {
  content: "\f219";
}
.fa-ship:before {
  content: "\f21a";
}
.fa-user-secret:before {
  content: "\f21b";
}
.fa-motorcycle:before {
  content: "\f21c";
}
.fa-street-view:before {
  content: "\f21d";
}
.fa-heartbeat:before {
  content: "\f21e";
}
.fa-venus:before {
  content: "\f221";
}
.fa-mars:before {
  content: "\f222";
}
.fa-mercury:before {
  content: "\f223";
}
.fa-intersex:before,
.fa-transgender:before {
  content: "\f224";
}
.fa-transgender-alt:before {
  content: "\f225";
}
.fa-venus-double:before {
  content: "\f226";
}
.fa-mars-double:before {
  content: "\f227";
}
.fa-venus-mars:before {
  content: "\f228";
}
.fa-mars-stroke:before {
  content: "\f229";
}
.fa-mars-stroke-v:before {
  content: "\f22a";
}
.fa-mars-stroke-h:before {
  content: "\f22b";
}
.fa-neuter:before {
  content: "\f22c";
}
.fa-genderless:before {
  content: "\f22d";
}
.fa-facebook-official:before {
  content: "\f230";
}
.fa-pinterest-p:before {
  content: "\f231";
}
.fa-whatsapp:before {
  content: "\f232";
}
.fa-server:before {
  content: "\f233";
}
.fa-user-plus:before {
  content: "\f234";
}
.fa-user-times:before {
  content: "\f235";
}
.fa-hotel:before,
.fa-bed:before {
  content: "\f236";
}
.fa-viacoin:before {
  content: "\f237";
}
.fa-train:before {
  content: "\f238";
}
.fa-subway:before {
  content: "\f239";
}
.fa-medium:before {
  content: "\f23a";
}
.fa-yc:before,
.fa-y-combinator:before {
  content: "\f23b";
}
.fa-optin-monster:before {
  content: "\f23c";
}
.fa-opencart:before {
  content: "\f23d";
}
.fa-expeditedssl:before {
  content: "\f23e";
}
.fa-battery-4:before,
.fa-battery:before,
.fa-battery-full:before {
  content: "\f240";
}
.fa-battery-3:before,
.fa-battery-three-quarters:before {
  content: "\f241";
}
.fa-battery-2:before,
.fa-battery-half:before {
  content: "\f242";
}
.fa-battery-1:before,
.fa-battery-quarter:before {
  content: "\f243";
}
.fa-battery-0:before,
.fa-battery-empty:before {
  content: "\f244";
}
.fa-mouse-pointer:before {
  content: "\f245";
}
.fa-i-cursor:before {
  content: "\f246";
}
.fa-object-group:before {
  content: "\f247";
}
.fa-object-ungroup:before {
  content: "\f248";
}
.fa-sticky-note:before {
  content: "\f249";
}
.fa-sticky-note-o:before {
  content: "\f24a";
}
.fa-cc-jcb:before {
  content: "\f24b";
}
.fa-cc-diners-club:before {
  content: "\f24c";
}
.fa-clone:before {
  content: "\f24d";
}
.fa-balance-scale:before {
  content: "\f24e";
}
.fa-hourglass-o:before {
  content: "\f250";
}
.fa-hourglass-1:before,
.fa-hourglass-start:before {
  content: "\f251";
}
.fa-hourglass-2:before,
.fa-hourglass-half:before {
  content: "\f252";
}
.fa-hourglass-3:before,
.fa-hourglass-end:before {
  content: "\f253";
}
.fa-hourglass:before {
  content: "\f254";
}
.fa-hand-grab-o:before,
.fa-hand-rock-o:before {
  content: "\f255";
}
.fa-hand-stop-o:before,
.fa-hand-paper-o:before {
  content: "\f256";
}
.fa-hand-scissors-o:before {
  content: "\f257";
}
.fa-hand-lizard-o:before {
  content: "\f258";
}
.fa-hand-spock-o:before {
  content: "\f259";
}
.fa-hand-pointer-o:before {
  content: "\f25a";
}
.fa-hand-peace-o:before {
  content: "\f25b";
}
.fa-trademark:before {
  content: "\f25c";
}
.fa-registered:before {
  content: "\f25d";
}
.fa-creative-commons:before {
  content: "\f25e";
}
.fa-gg:before {
  content: "\f260";
}
.fa-gg-circle:before {
  content: "\f261";
}
.fa-tripadvisor:before {
  content: "\f262";
}
.fa-odnoklassniki:before {
  content: "\f263";
}
.fa-odnoklassniki-square:before {
  content: "\f264";
}
.fa-get-pocket:before {
  content: "\f265";
}
.fa-wikipedia-w:before {
  content: "\f266";
}
.fa-safari:before {
  content: "\f267";
}
.fa-chrome:before {
  content: "\f268";
}
.fa-firefox:before {
  content: "\f269";
}
.fa-opera:before {
  content: "\f26a";
}
.fa-internet-explorer:before {
  content: "\f26b";
}
.fa-tv:before,
.fa-television:before {
  content: "\f26c";
}
.fa-contao:before {
  content: "\f26d";
}
.fa-500px:before {
  content: "\f26e";
}
.fa-amazon:before {
  content: "\f270";
}
.fa-calendar-plus-o:before {
  content: "\f271";
}
.fa-calendar-minus-o:before {
  content: "\f272";
}
.fa-calendar-times-o:before {
  content: "\f273";
}
.fa-calendar-check-o:before {
  content: "\f274";
}
.fa-industry:before {
  content: "\f275";
}
.fa-map-pin:before {
  content: "\f276";
}
.fa-map-signs:before {
  content: "\f277";
}
.fa-map-o:before {
  content: "\f278";
}
.fa-map:before {
  content: "\f279";
}
.fa-commenting:before {
  content: "\f27a";
}
.fa-commenting-o:before {
  content: "\f27b";
}
.fa-houzz:before {
  content: "\f27c";
}
.fa-vimeo:before {
  content: "\f27d";
}
.fa-black-tie:before {
  content: "\f27e";
}
.fa-fonticons:before {
  content: "\f280";
}
.fa-reddit-alien:before {
  content: "\f281";
}
.fa-edge:before {
  content: "\f282";
}
.fa-credit-card-alt:before {
  content: "\f283";
}
.fa-codiepie:before {
  content: "\f284";
}
.fa-modx:before {
  content: "\f285";
}
.fa-fort-awesome:before {
  content: "\f286";
}
.fa-usb:before {
  content: "\f287";
}
.fa-product-hunt:before {
  content: "\f288";
}
.fa-mixcloud:before {
  content: "\f289";
}
.fa-scribd:before {
  content: "\f28a";
}
.fa-pause-circle:before {
  content: "\f28b";
}
.fa-pause-circle-o:before {
  content: "\f28c";
}
.fa-stop-circle:before {
  content: "\f28d";
}
.fa-stop-circle-o:before {
  content: "\f28e";
}
.fa-shopping-bag:before {
  content: "\f290";
}
.fa-shopping-basket:before {
  content: "\f291";
}
.fa-hashtag:before {
  content: "\f292";
}
.fa-bluetooth:before {
  content: "\f293";
}
.fa-bluetooth-b:before {
  content: "\f294";
}
.fa-percent:before {
  content: "\f295";
}
.fa-gitlab:before {
  content: "\f296";
}
.fa-wpbeginner:before {
  content: "\f297";
}
.fa-wpforms:before {
  content: "\f298";
}
.fa-envira:before {
  content: "\f299";
}
.fa-universal-access:before {
  content: "\f29a";
}
.fa-wheelchair-alt:before {
  content: "\f29b";
}
.fa-question-circle-o:before {
  content: "\f29c";
}
.fa-blind:before {
  content: "\f29d";
}
.fa-audio-description:before {
  content: "\f29e";
}
.fa-volume-control-phone:before {
  content: "\f2a0";
}
.fa-braille:before {
  content: "\f2a1";
}
.fa-assistive-listening-systems:before {
  content: "\f2a2";
}
.fa-asl-interpreting:before,
.fa-american-sign-language-interpreting:before {
  content: "\f2a3";
}
.fa-deafness:before,
.fa-hard-of-hearing:before,
.fa-deaf:before {
  content: "\f2a4";
}
.fa-glide:before {
  content: "\f2a5";
}
.fa-glide-g:before {
  content: "\f2a6";
}
.fa-signing:before,
.fa-sign-language:before {
  content: "\f2a7";
}
.fa-low-vision:before {
  content: "\f2a8";
}
.fa-viadeo:before {
  content: "\f2a9";
}
.fa-viadeo-square:before {
  content: "\f2aa";
}
.fa-snapchat:before {
  content: "\f2ab";
}
.fa-snapchat-ghost:before {
  content: "\f2ac";
}
.fa-snapchat-square:before {
  content: "\f2ad";
}
.fa-pied-piper:before {
  content: "\f2ae";
}
.fa-first-order:before {
  content: "\f2b0";
}
.fa-yoast:before {
  content: "\f2b1";
}
.fa-themeisle:before {
  content: "\f2b2";
}
.fa-google-plus-circle:before,
.fa-google-plus-official:before {
  content: "\f2b3";
}
.fa-fa:before,
.fa-font-awesome:before {
  content: "\f2b4";
}
.fa-handshake-o:before {
  content: "\f2b5";
}
.fa-envelope-open:before {
  content: "\f2b6";
}
.fa-envelope-open-o:before {
  content: "\f2b7";
}
.fa-linode:before {
  content: "\f2b8";
}
.fa-address-book:before {
  content: "\f2b9";
}
.fa-address-book-o:before {
  content: "\f2ba";
}
.fa-vcard:before,
.fa-address-card:before {
  content: "\f2bb";
}
.fa-vcard-o:before,
.fa-address-card-o:before {
  content: "\f2bc";
}
.fa-user-circle:before {
  content: "\f2bd";
}
.fa-user-circle-o:before {
  content: "\f2be";
}
.fa-user-o:before {
  content: "\f2c0";
}
.fa-id-badge:before {
  content: "\f2c1";
}
.fa-drivers-license:before,
.fa-id-card:before {
  content: "\f2c2";
}
.fa-drivers-license-o:before,
.fa-id-card-o:before {
  content: "\f2c3";
}
.fa-quora:before {
  content: "\f2c4";
}
.fa-free-code-camp:before {
  content: "\f2c5";
}
.fa-telegram:before {
  content: "\f2c6";
}
.fa-thermometer-4:before,
.fa-thermometer:before,
.fa-thermometer-full:before {
  content: "\f2c7";
}
.fa-thermometer-3:before,
.fa-thermometer-three-quarters:before {
  content: "\f2c8";
}
.fa-thermometer-2:before,
.fa-thermometer-half:before {
  content: "\f2c9";
}
.fa-thermometer-1:before,
.fa-thermometer-quarter:before {
  content: "\f2ca";
}
.fa-thermometer-0:before,
.fa-thermometer-empty:before {
  content: "\f2cb";
}
.fa-shower:before {
  content: "\f2cc";
}
.fa-bathtub:before,
.fa-s15:before,
.fa-bath:before {
  content: "\f2cd";
}
.fa-podcast:before {
  content: "\f2ce";
}
.fa-window-maximize:before {
  content: "\f2d0";
}
.fa-window-minimize:before {
  content: "\f2d1";
}
.fa-window-restore:before {
  content: "\f2d2";
}
.fa-times-rectangle:before,
.fa-window-close:before {
  content: "\f2d3";
}
.fa-times-rectangle-o:before,
.fa-window-close-o:before {
  content: "\f2d4";
}
.fa-bandcamp:before {
  content: "\f2d5";
}
.fa-grav:before {
  content: "\f2d6";
}
.fa-etsy:before {
  content: "\f2d7";
}
.fa-imdb:before {
  content: "\f2d8";
}
.fa-ravelry:before {
  content: "\f2d9";
}
.fa-eercast:before {
  content: "\f2da";
}
.fa-microchip:before {
  content: "\f2db";
}
.fa-snowflake-o:before {
  content: "\f2dc";
}
.fa-superpowers:before {
  content: "\f2dd";
}
.fa-wpexplorer:before {
  content: "\f2de";
}
.fa-meetup:before {
  content: "\f2e0";
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  padding: 0;
  margin: -1px;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

    Firefox 28.0+
    Chrome 29.0+
    Internet Explorer 11+ 
    Opera 17.0+

Browsers not listed, including Safari, are supported via the styling under the
`Old browsers` comments below.
*/
.hbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
.hbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.vbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
.vbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.hbox.reverse,
.vbox.reverse,
.reverse {
  /* Old browsers */
  -webkit-box-direction: reverse;
  -moz-box-direction: reverse;
  box-direction: reverse;
  /* Modern browsers */
  flex-direction: row-reverse;
}
.hbox.box-flex0,
.vbox.box-flex0,
.box-flex0 {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
  width: auto;
}
.hbox.box-flex1,
.vbox.box-flex1,
.box-flex1 {
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex,
.vbox.box-flex,
.box-flex {
  /* Old browsers */
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex2,
.vbox.box-flex2,
.box-flex2 {
  /* Old browsers */
  -webkit-box-flex: 2;
  -moz-box-flex: 2;
  box-flex: 2;
  /* Modern browsers */
  flex: 2;
}
.box-group1 {
  /*  Deprecated */
  -webkit-box-flex-group: 1;
  -moz-box-flex-group: 1;
  box-flex-group: 1;
}
.box-group2 {
  /* Deprecated */
  -webkit-box-flex-group: 2;
  -moz-box-flex-group: 2;
  box-flex-group: 2;
}
.hbox.start,
.vbox.start,
.start {
  /* Old browsers */
  -webkit-box-pack: start;
  -moz-box-pack: start;
  box-pack: start;
  /* Modern browsers */
  justify-content: flex-start;
}
.hbox.end,
.vbox.end,
.end {
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
}
.hbox.center,
.vbox.center,
.center {
  /* Old browsers */
  -webkit-box-pack: center;
  -moz-box-pack: center;
  box-pack: center;
  /* Modern browsers */
  justify-content: center;
}
.hbox.baseline,
.vbox.baseline,
.baseline {
  /* Old browsers */
  -webkit-box-pack: baseline;
  -moz-box-pack: baseline;
  box-pack: baseline;
  /* Modern browsers */
  justify-content: baseline;
}
.hbox.stretch,
.vbox.stretch,
.stretch {
  /* Old browsers */
  -webkit-box-pack: stretch;
  -moz-box-pack: stretch;
  box-pack: stretch;
  /* Modern browsers */
  justify-content: stretch;
}
.hbox.align-start,
.vbox.align-start,
.align-start {
  /* Old browsers */
  -webkit-box-align: start;
  -moz-box-align: start;
  box-align: start;
  /* Modern browsers */
  align-items: flex-start;
}
.hbox.align-end,
.vbox.align-end,
.align-end {
  /* Old browsers */
  -webkit-box-align: end;
  -moz-box-align: end;
  box-align: end;
  /* Modern browsers */
  align-items: flex-end;
}
.hbox.align-center,
.vbox.align-center,
.align-center {
  /* Old browsers */
  -webkit-box-align: center;
  -moz-box-align: center;
  box-align: center;
  /* Modern browsers */
  align-items: center;
}
.hbox.align-baseline,
.vbox.align-baseline,
.align-baseline {
  /* Old browsers */
  -webkit-box-align: baseline;
  -moz-box-align: baseline;
  box-align: baseline;
  /* Modern browsers */
  align-items: baseline;
}
.hbox.align-stretch,
.vbox.align-stretch,
.align-stretch {
  /* Old browsers */
  -webkit-box-align: stretch;
  -moz-box-align: stretch;
  box-align: stretch;
  /* Modern browsers */
  align-items: stretch;
}
div.error {
  margin: 2em;
  text-align: center;
}
div.error > h1 {
  font-size: 500%;
  line-height: normal;
}
div.error > p {
  font-size: 200%;
  line-height: normal;
}
div.traceback-wrapper {
  text-align: left;
  max-width: 800px;
  margin: auto;
}
div.traceback-wrapper pre.traceback {
  max-height: 600px;
  overflow: auto;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
body {
  background-color: #fff;
  /* This makes sure that the body covers the entire window and needs to
       be in a different element than the display: box in wrapper below */
  position: absolute;
  left: 0px;
  right: 0px;
  top: 0px;
  bottom: 0px;
  overflow: visible;
}
body > #header {
  /* Initially hidden to prevent FLOUC */
  display: none;
  background-color: #fff;
  /* Display over codemirror */
  position: relative;
  z-index: 100;
}
body > #header #header-container {
  display: flex;
  flex-direction: row;
  justify-content: space-between;
  padding: 5px;
  padding-bottom: 5px;
  padding-top: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
body > #header .header-bar {
  width: 100%;
  height: 1px;
  background: #e7e7e7;
  margin-bottom: -1px;
}
@media print {
  body > #header {
    display: none !important;
  }
}
#header-spacer {
  width: 100%;
  visibility: hidden;
}
@media print {
  #header-spacer {
    display: none;
  }
}
#ipython_notebook {
  padding-left: 0px;
  padding-top: 1px;
  padding-bottom: 1px;
}
[dir="rtl"] #ipython_notebook {
  margin-right: 10px;
  margin-left: 0;
}
[dir="rtl"] #ipython_notebook.pull-left {
  float: right !important;
  float: right;
}
.flex-spacer {
  flex: 1;
}
#noscript {
  width: auto;
  padding-top: 16px;
  padding-bottom: 16px;
  text-align: center;
  font-size: 22px;
  color: red;
  font-weight: bold;
}
#ipython_notebook img {
  height: 28px;
}
#site {
  width: 100%;
  display: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  overflow: auto;
}
@media print {
  #site {
    height: auto !important;
  }
}
/* Smaller buttons */
.ui-button .ui-button-text {
  padding: 0.2em 0.8em;
  font-size: 77%;
}
input.ui-button {
  padding: 0.3em 0.9em;
}
span#kernel_logo_widget {
  margin: 0 10px;
}
span#login_widget {
  float: right;
}
[dir="rtl"] span#login_widget {
  float: left;
}
span#login_widget > .button,
#logout {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button:focus,
#logout:focus,
span#login_widget > .button.focus,
#logout.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
span#login_widget > .button:hover,
#logout:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active:hover,
#logout:active:hover,
span#login_widget > .button.active:hover,
#logout.active:hover,
.open > .dropdown-togglespan#login_widget > .button:hover,
.open > .dropdown-toggle#logout:hover,
span#login_widget > .button:active:focus,
#logout:active:focus,
span#login_widget > .button.active:focus,
#logout.active:focus,
.open > .dropdown-togglespan#login_widget > .button:focus,
.open > .dropdown-toggle#logout:focus,
span#login_widget > .button:active.focus,
#logout:active.focus,
span#login_widget > .button.active.focus,
#logout.active.focus,
.open > .dropdown-togglespan#login_widget > .button.focus,
.open > .dropdown-toggle#logout.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  background-image: none;
}
span#login_widget > .button.disabled:hover,
#logout.disabled:hover,
span#login_widget > .button[disabled]:hover,
#logout[disabled]:hover,
fieldset[disabled] span#login_widget > .button:hover,
fieldset[disabled] #logout:hover,
span#login_widget > .button.disabled:focus,
#logout.disabled:focus,
span#login_widget > .button[disabled]:focus,
#logout[disabled]:focus,
fieldset[disabled] span#login_widget > .button:focus,
fieldset[disabled] #logout:focus,
span#login_widget > .button.disabled.focus,
#logout.disabled.focus,
span#login_widget > .button[disabled].focus,
#logout[disabled].focus,
fieldset[disabled] span#login_widget > .button.focus,
fieldset[disabled] #logout.focus {
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button .badge,
#logout .badge {
  color: #fff;
  background-color: #333;
}
.nav-header {
  text-transform: none;
}
#header > span {
  margin-top: 10px;
}
.modal_stretch .modal-dialog {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  min-height: 80vh;
}
.modal_stretch .modal-dialog .modal-body {
  max-height: calc(100vh - 200px);
  overflow: auto;
  flex: 1;
}
.modal-header {
  cursor: move;
}
@media (min-width: 768px) {
  .modal .modal-dialog {
    width: 700px;
  }
}
@media (min-width: 768px) {
  select.form-control {
    margin-left: 12px;
    margin-right: 12px;
  }
}
/*!
*
* IPython auth
*
*/
.center-nav {
  display: inline-block;
  margin-bottom: -4px;
}
[dir="rtl"] .center-nav form.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] .center-nav .navbar-text {
  float: right;
}
[dir="rtl"] .navbar-inner {
  text-align: right;
}
[dir="rtl"] div.text-left {
  text-align: right;
}
/*!
*
* IPython tree view
*
*/
/* We need an invisible input field on top of the sentense*/
/* "Drag file onto the list ..." */
.alternate_upload {
  background-color: none;
  display: inline;
}
.alternate_upload.form {
  padding: 0;
  margin: 0;
}
.alternate_upload input.fileinput {
  position: absolute;
  display: block;
  width: 100%;
  height: 100%;
  overflow: hidden;
  cursor: pointer;
  opacity: 0;
  z-index: 2;
}
.alternate_upload .btn-xs > input.fileinput {
  margin: -1px -5px;
}
.alternate_upload .btn-upload {
  position: relative;
  height: 22px;
}
::-webkit-file-upload-button {
  cursor: pointer;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
ul#tabs {
  margin-bottom: 4px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
[dir="rtl"] ul#tabs.nav-tabs > li {
  float: right;
}
[dir="rtl"] ul#tabs.nav.nav-tabs {
  padding-right: 0;
}
ul.breadcrumb a:focus,
ul.breadcrumb a:hover {
  text-decoration: none;
}
ul.breadcrumb i.icon-home {
  font-size: 16px;
  margin-right: 4px;
}
ul.breadcrumb span {
  color: #5e5e5e;
}
.list_toolbar {
  padding: 4px 0 4px 0;
  vertical-align: middle;
}
.list_toolbar .tree-buttons {
  padding-top: 1px;
}
[dir="rtl"] .list_toolbar .tree-buttons .pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .list_toolbar .col-sm-4,
[dir="rtl"] .list_toolbar .col-sm-8 {
  float: right;
}
.dynamic-buttons {
  padding-top: 3px;
  display: inline-block;
}
.list_toolbar [class*="span"] {
  min-height: 24px;
}
.list_header {
  font-weight: bold;
  background-color: #EEE;
}
.list_placeholder {
  font-weight: bold;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
}
.list_container {
  margin-top: 4px;
  margin-bottom: 20px;
  border: 1px solid #ddd;
  border-radius: 2px;
}
.list_container > div {
  border-bottom: 1px solid #ddd;
}
.list_container > div:hover .list-item {
  background-color: red;
}
.list_container > div:last-child {
  border: none;
}
.list_item:hover .list_item {
  background-color: #ddd;
}
.list_item a {
  text-decoration: none;
}
.list_item:hover {
  background-color: #fafafa;
}
.list_header > div,
.list_item > div {
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
.list_header > div input,
.list_item > div input {
  margin-right: 7px;
  margin-left: 14px;
  vertical-align: text-bottom;
  line-height: 22px;
  position: relative;
  top: -1px;
}
.list_header > div .item_link,
.list_item > div .item_link {
  margin-left: -1px;
  vertical-align: baseline;
  line-height: 22px;
}
[dir="rtl"] .list_item > div input {
  margin-right: 0;
}
.new-file input[type=checkbox] {
  visibility: hidden;
}
.item_name {
  line-height: 22px;
  height: 24px;
}
.item_icon {
  font-size: 14px;
  color: #5e5e5e;
  margin-right: 7px;
  margin-left: 7px;
  line-height: 22px;
  vertical-align: baseline;
}
.item_modified {
  margin-right: 7px;
  margin-left: 7px;
}
[dir="rtl"] .item_modified.pull-right {
  float: left !important;
  float: left;
}
.item_buttons {
  line-height: 1em;
  margin-left: -5px;
}
.item_buttons .btn,
.item_buttons .btn-group,
.item_buttons .input-group {
  float: left;
}
.item_buttons > .btn,
.item_buttons > .btn-group,
.item_buttons > .input-group {
  margin-left: 5px;
}
.item_buttons .btn {
  min-width: 13ex;
}
.item_buttons .running-indicator {
  padding-top: 4px;
  color: #5cb85c;
}
.item_buttons .kernel-name {
  padding-top: 4px;
  color: #5bc0de;
  margin-right: 7px;
  float: left;
}
[dir="rtl"] .item_buttons.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .item_buttons .kernel-name {
  margin-left: 7px;
  float: right;
}
.toolbar_info {
  height: 24px;
  line-height: 24px;
}
.list_item input:not([type=checkbox]) {
  padding-top: 3px;
  padding-bottom: 3px;
  height: 22px;
  line-height: 14px;
  margin: 0px;
}
.highlight_text {
  color: blue;
}
#project_name {
  display: inline-block;
  padding-left: 7px;
  margin-left: -2px;
}
#project_name > .breadcrumb {
  padding: 0px;
  margin-bottom: 0px;
  background-color: transparent;
  font-weight: bold;
}
.sort_button {
  display: inline-block;
  padding-left: 7px;
}
[dir="rtl"] .sort_button.pull-right {
  float: left !important;
  float: left;
}
#tree-selector {
  padding-right: 0px;
}
#button-select-all {
  min-width: 50px;
}
[dir="rtl"] #button-select-all.btn {
  float: right ;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
  margin-top: 2px;
  height: 16px;
}
[dir="rtl"] #select-all.pull-left {
  float: right !important;
  float: right;
}
.menu_icon {
  margin-right: 2px;
}
.tab-content .row {
  margin-left: 0px;
  margin-right: 0px;
}
.folder_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f114";
}
.folder_icon:before.fa-pull-left {
  margin-right: .3em;
}
.folder_icon:before.fa-pull-right {
  margin-left: .3em;
}
.folder_icon:before.pull-left {
  margin-right: .3em;
}
.folder_icon:before.pull-right {
  margin-left: .3em;
}
.notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
}
.notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.notebook_icon:before.pull-left {
  margin-right: .3em;
}
.notebook_icon:before.pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
  color: #5cb85c;
}
.running_notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before.pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.pull-right {
  margin-left: .3em;
}
.file_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f016";
  position: relative;
  top: -2px;
}
.file_icon:before.fa-pull-left {
  margin-right: .3em;
}
.file_icon:before.fa-pull-right {
  margin-left: .3em;
}
.file_icon:before.pull-left {
  margin-right: .3em;
}
.file_icon:before.pull-right {
  margin-left: .3em;
}
#notebook_toolbar .pull-right {
  padding-top: 0px;
  margin-right: -1px;
}
ul#new-menu {
  left: auto;
  right: 0;
}
#new-menu .dropdown-header {
  font-size: 10px;
  border-bottom: 1px solid #e5e5e5;
  padding: 0 0 3px;
  margin: -3px 20px 0;
}
.kernel-menu-icon {
  padding-right: 12px;
  width: 24px;
  content: "\f096";
}
.kernel-menu-icon:before {
  content: "\f096";
}
.kernel-menu-icon-current:before {
  content: "\f00c";
}
#tab_content {
  padding-top: 20px;
}
#running .panel-group .panel {
  margin-top: 3px;
  margin-bottom: 1em;
}
#running .panel-group .panel .panel-heading {
  background-color: #EEE;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
  text-decoration: none;
}
#running .panel-group .panel .panel-body {
  padding: 0px;
}
#running .panel-group .panel .panel-body .list_container {
  margin-top: 0px;
  margin-bottom: 0px;
  border: 0px;
  border-radius: 0px;
}
#running .panel-group .panel .panel-body .list_container .list_item {
  border-bottom: 1px solid #ddd;
}
#running .panel-group .panel .panel-body .list_container .list_item:last-child {
  border-bottom: 0px;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-button {
  display: none;
}
.move-button {
  display: none;
}
.download-button {
  display: none;
}
.shutdown-button {
  display: none;
}
.dynamic-instructions {
  display: inline-block;
  padding-top: 4px;
}
/*!
*
* IPython text editor webapp
*
*/
.selected-keymap i.fa {
  padding: 0px 5px;
}
.selected-keymap i.fa:before {
  content: "\f00c";
}
#mode-menu {
  overflow: auto;
  max-height: 20em;
}
.edit_app #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.edit_app #menubar .navbar {
  /* Use a negative 1 bottom margin, so the border overlaps the border of the
    header */
  margin-bottom: -1px;
}
.dirty-indicator {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator.pull-left {
  margin-right: .3em;
}
.dirty-indicator.pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-dirty.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty.pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-clean.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f00c";
}
.dirty-indicator-clean:before.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.pull-right {
  margin-left: .3em;
}
#filename {
  font-size: 16pt;
  display: table;
  padding: 0px 5px;
}
#current-mode {
  padding-left: 5px;
  padding-right: 5px;
}
#texteditor-backdrop {
  padding-top: 20px;
  padding-bottom: 20px;
}
@media not print {
  #texteditor-backdrop {
    background-color: #EEE;
  }
}
@media print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container {
    padding: 0px;
    background-color: #fff;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
.CodeMirror-dialog {
  background-color: #fff;
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI escape sequences */
/* The color values are a mix of
   http://www.xcolors.net/dl/baskerville-ivorylight and
   http://www.xcolors.net/dl/euphrasia */
.ansi-black-fg {
  color: #3E424D;
}
.ansi-black-bg {
  background-color: #3E424D;
}
.ansi-black-intense-fg {
  color: #282C36;
}
.ansi-black-intense-bg {
  background-color: #282C36;
}
.ansi-red-fg {
  color: #E75C58;
}
.ansi-red-bg {
  background-color: #E75C58;
}
.ansi-red-intense-fg {
  color: #B22B31;
}
.ansi-red-intense-bg {
  background-color: #B22B31;
}
.ansi-green-fg {
  color: #00A250;
}
.ansi-green-bg {
  background-color: #00A250;
}
.ansi-green-intense-fg {
  color: #007427;
}
.ansi-green-intense-bg {
  background-color: #007427;
}
.ansi-yellow-fg {
  color: #DDB62B;
}
.ansi-yellow-bg {
  background-color: #DDB62B;
}
.ansi-yellow-intense-fg {
  color: #B27D12;
}
.ansi-yellow-intense-bg {
  background-color: #B27D12;
}
.ansi-blue-fg {
  color: #208FFB;
}
.ansi-blue-bg {
  background-color: #208FFB;
}
.ansi-blue-intense-fg {
  color: #0065CA;
}
.ansi-blue-intense-bg {
  background-color: #0065CA;
}
.ansi-magenta-fg {
  color: #D160C4;
}
.ansi-magenta-bg {
  background-color: #D160C4;
}
.ansi-magenta-intense-fg {
  color: #A03196;
}
.ansi-magenta-intense-bg {
  background-color: #A03196;
}
.ansi-cyan-fg {
  color: #60C6C8;
}
.ansi-cyan-bg {
  background-color: #60C6C8;
}
.ansi-cyan-intense-fg {
  color: #258F8F;
}
.ansi-cyan-intense-bg {
  background-color: #258F8F;
}
.ansi-white-fg {
  color: #C5C1B4;
}
.ansi-white-bg {
  background-color: #C5C1B4;
}
.ansi-white-intense-fg {
  color: #A1A6B2;
}
.ansi-white-intense-bg {
  background-color: #A1A6B2;
}
.ansi-default-inverse-fg {
  color: #FFFFFF;
}
.ansi-default-inverse-bg {
  background-color: #000000;
}
.ansi-bold {
  font-weight: bold;
}
.ansi-underline {
  text-decoration: underline;
}
/* The following styles are deprecated an will be removed in a future version */
.ansibold {
  font-weight: bold;
}
.ansi-inverse {
  outline: 0.5px dotted;
}
/* use dark versions for foreground, to improve visibility */
.ansiblack {
  color: black;
}
.ansired {
  color: darkred;
}
.ansigreen {
  color: darkgreen;
}
.ansiyellow {
  color: #c4a000;
}
.ansiblue {
  color: darkblue;
}
.ansipurple {
  color: darkviolet;
}
.ansicyan {
  color: steelblue;
}
.ansigray {
  color: gray;
}
/* and light for background, for the same reason */
.ansibgblack {
  background-color: black;
}
.ansibgred {
  background-color: red;
}
.ansibggreen {
  background-color: green;
}
.ansibgyellow {
  background-color: yellow;
}
.ansibgblue {
  background-color: blue;
}
.ansibgpurple {
  background-color: magenta;
}
.ansibgcyan {
  background-color: cyan;
}
.ansibggray {
  background-color: gray;
}
div.cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  border-radius: 2px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  border-width: 1px;
  border-style: solid;
  border-color: transparent;
  width: 100%;
  padding: 5px;
  /* This acts as a spacer between cells, that is outside the border */
  margin: 0px;
  outline: none;
  position: relative;
  overflow: visible;
}
div.cell:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: transparent;
}
div.cell.jupyter-soft-selected {
  border-left-color: #E3F2FD;
  border-left-width: 1px;
  padding-left: 5px;
  border-right-color: #E3F2FD;
  border-right-width: 1px;
  background: #E3F2FD;
}
@media print {
  div.cell.jupyter-soft-selected {
    border-color: transparent;
  }
}
div.cell.selected,
div.cell.selected.jupyter-soft-selected {
  border-color: #ababab;
}
div.cell.selected:before,
div.cell.selected.jupyter-soft-selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #42A5F5;
}
@media print {
  div.cell.selected,
  div.cell.selected.jupyter-soft-selected {
    border-color: transparent;
  }
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
}
.edit_mode div.cell.selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #66BB6A;
}
@media print {
  .edit_mode div.cell.selected {
    border-color: transparent;
  }
}
.prompt {
  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
  min-width: 14ex;
  /* This padding is tuned to match the padding on the CodeMirror editor. */
  padding: 0.4em;
  margin: 0px;
  font-family: monospace;
  text-align: right;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
  /* Don't highlight prompt number selection */
  -webkit-touch-callout: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
  /* Use default cursor */
  cursor: default;
}
@media (max-width: 540px) {
  .prompt {
    text-align: left;
  }
}
div.inner_cell {
  min-width: 0;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
  border: 1px solid #cfcfcf;
  border-radius: 2px;
  background: #f7f7f7;
  line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
   is no content in the output_subarea and the prompt. The main purpose of this is
   to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
  padding-top: 0;
  padding-bottom: 0;
}
div.unrecognized_cell {
  padding: 5px 5px 5px 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.unrecognized_cell .inner_cell {
  border-radius: 2px;
  padding: 5px;
  font-weight: bold;
  color: red;
  border: 1px solid #cfcfcf;
  background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
  color: inherit;
  text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
  color: inherit;
  text-decoration: none;
}
@media (max-width: 540px) {
  div.unrecognized_cell > div.prompt {
    display: none;
  }
}
div.code_cell {
  /* avoid page breaking on code cells when printing */
}
@media print {
  div.code_cell {
    page-break-inside: avoid;
  }
}
/* any special styling for code cells that are currently running goes here */
div.input {
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.input {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
  color: #303F9F;
  border-top: 1px solid transparent;
}
div.input_area > div.highlight {
  margin: 0.4em;
  border: none;
  padding: 0px;
  background-color: transparent;
}
div.input_area > div.highlight > pre {
  margin: 0px;
  border: none;
  padding: 0px;
  background-color: transparent;
}
/* The following gets added to the <head> if it is detected that the user has a
 * monospace font with inconsistent normal/bold/italic height.  See
 * notebookmain.js.  Such fonts will have keywords vertically offset with
 * respect to the rest of the text.  The user should select a better font.
 * See: https://github.com/ipython/ipython/issues/1503
 *
 * .CodeMirror span {
 *      vertical-align: bottom;
 * }
 */
.CodeMirror {
  line-height: 1.21429em;
  /* Changed from 1em to our global default */
  font-size: 14px;
  height: auto;
  /* Changed to auto to autogrow */
  background: none;
  /* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
  overflow-y: hidden;
  overflow-x: auto;
}
.CodeMirror-lines {
  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
  /* we have set a different line-height and want this to scale with that. */
  /* Note that this should set vertical padding only, since CodeMirror assumes
       that horizontal padding will be set on CodeMirror pre */
  padding: 0.4em 0;
}
.CodeMirror-linenumber {
  padding: 0 8px 0 4px;
}
.CodeMirror-gutters {
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.CodeMirror pre {
  /* In CM3 this went to 4px from 0 in CM2. This sets horizontal padding only,
    use .CodeMirror-lines for vertical */
  padding: 0 0.4em;
  border: 0;
  border-radius: 0;
}
.CodeMirror-cursor {
  border-left: 1.4px solid black;
}
@media screen and (min-width: 2138px) and (max-width: 4319px) {
  .CodeMirror-cursor {
    border-left: 2px solid black;
  }
}
@media screen and (min-width: 4320px) {
  .CodeMirror-cursor {
    border-left: 4px solid black;
  }
}
/*

Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
Adapted from GitHub theme

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
div.output_area .mglyph > img {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 1px 0 1px 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  height: 30px;
  margin-top: 4px;
  display: flex;
  justify-content: flex-start;
  align-items: baseline;
  width: 50%;
  flex: 1;
}
span.save_widget span.filename {
  height: 100%;
  line-height: 1em;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  text-overflow: ellipsis;
  overflow: hidden;
  white-space: nowrap;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
[dir="rtl"] span.save_widget.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] span.save_widget span.filename {
  margin-left: 0;
  margin-right: 16px;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
  white-space: nowrap;
  padding: 0 5px;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
    padding: 0 0 0 5px;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
.toolbar-btn-label {
  margin-left: 6px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
[dir="rtl"] .btn-group > .btn,
.btn-group-vertical > .btn {
  float: right;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
 * of chance of beeing generated from the ../less/[samename].less file, you can
 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
 * have some strange css to have the scroll bar on
 * the left with fix button on the top right of the tooltip
 */
@-moz-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-webkit-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-moz-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
@-webkit-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
/*properties of tooltip after "expand"*/
.bigtooltip {
  overflow: auto;
  height: 200px;
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
  text-overflow: ellipsis;
  overflow: hidden;
  height: 80px;
}
.tooltipbuttons {
  position: absolute;
  padding-right: 15px;
  top: 0px;
  right: 0px;
}
.tooltiptext {
  /*avoid the button to overlap on some docstring*/
  padding-right: 30px;
}
.ipython_tooltip {
  max-width: 700px;
  /*fade-in animation when inserted*/
  -webkit-animation: fadeOut 400ms;
  -moz-animation: fadeOut 400ms;
  animation: fadeOut 400ms;
  -webkit-animation: fadeIn 400ms;
  -moz-animation: fadeIn 400ms;
  animation: fadeIn 400ms;
  vertical-align: middle;
  background-color: #f7f7f7;
  overflow: visible;
  border: #ababab 1px solid;
  outline: none;
  padding: 3px;
  margin: 0px;
  padding-left: 7px;
  font-family: monospace;
  min-height: 50px;
  -moz-box-shadow: 0px 6px 10px -1px #adadad;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  border-radius: 2px;
  position: absolute;
  z-index: 1000;
}
.ipython_tooltip a {
  float: right;
}
.ipython_tooltip .tooltiptext pre {
  border: 0;
  border-radius: 0;
  font-size: 100%;
  background-color: #f7f7f7;
}
.pretooltiparrow {
  left: 0px;
  margin: 0px;
  top: -16px;
  width: 40px;
  height: 16px;
  overflow: hidden;
  position: absolute;
}
.pretooltiparrow:before {
  background-color: #f7f7f7;
  border: 1px #ababab solid;
  z-index: 11;
  content: "";
  position: absolute;
  left: 15px;
  top: 10px;
  width: 25px;
  height: 25px;
  -webkit-transform: rotate(45deg);
  -moz-transform: rotate(45deg);
  -ms-transform: rotate(45deg);
  -o-transform: rotate(45deg);
}
ul.typeahead-list i {
  margin-left: -10px;
  width: 18px;
}
[dir="rtl"] ul.typeahead-list i {
  margin-left: 0;
  margin-right: -10px;
}
ul.typeahead-list {
  max-height: 80vh;
  overflow: auto;
}
ul.typeahead-list > li > a {
  /** Firefox bug **/
  /* see https://github.com/jupyter/notebook/issues/559 */
  white-space: normal;
}
ul.typeahead-list  > li > a.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .typeahead-list {
  text-align: right;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  min-width: 20px;
  color: transparent;
}
[dir="rtl"] .no-shortcut.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .command-shortcut.pull-right {
  float: left !important;
  float: left;
}
.command-shortcut:before {
  content: "(command mode)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
[dir="rtl"] .edit-shortcut.pull-right {
  float: left !important;
  float: left;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
  background-color: #BBDEFB;
  border-color: #90CAF9;
  border-style: solid;
  border-width: 1px;
  border-radius: 0px;
}
[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
  border-left: none;
}
[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
  border-right: none;
}
#find-and-replace #replace-preview .replace .match {
  background-color: #FFCDD2;
  border-color: #EF9A9A;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
  background-color: #C8E6C9;
  border-color: #A5D6A7;
  border-radius: 0px;
}
#find-and-replace #replace-preview {
  max-height: 60vh;
  overflow: auto;
}
#find-and-replace #replace-preview pre {
  padding: 5px 10px;
}
.terminal-app {
  background: #EEE;
}
.terminal-app #header {
  background: #fff;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
  width: 100%;
  float: left;
  font-family: monospace;
  color: white;
  background: black;
  padding: 0.4em;
  border-radius: 2px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
.terminal-app .terminal dummy-screen {
  line-height: 1em;
  font-size: 14px;
}
.terminal-app .terminal .xterm-rows {
  padding: 10px;
}
.terminal-app .terminal-cursor {
  color: black;
  background: white;
}
.terminal-app #terminado-container {
  margin-top: 20px;
}
/*# sourceMappingURL=style.min.css.map */
    </style>
<style type="text/css">
    .highlight .hll { background-color: #ffffcc }
.highlight  { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #888888 } /* Generic.Output */
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #B00040 } /* Keyword.Type */
.highlight .m { color: #666666 } /* Literal.Number */
.highlight .s { color: #BA2121 } /* Literal.String */
.highlight .na { color: #7D9029 } /* Name.Attribute */
.highlight .nb { color: #008000 } /* Name.Builtin */
.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.highlight .no { color: #880000 } /* Name.Constant */
.highlight .nd { color: #AA22FF } /* Name.Decorator */
.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.highlight .nf { color: #0000FF } /* Name.Function */
.highlight .nl { color: #A0A000 } /* Name.Label */
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #19177C } /* Name.Variable */
.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
.highlight .mb { color: #666666 } /* Literal.Number.Bin */
.highlight .mf { color: #666666 } /* Literal.Number.Float */
.highlight .mh { color: #666666 } /* Literal.Number.Hex */
.highlight .mi { color: #666666 } /* Literal.Number.Integer */
.highlight .mo { color: #666666 } /* Literal.Number.Oct */
.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.highlight .sx { color: #008000 } /* Literal.String.Other */
.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
.highlight .ss { color: #19177C } /* Literal.String.Symbol */
.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
.highlight .fm { color: #0000FF } /* Name.Function.Magic */
.highlight .vc { color: #19177C } /* Name.Variable.Class */
.highlight .vg { color: #19177C } /* Name.Variable.Global */
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
.highlight .vm { color: #19177C } /* Name.Variable.Magic */
.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
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<style type="text/css">
    
/* Temporary definitions which will become obsolete with Notebook release 5.0 */
.ansi-black-fg { color: #3E424D; }
.ansi-black-bg { background-color: #3E424D; }
.ansi-black-intense-fg { color: #282C36; }
.ansi-black-intense-bg { background-color: #282C36; }
.ansi-red-fg { color: #E75C58; }
.ansi-red-bg { background-color: #E75C58; }
.ansi-red-intense-fg { color: #B22B31; }
.ansi-red-intense-bg { background-color: #B22B31; }
.ansi-green-fg { color: #00A250; }
.ansi-green-bg { background-color: #00A250; }
.ansi-green-intense-fg { color: #007427; }
.ansi-green-intense-bg { background-color: #007427; }
.ansi-yellow-fg { color: #DDB62B; }
.ansi-yellow-bg { background-color: #DDB62B; }
.ansi-yellow-intense-fg { color: #B27D12; }
.ansi-yellow-intense-bg { background-color: #B27D12; }
.ansi-blue-fg { color: #208FFB; }
.ansi-blue-bg { background-color: #208FFB; }
.ansi-blue-intense-fg { color: #0065CA; }
.ansi-blue-intense-bg { background-color: #0065CA; }
.ansi-magenta-fg { color: #D160C4; }
.ansi-magenta-bg { background-color: #D160C4; }
.ansi-magenta-intense-fg { color: #A03196; }
.ansi-magenta-intense-bg { background-color: #A03196; }
.ansi-cyan-fg { color: #60C6C8; }
.ansi-cyan-bg { background-color: #60C6C8; }
.ansi-cyan-intense-fg { color: #258F8F; }
.ansi-cyan-intense-bg { background-color: #258F8F; }
.ansi-white-fg { color: #C5C1B4; }
.ansi-white-bg { background-color: #C5C1B4; }
.ansi-white-intense-fg { color: #A1A6B2; }
.ansi-white-intense-bg { background-color: #A1A6B2; }

.ansi-bold { font-weight: bold; }

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<h1 id="Convolutional-Neural-Networks">Convolutional Neural Networks<a class="anchor-link" href="#Convolutional-Neural-Networks">&#182;</a></h1><h2 id="Project:-Write-an-Algorithm-for-a-Dog-Identification-App">Project: Write an Algorithm for a Dog Identification App<a class="anchor-link" href="#Project:-Write-an-Algorithm-for-a-Dog-Identification-App">&#182;</a></h2><hr>
<p>In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with <strong>'(IMPLEMENTATION)'</strong> in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!</p>
<blockquote><p><strong>Note</strong>: Once you have completed all of the code implementations, you need to finalize your work by exporting the Jupyter Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to <strong>File -&gt; Download as -&gt; HTML (.html)</strong>. Include the finished document along with this notebook as your submission.</p>
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<p>In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a <strong>'Question X'</strong> header. Carefully read each question and provide thorough answers in the following text boxes that begin with <strong>'Answer:'</strong>. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.</p>
<blockquote><p><strong>Note:</strong> Code and Markdown cells can be executed using the <strong>Shift + Enter</strong> keyboard shortcut.  Markdown cells can be edited by double-clicking the cell to enter edit mode.</p>
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<p>The rubric contains <em>optional</em> "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this Jupyter notebook.</p>
<hr>
<h3 id="Why-We're-Here">Why We're Here<a class="anchor-link" href="#Why-We're-Here">&#182;</a></h3><p>In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app.  At the end of this project, your code will accept any user-supplied image as input.  If a dog is detected in the image, it will provide an estimate of the dog's breed.  If a human is detected, it will provide an estimate of the dog breed that is most resembling.  The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).</p>
<p><img src="images/sample_dog_output.png" alt="Sample Dog Output"></p>
<p>In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed.  There are many points of possible failure, and no perfect algorithm exists.  Your imperfect solution will nonetheless create a fun user experience!</p>
<h3 id="The-Road-Ahead">The Road Ahead<a class="anchor-link" href="#The-Road-Ahead">&#182;</a></h3><p>We break the notebook into separate steps.  Feel free to use the links below to navigate the notebook.</p>
<ul>
<li><a href="#step0">Step 0</a>: Import Datasets</li>
<li><a href="#step1">Step 1</a>: Detect Humans</li>
<li><a href="#step2">Step 2</a>: Detect Dogs</li>
<li><a href="#step3">Step 3</a>: Create a CNN to Classify Dog Breeds (from Scratch)</li>
<li><a href="#step4">Step 4</a>: Create a CNN to Classify Dog Breeds (using Transfer Learning)</li>
<li><a href="#step5">Step 5</a>: Write your Algorithm</li>
<li><a href="#step6">Step 6</a>: Test Your Algorithm</li>
</ul>
<hr>
<p><a id='step0'></a></p>
<h2 id="Step-0:-Import-Datasets">Step 0: Import Datasets<a class="anchor-link" href="#Step-0:-Import-Datasets">&#182;</a></h2><p>Make sure that you've downloaded the required human and dog datasets:</p>
<ul>
<li><p>Download the <a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/dogImages.zip">dog dataset</a>.  Unzip the folder and place it in this project's home directory, at the location <code>/dog_images</code>.</p>
</li>
<li><p>Download the <a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/lfw.zip">human dataset</a>.  Unzip the folder and place it in the home directory, at location <code>/lfw</code>.</p>
</li>
</ul>
<p><em>Note: If you are using a Windows machine, you are encouraged to use <a href="http://www.7-zip.org/">7zip</a> to extract the folder.</em></p>
<p>In the code cell below, we save the file paths for both the human (LFW) dataset and dog dataset in the numpy arrays <code>human_files</code> and <code>dog_files</code>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">glob</span> <span class="k">import</span> <span class="n">glob</span>

<span class="c1"># load filenames for human and dog images</span>
<span class="n">human_files</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">glob</span><span class="p">(</span><span class="s2">&quot;/data/lfw/*/*&quot;</span><span class="p">))</span>
<span class="n">dog_files</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">glob</span><span class="p">(</span><span class="s2">&quot;/data/dog_images/*/*/*&quot;</span><span class="p">))</span>

<span class="c1"># print number of images in each dataset</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;There are </span><span class="si">%d</span><span class="s1"> total human images.&#39;</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">human_files</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;There are </span><span class="si">%d</span><span class="s1"> total dog images.&#39;</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">dog_files</span><span class="p">))</span>
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<pre>There are 13233 total human images.
There are 8351 total dog images.
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<p><a id='step1'></a></p>
<h2 id="Step-1:-Detect-Humans">Step 1: Detect Humans<a class="anchor-link" href="#Step-1:-Detect-Humans">&#182;</a></h2><p>In this section, we use OpenCV's implementation of <a href="http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html">Haar feature-based cascade classifiers</a> to detect human faces in images.</p>
<p>OpenCV provides many pre-trained face detectors, stored as XML files on <a href="https://github.com/opencv/opencv/tree/master/data/haarcascades">github</a>.  We have downloaded one of these detectors and stored it in the <code>haarcascades</code> directory.  In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">cv2</span>                
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>                        
<span class="o">%</span><span class="k">matplotlib</span> inline                               

<span class="c1"># extract pre-trained face detector</span>
<span class="n">face_cascade</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">CascadeClassifier</span><span class="p">(</span><span class="s1">&#39;haarcascades/haarcascade_frontalface_alt.xml&#39;</span><span class="p">)</span>

<span class="c1"># load color (BGR) image</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">human_files</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="c1"># convert BGR image to grayscale</span>
<span class="n">gray</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2GRAY</span><span class="p">)</span>

<span class="c1"># find faces in image</span>
<span class="n">faces</span> <span class="o">=</span> <span class="n">face_cascade</span><span class="o">.</span><span class="n">detectMultiScale</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span>

<span class="c1"># print number of faces detected in the image</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Number of faces detected:&#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">faces</span><span class="p">))</span>

<span class="c1"># get bounding box for each detected face</span>
<span class="k">for</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">w</span><span class="p">,</span><span class="n">h</span><span class="p">)</span> <span class="ow">in</span> <span class="n">faces</span><span class="p">:</span>
    <span class="c1"># add bounding box to color image</span>
    <span class="n">cv2</span><span class="o">.</span><span class="n">rectangle</span><span class="p">(</span><span class="n">img</span><span class="p">,(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">),(</span><span class="n">x</span><span class="o">+</span><span class="n">w</span><span class="p">,</span><span class="n">y</span><span class="o">+</span><span class="n">h</span><span class="p">),(</span><span class="mi">255</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span><span class="mi">2</span><span class="p">)</span>
    
<span class="c1"># convert BGR image to RGB for plotting</span>
<span class="n">cv_rgb</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2RGB</span><span class="p">)</span>

<span class="c1"># display the image, along with bounding box</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">cv_rgb</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>Number of faces detected: 1
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RaolYlRGSPE0cCiEpJGdIIjoOrKRMlN1clxaPc4jonj8djAZrNZo10geatnt7/n/v6e29tbdvcPHHZ7hmFovEaMCYkF+BTMqppQZwO763wBVQUyOcHxGA1YxpE37o4QYL1Z8fL6ipcvX3B9dUEcrrm+umC97s3PQjzO1UjXicCs6kEdO8UVDJyZP9VZ2xJaHExdW5zGnJAYUTxZEhSrU8JZ9KwD77v2jB+Nhhn5+RwYfAhv8dyxp3Uv1MmTdpzjZn5s+STAIufE8fgAnEHqXDD/hB8w85a2B3x67rLT3Ml35/VYETM56uz3eaenspyICJKrS45AVluV64p9Ml58ub6WHzxQtC4ydbWfqVgATnBFzcqSiuRSrAZSLAXZ+u5wGLh/u+P+7oH9/sjx6BmOIA5ycgxRGYbI/nBgGAbuH+4MWBIMw2Amz+NIGiPrjZkWU0rEaA5YVWJ5d/u2+VnEaEAzjkAqPgkCIdgEm1sT7GX9UC0kIsJhvyfnRBwz+3jkcH/kt7/+FnHw6mbN1eWGm5trvvziJa9evWS9Xlud1eVdPDmPpo5KxrtMIpI1W995EO/Aq4GaF47jyP1+R7jriJrpVj0aOvAO53xZiQXRqmI+TYyeG3d1bCbi2Ymqqs1E+1w5d+6zE7/kTKn8kyuOcN77jwYYnwRYGBNeRCZ/ojLkJbKeQ9NWx/ewxU89gInZnqkmZ+pIMrbz5lKFFOvKKVhM13OLk+aShPiy0pX7WuZYsHDwHJXQ/ABSa2OvHt8lcj7wcFD63Ug3ZMCx3+8Y44HjIbE7jOx2B97d3/Pw8MBufz/dZxZikRKGYWC73VoCmDQ20TarDfx6TM5AnojHVNwdVB9HD0/PzTXLhvfmELXd+Bb16UikNBLHIzFm3r07cNiZhyhZ6bqOruvYbDakfLQFQFPpEwpxm0AV1Tg5aKFEzayCEIIj9N7q6j2h7wjBkYOg4nDVeU61qRxVYhSkGVnqPTUOYfas6z0nnVTP0/E3VyfO9dUPKTEux3J1Z6/j5mPQFp8EWDgnbLd9u9GnJrecdmjLYvT0OfW75fcTn3FqNTkXTd7Uh5kWMonWINVpSs8TTE5qYphJMjKwyA0s4iwXRXupJyfQLtP7mnchE2NkzImHuwOH48hxjAzHyOE4cjiM3N3t+c1vvmO3H9nvRh72Aw8PD9y9e+Bw3DGOoxGFrsR0qJQkNhYTIZLMr6EMOMWRRot+NeIQEHNmEhFIEKvVIGXibMCPowHstNpN0sVmtcVLwHtHcB0hBDofyDqSxgMxwm635/Xr1/hgvfriRaJfWT9YmIk344NYdKuooOoRCz5BNRGzsA4GDpvtiourC65vXnB9fUm36olFSqljSzRBTjigm88QnZy2VI3Pmf14OmhmE3/JHbUzzqgVp0DyFHicgkvMZZHRJWjEKhmdreXDyicDFpdXm5Nvl0TlvNQOcvp08+edXkVk+1ziKOAROD1V2vUegUVtaVEXVG2S6fIhN6dqMUtP5SzAXKxNsjCJQEpjRQSJlpSFrIxAEEfKiTFlHh4O/Oo33/Lu/sD9w8Dr2x3fvX7L3f2B33x75Fe//IbdfuR4jByGyH5/5OHhgYyRmV4cOQRC6Ns9ei8cj0ecm3gYLcluhqHEhKhr/VCT8mSnpiBl8xad33tS8GIkY0qZKBMZnEaKxODpvBCCEHxH5z3ZK+gIqtzf7xpI7vd7fud3foY4LTEe4J1HXAbx5g1qDvF4STZps127+nxstisuL7dcX1/Tr1dk50kqk6qbI+SEF8W7aUGq/TSZutezMWYKZHuuLe3H+SC908/zY09/PyX253W29uhSomsyrRYjwV8UyWLRyXnuO/BEVGLpmMhkLjp9KM8x5vW7ZdDU0kFpYXqtaoiODbnhhO6sbHTKExlbLSPzgUZqlpbMfFIV/wiZ1BCnAVFlHBIh9DjnOBwmf4df/Opb9ofE/qjc3w+8vn3Hw8Oe33535JvvXjMOyjCMHEctYODRqLgKbsVTk2SkYXQDOZpo3/WeEEJbPQ+HA5qFzhcpSWdu6VIT3kBGiwmyWnfMwkXhAqw/CvE7puLbIaxWHVvXI70n+IDvHN5lVBO73QO3tzuqGfzm5so8QJv5Ulrfu2Ben1kHmyTO+Iys2YLoxPqhqjUhBKJ6xE8TUdQXySLjXTo7fqbx4hZjpI6fUaaxOX8/reNcmfvJPGVNmb+blNUt6s05oymjMZ1dcH9I+STAIsbIt9+9Buokn6bh/P8F8qprk7J1zhnQOFdOGeY5eTpH4VMAqeg9j9WcTKXJhmyqnpRxVodNrKi5cQ6ZGbABMWvRqmaE6hiL6jH1xW63Y7fbcfv2HW8fjuTcMSbHw/3Am9t7drsDr+9G3t6+A0yNyYhFnOYlw54zkEbLCB4j1cIbAuTco72g3TRgg6tkmVu4lc/vxfrKHplrg3zS7a1PKL4MiqjgcagKUTMhKqKRbu1KSkHjK5QjwxC5vb3l7vbCyDtZAQ4VqydrxiWl+kpNk8z8HVQzMQ7FXX1HCD3dEFHfmeN4WwQsAM1pBnc4qWsai957aL4+J5Y69xgk5mCilWg5MybtWqdgMpHz83cA55QQlmDRXsmsYO7p6fDe5ZMAi8PhyN//e/+o2eVbYBOQdVrlU1oCQUoTWMw/Lzt56f5sxFRYnDeXZlKJvCQvgcckms2iTtPdy7nVTyRbCPg8x8RQRGC7TmqTS7U4ZGXhOFQQsWAwI6iMW4iF0HBSgsQ0m4UiOlR6hhHu70fevr3nfjdw+zDy7t2uOKN1liM7m44tQiMVfemPnEwaoI1faQCXTZJnHMwiI+JPANr6A7SZT9UV1a/pbdKuQ+3TLHRSCNG6amYDDFFhTEoo4fehNwsNkjmOke++fVOiRkFk3RykVJOZSKGAg/l4iDiyGon78G7Ht7xmOEbe3r6jX69R16POI86XADItDlwZ38UFB9AmPL6NFeeqtDWNlexOJ//jCOVTns1C8lO7Vq0TSu6SmZfvqcTRolSKpFUtgN4Jwf8FsoZ8880b/tZ/9t9RSSNVIVVzaaodVsyaepJk9rRIfvTbJHXY4E4zFtMm9pzlnnEcp5yV9rh6joCTuupq8TTMONW2YFQJxQVpx0Fz9iypND0pKfuD+UbMI1v7NSVqsysMvqkiWSDGzP6YGSKmiuwi+6OSYuYwRIYRhETOqd2vpeYvVgsFkWT3gUck4DpHjAMxgR4GhuOIYipKCI6+7wluSvZbV7OEreZpJmF47xAfANuCIKdMFiVrRtXyRSRN5JRJmkg4kkYSnuRNqhhyMpOoF1adb0B1d3dHSomHhwdevnzBi+tL+t43s7OixU3c2covinc9x13i7s1veXj3J9zf79gfjgwxkfzG+A6pkpNtP+ABxMSthZWqSBhVuvCuW4w3ESGdhIzMz23RtieT3jmHD6lZMuYgslqtZiH+7tHLy9JsGkKgD4Gu687Pkx9QPgmwiDHz228eyJmy0tv3qvasze+B5oE300RaWVo8Hud3MBLI6slZZ+cUm/cZbqSZQuuKkEY7Vsy12AfovMN5Q36nQpKIr7xG9aA0zwryLKZB6x8x3wmtrH6a1JbDMRuB520ajDmR80jSTFbhOMIY4TjCcRRiUnIOaI7Fy9SRcyJrTfCSyMkIx9q/ZIilw12w9og4XHAEN/VfKjrKJPFlfAn77qSYeKFJTeItZ8QYzQOzbQugDpNCPGVBNn8ZcpGAbKIJAddJIY8zMWPciGoLWqvu4MELQS5YrwNg1gyHVLNNmUjFXSsq+/3A29t77u4HDkc45kNJuOMLL2Ju9l6gGLJmYv+MX0tGzlfSfEFOnslhMp/8p+N2ssYdHkkNIhafM0/WM4+idc7hdCl11AQ/ITzOTv5DyycBFpqF4WhiVhxdE29NrJ3S4KcZqQY8MnNOnXLGzZbUQCKlyQFWZscvygxQ6oPReDDJw4GGYhFpiG5XceKMjZ/Rz3XjnIqCzQ05g0oip3pfk89FzoovC+MQS3avXMBNHOMQOY5qDlejMIzZJmZKOBdI1TKRBYTi2TlJTROuTn0WixctIaPq7ZolxwQqS5VNBQkO75xZLxrHaKnpJHi8C8SiHuaciYVINavPjBQU5TSPZRYKGHkDuVzVARhzMgmURHBC13vWXWC7vUDzWIhN8C0/qUOzs+C77MjJkZKQzOhhfiK4tpgUYcSe8zAfX/Y0KyaYv2Adj7l9r0rjTU6pM5Hz3019kdt3y+seZsfaq1mlRB7HJLVjZAFOP6Z8EmCRFcbBGPacvE2aAhQpTpaRpBl0huAz9J5Q3QbHImWaTKu16ctLHRPKhJdlfc3yoTVDFdQgJTvHJAofXBnsRdUQ09+rZJJPMnC3Cjw26RxF7ATEkaMlbhkieA9BHTFlI0/LIMgijCmTkhrYVB9PJ+Q0NJXN2mkrUM7R1JBcBno2stb7ulJ5QFu0qfWmNBJvYWFiyjKFCUXWnxS1xPS0MxLeNBEsstXcuJOY2bnuO7IknRMpjjPeICPZfDj2+z273Y7h6hK4KOdmXDRAnxQThULypqTEMRNHSBEU2+6xcjtV4nKZ5iwoUuJFxLfn1/KXFom3fm9S7FMk+xmfhyrYiix8ceqEz+V5TeBwSnQuVenpe8q4PJ9b40PLJwEWqsqYhqIi2HfVElnGtKF9EwhKevqZWN+kkao6FKcamYUU14fnYmp1NygS69Layd6BeCnirInIqWybEbxNYucohFQ00tAtfTHmapIycRgyu05OigtC0mSxCNls9DmWVwaXq3ksgPOkZJvlxNogSWg6kMt9hcEbaVkGrakiQoe0oC8TBSqjaXkZxqB4MZVJUbyaLqzOE7xnzCPN+uEyySWyS1zgbJI7tfR5mLlUNaIu0TJWFdB2mux6HjPVCnTOkztHioCH6AKhC4UzsSTFmjKIkNcd4iJZlWM+cBwf2I93OH+NIsRoGcerE1vOiW2xnFkSHY+4FQSbYZIt0lUonA6zcdRWCEO5avA2otwAOvuJPK9jUPMSEiaVYhrj89LIUU4GTSlOHTku86bOpeeYRk7Lgpw/rfAHlE8CLKq4ZGV5Q87ZwAPzhKxxG2binFYskfKQZ3XOX3MgKQvohNCOFsZexcBGGM1EAlfAoO/Ms6/vHF0nBG8IblaGWc6LsrJq2QBokrTrEmTBSognqenjWieGJnwBzxhzCZlOuK5YI/I0OHPOpKzEmpG6qjNllcxCI4WNv6Ck17N+D8HhfUe3EdadedJ6cQQTykr/KkMMDCkyjoOpGzmTU4JQPDOlBMkJ5AIgwUFyM4LQWYNkYYA+MffpXOo7SfSrxZpVLEspaQmGGxmH1PrVQmiKumMhZbbKeocLHt8ZCZhyJrWU/nWFL6IF5nNx6uOQc/GQmcX61JFbjBHk2R4q7Vet0hiL+pb3ObkK1Ot6xBIXy6SSL87NSheWmyEt667lMaB8SPlkwKLvzNXap3kU5qROKJPuldT04qqi2ISRxknMLRqVgGqIr9o21TJfgLCM9Czii3cFALQcKDZBxcF65emCEAL0AVNDiGUFLW7DMz00pSlIzNqnhUsQtIq2GA+gzhVnILNApGj+iJqV5I23ULW0+zk7csqMydx9c0lyO8RMzNqEzyatMRdPTf3pQmC1WtF1HX7rubq8pPehuGVPoraIcDjs2R8PPOzNZVxF6HCGQCUNHlI5GVfUNHB+7jxndZppc6mbz8EiRSUG8Ewgp4IF8GWTNFOCAdjvBh7ujzzcD6zWHbbtYF39Ban9h4HwZPo1a5Hz/dQGqTxH6R9ZRp3mwsnknBsALwYyxX+E5QZHpqYYnNT+rFxF48y0a6pXMzrPpAdbIA0oJg28jtvalqnuOhc+VvkkwCKEwM9+/somj1bRuKyIOTeSM1WyDZvULk3oOffNSM0Xf0ZOzolRneUFOLFQNK6jEHCVqKymV+ccm5Xp2d6ZRUTEAr2Q6kthJOVkV9/aijLTViufkEre+ywH8yORQvT6iIs9yVkei5jsPasQk3AcEw+7kXFMxDGV8PTSn5oNKGoWc3GlT8reHYDzynYdeHFzyatXN1xfXnF1dcXFxQWd93hxJaeFgVxKiX3xHn1z95a7uzuOYw11Lw5oladwDt93dP2aN2/viUOihdsXSc4744IWwXe57teqHI92fOhKEJQrWx1mrJbTAAAgAElEQVT4bGREduS055iUuzSSx3t69y1ff/UFNy8vcaE8bx2BTJTEmDOHeOCQ9ox5RDz43kOepACTQUz5tc3Ya+Ki0s7yqiboNilnfMJ8XM0lplOJYbn6G3hJWKrMlQ13IiU+ZuZ82BIucUL2Twhxytv/mPJJgEXXBb786quZblU6eZbwRFUZ4+RDISL4U7BgKXqpLCNK27ElItAm0lOpQXIjtyaAUhPPO4u2rCuDEyMFVZdOXlXKqIFkS1a6DgoTx7t+z2GIRaweURlwKTAQzcOyiMfifHEJtzYlxbYUdKZ1A2QZIbtWt914BShltfZs1x1X11u+eHXDV199wdX1JS8urum7zjJpl6Av2zbRgG+73rBZr+m7jpUPPOx2xBh5ODwYoVqsHRIgiNAFR9cHi/YMgRRqmn5zmAqINblsmqRZ0JRJqgxRLLxcvInhZa8Uy3BuUaJoIOfIMSk5Hbl7u2e7PlgOjI0niJlBxSWSK1nMq2Tlyz0i+BBam6atFhUktwVpMa7a++PJOPnQzD07K8FdUhwsFBd7r6peoYpn/jxVsnCzcVjiazztt5iXJOZSrfs44sUnAhYdv/u7v9smZpqZp2pRaOTcBBaTeDt3OZ7UmAoIS5v1fH+MuvHt4lpV8lhIFalYLLR5xBlPUR2c7LuagEZ1JkKezVU0rTwZR79+MGZ/fyA/PCBjZMxVYjIVS7wUS0a2Fdx3eG9qRtWXDTiLeuPkJIAo0wfPV1/c8OLmkpsXF7x8dc0XNy+4vNqyCtu2y5sXU0Vyzmi0iRZcb5YSzWgyU+YwDGSNHIahkNSZHBMaOwtYQ/AidE7IvTe1KmZTK5iyibX8poXd1xgbuJofhTPyte41qEbyqli2Lz0mbm/vCZ35vVy/WHF5GQhdQohGQooQ+sB6u2FUR+iTmVDTFHFqHrlTVvUwt6KVsXkuY1cjE6uKoxMQwAQuQAn5Z/FdEzrLw5w/T1EtfJlbcGiL8RXnY1Xb+Vn1x/KarXwSYOFDx8svv2x6WUt/xhIcqvmzebzN0DTNEHSevAZAvFuAhcwiPKfVvgQE+QkcYFI/VBXnDdFrVOZ8C7tKBMaYm4j4yNdjgYB2XdNvHeI6nO/J6jgeR3ADY4zEpFBACpGSNTsX+s32G62Je00Hz7g8ibzT9ntmXN2u13z5xSu++PKKVzdXXL/YcnV1wWa9wmXfwu2D9yYR5UwcC+nrAzln1us1XdexWq0swKzkldgPCuNoMTDZcm+a+7v1U+c8zls4fs6xEdRTwJ02VYqUGVMqnFPAOwM/A0mPTwL0ePXkmEh55O27e0JwdJ3Dh0tWq605zDlzPBPn6VZrLl/09OtMipCyb3k26zM1Ja6QjXUszsy5pyv2xIHM1dAaGyRnj50+s6i77SU7C3OAAhTI7FoziVpHfKiLT+XwyoIrM0D6keWTAAvnHP3qAjiZUEURPJdjAlg6PlU0nRFRQNtlaj5puxlPMV/1MzWhSW4DBwyImiv3rDR2H1pqja6bD4jqEnwa2eoaAZpzRvCs1omH3cAYM8chMg4K3tF3a8vUpyXQTA38SBb8lfLY/CWEXKwEFlzknBC8cQ+rdcfFdsXVxZbf/72veXF9wfaiZ73pWAdHcNaOEByuxKY4J0gOhFBWODzqnG3z55R+FXi437M/7ozD8cI9OygZv4+HnUneWUlxYBzNhIs4QufNF0IVS4EnzTElqhGYxCkuI4krGayEusVhnVS+Czj1xMMDD4c963ee1Qr6leK8peUzK5J5pm7Cms2mQ9Wj2YH2s3FWHVDM5Fod5ioQnMvmfu6V8vDouDouVR8DTE2H6JBH12kpFc6cV4FDZrzc/LWs6935ifSe5ZMAC2OejXWei1lQeemnPNAmPqN8WIpz2Pp66jpbPi2rUtfWevCoUyjmVFf1z5kk0/iVatYNdjUj1aSs7hXExsKM+Qbz0iQbOz7jOY6Zu/s97x4OjGOk6y/sOsVMiQOXjHPJ2TJLpTTahNRiCoWW2KbrPZebLdv1mottz9X1BV99ccPv/+7PWa8D3mlxVbfYlhBsfxDBvDJrzENl/y1BjJu4Gudw3nP1cFV6TlmNwzSZs816hyVdTiWQLARnPiNti8JqQaoBc2Vt12kDpFhGgXpPlOIVWgnvbJ5mWSAm5WF/oH+XTQVxG66ko3e2C1kIPV3YEPwGJz3gSdFNC03xJs1FHUk5PJIoTsfAOW7A6XE5vGaTt0qe5wDDfFGm46oEDCXIcQEY0+d4Ekh5nq/4iwAW4tBuu7ix6fPTrqpzb7dTEseFpW43f3cnD8qpO/Hlr1JJbv8rNEADE1If+9ybq7dAARsrMWWcs9XaomilHe+8I8bIN28f+PPfvObX39xyPB7Np78r8KUecQHvIOuIiGcc9+g4EKDtCJbGSFQLJru+vODF9Zavv3zFF69e8OrmkuurLavecXPdY4nRM6Hz9H1vKeu6S+rGOc45XAiE0FEdwGLMJBXGsS9Jga8Zx5FV33P79i3v7u94e3fHbrfjGC3K83CMsBK8etbDSI3/SSkzioX1u7ZJsdSexkycMI4RTZEoI7Ecl3spGb7KM8RW5KiO3RAZUuQ47NntH9gfr0jphi9/1iMayLlD6XDSE/zGVJyyaXR1yxeX2rCLOplVH42/tNzYuY4xU7mecpJ6srqiIk+pDRbnza41BxAwiSPOFs5TlWc670+evvh7lE8CLIyIMaPUdGPn9PzT8+aTdTlx3RxktB5R7eA0122LcG1cZhGLpbrklC/NB2ROFtZw7PbQdNIZZbqktaU4zGQVy5Qw00ezwjBmvv3ultu394xREdeRVdjvhonMLVGIqRnvLNDNe0/nnenWJLzaHqlfvbzhi1fX/PzrL3j16pIX11uuLlZ4p6z76gNhINGVlH2bbtXMzDUQzJfdwnLfM4zGiXgRYln5us5z8/Il3apjs12zWq142O04Ho8cxoE3t28NHDpfwqyVw3Ek5yMp5eIYaY5WVepzoriq62NCokgmVz8G7Y2LESznhBRXbN8hGlFNjBEOR+WwtwhcVY/5f4tFMnsjSJ0EcjY/l0wxTRYD6jkHqYXFrvjhzCdkkzbIj84RaFLaaWk8nM5/m0jwRGq5Uxa+GUwWuXrsOX7kY5RPAiwAajZDmZkXba15usydYvzpKq8ztK9AUOc1M0/BAiELziNB9qYqqGRE5jmeGvVFdb01NVzKwGGRTQvA+d4eeCoOVq4EZRU2an8cefP2Hbv9ES1ivqqyP4wNKEKw5DOqZuD3EqxdakAhqvhCaK27nq9fveSrr2/46ssbrq/XXG47NuuAI7FedQTn227i1Y+lE0fwwbgbCTgfCD4gzjWyORfLQcg2kaL3pEuh6825q+97tvt7DocDx8NombqOI8NgSXRxdh9RJqVv9kQLiPkS3FWIZZ2AvhxFVvMypez5mikh86XCmBOHY+JhHznsjTSX4qnqfV88dg0wLDgPnGrZAsA8cVUEKbHmMpuclHHiS9DgfOjV9irBxsLJxKbyYnrq+wOImcBVpw2668B1QnMVraA6gZAnacmjsliqtI2lj1E+CbCoEw6W4HDGhX5Rlig/MRuilIcoU5AJlGS00qzdk+ZRB4RREFkskAtnZqmqjtTckVosfM1Ls7XBKqjPRopFpW4NmCmDO1mzLJ3+kbt3Dzw87NkPIxonXmSMZad0TPdXXwaJTn4Q5sgUIZtj2Hrdc3X1kp99/SVffHnNi+sNF2vHykPA0sz1QSzfga85OB0xZwMLV0K5xdh3NwPcII4c6i5q1efFs9Ie52tYtLDZrNgfbVf1wzgieLzcsT8MlpK/U7xsOMSheLfWl7SH4LxQo3CdVitU8f3IkJP5GbjSB5FEUPNt8WVQxZjY70bu3j4wjtd03dpA0BWza5YSIuOaybayZArF7f7MRKskZh09BRRsslKCyOoYrbTWJDFXn4myVpxWjalj0ykT7zaLlp6rIfX46hN08v6xZIsfBRYi8qcYa5KAqKr/soi8Av4b4J8F/hT4a6r65tl6mDr71LHqVDc7uf5T7VrqkHlZVxMVhSLuzqSA6l4ukJmceJZlikBVBXXSvBhda+c8QlOLB6Vg/gG28o0p8e5hz+3tLQ+7shKPh+YUlbRHVZAM0TxNLFVdViNkXUDTQIrmzxBWKy62W7766iu+/PILXt5ccrENBB/xbsR5CM7RByP7vK9Wj0AAfHJT6vtZ3I0WHwjvfcmuFVr/uuQYSidZ0hZhvV6zOpgLeYoQfI/3HW/v7tnvD0UdgTcHGzrNNF7B1zmkmFtPwUKYCFdxznxKNJOjKY7eAcGC0zUnjofIw/2Rw+FA161YrwRXeArvO9BgZveSns9I6qKK1Oss1IglD+CRaXIuR+FsfC4lkmrFqepkHZPnQslbv2S3GNPz66Vn5e/n99r9kPIxJIt/Q1W/nf3/R8D/pqp/U0T+qPz/17+vEj/b4m9eND+Ni7Iwgc7FVNqGPuoypz5ReUGaurJ6Fsa5yh1qOSksT0B1wrEH5U/IJI3a1KBpgExi5lgGhyORk6kWa5/Z392S9jt2774lj/ekeE8cRsayZG3WG1Q9KXdkF0ihQ7wv/gYZ1dFYewHphNXlmldff8nPf/YFX7x6xXod6ILQhRV9gH5l2wGKExTPmMzd3JfMTDmEKXW8a0tiuw9xAt6xCp6Mmu+CetYIgyRUhN4FoodAR+82dFqkk3HAa2YfPPuDbVq0DT27uEedmtrnMQuHaFnbzVxa9wuVkibAFT+ZmHOJkcmknAnOUxQMA9s4oIMlu3l3j+W9WAeCC8SmJmRCWBHjQE4ZdWqJgnNCJdP5ZdZ5W/iLU3hORaV9vHBpUJ5a059SC8x3dDlYXVtzMkhj8mjmfYpkE1YlwG3Jn4jwaUgWT5S/Cvzr5fN/AfzvvAdYPFWekh6AxzLc/DwA8vmekom4mowWFTCm3Jp2/crOT5vKNJHzPSQbAFIultPHFpq7uzt2Dw/EwXwTHOA618Krx5SQzrNedXT9ipwSw3EgjUfb8jFDCMLFdsvNi2tubl5wc3NNVwjFrvP0ndB5JTjLt+F9TRFnkkXwXXH6WWZgqvxRELG9QIyJtqDOmhRDy/4ZabLnVymk6zqurjpiLlYNHH1/ZLU2S8m74YhqZkzTpkDeeyR4hmEo21baY/GYaiLiLYOXN9Wzxgs186JmhmREoXMBL6bL397e0ndrNutLum5N0LpHR8b3q0fPsnr+Pvdsz57D93MEz43p02wXc2VjWW/xZKX4Ep255sckN+HHg4UC/4vYjPpbqvrHwM9U9VcAqvorEfn63Iki8ofAHwK8vLnCnwvy5/tueC4h1OMKv/BMdiD1NUuGBX/VHcWmxk31VOAQmR6cam6DG51yUrZ26PLlxfarGIdEPJqzToyZd2/e8ebbN3z3zWse3j2UhMTmfRdzJHuHeMfF5TWr9QbnHMfDgZwTcRgJIvRrz8vrF/z85z/n66+/5quvvuLrL3/GxdbyZa7WHV2A4AwsVBMh+GJd8Q0cnARC0+cLP+BKhm0RQkpELUldUs2r4MgOXlysmaQvcxYbhsGS23Q91zevePHihm+/e8PbQuTGmEgivFlvuH33lv3xYPENzoja3nlTA0sqAuOJzOfF+YALHhfsXdXiaXb3O8sQNkSyy2w6z6rfMEblz375DcchgQS6fsuql9bfLo8Gfk6Rso2hkkBTCwhcjB8oqscS/GfsGM8lnHkOLNx8J6sFa+cadmmVduep/Gbq0ccGiVp+LFj8a6r6ywII/6uI/L33PbEAyx8D/P7v/ewH3d2y02vHnnkQsgSiifuoEsYTv5ddrWZtfvL9qVVFREjVm3QWwKZq+48+PDzwcL8jZ/OXECzqdMwjMSc6FxaRszFG0jjiRVltV1ysem5eXvPFqxte3lxzdbll1Qe63vgFy8tRImgdULwz6/8tY7XkSaJY0vvTfSXrD1dWXSkMsYRV83xNKTHI0Hii9WZD6NegnpikpbA7Hgcutxcc90aE7vd7cixOR8UBy0nxf8la0vthwWrOmVu6U/rOfFBEhB274uyGhe6TOIjSB3ARHvZHbt/dc/WwZ726LKBAC0IUse0KnbMs48Vx9oxZtI6eGYfwSIR9mp5/bi7LIrBx/jkv2qFtgVtWdg4oHkm6P7D8KLBQ1V+W99+KyN8G/grwGxH5S0Wq+EvAb9+nrqew9lk1RGylfKJt5ZjHqsjcmatKBovfTz6cA4L55+eAwkymY9vxvRJrx6NtOLzfHYgx03frRkTFmMHZihoV0oy3iTFyOOzonbLdbrm+2PLi6pKXN1dcX12wWfe2w1cXCM6S9HgU54v64RTvZhKFmzJNF6+vdi/z1PM5WQ5TAxkBJzPQCZZQCLcYmN57QrcmeCVt4ars1J6SqXQ319cMhwP39/d0zhOJbUc3U4OKxaCK2Vkn87cmtCSmCcFiVrzvikt4huiI6Ug+RDQI240jJmG3P/LwsOfqamS9tlD8+aTPlXNUFttQnE6408/PjtMPKScc3SS8TGSolWSJhsp/FVaeAoaP0b4fDBYicgE4VX1XPv/bwH8K/A/Afwj8zfL+379PfU/h3vM3+T3GVTn/+2kkppXJAeb0uhMghcUx8+POOebUz947vHfFTFh9KA68fnPL/jigOFabLSkljuNgKgiK61a2/2dJ555TQpPZXZ0Tri+3XF9f8fLmBS9fXPHi8pL1xZZ+ZdsBOmfh/15M7fLemcPTSRr5yUpk2b7mwTjze3Il65QlxpkkFJ1nflLb9tD1PV23IpfNfUJJsnNxccEYs/lSjIn9xSX3F/cch0MxEydLFiG5mRgtWMrcxmmWJl8S4di91fotOtWh48BhnxkPe8jCRXF0G46Jh92ew/HIqt8gneVHNHOwtr1UUpEoGpCcjgkmCWGpgtCO/xhlnqtishiadDZvhLjzi9XHLD9GsvgZ8LdLgwLwX6nq/yQifxf4b0XkPwL+MfDvfl9FCk+YKOuvz5ST85Yd9sS5ek5lOc0H4Gah55MD0TmgEJGZL//yN4DQe3wXYMxkMimO3O92vH7zlv1hICP4rmOMkcMwNqKw7zrWmws2m43p5WWl71eBlXdcXV6YW/fVBdfX11xebVmvbbMREcU7KUl6vKm4MmVQqm2cqx110EsD0CmlXCgqgUkgBTCKlSLmKYajSiPOO4IEjiWS0ovQB89qtWK7LuHtuyPr9ZqLzYbj0eJgYhxKkFhq/Ro1mnXi5BnPg6RCCPR9j3cdvfMkHxjHgUEPxKSMCUKCISYOxVEsXiSCdMUNv2x1oArMYi+Kv85TYvzHnpCn4Q16suCprTaLNp2Cybl2PSWBf0j5wWChqn8C/Etnvv8O+Dc/uMIz+yyU+p47iUfSxUIlOPM7RWR7IpR1bhvPbukl6hZ1nz/vrNThLGtzzEaY3d7v+NN//Atev71jfxxZb7eMKXKII/txICXoe8/aWxj4vL7gAO/YbtdcX17w6uaaL17ecLFZs+47ghNcsM2C8F2z9tRcHKDNv2K+QXRtq3Ou5WRwgkkQysLSYQnMy5qrDi9SWHwhFSuDy86cyNrqaxLNqus5ro7E5HDeomJXfc+q6xmGAY2jkYozXkWka8BR+9iSE2dS8qiuTYILgRwzo5rk1fcruLyCZBsqQ6BfWzrG/WFgGCJdlwjBEYszXHVCsw1/XNn0SheAOnvqZ8fQubKUVJ8b0yfjdUa+1+k+nS7NRaAR8ifj0Kxq590SPrR8Eh6cQNlF/HF53nQKCySeB5a1T/X32W9pecSiSp0S50xLWZEs5CTKlcdm1KWNu0G+mbeC57A/8N3r1/z22294e3dP6FfgAsM4mOgrDvHZ2P4iHg/DQE6J8Xgk50zXdVxsNrx8+ZIvX73ixc0V24s1fR/KKgmheFO6oo5I8VgVCWhxRJsMOJYPk5LlPqm2XjNuYLkbV7u3bG7RWVIhGjJebO8USqIWFaEvF8q9cS598MRQMnIV6c07V9QcbINj51r8RwOJOiEwslhT8YxNiexsk+c4WPrd4Mxxo+tXaA4McUeXTKiMarzQcRhYrTr6PiyklWnovO8EPz+OTv9/H9Pq4zWstEfds+dV/mL+Aswd8CMJP58IWEhLPXdanu+gUzPTLBpv0UGTXwXUzWeWv9cEwVonirp2jrH+riU0Mae9CQhEytaKULJw20ZA9bfDOJCPGbTjze0d//Af/im/+OVviSnT+UBMyjAmnPesLramQngPWTns9oS+tzD1ZHEdLy9e8Jd/50v+uT/4Z7i82LBd9XROShZoR3COEDrLoenM+9EX1UGrf4G6peuzGp9SYyCEMnDVgph8DSNX80Zt/gA66fNS0wu6koy2iPAZZ4mNBbxs6Z2wXffk4YgPysXlipfXV3z75oLvvvuOdw/3hN78NPDT/iBjidOITO7UlUBGbNeuGDOHw5E0jjiUvg8E7yD1jINw/zDSdfd4Z16c/SpwcbEhdCVdn05baAbxqC5X5aWU8eTQLFs3TueUrpqNp/Mlxad4uFne2LnJ9uQaZ9vykVSlTwQslHORePB+nVB5BZFKDz9OUrKss070ChL1vEJSWn5+qp3k1FGmtqtKFfPP5x5M5zwxC4chsd/tuL+/57jbg+tIYya7REomDvc+NP3yeDgiImyds+zboqyDsF73XG629H2g9zOiMpQJVgi/OlmdmDnQO9vMdzKR2o5fTdetUgFmfWigoYq6KXtTywhSwHDuNWjF9jPVkmPC+AxLuOwFVl0AWbFe92yHTeM7Hg4WiBaOtg2h9b3HuYQ4hysSTqzZ0p3iZ/kfUsk3YgAFw3AkxoGuD3QipJgZhmjbBpQkx1XNACMJbUBMKfffRxo4O8ZmSYDnwAbgngELd0rKn5Uo6v8n/MYZH4uPaan5RMBCHjtGlXKaDGde2kNutbSzlr4ST1bglseUiM66YXGFCij275OmzB9CjVWo5BPlpVA8M4XD7oF3t3fs7vekUfEB4phxLpJjIrsaPVu2MKzbYJQ6gw+sVoHNamVmydl+liEEXPm/tV7mAVgWhl1NnXM1ybqx2AuZiE7zp6DsRD6Z7ur9VV5BNVL3MIUpbBuXi8VCyanGzphr+QpKuy2exBWJyDnw4kiUFIFz57oawVf7hFmSZBX2+4OlACx6ZEqJcUyMeeSis82TRIRxs25AMRwjYzRHuRDCYhSllGwdmfuanHn2T5X5AjJ/f6oO1aW3ht1jKo/m8fww61DNefK0Wm3l+8Iyv798ImDxNCg891Cq8CAIVH23FN8G2RmnKybrixQzXP1BBZyUtPMtGKiCysxqcObhnNre6yvFSPA9d7fveP36NcfdHhHoQ4fDyEcdlTiOttmvsw17ui5AccryYrkjtusNl5eXrNdmVq0b4HrvW2q1ulcKM7XC2lQlsJkZLpcJLmJkjjytG0+SxJnnInmhcNvmPo4sFkuTa9o3C9lCUfpVx2rdM6Y1x+ORruvaPQnOnqkUsPO+TTiZEXbNH0TNijSOoz2vwj2lZG1LjejLHA6H9joejwzD0HJcOh9K3ywlig/1XZCTsXLu2CVgV/GDxf/TVVMDBpFZ7jjJZ4HkpyifDFg8NUCfUydql02isYHG4/rmDypPcQ7l/0U7il9AFbW1Zb2x78+1ef6+MGnNmXXvub+/5+72rWXCEqHve3CeLgT2UPYnBQkB3wc2G3PxlpKcJoRgpsaLC66ursxU6CcHK04HHssBOXeysnaCzqI7RZ52FbbfMzoTfZsKVnffKoAxXdOsJDYRl1mvVZW+79lsNqDCOI4tEbD3Hu+ETCoRqB0hxAKEDikp5Go9tmmyZfwajrZjWlWfEhb/YxKIEbaHw8B+f+R4PJasXSUlnQ9IeV56xtfkcd88t5A9L0HU/p++s5c0KXd5fTk5vwnOIk0y/lBV6UPLpwEWCjpzbV3qXsuOXoj+c8tD4R2kSBIqpw9bGz8xZdFSU39a0JiUp+ZKngMhl53IIE3bKDJNSrP1a8lZkXGhbkJcE+c6NrIm0LN7d+D+fsfhMLLabAh9h4q3fUFxjJHCW6zoQocLWwMaRjqBjXdcX/S8vNpwebHBdw6/6ixa040EbwRj1p7s6316m+DiCN6I2M7ZFoW5gl+xSKgcJtVOZFrJnLSdyXwBF+tKOz/GsmeJYCzmbDulDuNJRDJRLQ4nqYFOD0jX0W2UeFhz33dc9oF9EJKDrI7QBxJKzL3Fcjgh+N4iVSlZ1F1PCB2hM+sIO7O6GIhmYiobIIvFCw8pMGjHIfccRle4qUzWA0pCvG07kcXhNSykg+nlF5Pz0XYPfto9Xk98cNqYLePW+JcCrnnVyOY6xqSqQm1I24InRWW1Z4epvXXuiAEjRSP+GF5inwZY8DQqnqLyXLyb/7awY5eIvPnAzxhbDzQSD/PpRSQseYvS8VlqRqLqlDWJhiYOMyMLSz6CM+RVHMx9+2Fv6ebMo7OzPUaYEsGaF2LXMk65GuhW4kY2mw3b9Yb1em0E5qwvJo9MKaRgEauZtU9Mwulc11bptvFOBQehcRpm1ix9VbxGjXWQIoVM0bh1gIqEpZhcncHaM5pWwbmKUT1VqyqSNeGLNOUE/GCTPxaASyk1c7sryYAqh+MKd2T9Wp6BmvenK1m/qoXlWMzRaLbsXS6WRMUVMM+PO4u3ORPIqFN/SRlryjTRmzTWFjcKaVTaOztuXuZ9dzrzl0AyL2Xh1Lkx9YeXTwIsYkq8efNmgd5gHZTKhkC1zDtxntSj6tKLIhOizxWIZh61/+xQpSVfseMLV1EnvzrLoDXTP+cDvrZh2qtSGsuvw47Qd/z2m++4Pxz59i7B3Q/qqs/lB5aHeWb+e/jTXy9//xt//a9ZQJrlIkfF0gmS4nLiVs4Gx2IT7PZzMeHPJWUm/soAYiZRzDglBDSP9SSqVWyuNtZyGlSdnyBRK5gjZ9Hkg8qnARbjyK9/85v2/zxeoZa53l07vlj6MIQAACAASURBVEY/1t/P6YYwOVOdfi8izfqwLOZMNN8HtZrj5iupqhLKythUj5Tb6lbDtdPwgJPAmBN+liH8c/l0SkoKvWVfT0lLciHb9e4RWYlj4WszKza2EvPkR+fMmU8VVyUv0w3nJ1GXsflbrW90y2tNn7NFC3+E8kmAxTCO/Pmf/znAI8li/t288+01/33JODdwkSU4AM39tYnn5Zy5FGJb400gUY+fg0U17YUQ6Ly3JDBZm8SjqmY61NH2voiZ0C/B4vJqA+LZbtd0ISzGx8Z7tus1X716wcvra15eb7m5vuLF9QUXmxWvXl7R94UQDNKu63xX7ssci+o+InX7u8cidV3ZqlnVF15mWiFF3OP0hOUeYzw2EXohdZGRsvVhzFOUZNTImCL7nakCu4OFqD88PPCbb7/h17/5hj/79S9JWZFgVp4xK2Mxv/rQk3NuOTNSSX9XU/Y5FyxUfrCMXHE0h7YxmcS/Wnm2F2s2m55/9GevATiOSt/bbmmqingFD16lEI+WO3OuduaTCbt8P+/tWxeWeT/O68jTwZwt587RupObkRM2N6YAyQYaP7J8EmARY+Tbb1+fFaGeUkFgUkOmAT/lzJzHEZyWpLmRlNUC0PaoLPUxC92u39UUbiEEyNpUpK7rCM4xjmPR5wWNqQEJkhjHyHEYTvRcC4P2zgbeMAzFYmHS1boPkDucatl82dMFTx8C637FuuttUyApE7uqRJXDwMLJjcd4HDRWjz39TmZp7GSeY937FmuianuM5rLzuRaROhdBHrBdvucktGCqYbR9MKyfpfVTv1mz3W7ZbC1/p46j7WOaM0mnybVarRjHsfENFRwna482c3IbO+JxzjZdTppLcNnkFXn79o5x3Nh4EGxbAe/oZwuL5QCd5wN52mS5zEsx3X8+E9awlEBMX3Ju2vz6tB57X0oQoe8X9dWpbf3yFyg2JOfMw2EPLDtuDhZN9Zgh+3Ok6CnoLEoZ8cu9I029aQOs+Co08IAFWNiKGm1X9RBwmFtycN62FhxHQjDzZ1LL4zAMsXAhy7bW9o7jUFh8oQ8d0lm8xKoPbFYdm1VvyXALAdp1JWKy+iNIwDtfErnU4K56IbXYCgnN/cp079O21Ezbbno1s95kqs5ZiWpek5JNZK7Zxw2KJ3FcJJvrtEDC4SURRRA/kZuqZq1ar9fNhBpTIuZcNso2C0oFFlWzjGix1Jw+9xY+P1sEcIJmtY2ScqKL04T+9tvX7NYr6ycB3/UgHp+nLRRPJYJleL/gZJIafHBnz4FpU6j5+Kxj0MvQgOjcWBe3nAPTPY6PxvopsPzY8kmABZyaS3ncSbOOPwWS+l0tp74ZcwISmMJ+dXnMog06iYWt0/2SS5nzKCnZLuu9C+wOD6Qxslmt2a43DHngsB/Mo/BEf6yh1cMwMI6mrvTFMakLnnXXcXFhIegX2y0XmxV9mCZYSgm8s2Q3ZTMj7/zkwapSfB9cYeZzkzwmyqdS/vWkzMLm5vykvuTlM7HPBg72uT4n+36ejUzVnLG0uFQ7ZxagcRwNNNTR94GLi4sSbSqkHIlF/Yk5lXgRh+jECwkly1dW64czk9FJsH4qkzJFJa2mZ/ndt2+566eEPqYyOaoSek51OwWD5evU1HoecE5/c3J8EiyamunO1DufyvMNu59SZ35A+STAohl2ZmDQyjM32+bDbJJPJrp5FScdJ1Jya4INZG36eIs4LRMniDzS9s4Cm3Mlx4NJBc53XKw3bLsVve/Y746ktw+cxglJ8GQxFURzxjlTa/q+Z90H+lVgu+7Zrles1ytL8BICOkvW6rCVf5nG56TNjXB3i8FmJ9R3LScbUEgxkUoL3FLrl4IjHme+C7FIgrXfVMtWAvP4GSlANZsgiz7UluS3782M6o5FrcvmoxELeXy6gIgYUV2J5erl4Zkmmrpya8WfQUXmW9dy97Cj208LUBZQcScbFD9trn806d3yQT8HFvXdJOd4Us/jFIin9agmOrd6si1/oTgL6gr5oac5ad55jVgCc6aa51+YbdwjIugsgs/OX0oskC33ozuRXsrnVCdXNskjx1j4AeX+uONyteFnX3/FzfULvPf84rtfE48Dw+FIOsG+NIxEFxnHI6vefCnWq46u8wSvXF6sePXymqvrLZvVihD+X/LeJNaWZFnT+szdo1nNbk6TefNm3VvviXqIRohpTZEQQoWQ3qiQGAFCqgnMqRnTmiIhIdUAQU1oxAQGzJAQE5pBPSEVAyQQqOrWuzfzZp485+y9mohwd2Ng7hGx1t7nZPPylY4unkqtfVYTK1aEu7nZb7/95mZXFG/1FOKdCdcWN1jzsqupVNxiEauRutqvd7ySP5YqmUft2GUutlbwT8z1d97jQyCOaQY4AduNc+nMNU1Qla7KdfPealdSniyMc6a14dUTt1uSZm5v95yGM/lUANIUmcZoJfqn0yVzNRsW0a7iduccfd8TNaP5xHGMRgbLlTAXSdMSyx8PA22w+N6MiwcRklhNSZ0f81xYebbz48pKO+Kz2ZA1znHtLVfMpaZM11SCy89dnoeqzlXB18eeOTA/w/gkjIVFtz/+B4mWwt2Csi8LXy8urrHAL1fp0gKgqCbndarKU2tNKgHLGH7lJiyRygz0hcYmahon2t0Ndze33N/eGcYhjiDGp5yusCYR24Vr7O7cGreBUNSlGm86lxazL+I14nwpiV9CBVfi+GXCWHWpkdGKi3q1cy07W+056lcFZyU1fDX5q1KVeIOLq5KWU5jbrmUtehdF3xITrolTXqjWpJlEFRy0jWfT93N3NDClrhFr/jxN03zu1rAoz5tNNQSVtDansQsBDy1EtAxOVptG1c9wJYwVqzqu9wEqVnspnDs/zpOx3NdVKvOj+NnVsBCtgJNlkqku2U9x61BvOWbVeF3OzT7s3M+n9v1JGIsP5ay/b1xfhDw3y70+3gL1qerKUJRXtRiFQphRmaPsEo1fuqL10WL4fEGIyTHRti33t3fc395yPp9nJSjgiUJ80zTknOn6liY08zlWZanWB9o24ENdHMsCoO4czrIO1qO0LPTiRbjS4k/EMg+i3vq3Xly34miowzqmVY/Czx5GQsnzgi+fQ0hqoRq+tA6o1yc6lAhNwBWRGjDtiVj5JynOVOg51Vp+0wziOk8yqiwiUlLQsWSZpBiC0jtEIcdEUvN0QghzSLL2BKoS+cV9cA3OaREmto5kJmyZloxOPU6dSyVzxdposADA9vf88WKQL6bljDDPRkmv5lf9rkqzz89gEarzcdZPmxfNR/Uzfsz4NIyFPuVC/JCxxMOLNZ1d5NmzkHkizZ+rIYtK8ShYBfXVo1jlzWck+lLU1zmHK6BTniI5WMHXtjcVq/vbWx7fP3B/c8vpdKJvO7IOMC3nUs+z3+ysYXAcoQB2m82GzXbJfMwVmSUjMOfsRWahmutsgBNXTOUKV9A6aZMJ2ZTiL5fDqsBWzEBkq00w7+Qpl8XCj6J0tcpAmGEyLCenpZ6kpmJretYVrGfe0ctx+75ns9kYRjGWdKJCzJbJCDE+deFVZhB57VWYK24TzXuoCsPXWQJVJceqheKp2c/ncQotGaJLQNOOUzYd/XBZ+vrc6+NFOKHLFmcGSi7TVysjU27pEzyjvh7jjw/xnxufhrHgw+7Zx4zHpbFYSq9xbt6lq7FYG4yaMgWHpmyxuKxcRtOixyNz8Zl9jkKAWXQWDFxzxBLrtiGw3W65v7UwhKy8fPmSYRjY79+gzvHdcfkN1X3ebrcEJxyPeW5xuN9v2e128y5buQPOOUJJm1JDgAomfujaqWVCLt3iPO9YAAGPaiKLI+dkeKez1Ke56eECoEzZznPmtNSdcg7Tys68GjV0cM4EfQFyNjxkncRqmmYGc+09y7nX8KV+dzUWroYYMZGDHayGdzkvFOkZA1idW8555s6klJEgBOfgioi2TqE5MfzI5t2lUM662HC+BVcbos01mY24wqwYp6rWunMuWahzdikgY30/awjowrwOqrei+uE19GPGJ2MsJBeLeB1CfDTcynWfQjXj1BBwjy7pNHnqCkpepRZL9WCd8NZwxjqe26IAkYCKxbmiKwZnjDhnC1i9sR5D03B3d0fTdWz3O4Zh4OWLPafjlvvbnqzj5S9II7UlX/CeJjiQhk3bsWk31nJvs2G721kf1FLQlZLSttUzyoRyrs6VBj21sEzBWHwguSlxcEHHneB0IWxVQpnTEso9SdHp7M1Y4sRIWoecCLJUpuaymOfb54ymtZCrPG3bc5pi8ZJa3BStXeGUkNhw7zY8uJ5daDmokMaIEyWguJyI0wTqmFIhWomQJJOChY9ZcpH/83gCIZSMkSrOGTEvxWVyDXHAU/qX5gQRIovDuQYb1+HMc7v5erHa/FsqQS83v+rt1TDC8B2bwRUTq17gyhixOrHyfbU4UpM+2TCea234U8YnYyzgMq78IWPt4l0gyzBP6mt3D0BYLL+UXH01AOYuOwRTj55jwYJlrI81eyxZCd5wgtZ79vs9N7vdTDAKg4UUL1+9MFzlN8tvcCJkTUzjiLQtkk3ZumJ7cz+MEMwLKgpS62tloe6Cy1xO3qeJVHttATkvcZjioq8+JyJkLFux/neuYZ/l9ZbzWV2nlNKsnFUlBapX5lKoqDS4S4JW5Z90RUt0EJmbBBsfxfgaKeW53+k6JTyHJsXV+ZD7X0fFVFhf17Ljf2hOPhdeLKMqh1G+/9kjlOfX7zMw/anK7kqkCai7nW2wC662Dp8rRvJT8MDnxidhLISPU2c/PNzVxRbsJjnTabwyPvXmejW9C6uBuJw03vuigu3n/hUV1PMF/U+kIuVWCD5lY4jjyObuBa9fvmK/3ZXaDFtENzc3/PrXvzZm4D9Yvi/GsbiOSuOEsO1oQ8N+v+P2bs/LV/dmyArHIDSNNdJZK0f5Zs6kADMGAYv77iXMQJftiGFODc87Jd7cWUqPEJyxVlWQBMkl/CQzdqFqxXZN081Gwyl41xBdRFOGZHT+rEpORWIvl3i/eCfeOTR4GrFUcNM0vMwwJeV4OnMYBk7jwDja3T0eB3I+IYSSRTOwt9+083XJKTMMJ5y01oqwitHk0pYwXeEVMZFF5z6wirUwvAYHn+XYyJpq/nS+fd9Yeyozr0MyzMzVAp7rqqht6UE2iw9d/1+Pvaa9/0XGJ2EskGW3ub4ZP3Rc7/bTNK16Vlxb1iXmAy7Sqrb41hCnw5W4vrI51/9bXG/p1Ay0bcu2tz4WaZwgW/4/pYR7fJrzNuS/YVvaDpKFprFdNYRgepQp4cXhQyFs+TBXtOIdzdwVfRGaXXCassuKziHU4g1YaFF/ty1g65cic0pIyK6AvslS3JIX0V+7mmsG4sI+zNGMqq8TWBYwUlXNZS6G1j4f5r4oXZdMEWy/Z9P1NK7hxGjtBqO1QBSXEPFETZeeoYhBt0lRN81CRLbLKpS+qU4ucyLzYq/g7MxiXebYNQbxXEiyno/fN5ZwRZ49/vp9lx+sSvMFp5JFA3XtyVhk/wcGcP7UsTYSsOyk9bm5b+ZFyLLKaqxSVfVRtIqkLHoCzhWWZ3Wly32Z6xKyfVfXtLShgZQNKFsZlhjHohG5jE3XsNl0c31JLr03vBQ+QrQdWlrF+0Djln4iUXVuiiNSCt20SP+khKrgVKmqTnkFZprWqJRrUftx1OtizWtcNRRqalLrbEUubr+lZv0MpKn3hTmpkAXvG0iFaSpLmrZ6g66g/g4x3ZiyC4YQ2HYbdrsd225rXlVVIi/X1hVgOudMckKbG2YcQHXOsq2zZeu5cj3qHEkpUcvQccu8WXCE+u8lbNEirjPPtedSnM+NK9BzPU8tvCyhsFvm3nL1dN7S5OI3rn+f8oGf+6PHp2Es9Omi//goaknlhtb05npnqccyF/yyYe8FAewZpFhEEJdmY2E75Qqj4PJ2iC76ln1Jc5LtvU0IbH0PmCFp2stLPhdNjSPJqfECQgJNqCamaSBrRMTovHN7QOdofenE5auXUCeqWsNztbRmKNciqZ2sOHAXu5hlfZyalJwAmoXoMpI9c61B7Zq+8iTWitv13HwBXFQsrMshzyHcXIOTM8blSCu6wSIoI2LhSN+W1LEPtCEgtfw/KUkMZ7BUqZKmiPiVS16PK8aH0NV6K47VTPmuZLecS6bFyyyAXOfSh8Zzry1dzp+GJs+FKst5VTOQZkMA1Zhceh3LZxNuVWV6baB+iqf+3PgkjIWyUF0/Pj7Mb0/oTP2+NhY4cCtKd7ryIi48kbrgUNa023q91y4jWush7LXg3AzKGZkJOh9obl5zPJ55+3Di9uHh4rzb4lEM48nwjeBQ9YhmUyhfue2kXBaIlWmHri3nStmbjYJNiksPUGnMEOQF9LPFat7DuvtarjgHOhOQtP4tQm0rIBXVlwUkXYO9dbdXtexJSgm9KowSMTwnC0bGgkKOK6laXdKsoWScgjN6eXCOqMlAVs0l3DBj5XD2XL2XxtCbDUUNe7xzBIFKQfAzDpTRrHM4FVcFJM+mPlev/dgQZD0ujMYz7Svs0OvjV0xjKVEwcJ4nxuhaaPqnjk/CWAAzSLjm169vQI2p16+tn5gr8vTpZ2ENgV5ZXbu6dryy8FRyYT0uJ1ONWc5LMVPbtnhxjKfzXKq+7TdsNht22+3M0c3bht3uht/89vc07hJsUtUimpPILtF0G+5ubnl5f8d2u10yH1kZ4zhneWYasy7lzUmN9hzPxvnYtBsUK1KLUwmpsrK7vbGGPVnwoZCWwBS4Rc0zcB6nMmcw1oI+FfNZwqs4/9bJ5cXDEMc4jRdGeB3jV0RWi+p21jz38wBKYZ3j/sUtL9/dz8xNTRFfcJVKGssK05QIQc0rWHmSS7VmvehPwfQ5/CjX1gyPv5hD15vZc8Zh7d0t3sB1qPw8LlHxpgsB6fL4nMr9RYYnr49x+bv+oLIh1VUz0IuLtNDa1bq+cc8Z8LpTOj5wgYSFvi2gs7VevUVlluKTq52lnpMRfYzU1Jc6hjyanH3XmO6EUyArQzPNJey77f7idIbTiZwjwQv9dsOLu1u+/PxzPv/8Na92G0SEb7/+iun2xP39/czcHIYBlxOhaVBxTKOpTp3HkdPDI999966kLe3aNs7jvVWzvnr1aiZ8abDWiCklWh/oNhYyxZiIOeEkENpmrgNRVSgGM4SAeDNAORWj5gFx+LBUgKpi7RdW43pSV0WzXP5TB+KNybnf79nvtxwOHVngZtMzpcwwKTqM5Ij1MI0RVW/VsPXwWnqXyHKPK5a1zipLMci5djhTo89/TApiZlheP6eX+rAXGMTaM+XKC6D4DlVVHaGqwa43OQuFzQtaFTJ8+ER/pvGJGItl2MVc/rbxfIjyMVGPJ/mPC9dsfZNzqSlZGaK5Q3ia06cOQBfsQ0TmfhN9qftQoWgx2G7dOG/sTmefjdGk4K6HQwjO03pTwNrtdtzu99ze7BARTieTndtsNrNnU3ur1hkfY+Q0DJzPZw6nI9++ecPxcCDFTPCetukX5WwgZqus3aD4qhTuHefz2cIFX/gdvsUFb8BsFlJOiyixlIK1fIkXzVkiZa7lqKzLlNITaXxximZIqzCzHq9tGzZFQavrOo7DmbZrCEkQJuKUSvtH0Ggok9Hy7X4mKotxFYbUMOojC2zetX/IGrzAE8tOvtpkZPWoLLjX+rfW19f8lsJoW6je83HMiLAKrT+0N/6c45MxFhc4Q74MR56+72PKDfUleVIwthykhAf186uYzrIKgqhVSpLTjP5XJK5iI7nMvqgZl5Subfniiy/oum5eNHUHrgv88fHx8lQUEJkb7vR9z7Yvili9SeNv3m+Yponz+czpdLAq1KYhFb2FMU4cTieOx2PRtLSsyzhNTFMs4UCR+XMe3wTGODFMI/f3t9ze3rLZbQ1oJpEUvCZUfSGBFT+fmmUwgDJnE4OVXLMazEai3svaNyMXI4EucXYuTFtdXfssoE7I2UhxtQXCbr+l61sOw5HGOwgBxTFMhecSk9HFUy4p1dK5PsOih2n/LzKDH5hfylxr8VPi/csMzNOsyMcAzw8d7wlomf36DRjfdG2W6ms/+vQ/OD4ZY1HHclH0YhdfXlvfvA/fSBE3V49ej1zTpag5ec9gG1AMhrOFYmnsYtDc5XnFGNExcrvf8+WXX9K2rT1HQn1GeqFt+7nL93rURsW7fsPtzQ33N7dsNhu6xghGjQ/c391xOBzQnHl8fKRte25f3EOG49mMxMPhwOFgfTCGaaTd9Khz5Mk6glu60XptvHv3jvP5zOPxwGk4owKhbSx82mxQXao7Y4xolhmnqUbCeqlGC/vSImQ8X0etFbCXi2d9n0VMFm/uwDdvxktja+ehbQP7/d46tL19W4xvQ87QeMfoHCLJKtALeczkBl3xZBaAs363E4e/2iR+rvHcb73GNT4Gll4ebMmqPD1+zdb8E3Ar+MSMxcUNk+W5D97HQob6qd+T0eJVuJnWW18R0bkcGFdvyApsLSreAkVERdntdrx8+RIy1kwIIbkI25auc2y3RgFfjwoGbjZWqfry5UtudntruOPt9tzdWavC49GaFD0+PhK6lqzC+8cHHh4eOI9F5zNacdd2v6NtO8MtsoGQ42hg4+l04nA68nB45Hw+k3PkNA589uIFtzd3c8MfcLNWRErpglOSSWiymplUirBQV/ojV13MjJYUsBn2cl2rSHJt1FM4CrlkoGbAtpjuEALbrdXImOAPOG/d1WvVqhRMwnABE0DOYvUfemEUrH6oyvDVURfdNeD4Qe/04zNsxsWWWbN+dXly/r55j1yKHC9GwSnWn1nCGbFQ+jnP4md0LT4hY/FUYwCW2PJDln9uRXr9fLmgidqZfHVMp+ZdVHdZst0IMZd4FrdxghQhlPmmFIQ+abKu3yLEcaLvOlOm3mw4H46G6Bfp+DiOVm6+2bDdbp/8PlFLhe73e25ubthsNhehy83NDU1jbe1OpaHv4+Mj4gKPj48cDotc3zr0cWWxo45Q2vmllDgcDgwF35hSIkvmOJx5+O477m7vubu748WLVwV/KWK6K4KZQhG1MUMiadXLpZTPAwunQlcp29VinFWc1p6dMDM9K81ZSpjWdc3ccX3NIJ2vY2EmzFkHtTDHXufie5/jItTYv3I06nF/7HjqDX94XHsd9px/8lztFrc8/0/Gm1iP7zUWIvKfAv868LWq/gvluZfAfwX8MfD/Av+Gqn4ndmX+I+BfA47Av62qf//7T0NRTJ/AlYa4ywnU1+sNWEIRpSkYw2LF5y5O5ZqaPuXKBVQgh0u7bbOftXy76qVLrUD2Ct6jPuDFkxN4H2gd7Not+36PxIzTzM2uh5zQNOFcwzic2HUd+93m4pdbuCE0wdF5IbiMk4kutKh4Nrst4h29XzIS337zHefzmRcvXuFVOB9POGfewKbb4EPHOEQk2GfGKYH3tPsN4zgRhkR0Z/M2suPbt0feHybavmO3ecf9/T1/9CvlxYtEEwJdaJhSxInxR5IT8yLEEbPiou3q2RvW5KKiJFxSk9yPRQagZIdc4YNIApVk9PLkluyUZlxONN4zpsh4OBBy5rbf0ok3mf7iuTStEJLAyUJFq0A2UR4VE/y1itcyH+q8EMX55R7HkPGd1QORBC9L0dz3jXVK+LmQ60lq9clC16u/8vzXbHjKRnbhkVwZj+tMy3Pf/xcZP8Sz+M+A/xj4e6vn/jbwP6jq3xGRv13+/R8AfwP4p8v/fx34T8rjR0dN7dWL5HFFPux6KOsLm0mg5im4K4NbY2VYHLrlog3Pn8jVV84TZX4ocWYeS12D+b7KhA/KZtfPhNBNv2XTd+SceZcPtL6l7zZ8+YsvL75jv9kSGsfrFy949eoVNzc72lJ5Sc4GSBbef9d13NzcMI0GlE7TxH6/h1Lw5b1J8J3OEylaUVTb9qaV6T3eW1e0zz//3IDQ45Fz8TJijDw8PPDVb3/HOI787+2fcXd3xy8+/5zPXr7iiy++4PXLV4SCXXi3FLNNaZjrVKJaQVaDGZbTOJCKDF7wHpVmNr6VjGeyAOa4m2aom4v1AELrkejY7TZ88cXnfPf/vCfliPN2sZ2DthXGUXHB4ZtARphSZIqRnK1e05f3ChmPt1oc60DBtjOjnXM2HQtvSmEf6ixfjcKcVZnX/zMVputMhiyA6/Lk+vX6fTq/v87FS6NTDUl5nL2lZ93sp8/9hPG9xkJV/ycR+eOrp/8U+JfK3/858D9ixuJPgb+n9qv+FxG5F5Ffqupvv+97nCwxoylK19RV9TIuRVvKizMhZZ2ys9eACpDqOr67JMV8bOeQKyadr0icGhdAxJkGgqh12SqNb4bR8IommDx/53urFi0pzMvvMPJS13X0bbc0PM7WRYuc0CglFSu4fkO6mWa3fixhiQt+DgPapoetI3QtXbcp8f1Ssj9ttpw3Z06bLcfNhtPpRIqRIUVELaZPKfH48IATYTydDe9Imb7rTMWr7eZrl8hkUVz1/pwU/KF4fMGbBumK6q2qJFl4MYt4iyOuUoKLzuZEaBy3+y1t2zLlxEZ6pjGSojK1ip8Gu+eZWWrPXELbMETKtMiWbltXHHuHoaNa2aR5XnfrKbLMwZodmu/kxeuX82q9WD+woOdxGSJ9KKRZexHmeawNzPX7PvJ1P2L8VMziF9UAqOpvReTz8vxfAf7R6n2/Kc991FgIrIAknQ21UEuL60XIC56giner4h0u3bvlQlJCGGaj4f33/+znUl0+Hy0lR0QQWyBezZ1W5TyeGOLAOI64JATXsO2VsCsd0dU9sfK1U/im72m7YJTvep5SOpYbCdpwjM527CZ0DDFxHs1LqpTogtdaDxMfnuh1Vixkpk87oW0apmmiGQbyFNn1GysrT4lpmnjz5g2Hw4EcE7cFU7nb39j5r8hHzpnKeL32F7ob3gRxcy0RnxeBNZw2CqkZmiqEVDtpORRRlkfuPwAAIABJREFUA0f7vmW77RnHiHaemDJTykxZCSESsexUQgup1G583cwVrAhNJmCRz3dqWiEOXWUg8jPLetnxLxfhpSdwDaquw4WPL1598vj8+y/fJ2sj9TN5Etfj5wY4n/tZz565iPwt4G8BhLASWpkfS+pshq2wKsOZwFKUq1aGwoxFsczKU4ad1EzG+jye0q/r58t5zn+7ej7Fa3HOQFBL9SXGcZgbBY3pzMlbKz/XeaYccbrItNXRN1bSfrPbs2k7E70p5+B9xWfc7EV57+nbjrGJuNI1a9tNNH2H86Hszp6pLMSUEjkpIVjBmaizJkUlvPECjfNMYbLmPKqzAM04jpwPRw6HA8fjka+//ppxGLi9vSWUQq+u69Bs2aAlMyFzCX09Z1+qUctFJukigyxSi7YCImn2gNY7pzgI3tF3Lfvdhgc9gmvZ7jLncWKM4NyIS8pUNEZqjL+urFUtiZtn0pu2kOs52nVPT/p//DAM4NobWf79PbyKq8LGtVfx3PfO18g9BXJ/7vFTjcVXNbwQkV8CX5fnfwP8evW+XwF//twBVPXvAn8XYNM3dl8vwB+ZmZR+teiXzIfDSZyNhCOU3V6eXNjl5lSUa30ml7X+cuUmqi6fV2msIAqILs87YRaYknUcS7kUcTkHKTOczkTNDHGy3foqXG3blu2mY7/f0vc93pnhCc68hUqGQpQUoylrZaXvOtQHmqab8QJfPIlxskrOGEfyaHU3TdPQ91uaBrzvaLzgu47ghK4xvY3jNKA507btnDmJ+xsOhwOHw4FxHDkej2awmnYmnwUU8R7nzPALJhI7DaOlgItgz5rQ5ZwrxWxlkZaFWu9vkBIkqOKKQetCg3Y9u92Ox8cjaKJvrSlRGKaSdl274DJnR2ppvNMS+ujTjMgFUJmvjMfV++DSaDwffizv/9jrF+/Lz7/+fUCrX7EYPxTG/EXHTzUW/x3wbwF/pzz+t6vn/30R+S8xYPPdD8Er4NKCwuXFr2BlZTvW15+kzqSI7D77DauWbu6yecf1xX2u0MhebFBf4vHasITKD9CiepbxInRtg/eOlCLffXfgfD7ju5581TjEie3ktYEQqvhVyFB/U6VOi1gnrZQyaUqMKXI8HpmmiSzQdT1SuoinpJyHiRgTwbf0/dk6nfW9hSOOGSdwztHmhMeqQYMPhK7H7W/Y7/e8ffuWr3/31cwkPR6P9EXkZ993eKDxnlD1NtVo4U2hsdt5JzPNpZ/I7EFkWJenwyq9WaTxvDia4NA20LeNhSiiONfOHeJTMuk575x9RVrdJ1c9xMuQZ33fn1tT12n5izm3KmF/6lNfCR098TSe+bIrb/jCwKxfU33y93Nh84f+/VPHD0md/hcYmPlaRH4D/IeYkfivReTfBf4h8DfL2/97LG36f2Gp03/nh57IWrHJxgIwzVmN4Od4XlWtOtQtYQe1v8WKE7Acc/k7yzT/vc6pgysyapcal3Ysh2hDRkkp4nxTnIdMCI4uNIgm+iYQ2kCDY9O0TMNIOia+/vprvvjyV09qQ3JM7LY9bWsNi33waCouvJr4bZSqqWHVkCG04BJ5zNa5Xax+5Hw2L0YVmtCh4vCuoSm1K6fTiWkyWrSQC95gOJD3nqTJQpxpmq95KGrlKSUe3r030d2UGIaB0+lECIHbvrcmSIXBmaaJaRhmqnwoVZwxWhewGpq4ZJ3Ck1x1kGNJW4ozjYlpMlC38YG+7wvzVRinAR8cm66BbM0NxAc6H8ga54ZBIrV0XWiDwzXNZQjqrQm11vCotDm8zGR82Gt4ukCf7wRWQdvnPN+1avx1CnbtnazfUx/9M+BmeeLJOfzU8UOyIf/mB176l595rwL/3o89CRElhFICHmuLe5AgaExzJ66a0XBlJ4l5CTlmEZPitdeGNyKKbV1111ISq8a5skj6A8tEnXed6j6b+xp8wHvQHEsKLoEomyDc7nprPegD+97YmtN54r1muu2GlCeUS6Wsv/KrX/D555/Rdc1cQq1Fdfvt+/eYEnY7pz5bJ6gzL2tTpPgMyBQeHx/nBsvjMJAUEiODDITQ4kPLSGI8V2NQ6jZy6eCVE912Q06JYUpMw0TTtahC32/4/PNfmPFLCe+CxdcZFEdWYRwsI3Qex5ktuicxxoE0RYZhWCQCvfV4tX5kWnZ+47DklHFth/dGU9c4mmJVts5it/sbfvHZa949HImngcYHtluhaQUicyjqxZFdOb9swsGN93RNw7Zr2WyWedB23ryVMl20GC8v6wW7nrMy4xcVQK/PmxecP2gs1nhYnZMVBK6SAIvi9/q7Lz2hWZwXE4x+blTj9HOMT4PBqQo6YDhFKpa6dNVqpEjj6QxQ1nyY9/ViCwSLgXMqrfByLHZAgERNx5pru6QvnVv6NNTHi8o/lgyGhBFSRPKIQwnCrCjdiOI0opoIoSc7ISYlonTbDbe3+0Khvhy7nXVGr8bQcA8zKC5VkLP+jssdp0rzt21g13ekNOG9sOs7hjbxeDzx/t2R43kAZ5hFaDqmQk+vug1ajEUjmfZ8RoIZqJqOVV3Uth1SvBk3h4e1+CthTXrGmJiSeRCVOzHqxOH0SIyRrrGiuVR2fecbskY0W9cznDApRMWUq9TKpJIY2N00DfvNnsPjuXhFQu+tWncYM5NCVodLRQoRoSu/1dtUIXihCcvia5zhXTlbmfyk5qEG9xR8r3/XmVixlZqJsNcuF/vywWtPZHafAfCusDdnLPg6O8K8aVZjJfJhB8Le8wekwSkCm8Z+bRJQrZ2+oSu6ESldKhHlouUwDxWTXHNCckLWOKs6UXZR54zsFVfxsi8svkW7c91C4Ao/8cUrkIR3QiMGYmbN+CxMBTvQDYxT4jydTSeia7i7v+U8jHM6sI7tdktb6kWSWnVmTGZ0urCxXqY+sJ4RWSPBt9YyAAiN8TRuVIltV4C/qrlRgT7rUN61HW/efMc4jogIMS5aGze9p+t7k7HrO3xjgGkYG3a7HaIwFgKXOj/zSsR5sjduxjiNDONAjHb8x+EAA5wOB96+fcs0nOcsyrbrcU1g1gjNkSRKDp4owjla+JHGiVzqS3zJuPR9f6W8ZcV4yGiK4mt6umYazCMz7EPog7BplunvXalB9s4kCYuKmH+m9d/FQi+gbZ3HM+bClcf6ARxhnWK2I+Z54a9JV+vN7HrtiMhSt/OM1fiD6kjmBFpfysbnH12a6cTBcvMpzZLzZlkzmobSO8JUnpsCpHmnkMCTEZSkEdGi9ajCQrYSRAIVXDMfdK1aXWNne7fXwQBJMTEVycoUE3mISLclDlYivt3doS6RculFmia6rmMYR9r20ru4udmx3fVWr5IzSeO884Qi8Y9bskRZFU0JN0vFm7CvkhCnhMaVtKaj77fc3rxgjLbTinimBF///g2nODBOp7k4zXsP+458W3CQcUB8kQncFLXyKTINhrmE7aLbkRxoTAzTaOnjOKFqu/d3D+84PDzy9u0b3r9/z3A+z4vj9YuXVkka2pmjUVsajDlyjJHT8cQ4ndGYmPu/qmVsDKzNxKwM0bCknDOykhDMOZNVmZJpkoRWaV2gbRxrOVTNE+LMaHonVmzsBK7Zllwv2Nm3uOD6+NWavTYYF1kXVVJRCbPFcBkyzPhcNRgXhqQaymWOrsOhZfwhhSFkNJ+pLr+INypSXb8o3pkRqLJyqkpMyeL7Ur3YzF6CQoq4wtIQEirJyE3i2PbTvCGE0p/UmIWyUt++lHYHaLJ5K03dN0TxXgmNsNs07LqWmCamojClPqAukNKAC9bu77qQ7Pb2lu12O3tLqmr07CD4tpmbIJnGxqJ1OcahnLedcxynWYyn9Z6m7XC+w/kW7zpUAlk9b96948WLF3jvLR06RM66LOBpmixdLeCyYxLBBW/SfONkLM6iXnV7e0vXdUyaicM4F6hpDY984PT+kW+++YY333zNw8MDw3AmTpZG/u7t25KmDbNhamumJgjTNJLGiSkO5DgVsDJzW4rbmqZhu3UMU2RMleOSZ4NvncwTa900lzMBaASa1RoKYtiTr5WwWPj0pEZILaxZdvqFEVwXrYG0z2Edy2cWyUbzemcNTf8czlGdlzUloBqecvwZB+HJvP0hJMQfMj4JY+GcY7fpi7Ws2Y16MVe9METMkNQQIjTzDfRiXoUW1iGbDi/gg4N8BvKsNxn9dytBWZ1DETMY7Xxe6+8SEbqxSMUpqwZCDjSgEnj58tXMT8jOFr9TywaISAlJLi/5ZrOhbVtyGoqHZAutaZbGx2kyI6WyNKFZaNDGbFwmHIzjwL7f2vO6KJQLnu12yxdffMFd0cjouo537zYgmU5M0cp5T1fL1KueR81g+MDNdsfNjaVUQwhM4himkYfDI6fTCVB829CnluF84jQcOZxPHM4nYpzmrmun04nH44EYjdUZQqDpSr8Ub5WmTbCMTdaEVqWt8WCAbOGPWL+gYT5HnMcqbRMhWYVxW+pMGm/Ubu/ArzALIwZazYsKBIUcZO7Hug4Dnv5/6TEYX+Sph7D+WwqoaUm8whVyroQh1yHFWkLysr9INRZp9jQuItZ5Y/05xidhLDY+88/fn0x1u9mSRIg4kgQSGBkKx/E8zSGHCDQum2fQrIqHpoloKXi8QB88u87jGXEkvEDiS8gRyHSNJVzJCYdlBnww/CO0ZoymZO5vowkDrdsCwLaI7xHXkXJLt7ml64XQ2Hw1gC6SshKCUaqvkeltH3CaGYrQTAieEFqa0NE3bSFyVcjMoYVMlCZH4z1pymhyBNcRI4zjGRpPmDLBWZPlnN/Rdlu6zQ190/DZL15bifrpxHbX8+67t6bpOTxwGge67Ya276xEX6xCVJPVTey2O272O3ZdRx9MBvCsE64JjK3j8d2Bdw8HM679juB74tjh5NbYrIzc3e3puob3jwN5GFBJVpsSM2400d1GYNNBasxz1BiJkxnU9+OZrm+IRNuXZcI1kSQT4syr6rXBT5l+iGhKeG3xLtJopuPEzide75aCwrv7R8QbWJqjWvGZgMtGCX/eSGTWG/2CNUCYlkVaSbvmUXBBzLsOcuK1ANZqFPz04n8Ri1zcyrO4/vzPYyo+EWNxd3/Pn/7Nf4UkwikJkyoPh4G37488HgfO48ThNPB4OM86lnHKTOPI+XRGj4prQtlli/6lD7Rdx+3tll9+fsN+27Dbdey3G/ZtW+jOCacjwSveJRyJrJHGO7LEhTegJXNQ1rnkBtQDAZENyIaUG45nB+3eZOtK+0TvA6hpSKS06FeuR9WorFWjtRvZXHUZwhKaaRHlkcxwHIi6yP6bgyGcHw7kU6YLHV3b0zYb8hR5GL8DFwih4ZQicRy53e+56Te8e/eOeFQ2qWd3s2ez2+GCL63/qnix0rcd+23PzX5L35vYb9d1uCZwm29nZaqvv33D7776hu3+ni503NzccXNzgxOl6xo7WfeesYQXqYRTw3AixkjbBU7DiXfvj4zDkTgOaDIhndYJ/a6361XwlJxLK0jfkaaRYTogCJ+93ht35OEbcspsN/DrX9/zJ3/yS/7qr1/x3/zPdg/+xr/612cdkJQSOVr4kqJ/1lCAbQTPGxEljJdAo6rO13Kt7XHhsQhEPvx9H3peRNBYz+n54wPwv33NX2R8EsbCBU+z30HMnN498HAaePvuwPvHE2/emZs6TplptNTiONoufD6Pc+4+ZQPmLLQwrkPXN7x923E87bi73fD65S2n2wnZbdj0JqbSOMvPBy94VZxvaANkTZbnL0xEdZlYSTxaQC0t3TqccBqUrJPpVwSFrBY/a0RT5HA42G/9QM5bxArFQuk5Uq6MtRz0Yn1IwcC7dTycF6BsdsOzkqeRKQnBBRqvOLGu72M8Q9cRR8tkdKHBFddcmgbfGOlpu93SdC2U1ohd1zGdBxzCrjR9bnxgKmlejYnhdOZwMLaqKW0rwzBxPo90oSmAZEvTdEDm1WevzcBTOBfJmirFGDk+vmNsPINXgodBII5nUlLG8YQPIEVJ3XqanCBlYh7QPEIlcHVbXr3ccfOFhzyw3wf+6K++5q/9tV/yy1/ezdfxj371elYIyyuGaYqXKev1/9WI1scLklR0z34uaX7yfP23OZDdjzIU61GNxIUS+8X4AzAWCjyOI8fTwD/++hu+/uYt794fOI+Zh8cTKgHBo9mRkjJNkTRlTufMOCqpAIPjmMgJnJsYfeY8JI6HM4fTe3bbhq/f7Li9u+H9rUnL39/e8Nl9XxrgCM4HXMjgFWLC+WxiPBkQy26IeCTnIpiqiLOSeic2weIQcS6b65kgZquaHKezgZdXxqLGuLUa1BVl6pQSAQ8ieKm3yRiPlaJd3++SlbjV44sa8SpNA4MKHm8kJ+dxTtFsWZOuqQxOKwpzdLi2mT2LpjMyWOVdjKFB1LqotbN3JgwoMY2cTiceHh44nU40TcOLF684nCZOxzPTGNli0v7ONyCZTbux723CHFvnQhL77tuvOR0fOT4GDo+WITk7hWkijVanIt7C0cqF8M4xDRYaBA/BTzRN4sV9yz/3xy9pPNzctHzxxS2//OKOu7slM3V/2+FKU2SyFuk9z5SeX6zA7Pmtd/g62tzMv+fCwKzSq88teJHNhYH40OOTz3r35PvWc8zG//qhJfiDxidhLMaY+Edff8vh8cQ//PPf8/U33/F4GMjqGCY1MMoLOUnJu5uY7GlKpEnJ2aGaiDFY1y4gigF/g8+cYub98cTb44n+zQNvd3tu91tevLjl/OvX3N903GxbthuhV0dDJqZEOzm8V2p9ZC6ZGbKSUzRuh4tAImfr93k+jyQ9mxydtHj14M/A85Nj9gicnw1J1eVMufZKsUlp5smAV4e3LMJq4jnfkrzSecfD9MCYYtmtJ1pvizI4IYrSNwHddIVWjvVbbTPtpmez3dL2LRKKcpQmKL1NQqlURdNMlpuGgfE8MJ7P9jiOuFJoFpodTTBD2XWVeu6IU6QJuRi9ZjF8xZZuuoaH99/xtl2AXhHBDycYLRsiWolQpftb1zJNZ3KGvoW+g75L3O0Df/JPfUbfeW5vOm5vWvY7T9su96PxybJqmqiqbEE82T2/UA2kXMYCLtqzTa7ex1XIcLFZPJNSva4pWR3/Q8bDJsdl1mb9uZ9rfBLG4ng882f/4P/mNIy8efPI4TQxjgklkJPDuYh3nhQzcUyMo3XRtpoCX1x+geyK4Vjt4Bma4IgRpqNyGAce30Pbnrn99sRxzNzftLy43XB32/LqfsN262h9yxQzPqVCwc2202NcDS0AhvWtVcZhQrJjOJ0ZhgOhdYTW4bwjlwKw65oAYMYqrvn+NjkEMkSNeDzZZcv6lNfJxjatXoadm5JxK5zD1J5SmkDAhwYXglWptn7uJCaAH3poPE3b4ptQ6mys2VDO2Xq1BlvY0zSRiZCVcTxbyrSc+zSODI9HxB95/Ytfzeli55xpfh4Oc53KMExI8LPkv/V+9aUmZW8uvVj2oO6aMRhnpOuMh3IezkzTxLZvS6hihuJmG7jZN9zdNbx63bHf9Gy3gcZH4EyMC/U+5YlcGjJpygRxTM7NihbPhwTLQlRqSULBuYq3+2ThX1W7XnMvNE/PEz9X1dCrJMnqDas+Je7qmM8Yn58yPg1jcR74P/7P3xBjZopq2Y/sqKLRhg0ke26CaRJSykSxPHpKmRxzSWXaIpuBKhFyMAFIFzPilEE8ekq8P57JvOF2Z0bi1f2Gh2Pk1f2G1y8bQjCXtupHdqmmtbLtagjiRpwEYnKI9KQ0MY6PNEnoVPBNJmJEJufcEwZnbQWg2SDttbHw4lDUWg2K4RbLDuzmYzpavGQmsboN1LHdWlrTSSClzJjNiIxpJDQeROn6lrCqjdnsWsZpsqKq4HC5iA/lxBQndm1L1wa8mJDx7AFNEY+y3254/fIlMStv3z0wRuXh3Tt805l3kJWUjWNxODzy1Vdfz0a06zr2t7fc39+z7Tucz1Y+HzpSf8O5PeFdmBdoxRecc6Q4Mp7ONN7RNMbHaVvYbITbvWO/D3S9o98KbSdoikzTCbdqBBRjxEko2QoTI7Zpk551/+1xMfCCtyI2KSpgaxnZK+Mwt4JcPfeE/s3lxvHcvy9G+S3r9C2ylOj/HOOTMBbjqPz5V2fb+ZwVVClF70DFZNgS5FSAmyTk7BhlNH2CbGXbo+YiHwfDFEFMVIVkhevKRCJzjBnRTBsmND/wftNwPAy8ezhyPE8MQ2S7+4wQ0+yW5qxoMRaNr3R0S+llPTDFwDgNhHbPeJx4OJx4PL1DvLUXrDesKl/X0TRNAWWNiFV3ghp/et+gOeEKXdsKvphVp6yobqk3yC5zypndzYa+6YuepdHPp5yICtHXBsdLi0GAJmakFVLOpdzbYvdcDVTxZrw3UDepkqeI88w8EhEzGsN5Yhje8+bNG16/+pz7e9MY/dWvv+TP/uzvk6aJ92/f8/79e47nE03ouL+/58XLO3a7HXe3OzYby7w0TUPb9oiYd6lOaDc99y9f473neDzy/v1bXNdYKOFg2zte3G25v9uy33qcy6Q0MU0JJRoBbmW4h7GGe3Y9hGzNo+Tp4p0fL4SM9OLRr4DnXOUfKanTtAIfnVwYC32STF3Gx0IKzZeiQev3/0EZC3BE3UIWYgrkBKqLsYgxkWLlVxhrUdUxaiqluQ41lUWcCqJLfwVxikwBr5SwJsGQCM6j2SNjy5iUb05n3v3+yPuvHvnqrmM6jtzsO7Z9oGkd3jtOzhryBjeiOs3FPM55kIZEILsDYRM4Pj7w29/9ntN55OXLf3HuMepce/HL+763Xc05mkJ7zjmTYqbJpsYVmr4IznTF2JjiVkZnafyUEuIMq2nbHu8bomYKY4mm8TS+w7cNGoygNQNiZfJ2TkmnEzlFpMjtdV07d+eSpLYrpzR3ZdOcOT4+8u7hLY/HA+fJKjc//+wFX375JeI6pslCreH4wDe/+3NaJ9zte77+3cQ0nBkOByZ3wpPIw4n3TcPhds9uv+HuZk/TBs5Hq1zVBC9efs7dzS193/P+/QOPx4PFgxppGthvHb/85Qs+e7nhs9db7m4853FgnM6A4RIi6QJsfngUZtaumtvv1BbIstiqSz/zQT88o/M1yEi5jlDLDaqBF5HZaGT5sLH42JA0PQlp6t9/UMZCEeJk3oGyigUL8SRlyEgp6rGGsFkVJ2Fmo0jRoKgtCTWbC2Y3rVyw7C2lhuA047KDWBORQlY4HRPCwO9++x3nuy03txtuthvaroEw0QZHDhbLK3FuWKMy4VyLIvgAmQOH4xsejyf2+3GmlT9XUFRl59aFUTZMBs85RxA/ewNiWVtrFVhgz5lhqQ4fAilGUqGAO+dwTcCHEltLY+nYWgxX+1TEMxRg2HmHD3Ihl299Tk2kNqvVpOSciePZrkfKjKcj5yniQ0O/zbx8dUMbdDZomiOvX9xyt9+gmnh1f8fj4yM15a1qlPPz6YDXSCDTNJ5hPKEp0Xih63p80zBME4fTEVVls9kwxCNd57m72/Pibsdua2GTw9LtNWvl1LyPNdY4Rj+zhh3ZuqmVEHQxDj984ZlD8vReL20RzehcewPrroTX42PfHXhqKOzfT8ldP3V8EsYChThpyblbA59stc2l3sGk61rfIpLJ5X8pQrS1aZBNZHu/wUHGScgx2U5BRrLOAkk5Z6aYkJKajFlImhhTZvPtiSk7puxJdPQZfJOIDfQ4UDHdBXTGEMQ5Uo6kpAzDiSmOpGmcX3+OY1HB2FnVm5UByW5G5WsdCFlLRmRF+7VuwLOXgXecHg9kbNJqMrXrFAcYR9y0yj7IoogespbO6B4XPK5UZaZ1Ok4t9ZpjtHqUKTKOZ1KaiGlkHM+czgNN2+ObjtPhkRgzNzc33N/d8Or+jqYNjONI1zgeXr+0JknRQszj4cThcODx4b0xzePEpBNxHHAofdfimx7xDbESuNqWRGZ6PLLtt9zd79nvd2w2nhAcion1mKixdQvTqOiq3YTpHluY6dRqO0QzWa9W7w9sgXfdyWI2BmITfln4ernA3U+rEFX/NPxYwpCfdMgn45MwFgvBpYi9psw4RmrrPArar2K8A5WMSsa7YGGG81Y7gIFTWqtHcy4AqAUqdTfOri4UR8RDgkktk6CaCB76YyY1CQ1K8pltVjqfGbuy++NNOctl053AMg8pwvE4cTxENDvadjtzKJ6LOVV1fr2OagjW3gZg/UrVXncS5utm52M8Ee/Fdug200hDDkuP01yxlyla3CxSDGqZWD4bSOjEuA/OMeXS/TxG8hQt9CrGYpoG4jjNzMthOJHiSBDou4bdpmN/s2M4nNE4EccTh0czasN4Yr9vadsb9ruOyhMZzhOHw4Hff1V5A2aUyJlNFwihp73ZIc7aMKoqoWvoXeb9Q6brm1lIyDkQzUzDSI4t2liomnOZJ6u+s2mGL0r2RcuVebLQ3PcYjHLMfC3hX3AOucQ+1vUcdiIfWykfHlqzWleG4uccn4SxWFI8lq2g9Mc0OfWMODHVa4kFJChEqKKgJd6hYjdfozNBWIoTqEZSIkthKjqy96Z94T0TkLIUuTi7pT4pzSEyuYlRzpxp2EZhHyb6tiouWePf4BzOwzSBc0pMwvt3Z46HieA39O2iu1AX9vVvr6nPtZu5ZgVaytA0OqqhCr5wLly+vIYuWB+SpiXjiiy+fXcW0wtNZe6GknGpqtwlD7xqWLN8fzUYqDV0qmGHeWcDwzAwDaZh0XVtUfdyxOFM04YSrkyM4cyma9hveiKm4NUFT0qFJi0g2sJnrzgPJ06nEzJmQGb9UDZbNEX6fiC0PS6O87XtOutWFmPE4YsEYEJ1Z9dgMj2QnC7d+hjz6jdnpAS01w2Blvt3xZB81oDohaG4eEUrlHnpWfhnSuKffvfTESU+ed8fKMDJEtNWklJxqWPMSCFlmTNN8RzU/pJaom5/Z0mzaIk4S//VXcIXVz06jzgPYulLXg7cAAAgAElEQVTVGe/IAIExT3z3eGbIlnKMOPZJUX9k6JrSa0NpfKYJ1qg3RtO6UNdyeBw5nxIiHV23XVzQZ4zFOhV6jbbPqUkrpyskKLE+GvUAV+k0EeE4ngtmMTCN46xDiXeMcZrj5uA8bTD1be9Avc6qT6zOod6XVFXIlFl2DiztmGOcz9d5+9z5dKJr91ZXExNRR8ZzRpPVwIR2MgXzcm9VBR8cTfB0bWOq3t4RU0PKGR8sJX7OmaZtuL295XQ68Hh4x+Pjo0kMFmXyS0Ng5QGkTHZuNhbrexFjnDELX1XPUKbrcGIVXzyvQFXuh16FF2VknoKOaz5E/onGIpOeGImfe3wSxkLEobS2cyIgHu8tlp8VkUJDnDLjNJErbz9MeBoUTxozOmU8jqbs1tX6D5IheKQJJOdwRGIc5qIn4zQUy+4F71uCBPIUOB0cmhKnw5mxPeHcidN5oms9bch0faDvW4JrTfMyTzxMkWNKVuLd9zgXcM5Uq67vY9NsitdhVG4Ttk2l6MzOLWVF1ErjRZ1l9MfJdDVrSbYIMSrDeSDmyMP7R4bB2JQxRlIpegLwBXBsGk/fd7PGZ9v4uYOZarRUawExVTMS7TpZqtURc4QMvbRMNEQdUOcINDR4Gt/SqOBipnXeOCjngXHIHB4fTbRnbkpbFhdGrtkEYdt0ID3qPFmV4/nE4+nM19/8Y969e4dz8O23v+ebb3/P6fDAP/vP/FVe3e252fd4lyycdQHXdZymdrWYyrRfZR6qkLKIXKik+njJi7mct88+Wx4WUtYFg1PEGjY/42ECs2jw+rn69+x98NRzeK5A8ecen4SxyKqMZXe60K9QS51WPoAt/iXWW/MR4NJizxWjJZYrAY31ai+7pXXdKmSp4npXTU5xgYydVzodOY8ebY7m8Xila4S2gc3Ulb6iAbwnp4nTOTJMGfEsmRjgOZCzhiE1ZZozxk5NihS156x59ipEDZMJPpBUidOESAJnrvc0TUzR2gPM/y7K2D6UGhBZyEaudDKza6WM04RLqRR25blSVkRMll9MBVtW5x5Km8YK0krJ7jQuWKFdSUVKMWo5F63RcWJMkUzGOeOctI15O6HrZw8hpsRQOC4eNUzj918xDANv375hPA80Lez21qm+aYrYjSbL3JQQ09bWU+8OFszi+qUcP7bTf8C9L3VCQBHQgWpEbO6uqNlX3+dWPIv1ucgqXKmtRerrWry9v+zxSRgLKClShUB1yRfmXIy2g9VJO3e48lxceDcveEfblN4Slg+hyr5nZO61uXz3YnDOOdNog5tM7wImavFWakaaxhD1thGa1shOwxRnRuEUHQ/HE2NMdASkqDrV81sDmfW5et7V1Z/l4PKiL1nFhsWX9oYU/CEnI/Ik2x2Pw9nUpYp2Rv1OIza19JsOVasXCU1Rp2q78n3luwtgWq9lvUZ1mAcA3jfAYiwab5wMFwKNt+da365AWisgyzmSfWRCYKAoi5lIUN8Y3XvTtKgq52lEYyTFEc2mTdp6x7brOR8fydFo7H0frNExRigTIuJMIl9VSNHCVjv/5XfUUZnf18ZC8kdW4Uc4EdZucTnYzAZQcOswRJkXv6paWFaPUT2Icr8FAb0MOWW1ef5lj0/CWNh1KxPbhdIdu5lrAVJKTOM474JL9kCXWBoKgFn4DB5kNtuXE35dviu1Wk9XvUKSwHlxHytDUcbJ6hYaR9c4uuiISRgmCCHStplxgsfjgEgDoUH9oub1Ic9i7SVVw2XnWN5b2g6KOGuBCMRkQjxZsxlAVYYcmVJknEasB4o1RA5l4XZdQ7/ZENNU5PcdKp6MszSiODMUqiUl7XDicaJMcWBRPS/Vr+W3NL6dsZeMWpWrCzQh0HfdDN7mkp0S71C81dqoI6QRHDS+McBVrPQ/akTjxDSeGYazpWjjSL9pef3ZS4bxyHl4IOfIzX5L1wQQExsSYpFZNEZqTOuuYE/5CNMHPIvnNDjX987G0/c4vWJmusWzUL3EVHQ1N+OKgl6w/Plz6/lS7sJynGeEhX/u8YkYi7qYlqa9ALnw7O1+mSqWbwKhTM6Ux3lhwXIhRYyfEVOViaNQo4tS+Kx4vdRF1PAGYErZiqiKUVl2fyXkTHvMpFaJGVKOTFFpm4YxWmHUGJ1VcTb9hWdRvaL1WFeaPld4tBSZ2cRXXWU2sul45Gw78DCYDqXGTNc1MxYhIrPn0zQeiZCSaUpUQ+pdQypCOoJN9gtSkVoYuGyR1YurHt3CmlU1CX6nrrQYsGsQdbJaFzGdVV+Iaim1pa7CzkajEomWDq81KGqErpwj5Mx209K1jq6keDfbhrZ1pnNRGlfPoGG2dorr+3BNkIvxw5mLq9k6/7V8/JmFqsWTqYYpr96bVlmvlccBluJfPAcummrNz5XPeFYen///ibGAZSGp81zSaFMB46w+wK90H6TyMLBwpN5sZXHltaQCLVZeuBxQL3Tpt+kWHMRJ5R+UQpzyd0zWFu/xnJiyY4t1LR9jZpgiTSNMU8L5Bt9tafoNuJaUaperK9ogNaZVYjZWqoq3NHA57zk1nKwhD4X3ICKcChYxTpHz+WyZDlU637DZWe/UttSH1DYIYC0HQmmxYN6UJ4TGQpto54GUbow4wOjaaJ3MhgmYO71iH2bIyRZ68HaeRIXG3uvFQ8E8IBO6hiY3dr/me5YMa0gGquZsyuwOY5x6J0xxMMmCHAnO2JheMkjCiSt3TFCsFkayMqYF4BV86Tmz3AeDJp668x9LO1689SokqT1L7T1LCLTc9w8cVBfPB6pnITOoX48pIsTympZ09l/2+GSMhQ9tqZJ0NktLvUKMlmMPoaVpmnkBOWc05UW49lIhaL17hOBm65yyNRw2w2GL2K3eG/NChrLwZDmWjiNJlWG0yk5nZh+fhZhMAyNnpWs8TejwoTPjUDzL51JplX+Ri7ezzrs7cTPnIRdPJyedVahOw4BifSHGOBE1lzCtpdv8f+y9S4wl3Xbn9Vt774jzyqys+qq+172+9rXNZyy3jWmEYQaWGIGQLCYIJqgFwgxACKkHdDMBybRgQDdqgdSSEYj2gJfEBCEkBEiIATQt6EHb7Re2ue77+F5VWZl5XhGxXwzW3hFxTmbV/V73UtftXUrlyVNx4pwTsffaa/3Xf/3XhsVSi7DGis+oLQOUWGRIORJixoeEcYl2sSTnUmqei0ByztoGsRiLqmFhxGh9CFnjP2ZpVgyxksFixBm9b86AnkSNdmMsuakGORYWqCcFSGEgRCVkpRBVuaxS64PXTuhRi9hyVo8j+F4/V2FpUuoz1LBMnpsZPdDJy5vKzc9Tna/ZsecL9Lw/bv17dsw8NZ5nBuFkPpQXqZelxXp6bfNoJPT/ps0N1DP6QY83wlhIKVFu2xZSpu/7sTFKs1ipZ9FqDr3m25umIUZbXMwwSuoBVPZjrdKEzDAMo95jiLqztssV64vVqJJdtTBjmkhRbnZDkoUcIoNPGLHQZfyQEJNorcHaRLNquHBrXLPUBsbhfsZmPkwpu7Z2Ul6yVuX1WuNwTVM0R8NEziJxHHq60iLQuobV5cVYsr12S1abtfJUkhoS0IXe971S0b1n8F1p1FxSqeuWx48fq4cQtDBp1S5Yrzcs2pbU98rkTEGBv6yFZMHHQqoSvXZZazFi2LNoOljKaIDbRtswphyIWVEQEQFjkGRJcVCpgr5nGDoO3YFu6Ig5EMkqwrxoePHiOULAEnCt8OhigbMJcirp98JMNcpKGcIAxQuKhNJoalaiHh+6P6qZUu7UK72MBxd98SweUsYaj8+neAUw9iydzl2FX22lFp2/U/nvr6Y3yOvGm2EsAIyUjlyKXSyXmoarcbdrys0q/pYvvSeqoQhn+fARLMzqpk5otKZlxThcs2CzvsQYg/ceYSDFnqpJoGnPSddQonY4z1kIPmr3kIK4h0Z3p41paZvl2HvyBEx9hWcx4RgTTmCMoXUTwShk7XxWmYc5Z8SakkVwJ4VqYg0xBy3OKxjEyMIshWVt2yIm02U1pNEP5KHD2oblcqnVuQi50c9lxNIsVgQ7EENpexi8GosQtNnSiO9YYknniQjGqWchhXKvt9sRs1f2rEAumhkhhFFsp3p01loVEEI9ECuG3faW436HcZmmcaxWDSKa+Yoxj16nXmM79hAdwYHEiWcR4in4WcOKNHocaZw7dR7Nw4LzkeKJ2zE9EjDm1HiMrxFOwg0Ac25g5nNn/g4/eJrFm2Es6iWri6BtmrJLMuIVxqK1B1GVsoahx4+77RyE1EsYQlDXt3gZuRSmGbG4tmZUtNy7gobV+GiGJZeYeeIa2BjBaXNiKQQpsu6kZNXKtLZRcdpiMHJKYKZeH+fZkPrZa1w6Tw9jdCHV3zEnYjnGOKsVo1DidB0iisFomft0vhgjoaZTrYYATbkOKrDrOfYdu91Oa27KnfFeazVa1yBGRgk8Abx36m3kKpYM4hzG2lICD26xpF0tsQgxerSXXLnrImCkdKZPhJQIBZNRYDbTpIaUoxq/GEgxklKkP+xJYWC9WrFeNlyslzijuE7OmUyA5BDryDmhvXsKfyfVazUt1kr3rgCi7uipNBuY369Jd3PqK3M2nwuo+sr5nh/Kn5TPxLxHyHScSffxlPmI+dXv91WNN8JYwGy3LBMIKHF8LMQk3QF9DPR9Vyb45EY2TXPi7sVC865jvlM1pV4jZeHY9RhRjkLfKz9B89ea085R9R5S1FYB6spO70XRnkgmY5dal+GsZdnqbj8MgwKWr7iZFays4yTdVnGWGfdiRM1n9SZ5pp0wktGyqj1lkzE1Le1cOWcBfsWUlOqCptGuYPvdgZ1sp1299H7d7Xaa4bDComlpW0cq1znGPGZoGmtxtiWUDERNwWqatXBejKi3V/EnMhL0+4ZokRS1/sVAkxtCCthkyaUvqyn329glm82K1VqbNot4aruEev00VFIAu4KOIEW4ZmYsRk4LVMxAwe/7HsDkKdbQ5TSzcs7aPHltMdzzcXL/X7Hms0gBmB8e6e81YwGMYFQfelIKo94jkghhII7MQ4Nzp8BVXXiZ0zixegug+W41QnmseYBEDL60LlQ3utaqTICpoS1AnynBZhx8cbdVZ8EitIWg1LqmAFCFup3DGAbMRw0LamyqhCcLpSw/pcRQBGc0W6KLr5bu64KYkXREGEKPS81kUEpT6JElGksrQJIa1fK6RbukM93Y42TZLFm1KzarNYdDh++OimOsl9oFPXq6ruN4PNL5oaSnDSl7YswEn0o6NxYpkaRYqDUYazFNOV5Agn6XNrdEhDh0RDS0nHuMqh5mefLkCTAoGatFQ5ycMZKUQi7F60taT2R8TRPnoip1msKeU/H1vdJ47R6ap3pcJdPNCFjZUGwUzAwXVPzidN7XtL1+UdVZed36mJLdZ0N+8Ev5jTEWoUy0pm2K3P9AjGHCIkQpwbWWQLOhdlwA9WbFsqgqEagalpCVxWmS7rqmVrKWUZsTjexQMfgM2aTSKVNojPLyDUIMnhBiaZRcGgg3LRcXF1ys1lhrCWHQ+gf3sEQ7TOnbMXszNmO+797WrImIlMIofexsowg/KIYQI6nQp02eql0b05TrODFGlT6s+IfNwmq1YrvdEkMmiNLGndFr6YNXwR07hXqHw4GXt7dst3vtXG+tMjuNw5iGIQY6P2CisnLbbHBO8QvDxDMAlHxmVKgn9GpsKuhccaP6+K0nT8gMIAPG1v8valHVsJBVy0OKbAG5BPdGQ41Z1F/FksbNxugiDg+xNKtxPinom7ANoMgdTPc7FQrHvMhtzGZUgd2soeZnlMw4+0h/j9SGUHf5ksGASLAGSTIW+BirBKWUVeNg7pLXhTZnfOpp87hz6SIrF7ScyzlHuygp0ugxZurhIbngHmNa1WDyMKJKigmoPL5tG9ZLVaZ+dHGpncEzpDLRcQ8DVPWzj6lSc1JPqiF9qb0IvqaSp65myZdUqZ0aE01GKQLNicfljBkl/Fxxs2sKGiAG7S7WHQdN/eWszYyPXanmLLcLxSi6TjGO7XbLbrcjxAExhqZZsFhtWK3acUGEkkXJ2ZTmTZlklLiV4V63tvq5Q6gGI4/GMgye9WYF4jQzZQWYsI5qLKrnVdkVlX+h/2lOtvnKF8klNVxDlnxGYBDuY07VY5nf03koUg3FdPzssRbqnL6Gh8OR0bvivncRzjzWH8R4I4yFGEOzWGKahoAQJZEtpGwgmTEFGoaeEDI5O6xpFFB0NS2HqlwNgzagGRehIWWLbRqaomG5WqrydbNssM4wDB3dcCT5HqGAbKHX+NpqvlsETF6UTt0RZxuazYJ/+B/6s7z33nss2oYUBqxJNG6hwrLBcbG4Ug5ACIQ44OLpJa8CvsYYyGZ0e40xZSJpR3YrXsueY8BklRS82e25vdvivWex3nB1pWK360drQgwYPJvVikXWfirGGFLMtK0amCzMFmNkuVrpgpaGw+HA/rDldrvjcDgQY2S9XuNa7f3Z9z277YH9fk9KRxaLBavLKxbtikXTsFysWZVmRJIiKQxkH5DGaigVgUGFjGLyxOhJ0ROD9iZNPpODqPcUMrnvSYcd8Xhg6XokdojNrNqsKmASaAq1nqShnMlWwesAg2jdSu2bS2lcVUfMmnY1lfkp6g3E8LB0/8n8vffUBHDms3RnmnkV5YnxNSIC6eFFb7Kc4BLnRw00J3/XKmryq1O+n3e8GcZCtD2eiBSAcaI5L0vq1FpLz9QPREROjIW1QkhhNBLTBRIWzYrGLVRDwbUsF5uRq5EkkrMlJgPZEUfhU6V3a2igu0Ct3kxJm/G8//77/NIv/RIXFxds72558enHWJOKKxvHGhY/6K7pvadxp7c5lUrOXFKNo9tanp+POdnMWa2d2e/33N7e4twtfd/z5MkTEpHVUhd1LShzVhdpW65zSFMvk6nnCKVATPUmKs5iYPQi4FAEZQoWkRKrRcvFesPFxYWWuBuDs1O/1iCqWZpipM9xvD+uKLGH6BWPin7EdaZa4ek61R8jWTMuJhWSmQ6DkI0UoecCNCbFEFJOIFYFcUXrimI+Pb/eNxVCrgVe6p1M87Smg0/vC2dzbgI5P8s4wUHGz3RODish9XjomW9Rvb5yrjALi16XRfk84/saCxH5z4B/Gvgk5/zz5bl/B/iXgU/LYf9Wzvl/KP/3F4F/CTV+/3rO+X/8vu9RSFkhBIbuOFZIigjr5XLkUSwWuuCnSTOBXllmaVZjZjdLWCyXtK1Sn+cSdzmnMbevC3G6werOmoms5SNhCCXrknjy7Cm/+Iu/yM///D/Ah9/7Dh8fDmy3W772zlN0g/CEs5DIe09s7xsLaxmNRR0555HFWv+ui7v+qPhLx+FwYBg8vuuJg2f38hZzdYkTw4BiF8ulKoS3rRqZ2E99Ya0YxCg3IGcNhKwxLNsF5kJYtnpvrm9e0ncenzLStKwXpZbHxdEQj2Fg8pAzvvXkCBTyHKVeJ4Sg4ZrRQq+kfpxybQo2EXLSMvycCDno32REEmI0tZoKDmERktQ+sDORYVSfNZK0crmCnzJxPgBi1vL3SCF1UYDK71srcnbPxscTZ+b0mHHWj6+ZG6OaAhU5D0VKer3yVM4+1jkD9avyJubjs3gW/znwHwO/cfb8f5hz/g/mT4jIzwH/HPBngK8B/7OI/Ex+WFJoHEbKLl8W5rz4SYHCuph1gsBMRaq058uSWCzasWBqyjroZHdtW+oBNKRISSdsP+xLpiFQJdSsCIEJT8g542NArBqPzfqSn/zmT/MLv/ALPH78mN/57d9id3tH3/csl8sCzlYBm0BTUpajYtNszElb8/HQzlT5FxWnePJkSYzqsRz2HVdXVzx58gQjhWhVsIrFeqUdyJIlkmmMNiLOUQu0pFxvawxxSEQfCN4TY8BKMRrLFW3bcjho9zFN2ZbdVDTsy7GUh4tgnJCzxfte+45AMRIen/SeGVe9mlNwLuZE7wcG35deHyrio9fVYysmI1FfK5rZyTkphpkzZEtUkgqQSZEis69eRcozDKte71L3kjOlE9k5rvF672K+UMco4IF7+CqgO+c8VUoXoHV8XQ1TCo38VXDmPaJXZm6hvtT4vsYi5/y/icg3P+P5fgX4r3LOPfD/isgfAP8I8H+87kVqGAze6+NqNACOaYrhYyFhTeCmIv8+BEXijYzA5Um44gTJiRx156jI+kjCSqotKXUhJe1wVjEPbVxkMBgOxyMffPD38/M///P89E9/wDAM7HY7bQZslXUaCq5Rd+lyHR9Mnc7rWc49i4e8ihqiqYdjtSHPlWIFV4+ueHL1mCdXl/zd735bOR5GQ7vdbkfKmbZtudpcjJNyJLVplE8OkRwiyat2hCkcCecca1NaEyDFEBaeiNHFSFlEZhQanpXcQ9m38yjB5w6Hcq8oNR4ZH5NyafzA0PeE2BVMqcfHQMypVPRoWlONRlJPIaknWa+Z8inm9R2VfVkPMyfXW9kdmp4Gc8KxuG8cHiZJTYu1nvt8ocrJc1P4+arXz85S5rzul6f/f95vRMuMZHr8FYwvg1n8ayLyLwD/F/Dnc84vga8Df2N2zHfKc/eGiPwq8KsA7UIb79QwIqU07oxDntiZo54kpzfPGEPwaSRUVe+kvk6zGnG0yiJTQ5amsaQkJcMyLdKaz7emIRlwNmnvChF+7ud+jl/4hV/kvffe4/d/93fY7XZ0XcdmvcSgxk6rJO2JjH51r+djzuWYx77nP6OxyEzehTgu1xvilWdr9trR/HhELi/42jvvMiRV9T70Hbv9vuzOHiPC0i1wSPEuCqAqU5/YpilAIJX7odemdE9VDyILi0LlDoSSkjY4qwSvSrDLAqSs7MqC/vsY2B1VeUzFc/R4HwL9EOj7nq7vCKFj8ApA+xDIRFUgK97EvNrTQOG91B9lh+ZkiMhkJequP1tEKWtxljaNqAYHOHk8/jl6GOdjXPSvCF/On6+Gor5urEXJ6QSAnZuGfP7hx/POvJFiXbKAYF/T5+yzjy9qLP4a8GvlI/0a8JeBf5GHgrT7plWfzPnXgV8H2Fxc5JEL0DSlkKykHefptFmxjx47VZ3iGTMV1VOp1GQNa2YNfKrOQtOwaIrydGCkkuekN8qaphSZgUjAh8R6dcE3f+qn+bEf/waXl5fc3Nxwc3Oj2YSlYiLOWFIB905SaGnS3qjjlH9xygKc/67fu1ZMGmNYtAuWS9XwjF7BzuuuR7zn537hz7BYr+iD56MXnzL4CTzsug7TClkMJCk8EhA3FXzlnEeAc055b5pGsaVSxFZL30V0aku5H+eFTTEHJZWh8zySyd4jMdPkRBY3Fr6FFNl3R7ruSIgdIR7p/UCMA2JyEeqZQgdroCrBQ1USq0bXkIoB12ZKqewZFknnS0hxhgmruC9U9PpQpCzYB0KM+vr5b/2/dGIsqi9aKmnGzxDPXmfGz1uub6miPjl3MRRfFRX8CxmLnPPH9bGI/CfAf1/+/A7wjdmhPwZ87/uer3xJ5xzNRqm7oxufZwzMseTYluyJ/u1DT9dP+EVKadypnHMsl8sTcLBtl2RUUWkhWnUavScMA5K1hD3azDAEbm52mv1oW1x7wU/9zM/wj/3j/wRP33rKx59+xG//9u8yHDuePH7MxWrN0HdAonWOQCLHScNiNFan1/JED/T8/0AxFF+OM02jQG+74GK9wYhl1S5YuiWffvopdzc3PP/wY34rRZ48e8rVk8e8/fgtfuz9ryHOcnt7y4ff+S7HQ8fglb9gxaiBW6toz5SRscgM++j7nqEI7Mzp5s6omlYfPc6o1xAH9WrcYmqeVD0TKOh+gQSMwJAiKfhRZLgLnj4FXQ/OYqWBVMIOE0mSMQWz0AUXyTFibUs0mRwSMRlVyUpCKszKJFltilUR5DriuGAL56HUDM3m+b17dz8MmYOWr1b+Pg9D6mtz1iwP1K6rE1/k7GVTxrUaNFsKH2fnTVmL1tIDhu+LjC9kLETk/Zzzh+XPfwb4rfL4vwP+CxH5KyjA+QHwNz/jOXVBV6IQheZbjEWMccQsqjfhnCn8BSm6DWbG+JuyEClpPxJl6RnIDSllcgrE1o3vrzhAiWtxeH/UcChknG25evyEn/77foZnz55hGsvhcORwOIyp34olRG/pS1xurQU7EaYeqguoBqMai/kONGlyKEg6iuOWa1SVpBprudxsFJxdLtmX0Ojm5ob3vv413vv611g3C+zVY5IPbG+3dPuDlpL7QEyJm9tbYt5MGaOciVlFi4dhYL/f6/Ejo1K/03rRalVsSgwhkn1WrgqRhSwmIV8RZWmK4MSRg+pk1lAlCyUrogYnQ2mh6JDYIjGccQ10MyGrToemStPMq5jS0XUh5VysUzpNN1TylHolJfNA0cWgmIEznOJVUECmZFz4DFmJucGZPzwBOEt4OPca5iSwsYXl9H6pvMb7WLCXL0/a+iyp0/8S+GXgmYh8B/i3gV8WkX8QtXXfAv4VgJzz3xGR/wb4bSAA/+r3y4RAze2XBZInQ2ELil6zG8d0oKakUoLeD0p4mi3AkeVWjEpGMx21AYQSX6a4PEav8m9GaBYLXGkoHEOvDM9mSRd7Ykw8fftdfuInvsliuR51B6qnIiI4K6wXS3ypjiVlFm1LNIyAamWk1jGlcU8rFWvmo+IFozE9q5MgQQ668y/bhZZvh0Dc+jEsO15d0e8PJB+4vLzk6+9/jePlgbu7O+7udtzeat+NEODlzc1o+DJqoKsMQO8HzSJY3fOSV4D5OHhyjkU/UtPYoTREGrwno+QzAfJYC1OAZ1GRnWym3VJ3xUJBb7SXCDGQvNWMSwH5ctIu95K190vKEcmmpEr13kesGqEa1jEDCWa6lTV1SqnL0XtSVNQ4zYRM8+yhMERn9Pzx5xm1Vy95kvzX95szU/O9s1a8iPK5a2ltjBHbuK+khP2zZEP++Qee/k9fc/xfAv7S5/kQIp1ZB0cAACAASURBVAVQy5kUToG9CVmXsR9mXUSmtOpTZmLEl36pWgQ2e4OUC9W4utfaH7NpVEFKTKZtF7TtkkW7ZL8/8vx4hx9i2ckjxlneffdd3v/6N/Q9cxgrNk2KrNrFuCOrkdO8v9LN9X1DCAxyaiyqFzV9tin7IzlrVetYzDalcufYghLUDK11mAzHkhGKJQIejh23z6/VCL77Dk+fPWOz2ZRwboVxDuMcx6Pn+fNPOA59CY1mn7VmqPLUl7UK6wxFsQtJGCdj5kNK9kZmfVPVwOk9cq0W20nW14oIkYxPkWwE6xxNA2KCsnlBsx5ZS9rJCclBiUw5TCmD8yxGsQ2psM7GbubnSy6bcW2fwqanIYecGZDp/s0ff36coBqoOuLsPc2JITqtMj39bJaUarMuS9tafIj3UvZfZLwhDM7ZF5Y4koNijJhCzqrgI0zsztV6fZZqnS5gSnaGU0B15cSAMAM4l0pNXixWNM1ilK27ubnBh6SaF2JZry545733ee+99/TzBG1Q1LgF2Q1cXl5ipIjLoGFDOOs5oWzG+zwLmOoSTjIgZbc4B0bnBoJCMkomjyXfV1dXDL4fPYvD3Za+6wghsL/bKh9ksaJdLrh49IjVZsM777zD9z78lLvdrR53PNB1h/H9QunIntLkDXmvYOnKtSQyxoBbKJ8lFe3UbKHNDSLtieaIGqpSO1LqOmo/1phrqClaTCmqzRGJhJRoapiRtDE1xFk+tAKdk4Aw1GtaKfT3u9nnXB17M1u0ZpaSnIOU83CkuhrTedRY3QczP8t4UOymeDgpZ+3Ze5YlIxvEKYg/D3WtVXX23XaP9z+EMOSHM2SqxlM640kmoV64OXsRGFOl812k/tTdz5ipaEzMlHZcLBas2gWXl5eslhvImaHvud3uuL5+WUKGgDGRpl1ydXXF+++/z1vP3tb4PCSq4lQcSsZlNqG01H4KqeY3eD7UyFVMYsqOiMxEbCrYO8M3YtRqVinnb63gnfIlvGTW67UyXwev4jRdD8B+t+PTjz9hsVyxubxgc3lFu1zQLFqePHvKN7JnGAZubm44HHYMw8Bxt1cQuBR2jT1OSwOj5x9/wvF4JElitVrRLhuc0+rRp8/eGq+/oxkNRdM0NMuFpmFLtegoMVBCBOO0N4wS5s40O2q9d93d5TwLkQqoqQzQuojrqGDm9Lf2gLW5sELRTMtDmY3XGYDzOft9MYvzc1MzdlA3kZN0LareJTUUySBGmZ0qjqRcllqCfzgcCmj8J8izqAVVOEcwqVQpptG1N8awXq+BafENQ38Chlaqt3OOvj9Sy7GttZgxn+9K16qGxliWyyWNc2y3W148f8nt7Zab213R+1QxXBFhs9nw9tvvcnFxQQwJRLGU1WqF73RRLVpzgkGMuIOdYQxnIxb5Ov1Op+y+hyZlStpJvRoQayzG6YQyxhBNwB8H/V7W4a3ThZw0lLIIx+NRZfDIxCwsg9ZzLNcbvvmTPwk5c3d3R98f2e12fPrpp2p4MwxDKEJBPYe9Us0P252Cn3Eo2qYNtlWKedO2NG2LaxqapsUgI52/Er5MUi9qDOFm2SOMht8V+qqGAWOQPDVD4uxanfNU7pGcCi4y/3u+wOvpxurT+T154P2m8xRD9Aq5bbln1Kb3B134AJiJ7zFtlrWtROIE/jSGcCaBUNfI3d0d2+32T46xMGJ4vH5UhG4igx1KEx11BauXsGg3hU1Zd1g1KEMc6LoDPvSlICliC9FLROtLFo124F4sllxstE4ihswf//Efs73RC3o89qWc2ICJtAvFUR5dbfjmT36Dn/rx98m+Y9k+QlLL5mLFW2+9RRwONI3qWXTbHTYa2tBgbEuDY28s2h3NEs/k1g5lYSNWGyORiSFgROglF/3RFolKKnPW0hgLxmkvUin8RAsRT8gBt2wwrUWiei7r1SW1BLuWqIOQtp7bm0/4pChQSbvg2bNnLFetdofHYpoly2dvj95B33Vst1tub7aYwSPOsFoJrbtks9nw5NEVq4sNm82G5WZNs16qi1yzOsUYWITVUunVoddamsNxT7/fMvQ7Li8XrDZLIonb2x2fvNxyc/uSlBJPHy9YrBLWRlojNK6lEYP02jqAPKjuhlgyLSm1xKSeJRFyKTiccZ5IrgCto/sRgTgCnCdhR93hz4z/3NjkV6QqFRe9j3+M71qrVdMUPtXjZKbbkmUSgTbZ4KQtWEWVJITDoePjT67Z7Q6k+PnCoYfGG2EsaupRqb9qAaVoSYCZ+oTIFPMq0FlSSskRwkCu/S1IRYFKd/bLzYZF0xbNTfAh0Xee4/HI8+cvuL29xXfKz2gWpVFxzmOn7eVyyeXl5ejZ1G5o7WLB5dUjtjdLrNGQpw/aRUs9HIuR15cI19RuJTjV63E+VIrfjKQvM8+ilO3spHKyCMZa2yALi0gR9PF+pDEPMRBCGsv6/XaPHwbathbbFQ5IVKwo5czxeOTu7o7dbsfh0KkcQIZVu2Cz1F6j68WycEFalus1sbyfMdpWcgSoTcnWDD2744F9d+Q49PS959mzt1guFnT9nr733N1sefHimpgCJm/YeMtymaDJOFptfC0K8ElKJ+IzCno73a0L2JzNqbhQKn1DKkZRKdVJ4kl4W6/568IRnaN2vO/nYfKrlN7P18Sr3kPODJiGrFa5FklTytFHDn1Xrqk/ExD+YuONMRY1XGiaIpXHBBIpTmERUXXr2pPTNQWfMLqgXXBoybIai6YI/64XSwRGCbjj8aB6Ddsd2+1OSV9G8/l1YqWciGiOWoldK42xi+HyQTMqV48v+fSjhWo2kEupvGCaSXFLOFWnmo956fXJzpQzrmRDsPUaTRR2EZl4AbPJK6JpyD4NSuUuamJtZbJaQzcEfOwLq1FwTaO/bQYf6b2yMxOx1M8MZRH3dH1f9E/DCKRdXVzSNg0XFxesVysWbcvCNbNmyVMq2zpHW/RSw9Bz7Dr2uwPbO9XNCF7v22Kh5LDjsePm5o6bmztub3fkDNYkBm/YrC1hZSFZpM20GExOhKwamykBEVKUieRUVdDNaefxXBXKSiPuGg44O83P+cKt2NH879mMhlfgHfePPf1/k6b3OTcTZsZCzmgWT/kXppQqKNjpw1CM+o5hCAQfeSAC/tzjjTAWwJjaMUYnmCpi1R1JJ3zjFjPdzOk1kYxzJWuSIEbFSStott1uCd6Pik6Hw17LuruhyO/VANUQYsA5Q0XFBcHWJr+tVrV2fkBSYr1ec3V1VdKciRA8pnE0KJ+AXLn5p30vz8ercIrRCFAMZvGs1EhMClhSjq3CORWYq1XSVcjFGME1LdkHQlGeykYwjcMItChoPJSy/Rg9fdCqz94PbA97ff5sZ3y03qhHsV4X8d8GV3CKtm1JZpL2G1sqAjvv2R2P7I8HDl2vXd3aRkV2mhX7wy0vXt5y/eIld3c7jgctOLvbDngPfQ9haJBosKsG2zpyNqphkYSUhCEoMemYNPwyYaKozzGLbkiliVEROqrGwmhqv/J7Rm8ulU73o4GfjIC+Np5co/k9feg+P/T7/HWvOlfOGe9DyZxF+s6PoeLx2KsQU7L3zvl5xxthLGrhWAiBtl2WiaVIeAX/nG0xrVEh2LKrDX4/Mx4l3RryqFFR063dfkfwvjTY6UdKOEao/U9rOq3yNrTvRKklKAuzbVuwhmEYWLaG1cWGi0ePcG2DP6rK08Y5JS4ZQ5y9z6uMxSmodipGY1BG6wj8Fen5es1iUb8CIKYxa0Cobraqb+l/qxUV47Q83QiIwZb3zAL4knWIiRSiNhQKg5K+MkjSBcUsnZtzprGORdPSWDdRlEs6O5dQUDMbpVo2q6Hves/h2LPvAyFpQ6mLiwsuLzdghf3Rc3uz5257pOu10M8YGIbCm8ggOeAk4Ii00mhbBGlIkvAh0h0j+z6zD72yRWs4YfJIwwc47PtZtk29DyeGMPPY5pyYyZubCFp5JG5NhuV8oxizWieToPzOebxf8w2kcjjm/xQXKZ63VHBcyYrDEDjsO3a7A0MfsNYx52980fFGGIv5hazeQu3J4IpKthHtzA2TZ9FKq+m7odeioxDGblu73Q4ftM6gPxxVOLfcqFTUmE0pXjKjMnLNrDgy2qxHgpAiowZm/ZzGOIyzLNcbLq8e8aI/qkpT2f3bxYJBhKHritHJ1M5n8zHHLPS9J2NRr81YDIcphq3Qw31Q/MIYJGkneD3pnOxVxZBVv9L3XaHIV6UsSsiXsWPV7yQGZIuC+uA9OQZ8mgrLKNTp9XLFarGkbQqXwjmatoVm1vjIzPRJBjXchyFyGCL9EBDXcHlxyeXlJW3bst3dcnu35/Zuz+HoiVE7pKdsCLFUsOoHR2JP8o5WHI0TxGaGmDgcAte3ge0+sR2K9pbMRITctIBubvfl2utmYYs0gbH+JAyZG/waWs1DlPHcdvr7/s/pvJ/OKUiaewz1uMrq1PoWpHJy6u1Wwx9iousGtrsD2/2BYzeor5ngdX1MPut4I4yFc4633nqLruvwvqC5xo0WGygCsVv63vPy5Uuur6/ph51WUfqhaEGmMcU27riFQCsjTTyPKaiUJ6XvCqaCItLG2JI20xjWp0zXe5pFS7tYIVYIMXPx6BE/9uPfZOiOHO7AFK2FpmmU8+ADttEK1Ha5wLhTd7AbepqcsI0Dr266ccppkDxhGmJUHbsW03V9j0mZLEW0t/auSBkpximG0sZxsSyGNJTf/RimpL7yJXpyyKzaBdI6rBVMEEXRk2L72bWa0iaBmSp6aypa06YL1hcb7KIlGLBNA0bG8nSfIrv9juvraz69vSMMgeSWmkl59nYR2DnwW7/3h3zrW99SRunxSAiGyArJQj94BM9eoN3BtvE8Wh/I3rFaG3BwHHqudwMffdrxycvM0W/VG6FkPg3Tjg787b/zB1PPWyx29CbSyaKe/8wB6WowxlqextwzMpWXAxPfZ6T0j+8dxw1jAvZP378+rvPcOYfvBo7HPTc3N7y8eTG2czC2Yei1veSXXqdf+gxf0ahuVy19TlmLf9SABPb7PS+vb8f6iq7ryGjqtJJudBfWm6HgU9WxPFWjmkue5VHnshR7xYyMO7su0CEG/KAVrWSjhWHGkI3g2pb1xYblekN3PGJiBFNSZ6aU3M8Yf+ehyLxE/R5QWZzVlHNh9pUKwurKFhIOqEdtChXapNLasKm7+kRmG4FWBTr0WodICsqcTKVTuWpDRO3ZKVr56oxFbBq1KY3oDmwbd/KD1Wtka8WjVEOdRrEgrUVJuFYxjov1BtNoU6a7uzs++vATPvn4OS/vbk/i9UQi4vRK5EgQ8EDfJbb7jojFLQxdTAxB6KNiWHOaQRZUL2d2K4bieRgi4BUYzmf8Dk4X7HxO3fMezEQirGPOBZrfi/m5G/ewsTg3TvPnnHOkMJRru+V4UH3UnCj4W2mGfU/m9/ONN8JYeO/56KOP6Pue/V6LsHwhZR2P/bgb7nb7E4CzglBITS3qTRKRInhTG75UgksBAAVVgK43HaMLEFXXtrPnUmJslnPsB5QZJ+PObaxltblkffGIu5uXSpZJEw7StktM6sfveg5eTfJ797Mi9fgpfi2Lu4RRKas6lHJHBWyhgIN2KC80YUFDLucm0Hf8SbMUYwFDMTLNK9HrppPztCZFJ7yGa8ZppiUbIUop7LRCyAlT4uXee7Z3d2PhGk3LatGyXq9ZLtcQEzc3N3z88cd8/PHHXF9fc+g7nCu1QyWrkkTrZkQofAcYep0rxjpao7hRSpCyJeYwxvcVMNZ5MF3nqGJbxJHoeb6w8tnvCfKY39K5AVI8Y/63nPakrZvCDJ8w+BNDcO6xnP+M7FhJxOjHNHgM+vly2XS/ivFGGIu+7/mjP/oj1ZI8dOoWF4BSEd7p2Ao4jvRwyoSJ93dn3YznKaxiwbNhvOm1eChLKfiak2nUeFSgtO98aTqcyAFSGrAZFqsV64sNzmkXeBGIKZaGPA0GP55xZKqWMReXqXUg8zRrFayVSkGexbsicuJKa0EWGm9ji3aDkI3iETVtWHGheh1todjXc8ao23HFR5DSS9Po6qwpvDqZszUo5yeTcyrtB/V1KSVtcxgj+/2em5cv2e12RB9YbzZsVguWi4YUe27utnz04ff47ne+x257S8oBZ6brNH5Poxoc1gg2R0hKdQ4hE6PS7GuImZIKAdfQUxdvOqk4hbLxpBmeMAKN9yuap8k4wxc4xRkeHpkYIsY8nNnQNGj1ZLj325wB5fWxc47GTanUJIAtVTIxat+c132szzjeGGPx7W9/G5gorWlmbUdqc2Gm1fRWbWbLK1xDfaJiEWeS6CKaATEFyRbG48imFEwpuJRSpu819BGU+ZlFtSRbZ2nbls3mksVqrRkEk5BkyKLpU+fcPeLV+PHyVDRXb3wFOOP8/6yZzJgRpJDVkCIPn3ShZqARO4nMiFZ11grUXAvXoHRgt0jbkpwDa8kxEmMgJl8KtdRri6KZDZAiwz+5yql4FMmKOiUCtryfD4EhBo7HI9ubW+5ub4mDp7GOy9WCdesgBba3t3z80Ud899vf4cPvfY/+uKNxGurEoP1kQaUMDBoqNaJelUkZm0FSJodIDJDEQkrKaowULZOaWlbPSWaU7JQovXHvF5nN75W+/ovH/yknUlV8O2NSvO68Ou91w9DPMn8+sliU0ghXDYp6xX6IJ4D5lxlvhLHIWRe+7lSpVMtNKUJTUqhV/SqVSVAZmarrWBlvduZhzHYFlNmpxqWkJmcA6liVOuMAS+kdEkKZ7NsdMSZ16yzEUECrxYrles1qtWK/25aFrKsm5zT2z3ioNmTOSK2KWdW1rEIwIUWyV0zCGIOtrqm1ugBiTWMqKBqNMjarYdJwBUJO+CFos+fyUUSk0MctYh0xD3ipndFkDDWMs5iond2lZjlcaRhUStCdc8rZcKoiTlJ25uGg2hm72zv6rmPhlHTV2Ez0R2XSfvIxH3/4XZ5/8l1ur18Qw0DTWBU0yol6K4WMpKheBQmblU7VOGHpjOoMVZyhhBzj7owtRWLFaMx7J+ZSlVpCPVPmQXrtAn54g3qVsTkfo79bN7ucRw4LnHkXopIH9f3ymA1S7VAXwVqlz9eSiBpK6Vr4TB/pteONMBbWGi4uLgoAVtiBeQIl52rdwFjxOBqUWOO+6WKqu18MBFCrEyvgV0e17mPc+ABoVXt6brfa/SvRsrAtPkYwFicZ5xraZslt2aGapkVswg8Dzk3vce5ZzI0FnAr41lGfM6Lfqxo5Ed1ls9SmegqKeu8JXl3tXBq6ptm1GWPixEkHdkp5fVsMRyoKVPVz0hRtTVeaNmuzVayowbZtg2sbkiiPwofEvjtwt71jt9UyeSMyErhif2Rf1Lw+/vBDrj95TrfbkUPPqlEdECFhGkvjCgCbNBWci/6yMbBwjs3SsVk7bBtIRlW2xOgMsILiKTkXGvccE6oPZuDhCQbx6oX/Oibm5xnz483c2yhztupxMBqLs3Cn1DJhqteXyVFng3GZGBJfQeb0zTAWKWe64ag30xTG5Az8GZHwmdV1zhGqIEqqC0D/rBkQ/Vuw5pS3MArUikBltqXimhtLignXKjJvW1Xpur6+5vnHH7K7fcl7bz1RUFEactDfbbPmydN3+N63v4WxGZaqaxl6j8tGqxAdJ0Qg0L9jzjgEiZnQDcQsNFlwbkEsu7OCWYoh5JhZNI0Cl8aowRMgW1LIpNgTvIZMFZOoHldTsIZsDSFDSJlQCvKir4bWsGxaggSGkIgxFAKbKjEJWvMSM6SQCKuEWWT6NnIk40Ni6D0+Jra3O7pDR45wsdmwWTUs2wZjA9/9g9/n+Scfsb2+4bjbMhx2LELkaQN+AX2EIIAkQs70gxKynIBJuj4a41g3jqu14/GjhJiEF0s8QuwC0VcP8Yxqf+55jhYilwxZurd5/DDGPU9mNv9PkdTJkGnGaQHGgWkUvDKBFHukqSX/BvpT4aXPO94IY6E7vxk9gNGKjmGCjMfVG/jV4Lv3xznltk4yX+jilWlau5VFMs5aLi4u6C4udHFiFVCkeBK5yvine5+7GsBs8rhzKDCXxu87gl9zULe4liklxSyyIc87ijmHMVW5a+qDWXfXigHNAWHVJVUBGim4kYhmO1KMJ6CgaiboHIxZwyexhoTBB0/fe7pBmyfnnGlbx3q1pG0EH3r67YEXL15wd3fHUDYKJa1FUobGWbAJh6g8f8wkmzAtDJ5SVXqayjTGkI0rnsGUNVNdzYdChYfl/H8Ux3madu5pa7Nxy2H/J8BYVOLTKReiPj/HFQDmwqRf7E5nYXQ18/nZC+o/r39IKdJ1R25ubrSIKifsjGtvrWW9vuCiGovy+UJUJqQVSxZDEvNgw5dUFqhSqfWAkFQyLhLHwjqT8+heiyj4R1b2pjpRtZCsdBhzjqZZjABXZYtWjEPQrIk2IY+4jFbv5kwInpC0xSFStGaoknnF4AiIUSGWirnErGnm/eHA8dABhuVywapxLBpDDB031y+4fvmc588/od/vMCnjnGClxQAmBg1+SotCooK3rSl4dRHGMokCyAopFYavrcYva31ILKK9Y2HX1PbyR8VQzL3rOuZ/T4SuqS3nuAlknQfneh5fZLwhxgKqYTg3GDUFOk8XjRfvSygWT9kPNRhjLVlOWCmFbKLszhhzUcq+43DoRost2MKLMDhnlMloNWc1UdeVzZfLLn7uZv7Ff+8/+sLf4U9HGVu06+4ffbbDv7zA3Js4pj4pqhg0bbRGNHv2ZceXP8NXNbIZf2Tse1Uo19nc+10BqS/9tg/FM4YxzVbBRx8Du+2e7XZbdtE8Aq29116fi8WKRbsCUMKWcWN3eFdTjfLmXPI/HT864xw0Pd1UqwzlaYZHmIrizoH1LzLekJlbuRAqGlIfw1RNVx/Pn/+iQyVdzdiRuvasqD/1mJFhJ5qWPB6P3N7cjWlaUD6GCuNqpmKxXhFjHsWFndMY2qAGwxjDb/yVf/cLf/Y/HV/9kB8RAz5nep7/HeOkW8sDRuLzZmgeGm9MGDLPWZ9fkIeO+XJjloY9B5pF294rH0MrEUABpOPxyPX1NZUl56q+RFRi2cI5rq6ecPviOcMQaXLDXOXZYsAogPnX//KvqQZmVDn9/fFY+m6gmhhNQ7NQzKFpmrGXaxXMXTYLHq0ucaYZpfZsqZ61iZER6ooIjTHqjg6DFhzlHLU4LFeBm0Dqo9Laj3t8GFSSPxcqOsoEnIdssRjNYw4McRg/v9aFaKHZ7fUtL6+fc/P8Y25vXjB0O6zJNK3lsoFGtD+GTQmT8hhahgKoZiNjLD5NipYYE/0Q6PwAkmlbw5O3V7jWYtySgxeeX/d8+OmBm23gZThNG1fq+I/imIPv9W81FA3Kjambq5twqj8pqdM8lzMrlOKRa1DAQq1vMKcU589wr8/Tr/Wc9fFYMyDTZIo54KzFuQY/dFgDbWM5HA783u/9Hl1/ZL15i9APLJZL/KCtAJfLlp/64AMg8dH3vkN/PLA7dlwt25GhSdIQx4rgxBCLwfExIjGMnb2OfcfRZxaLgcuLK4wztNJqqXnQStsw3LBerFk0LYM4rNVeJktxmgJOaVbyr02QB9+Tg7L6rBNyttOEMgoMtm0LkslDJqaIWKvl8AtNI2cMPgb2exUqvstaz9AuG8RZ/HEghAN3dzv+8Pf/H7Yvr0mxp7XgbMRYQ/KZaDK2CNPagv3U+2RzBlOTYrGUjLsS0kWyWEJ0Km6TYkE7B8gNpIF1u+TttzYcu0B3GGhxY7uILDJ2XDdG6fvjfJktKlMA3R/ueMjLedWnmG2qGMXZyOSkHq+uoaqp8eU/2RthLOA+4jsHO88Lq76yIXZKx1JRZai075yrBoai6F1/5HDcj3UK2UxZgKZ1hBRZX17w9Nk77Pf70smrLx28tLqzvo8kpf5aY0rtgsGUzAYouzTmoGI+3mtWwyiQGjP40imsNi1yJuJoVQaQade0aSamElUwRzMEgVRSqN5rRW0uxXvza17l/60tWhRFfKgf+oLXeLIVTFEX84OGa/vjgZvr27HDvKC1MgJYSUg25BRIWEyapWKL0Y6Fvi0y59AUtSspdRBkXGYkjkkM2GxRemrGmsyqFZYLyLswpr5MHjmcr5hPP1oeh8gpcXEqSoSaPZSv4Cu9QcZCJfE1ftS06CiuOuMWnIYi1QqfC6jOzWjxnU9Ye1XFqMyfMT07b/hTL3jAikBKdN2B/X5L3x+JUYvDIlq3kUXwIbFZX/DWu29zOO449gf6l55DN2CMYdkudbdKk0iucjCExjQqIyiq/QDq7cSooYHBYkvDpRqLDmngOBzxKeLE4JxK8dvmgtqBXqXk/KiNAZBiQJLgYjEWwRfhXk+ahRLiLNp7UdPJuYjvHPuOQ9fR9171Oi5X5JTZFyOx36uor8rhbUlDxKmeLJK1biSaiScSEWUbltRwLfiqSgKC6nZUyo0I2o2uptDFkLM2HJKccNX7dJaL9YLNeg+7qT2wigXFcTGNO3Q2VRTgC87iH9QoNUuzUWlI8+rTubGorF8RwVnzlWRD3gxjcYZTzA3COU5x8ruGGF/irfX81UBNIUvOGVKAolNZO3V33YHjcc8wdIXw1NK0RftAQJzl0dVjnr33PvvjgSFEhu3LUZtSQ5FJrWkYhnJD7YzmncgF48g5E4bIIIOSrGYVnxoyJQgDUWzhf0RsmLwJyXVCWUytjSky89Goox0ShLK711i+am0mowX7gQzBcxh6tvuj6nTmRJRM6xzd8cChO3J3t2W/33P9QgWKhj7gmOocYlT7E3wktRTtDwCld+cSpinOPWl6YERbJRjUcJTvYozgtE+X9sUVQ2OEZLSo7WKz5HKzpDFdWTyM3oqRGVXrLLv2gyL9fZVjpO0b7bpX520u80i5NBZpqkTDlxtvhrEYsyH1N6hnIbPH09G6oGoKdaLpTrf4zLO499x06SUVjQAAIABJREFUnoe4/3OPwxgpJd+iO3m359jtGWLPwq7IJmnbw1KhmcTQrJe89c7bHLojfQzc9UeMdfiku6dkra5dNnZscWhFS5dNZrzZ2TjFc0IkDJ6BfmqkJIZgpeTVdYGFlIg58KLrWK83WJk6oZmiMSq5Vm5qAZKIMiQjGZwl+KT9S4eBWOT1cvHOjv3Aru/Z9UdSzljrSKICRbd3W3wYOHRHDseO2+0d+72W8GdLASkzIlrEVqvfU5YZMdQokJrANjI6hFEAScRZ2BjL/XbGjHuuNdCKxZpGSXDWsVnB5cUS51SlPOYHQtmvKA3/wxzqleVRs0LV1DIkM5LkdLNQYPxPjmcBPGQsJgDnPMyo3oX6pRWs+qzGwhQgSHdZIRdMYnyFZKwUVXGmndz7I7vdlkNRuW7bSIymtI0LYCwxZ1rbsL644MnTp3TDQPf8+Sg0I6ZM+JTISY9VvQqDFS1K029ewMXyPb2PiHgsghSXU6tZ81itWnubOlnQpgXiWlLOeD8Qg16zxioACiBloSqPRD2nIQb66AlZl6OxyjodhoHt8cDheKTzHrGWTKaPgXDwXF9fk8js9/uihHUgaBeDkY2bxRTpOkrNSsAaO2pTGBQAThJJWUgyLwLUcMSTSvakKHGj+h1SmLKSTdHRUq+kbVo2mzXLtnSVD6WnSpkylfT35oxJDvJ8PERajDGTC7U9FY9RmDwLdQgj8hWAFm+QsZjGvB7ioXGvKOgLjBo2zN/z5HHWvqGjGleOEBP7w7aAl1N/zoqyxxiJTkvBnTFcPHrEOzmR7l7y4sULjrdbmoVWjYao6tnOOSQq5pDEkIt+po9VCg2IpeXB4BkyY2sCHdrfxMdIX3qPLpzh7rDHOW0AVBsamwwYwdHo9ylfOcZI9B6fe3zfE0LUik8jhByJIbI77Lnbbel9oI9BXf2sjWx8SOyPB1YrbYMYEWIE51C3gtLDAwVo20Y1PmtjJil0f1UaaHBGGHIYfUURZV3mnFTJGq1+NUX6LlVt0phAYvldBIcbYb1acHG5Li0OOqocpd7yuUf6ozUqbmGtFIOh2ivkqXL5devo8443w1gI975Q/ft1ufCqOZlrvqt6HvPQQrSEez4f5oSrcaKV9zNGtTUWzaSSpfiAaltIyrx4ec1+f+Cdd9/jbn8Al1Rot2nog8dkp9JyiwuevX/Jk3fe4tvf+mP+9v/+NzncbFk7x6PlGtc05BDp+8DSOKwxHIaeICqCE30pDXeli3xIDGEgZ82wVFEd4yyryw3teknf99ze7Pnu9XNqBzPlamjHN5czq8KfIEdEVE1JGwl5YhhUqaxXxbK+7/GhZD4GzZbYpiWYTN/1HI+eHHsuL6/45k//FBebR/zu7/8ed9sjx+OelCO2MeTW4nPCNS3NZlnqTHqGAjJ6hKaI6uRsENqJc1EMmzMOZwzeZbAOcU7vS0wapvmEiY6mBxsS1iSaGHmM8OxqpSrgsWffa/5ElTAqsK0z480xG/dBzZNSdsOY/taq4iL0M8PwtCSiGNCv4BO9GcZi9k3mXsMJ2PjQyx4wMOfp11f933gMjM1maiXn/PixDDhC27YqDXf9UhsCHw5jJWjtIWpySZFaivKStmZ8+vQpT5484TYk8CoZaI1VaX0pbrkIC9eMgje1x2tNhdlCGMs5Q0wEwnh9vNfsjLWWy8tLOq+4gyp3R4zpsFab/vRGyDkW3WHV6xh67fMaw1D6qxx1AQ4DIQ5qtGd9S9RbUY9nuWx59OgxH3zwAUYc3/ned/WalQTGaPiBkCJDH0AS60WplsSAWCJmpmgey73QDBUma/ovCs7Uzm+qZlYl/KPvJ3c8Z4IEQsqlwbV+Dj02EhMK9H4VBIQfyPiMYcNZn5FcMkrqkcmDc/6LjjfDWJTxKj7F5zEYD53r/NiTV5x1yda0qoJHKlBrRoZcu1yw3x95/vw5+/2e4/HIZnOJKyrXTgxZNJNgx25VCefgyZMnPH37Gf7QsX95y647smpaXBG8FTJOhOQcTU5a1Vm6qlXPR0QgarNedTO1DUCO84VgWV20PAqXHI8d3XEoKU79GQYtGZcUdVcSw1BaE8acIOWx30ouVbMKpqlcAjADzxRg22w2vP/++/zsz/4sL2/uxnDEGAMlRelDhHk1pAh9Y3Biizygw5bUbAqJGDW8qNkhALHl+4ZE9InlYkGDUwVrG4vMIpAtCa9d1zDaLqJzyiV5gLvzozbqR58MBCeUg3qQEZWNNON3/RNRon6aLj33Al4Xc72udPezv+9D56ihiYXS89N67Z364sUL9tudCs2UXPbY7FeM8gCquGoW/OBZLRreevaMw92O43bH9u6A957L1VqZSKVS1WZojcU5IZdzRxNprFNOhwgSI0OeRGuIcvIZovcsl8vSM3TgcOwZhjB6QHHwSErkkAmAL5yPoTR0jsEXdF1oCmtSRAhpkgbMMumFPn36lPe+/jXeffddbm63pbcIuLYl+l5l8YoWv8oSqnbFMVhsNrikcnhjOXo09ENPDp6a+AK9FdYmJGRioz09lo1mWkBDxCQJTyAmQ+8jMQk+ZY7HSN8PDEMiJkro+vq58v8P7vnZPIrKUQEQccxbFpy0GihY7leR7/m+xkJEvgH8BvAe+k1+Pef8V0XkLeC/Br4JfAv4Z3POL0VX2l8F/ingAPy5nPPf+j5vMrn7M0m5udjLKz7bK/9+nfExMpdhB21deKZ9OAtH6s9ut+Pq6oq7lze8fPmSEAeGrmfZLog+QMo0Tj2MtmlxVrGGlD0pweO3ntIfj+xu77Tvan8kkVm6htZZZW+mjMuCGIcXDS2cYSxGo8jJOQODD0oTF8G1jWYzho6IsGhXONeAGMQY2jaWrIontoPStqNmT1LjWEA5RyYaS0wK4FqZJiB+vjMZalvGr3/96/z4j/84m82mNJ4+TvfAGjVKlWgVIUukRbjttam1SNY6G7QZQ46JoZ818y1AXlN62HaofV1FrT4eF4615KA6lkPMdCEp2zUJISbtGh/rcjQFH3ntzHzjhswmZ/3eMWrWrGJrlW2rnt0PF+AMwJ/POf8tEbkE/m8R+Z+APwf8Lznnf19E/gLwF4B/E/gngQ/Kzz8K/LXy+7VjJJRUXoC537X6da+d/56fb/76BzGLalROXpeKxZi0PY0x+K5nuVyy3W41u7E/sHDL0cIbBGk0m1ENkgoOq3u9XK958vRtdl/bsu+O3F2/ZNdr28WnV48xBKIG11iExmohkKf0BTEGV65Pqtcnq6JV9IGEYhfZ2AJuxrGJUhXAsbYlOasehq/9VTT8qZkdZVnODXjAxzB6L6eduBxvv/cu77zzDhntVN+Vkv3aO0WbQ0fEaOPe4CER8D3YAk5rc8aivVDJRPW+R43DgwGXBZczxiRWPtINSV9X7tGQPYNXQ9GFTMiGQKmBSdr0KHOaCftRHBO2pqrfzroxnd40pVucmXrSyldgL76vscg5fwh8WB5vReR3gK8DvwL8cjnsrwP/K2osfgX4jawr9W+IyGMReb+c57XjHNw8f+6LjM/8+myoBHo1Dupqh9LQR1yDtdqc9+5uy/OPP+H6+ponj58SfcA0DblI0099QJTCbUXZkbZxbK4e8bVvfhOM8L2/+x2ef/Tx2DRITOlZ6gOpKnznhFNJKjUZM39y4ZSgVZsyhVrbYaDniPeqkBSzEqH0WmhBWcphnEiVAFfDFHLWnp+2go2m4CNmZKGKSAlzFrz99tsqK9h19H2vYsFoLw+EUqSn8gMB7ZRlkiGURjjKuUqF/KaktbaxRWE7EfKgqdhCxYkOMo4+Zg5dD8mydCpWmxjooucwJLpAMbSOIWoPlhp0ZjRRMI9E5vbjzXI4zkMT9cZy5Z8Uo62hh0P7BNuRTDdqtH7J8bkwCxH5JvBngf8TeLcagJzzhyLyTjns68C3Zy/7TnnutcbinOJdd6a51/BZPIX5/7/quXOPZXweizGqT7FYLHRHHTQrsb5YkXNmu93SdwO/+Zu/yU/8xE/wwU99wNB15BhZr9dUinVO02eMMdM0iiesL1suHj/irXfe5r0f+wZ//Id/wM3za3Yff0prtAfJ0QdCP5BMpSULDVq3EK0KAbcujy0GfNSWjsdS3BUTHO92uotmDYVy0nJvbSCUxu9+omRtMuvlasruiGGxbEhZMz3L5RJrLb5gIo8ePeLtt9/mg5/5WXIWnj9/TjcENRrek1IeM01IwWBMg7SorL1tGBv25owvWQyTE4PX7uzGaqm1SMS2C5brNU+fXbBeNdAfudle83Lbc7la8uzpE5xpEDnig9bM+GwZYsYHKTomNUFawhDyKyzDq8lRP7xx/72neS8FG9P5rBmvXFpqhPth+A/Ds6hDRC6A/xb4N3LOd69ZqA/9x72PKiK/Cvyq/mE+9+L/omj293uP+jZVeag+H3zEGjd6DbvdjufPrzkej6zX6xFrSUljbiV2qZxZtfzaZV3Vo9vlgtVmw5O3nxF94OZ7HyIx4Yp+BcayGwZ1JxFqA77G2FFqf9E0ZOdoUkNTq0KbwLH3GpYkjdklU5oZg8WRC8MvV8B0XM/C5eWF4jOHPTFkFosF6/UT/r/23i1Wkiw7z/vW3nHJy7nVtbu6ukrT3cPhXEGxqRnInLEIkBxbnBfaDwbkB5m2CVEPFGzD9gNFvRDQi21YMm3YEDAGBUi2YEKwZGhASLIuJE1bnhlqyO6Zmgtnurq7erq7um7nnDrXzIyIvbcf1t6RkVl5LnXprtPjswqFzJMZEbkjYsfaa/1rrX8VvZKtrS2MzVnqDbh48SLPPHuB8+fPU5Z99vf3IxO4m2nj0IXWAsTVMFIlNm6qTFLEB+X7SD1cczKSOeUxuCBU3mMmNW5SUTkwYrVJUt5DvIGswZYeazSXwlU1ftTgI16RiAl0jB+8DXG4tXu0cpp9VtSa8EzB9u73H7iyEJEcVRR/L4TwD+PHt5N7ISKXgDvx83eAK53dnwduzh8zhPBl4MsAxuYhfnboOI5SKMc4D4C2VFpYwMcYzAywam2OMWqOp1XV5hk7Ozu8/fbbbG5utlGBxFnhUjZn4rBIdRmd4xqb0xv0OXPuAvVozFsCtfcMrEGKTDtrVXF/I9ptK4bBjAhkWWvmG2PI2vRvR24rzclwjsZPO7r5GOFxMc8jxHElfML5GiuBrOyRZRnjakJm87bze+r3UZYlly5d4sqVK5w5cwZMxqROJDqujXikepB517J7P1tLLHW5DyG6W5nmfQTfaplQNwSZsLkdyI0nNFrot9QryfrLLJ89TzaaUEtJLXv4yiFNYFx7GlfTdH4+9VE5HLx4MDHqacqiHKQEPjs3bUmZZHptPyCAM0Y3fgv4Xgjhb3a++grwS8B/FV//UefzvyIiv40Cm1vHwSseVR43Xm5FaOYms4koeWoELDJtIehibsN4PObmzXe4ffs2y8vL9Ho9VSSxetQF5e0sigJfR5A03uy2n6lV0743GGCLHGk8ZNp2r/YVvbKMoGAsstATBtTCaKqmJW4REYosgyzDkmOxVKkfiPdt/YByYIBz04mnae1KVei9B6/jVsxkSvx69sIFLly4wGAw4OrVq1y4cIGiKLi/u0NdKzlxG80KWgmJTz3AppZGmrva9yz50937aMGq9dEQ2xN4j3Xak8S5SawlaSitIS8stZSMGkMVLI3NIetjfY0DbBbwfjS1FNs+IvrX/K+/32yTTxqDE4nNpjoBgrSNf0KKAo5nWXwe+IvANRF5NX7266iS+Psi8svAD4F/L373j9Gw6XU0dPofHf0Tj3ZCx1EURyXhLEr+Egw+NBr3j0xOznslafEoUNdox+93332bK1cuE8Iyde3JsiJWVKr/WBRTS6Wp9aFN0QQjlrIs6Q2G5EWPxo+o8fig/UgGvWEb4TBBxxKCAnXBB6Rtew4SyXMwQm49LrZ29OLx0tCk8KKxBDttV5B4QRWw7GvGowsKJmYFYjKyIifLcs6fu8iV569y5swZLj9/iSzL2N3dZX80AbEYm9O40JLGppUPiGNN6fmz36VEuNAJWTexgMPEfAgjGU6dKapGC/00UuTwVEi2z9JKg4Z0+2SZxeEINBhTae5dmKb6/6iJcr9oy4Tp6zRq8iTkONGQ/4eDsdSfW7B9AH71YQdylIvRnVxp+4c9xqLtQbAypU9Lv6Mr3jR82z1uoqobjSa89dZb/MRP/ISWAct0ZU3vvffksQ2A8x7EtynORVEgRc7y8jL9pSF7rtYKVgmQ5XGFnuaJpDMOTgvHJCgPaAsIA3jt05nFzuhNUEXiG61yNeh4SElkRsjMtIq1dg218wyWlhCbaz1IgHLQ5yMvvsCVK1cYDAYMhssKZFZNBHA1SlJVVVtPk67bvEIWiEohsVAkRaGUA9qjRPdxaGhUy8osQsAGrWDFC41vqPcrGnYoe5vkeRGbJwGSqRKVQo+dUsY711K11KLZ8XTK1o8zf+efg26H+e42T8r9SHJiMjgXyaIkKzi+GXfYdu0D2PH9ugrJGptSXwB1TTwgEZswxtA0DW+++Sbr6+ucPXuWsiw1dNg09IdDfNNQNU3b69TYiMJLZBewFivCcDjk3IXz4Bua0STmSsBkrG0L20K7AC5lUab4uUg02UMLEhoMNlicOHV9Yv6Cjw9em20a08OTZSFGeS/6Rcny8jIeAzIhiPDss8/ysY99jLW1NepaE7yappnya2Q5de3Y3d2P5fQRWwmBIFro9KCV17k/wbSEwGAINjKZBacJXQkYxeO8iT1aBUT7tIwmgdfefJteoQBxnpcMyl7bjWv25ptp5t0TfqCSvJ9g/fz2zrlogTEFbsM0yvOkTvFkKItO1GFRWDM9xN3JdpDmPOxGtElGnR8OJABtdpspGDo1030T6OU9/W0fqCYN11+7wbVvfY+V5bNcvXoV18ROWLUnt6U+4E6tjix1HxfN31C0IJCVA37sk59mZfUst27epNnYYG9vj8IqZtJUCh6WWU5uMqqmApOxU+1TO48XTXgK0U1pZDulHgFembOB3OZYhCyz2vm8s3gGgb61hH6GFAVFr6QJlvMXnuHKn/pTXLp8hV5vwP3726o4vdekMQfGeib1PnfX77C5tcH2/h5ODA5ogkFwLY6oiVcG52u8lDP3ynR1RxsdkRbX0HticbF8XbWodnL3tfKE7k9q/GiCyJ4qjWjVNbbU7m7BtQkWOocin2qnT4yIj5wQmh8yK3P0dnPfThmpukBj5/ukp2YkLlyiV2gGEA5hqtgSJttOVn1xuKk6VDw9lg/EEaX9HlNrnAxlEU/mONr4KAxi5rAHWCbz28wjyAd91z1G4tEMIXDv3r3Ys1OrPJPVAbMp6yFS5weZNXJtnrG8vEx1/lyk+9cEK2qtmExRjGRJpGNaa9Fi62hWOwVWg43cDj4yb4kmLAGRq1OVRaKXSw9ybi1ODGIteVbSK3usnT3LubMX6A8HTCK5DmguSmrNHUKgqpq2uC71gzUmi4i84YG4UzBHJgodbEqb2Dl+WpjmAB8rbyUqAOcctbUxH6GYAf5a100SDV1naF3w5IhoiDqyR0s62oPbmvab5LrqGDpWz/w16Ooj4cFBxL8lAscfWDTkg5EHS2kXKYf3O9fiIKVxEBaS2Klu3rzJxsZG64L0ej1lrIqMy97X7Wos0Uf2IhgCjaY/UA6GrK01hMbFCtGGZqLJVsF7giipToqqNEGxj1wEG4LyYYaA9eqG+JSwE893im3E1G8bq0LN9NxsCPigpDJFUTBcWWFt7Qz9pSFgYr+RQGFMWxKfiHwnkwk7Ozvs7ysPhveQZQZfJysgMqZ3FoZHncKtZanUv0gCTjvFJAbfKl1rLabozdzTKaaiNkWbIzOzOBxvPK0i6JzTA/u2z/38PE65ETHfpW2N2dku1S2F2WNNBzr/UXyeIjj8I6YsZqW7AqS/u69JcRzWku0gi2CRzCeydMfQTW4hav7k5ydwaX19nbt37zIajVqFMO0OpQuDdjvXXpTtwiCxM0W0G8vegNWznnGtVaDbe/vUrsFV+tuJTNdDZO9WNisrWt2aBYO3gImFRdHPTyS/YqbnEYzmbxiDMnmLYBqPr5XNvNcbsLKy1malNk3TXnMR5dUgMoiHEBiNRrEX7L42QoqWTxOvnWInJrrSjzd5Z8Fuiw8eIz6yjDktuJOs5TKFBy2K6TEiqOpVcczOu+MOiFnQNH3Wvo9csdGq1IN3ktUi2KuSFJ6ZOcfptgsGFWLSnQ4DLVdX8Hr6nGjZ/+PIiVEWMw/lAnm/rYp5rKKrsLq/3eZIyDQhZnt7m9u3b7O1tTVNgErVgCJYphreh4ANVn1X9Mb64BAMJs8oh0usrikrFduaFVlPGjJFrgh+2gk9uJqU8GVAqeZi5aExBhvHaUIi5o0me/CRBFfzJ3RiqvvhxzV52WO4sszKmTUGS0M9V2vpDfp6fjHMGUJgUo9pmobd3X02NzcZ7U+mxUttK8pZwuUpm/rh9+nAuZD4WkXxppQjIolajogdGC0wC35xxGCRAnnADT0wHZypQpifUxFU7n7eOjZeSDc/WX3d8Zg05zoYioqZHluY6WkKTK2quCi0neK799k9npI+McriOPKwodGDMIvDJmK398L8fmkM3eQtay27u7vcvXuX9fX1VlEky0Pp2D0Yg/VMF4mOn+lRPkoJGTYzlIM+S6sryPnzWvOxu0cg8obSQCwKkxBBwRDQ7hsGKxnWWHKTxehL3C+EaQGVUkLjfNBoZUzcIhgkyxkMl1leWWNleZWiP8BpAUK0KHwMIgS8OG0X4GF/f5/trV3tcRLAYslkGj6NdLyaHCZdIPDh72X7kMcWAIhXsx2rCs83qgIjF0cIgSpGsLr3t72PrbKYA31jFObAR2zRXJx5uDu5JISYpBOVVkALB4U2oYoOliVtfnabnB7nnt73qeo7ePFTDCRt+fjZqCdGWSyyHA6yJmbN0MVyHHCz62p0J9GilSZt212N0uTb39/nzTff5PU3XuPzX/g3GI2FwWCA8zXO1+RW2add8IhXLs9gpOUzJ2g2p/eBxmu69erZc5yTEpsVbGxsUo3GGIQ8K7Vbj2vo59pHxIcps7dpPIU3sXfotO6jBUkJ5HmGt4IJnonzVDETdBQMz3/kBV786Ec5f/FZ8l6JMwbnArVrGE80pJvnOSEbUXtPFWrqynF/c5s7d+6wtzeiaXx8GMEYG/ukKGdI8EZXRZFIO3jYpJj67tNFXDlVAxC8m6lrCSE2oraGajxBvHKjihFwml2beEunIKxBrHTmwJSmTs+h6TyOZlZxhKT540PY9jTwQIZ2aU2n4rQuSNcN8gyyzJNZaYmSAg6r5KBxDMmF1VqZdLl8wj5DAs+NWooAJmCMQ4wqPyXx9UzV0KPLyVAW8cbArCKYd00WuQXd7Y6KpnT3n/VbH7Qk5iMfoPcw8UJ0x2GMYXdvm3feeaf9bjxW+rqVlZWWni6BgsaYdmVtvJZeB6YPtAtCEEs56LO2tkavP8QEaKo6thqweO+mCotC1+08cloGyONDEfCxz4hEbk9PHb/HB1yleRGIUAz6fOzjH+fMuQv0l5bZn4zZ39vH9gpNbsoznKvZ3rkf62GE/f3A9p5aFKkepmkayrJHVVVkxupD0sE7pqXxs/ewe+0PtSLb71NERZf/hBUBZHmpWItTzCKl4acIlojEHAy0lYT4B+aVjrdoj5ksFd8BQzObtX8bifk4XpR13Dk8MQs1OMDTK3S4RQH90pDlhtxajFVa/zxFzEJolUHjPeNRJJP2TLGH4AliaGpHXqTPQDKDNRaMZXt3FAvomsXX8iHkZCgLOT4G8ahYxaPKot+bR819aNje3ubmzZsRs9CWg0VRKKntgiG3Hcw63yk2YBCvSVVZXrJ65hxrZ8+w7hzOBQwBm+ex3kNdGJGAsXkLXtJRYopHxJ6lXslwmhBoRhNGdcPY1eRFj8HSkHOXLvPM5ecRk9GEiA1YtQQSRtGtriWyn08mE/bGIyaTyQytQHAeOQSEfpISEvj/ENLeW4HY4YQHmoKGaZ6CulKCBHV7smipaG6Ei/wfjbKRO5DoLJgAxkKWCatLOUWZUeSGPBcyA8aqInV1wM7ldSSFNykrgheaAE0dcA60da4n6xUEW0fLFFzjCX5CCEKZG5x4miBtCv2jyslQFgtCp09T5le1Rf4yzLosdV1z69YtxuN9lpa0ZD2FTtvq0/YYnhDMbBIS6juHiINI0LqOYtint7SM3N+mMSNo1AXxooQ8tW8iS5QmGIXgKawl2AyJZDwhKON47Tw2N/gA40nN3niCFDkrZ8/y7LPP8uyVj9DrDxnX6tbUPsTqzE54McYyXGiiJdEwGo3aBshtLgizluBMlCGG+h69bDrwQLDwIY71gPXYDefOV6GayKsaFiwcrsHTIEHT+INHSYYCFOIUaDRQFBm9MqPXKzh7ZkgR+UPF+Jgr4hACLs8pjJ0dG1A7R1UZxZiCUFeBcVVRV3rSg0GBl0SC5BnXDtfodTYGJiEQHI9tW5wQZTErB4GPx3E3nqQclfsRQsBY9cuN0VyDmzdvsra2NkM/l7aVaDJP8Q+J5e/6eQpzBtH8B59lmADlcMBgdVlJbiYjLbXOA3lRqqlbN0oiI+qfVk4zU40YGq+uQe0bGu90hYlUc1IWDFfXuHDpOZ67+hHWLlxgc2cXL2aa8GUMYjNNdDKCeHV9fGjwkZlrMhkxHo8je7Y74D5NMZPHvi8P6oqHP0YX/zIHA95JgShuounmJkKO1kAmWvgnQf+WXN3KQaHXsCgz+v0evV7BsF8yHOTaAd5XNE2t5fyuidetoTBTPCyNMc+E3KDh4CBUpaescupK826K0uC8AFolPHDKY9J4w95YG03VNUweU1ucSGXxNOWoiMs89pHmWLq5f/Inf8Lly5c5e/ZsfJAmSGYWKp7QJuDMQu7JWnECdfAM1tZYrieM6wqAq9UoAAAgAElEQVRvBGuUUm91OKCuKvb29qijC4DzOOuwVklufBAa8YTcIFlP+5gaobeyzNmVVc5efIYLzz3LYHUVspzJaESWG4zNKKyFmF/h3dT1EhGkw5OhyU/VDA4xxSD0yZ5PRIq21CPepScjiyJkB0W/QggKrAaHMWCN0CtyitxgxGFw5LnVBzu3LJVJWSj1YFFklLmytnunvVwCFbhJ67paCQQXYpRE09yDaNf5YGJDKEy8/xm+L3iv2Ihz2m5TNVZBQDvDZbsGYY+6DmyPH+96nThlcVi22dN2VbrRkba6NEZEtKO68Nprr/GpT32K8+fPYYzgXIO1WpGZQuOpra/G8O2Bv+Uyg8UyWFuhX43pjfaRXsGg12NpMKSfZ+zv7lEjjH1DPXHK/I2ltuiKaAIhTKtKM9Nn0BuwfOYMF569xNqFCxS9AT4IVV3TGw71HI1gjCUDRqMRdaN1FYlJOgGZzqWM05rU90o6IboHlOuDdENPTQ5bGNKYna87n2lmqLGGzBpWhiWry0P6PYuxgTIX8kyjPr1sMqXjl0BuPcZ4gm+01UJd4asJodHEthbfME6bLmEg4rde2y/hfRN7gWRkRYGIpXae/f0x1ihJr1gDRtnFxGYUvZyi1KK/Wxujx7peJ0NZdKIh8CBG8DSUxKIM0gTWzYdwk8uxv7/PN77xDdbWVrl48QJXrlyZPiCCmp84QtDiJnxAYiMh04bJfGTyEkKvR1VPePvuLXb3dsmWhqxeuMDS0oBhv89oZ49+ntNkghn2Wpwkw2sfVYHMWnIjsRIzZ+3ceZ5/4SWcGPYmFZPGEYzVsnWPFrAVPbwL1FVMsIrRBLzDN0qoUkWLZlKNtK3B7i5VNW5N6IAj+KlFpeCrf2z3AVQZhS5I8Qhd0GeSr5gNkU9fBbGRtT0Rq7pG69CMEJwwGCxz/uwKeebVqrCByXhENdqAkGlRYVAVabzENglGu9ZBy0QWYuTDUGtI2TeKM8R5EUTdWMkcuTVYq02mcDWTUUUmFaYoMGTU1YQqaHHh0pkzDJdW8aHh2g9+FJQFDz6c837vwyqM+RyJ7mfdCZxwhe426e95RWVE2rBdjEOQmZwyL9jb28FaSzUZ89aNG9y5fZtzsWw9Cxbjjb4KOlF8ICtznGvIbEbtPZlVEtnR/oQ8zxlXI9bX17l1+y4hKK1dIYaxF9y4wduMMBhSljn56hrWRqVQTzkl8l7ZsnAbkzEcDtkLHu8aGl/Hc3I01YTaCWWvQCQS7gjYTMhsjnM1zajB+TqSGmubw8Y1+NBw//59BXq9U+4Jr20Nm6qe5qy0eR/TnqIz9zVGcxbdi9l5IockdXXIiDv7SJiC0VP3x8QEs04EpL23cXw+NuyRlACWEXxDVXvGVeDWrU3G4zFnV5ZYGuZUOKrJiLqqMUYbX+eZKoLaVRqZqjQ/xApY77He4j2YkFGZTBPk0nz1mj9TlKqwCnpYl9PUQl0JrsmRkaGWQHA5uc/w0uBdTRMqerubZDZlET+enBhlcZQymJ9A74fMJ10d5Mfq59oDI5nhab/d3V1u3rzJjRs3eP755xkOh61vn3z4aXadWgLjyZg8L2mcjwVYmiNx8533uHbtGltbW/R6/Zbqf2tri/3dPTY21tUtMKr0ykyZt0po3Y7l5WUuP/88q2tnyfOcqlL+fWOUCDjlEFhrldxXNNvSxorRgG/zIloMRqbp03Vds7c7Gw0JIeCDb3uStNe3vX9JCRx8Lxbd58Nc1EX7L97+QSukq5S644MpHeB8NEcCjKsJwYF3E+rxmP3lgl6ZkRmvYVaxWtQXAWyCwbnYYjGGzU1mVfU5g0ZcpyCwDWl86X7GlHyUm9SjYfA6ZmmGusbjCEbZ1FyAqqpxolwljysnRlnA0UlVx932KDnI0gDaMKeZcznSft2ELtAHvs0EjMdbX1/n9ddf5xOf+ATPPPPMjIsVQoj4hnbw1v6jDVlWMBqNVGk0DTdv3uSVb77C7/7u7zIYDFlZWWnJcne39rh1+ybvvfcezulvp27a1lqYTBgMBhRFwcWLF/nJn/xJbNbj0qVLeBMp+UQwWY/GVzFZTGtJNA25m6wWYhNk314f75v2OjWNWhWpRB9omcC895qU1b1mM5bd3H3hoOTl+H0KcR4677vK4Hj4yIPzYNba8YQ2zK2KIraJbGr260BVe6q6pqpzlpd6DPoFy3lJZrIY7YpFeCLEtq4aYkUI4nHB4CUgVnBuGl2JPP9a6GcttlAswjmtMq59wHmhcQ4Ri3fgJCA24EXTuyd1wMQm2I8rJ0NZHJGUdZCL8qTkqBVrHtTsKoZ5N2nQV4Dw7bff5t133+XKlSssLS21tSIh+LZrVAiaXFOWNloZBWWZsbGxwbe//W1+7/d+n6997essDYYsLy9rqjewtbXF7u4u2zv342/rQ5zAuPHumMFggDGGCxcu8O6te+xPHD/zMz/DcLjUEgv3+znGZvjoKmTWttZLNwqgCmJaRds0WjqfiuU2NrQlgnOdzNj4ZM+HSgPM5DXMfM5iC3JaqXmMm9kRTXeOULLEC9Uey8xvPP39mYivFvR0x+CFSMVowCuDelXDaNIgtiZgWLIFRnIM4HygcTXGCCaDLIukzo2m0QfvINb1zGa3av6MEUtWaEzWe0/lPbVzNMHijNBIwESqA2MFBcL03jW1x2oY5eEv4JycDGURjs6teD/dkK7J2k72OYtifvtU3ZneO6cgbVGohfDO2+9y/fp1XnrpJVZXV2PTohQy1GNYoyuzUvgHssyyubnNH//RK/zB//V/881XXmV3e4ftzR16vfttu4Fu8pPNdDWqqkmLU3hrGDU1VVUxcYFJ/Sq9wTIvfvTHefHFF2lcoGpqyv6QrOiROT2HjGkp+vx/5xyTSlstjsdj9vf3WwW4vr7eWhXqXk2vm6Ytd+pqxEdo4mg+i0dVEkfKA1mSh/+IsmnFqFVUOj6IWk9BY0CVAyaBhoq6CaxmPazNKEqrwLZrNJENVTRBlHhHS0ECIl4tAjqkwsYgmURgU7N7Q/B4HHXwcVtLEyvLMoFCLMZYPA7nDI1vKIygvVceLxJ1MpQFR6/u8xGIx5GDsjO7xz8oIau7jTGziiwxZI3HY+7cucPr19/gvc/c4oWPvEg2zKLprhPVORdR8BA5MjK2t7f5+tf+kK985Sv8q6/+v+zs7Cmvp6/jNjnGCIPBUkvVFwTqZoINOVlRqMUSXQBkhAtw884dXv3WNT756jc5f/EZ1tbW1BJID60k10FzH+YVRTrHyWQS3aaq7UoGcP/+fWDat8R7j2S27XzWtS6SNZaAyoPkoHYeiqMcfv9nQGo0eiMLLQqZex/P1Qtt2nd7jztbBhNT7TMCjiYo1uRr7f1SNzU7ZQ5ZgCxDTAnWEfyYEBoqH7GcGOUkaJha+9NOM36TaynClH7AmrZZVWzwgItWBWIRm0dWNIMNnqpucGLiXP0RURaLkO/575+UVXGYsujSmqVJ3R1Tl8vCe9fS4PX7fUQsGxv3KQptOnTt2jVWV1e5evUqL+QvkBcZvV4vZnvWiLH0eiVV7fmjP36F3/md3+Gf/tN/xhtvvMF4PIagvUVXVlbweDxKkKv5GrliDSZgJNPuWyFE01YVSVn2SR3Svv/97/Obv/mbrK+v85f/0q+AD9STClcbJqMxoSh08kXgMrkjjau19mNvh9FoWv9RFCXGWDbW77O+vtlWNqaSaMFQew3zAR0OjM5170Y/525t90/tIqDuwVG4xnRnaZPCQwtaChw6h6LC6ICc81G1YGzcTCNhqfE0Eqh8g69r9ht4K4zo7TQMl8YMBzlFz5KZgkHf4nyFiXZBitpYMRhrKDqFa9bGuhTRe+gF8B4rASMBH1NZjYAttGCv8ZqnIVmOBOVG9Y0nZIvzeR5Gnm4K3Zx0FcLU1JeZB/QgpXGYJXDYdt3P53+n65p0x9LFLlIValIy6iJohWLTeN56621+8IMfsLe3N9NUeDKZYAxUtR7r+9//Pl//+r/m5s2bLaZRZCWTSU0IQlH02irJbgTGSkZqhktE4K1AU00wBIrMYgi4uuLtt27wnWvfYnP9LitLA5pqTFONKXOLwbfnlRRFAm+bRis1g49dzhrfukR37tyN+00jK91rvCirM1lTrQJZcEtENIktJbLNY0Pzlk9nTxarE/18Zr9gpv8fkNmQOqDsYtAOuPFa9ekRnIdARpb3MbZkrwpsTRo2dkbc2dplc3fMuAGTl+TlEGMLjag5kCDkxlKIjX1sc4osJzOxk7zzWEkeUAw/O2XkzYwaQcZCXqSGWHULqhIS583jL7QnzrI46H1XSXRX/YMe/sPcmkXg5HzYdH67+VCcvp9mRiaux6IoWoXSNA2bm5u89tprvPzyy1NMwXuyXDusV1XFrVu3cS5w9vx5xFpGeyOyrMA4Icuytqw6WSzd80hmv6sb6tTCzit+sLe7refiPK6uCK7m2jdf4a0332B1eYlhzNacgpjpIXRt2DSds/Jqeq18bBqWl5fZ2Nhga2uH8bhqLawUeg0xfOBBF/QwGzY9StyhQIVHxBygKB6UBxaYpBwWbj51Rw6TxMSd8jR8iFhEpMCqvFA7TzVxlB5qb4CCleUeZaZs6xgQmRChSazNsSafVjVHfMcTu6sEVVCapwM4hw+a/m+MRySxqE8XJWM65txjyolRFosSqJIswhUO2v84UZXDAMx5pTBv0UyjGolt22Ktb/34oiiii6KRgu3tbV577TU2Nzc1dOknZFlGUZbUdcPurjJi/9RnP8vy6ipV1fDVr36Vra0tlvrL5ISWhwE8ZVmqPyuCZHaG61OCug9FrEWpxxNym2njHSNk1vDO2z/k9p1bjMYfZWVpyO7enuZolCXjWKPQVSCJlLjLVu6aQL83ZOv+W9y5fa8l3qnrmjwrQGYzcoG2TQEi7dydrwaHqSWR1vpF96ZllGuVedq43Yl0lBD/2XDEHApm8YDSdzOjmo5NsVFVMB51JyDgTQ54vKtxPij/iKlY26+xg5JMLJk1hMwiIWBNSVH0EBctV7Q3i48d8IwJMRnOadPo6GMF11BYyHJtvO0iN0ciAhaRtp/M48qJckO6Mg+uPcp+R20zbykc9JtpYnUb8iRloA+rYG3e3ti0b0quun37Nq+//noMZU3p3XZ399ja2iKEwNraGh//+Cf5+Mc/zsrKGtVo0gKi+vsRLPSexlUEHJmhbWqcXJcsy7TKcFJRVxOdwjJtI2Ct5Rtf/0PeeO16tHQU7W+aKuYE+JY0xzlHVY1jijJRMeakVfDevQ3u3l2fumOdB3KGzIgHc1bmpetydO8JMMUe9CAz2xx8TDOzTTr+9L/p/I+fxavc3UabO8uB/xePRVrXUCQnBGFSw/6eY2NzxM5ezaQKeJ9jTB9je2BybJa39yg12zbGtEl3yY01QVMwjNEFIi8Mea40hkY0Pd3Ee+Bjot88Z+ejyIlQFoHZh3URGv/APnOfH0dJHBTl6FoK3dejJCmMLuem7g8aHdBin52dHV555ZV2EmQ2ZzQac+/ePQUL+8oqVRQFly5d4tyF8/SWl2YUUvvfT/uKeO+1J6tX3opMjFL+h4CEQCYWXGC8t894b78N01777ne48fYP2R3ttg9GMvudb1r3w/umLRJLom5XwWg04fbtu2xv72KtJc+KGRcpXe9F79N17z6885JW7UV3oas8H1b0AT4I7DvscZjlH5GgdouEpGJoOTyUr2SayOUdNDWMR3B/c8zGvT3u3x+zP3ZUHpAcbIE3FpNnZGWhhWLWxsjHNOFO/xusFazVKEee52pNBYf4KVm0q2qaWpm13GMS38AJc0OOwh8WafJFFsKibY/z+/PHTm5H+nvetJ7ff37bBBY2jeX69esAlGVJ7RTLuHv3LisrK/Tz5RgWDQyXV5RCv2koyiKWfgdEAtY5jFusWIHIsWBig+bYGNl76rrBI5RZwd7eHk3TxAjHiF5PO6whFqFp3ZoQXPs+rXb63jIYDFi/t8Gd23cZjyY0tWtXwiRJeXbv0VELwMz1POy7FEg5ELLoPPQJfzq02OwwMtvjradWEqNW0Lof0ZIAvEeCQYIjeKgmcN+PtaRcBAYleZ5hipKsV1JEC9B75UWVmANjYp6FtapQcvGIsZrQJTl11bm2PuCbEFsyEHGVHxGAU5iyarefzaHf81bBYsDx8NXsgd/tAJfzx+v2AU3H70ZC5o8TQmizMhPwmLqT7ezs8NZbb/Hd736Xq1evcnf9Hs45tTTKguFwyP7+mMYHLl68qH1Ti17rEpSlHrdyFdQp+9PRVHU0NV10gaYFW9s7OzpuIxEMbNja2uLCMxf587/wJT7+iU9Q1TV5USgHQvCI04naNJU2PZ5MAK/kw5EzIQRPr+zzB6/+K65d+w7r6xvs7+0xXFqiKIpo8aRoUDNVsJ2oRjtvDzEOjlL60sE+jpSHrUo9hKPPhNlhKxnONKSODwTxSK2Fh8bk2LLAxhLzyWSfegLOaQi6QSgHfbLBkKWza5jxHvWkYjwaM2lqghGKskc5yPC+Ie/lDI3gvOBdRt0PBJ8z2dew6aRucF6rVfFCkWkv2jIvgJ2Huw5zciKURWBKUdd+1sEOuhTuSXsuWrEWmbpHhVQP8jsPC9HOkwt3w4GJGDaFFr33WNGuWTdu3KAsy9YdGQ6XAaPgpQk0VUVZ5hpfNxpN8TggYhHeYo0ls4U2Om55FU1b8CWZxYvHFpGgdjJpFV9RFLz0wov8m1/4PM8//7yCmE0DDeSSLIGpGaulCVkMiXrqekRZ9tnd3eW9Wz9kUu2DOHr94UxjoaKwNE2FtVo4lfj0Ja62LeWeLMaPZhaKEGbuhR4jKfa0z7Qxj97kub8PFT/3yoyiWISjpGnRtSZ9tCrECiFYyJXiEDzSpMO61CieKhi296H2NUE8Zb/HyuoZeqFWAmAKmjDGETTaZjOwGWDJcrAOmtqTe2VaG3un3K3W4qoGP6nJrEPKBmsFTM3jyolQFkkOegjTg3iQHBQdmbcWFsmikCnMJl8d5/fmjzfbUsCzu7vL66+/zuXLl7l48aLyYcYKQmMMmRGsVeWSgEpd2TvAKp1Wg1EZpfGEENnD65rxaNQqtfRaFAVXrlzh5Zdf5sUXX6TX67UJVsldSpM/uRxVVbXuxWQyoSxLnHPcvXuX+/fvt1WzafsZst5juhsfNjmue9td7HwItHzukZBII1we52qsNfR6Baury4hRBneJ2ZhKAaKAsipgq7UlDoxoCBkHvcZRVQ11PWqfFWstxgpZZlCd9nhUWSdCWbSRrwWTa9EDv8gFOWybeYsl/d19oLvbJaxinuviMNN4/jfmlcVkMuH69et8+tOf5vLly4iNK2a0QvLM0jR6k3u9nlaX7u4utLZSp7H0cCeFkIq7JpMJ/X5/pk/GpUuX+Pmf/3l++qd/mqWlpdZFSuBlXWvWZ57b1v1JSipZSP1+n83NLd555x3W19fbSbmoQneRHKbAu58fhl89WTkoVHqwG9KVRefSnSPz0TUvYKzS4hECDo+vYWt7B4ywtLREM6jJc4vNMwqG1M0IsRZMRuMrsiynzEqcCzhxZFlBaAJFNYkg5jT/RJVFLG1/AiXqJyIaAtMHFKbhuynYdnCYc/7z+YrJ+e2POkYax0HWStqmq2QOW0G7E399fZ13332XqtIErhSSBChiXYeINijq9Xqdxspeke24+neVmtYOTEvlE06QmugAnDt3js985jP83M/9HJ/73Odai2UwGLTnWxRFe+1TElhZlm3CWbome3t73Lx5k52dnQie1u2Y5i2zeddx/hp2r/n8ZydBDruvh415Hvua/x/QKg0fOTarBjbvb/PDd97l5u173N8ZE6SkN1imv7xG2VsCyXDeYG2JLUqyLMdmBXlWYrKCPC9bizRFTbIsaxeedH8fR06GsuiEhrrl313SlUUuSvp70efzk7MrCyMJC/afVz4HHe/g05rmQBhj2N3a5a233ub+/fsUWc4ougsAmZmGxspSb7wxRtm+O2PtXossyxS76IRwQwgdQNKxtLTET/3UT/HFL36RT3/60zz33HOt1ZDGlcaoisG3CiR5fkmJhaDK4vbt2+zu7s9cn+41m7/Wh70/iYoihEA4BHxN2yQ5SmGISCxIM7iYaNU4r8laCC7A/gRu3dvgvTubbGztMq4cJu/R76/SH6wSyAhEMgzJwBSa8ZkXZFlBURTtgpPZvHUnh8MhZS+nKPLHvi4nxg1Job6u2dt9UNMqOrPfAROtO4kfZhLOm/yLJv5hOMgicFRElJSkCWxubvLuu++ysbHBSy+9xHA4bC2HtG2bWDW/SkvsnB40szLP8xZLSBmW3XEVRcHS0hKf+MQn+OIXv8jP/uzPcu7cOUajURvWTBZGit7MYxP7+/vkuU648XhMVVVsbW3FNoV7HWtwWny3WMkuBqBPvITYGHlOZsHWxXOhe0/bwwHBx1R1o+n0EiCVs9SNZ2PLY/JdBisjsl6fpeUeRZljfEOW1SAWQgbiMVlOnvUQPNa6GH43M89MUWSx38gH0JFMRK4Afxd4Fp0VXw4h/Pci8hvAXwLuxk1/PYTwj+M+fxX4ZdTa+k9CCP/nEb/Rrl4w+7CnmoSUG9B1EZLyeJxJ2LUoFgGr3W3S2Ob3nx/D/DbeB3q9Hrdu3cJay+///u/zzDPPcPHiRSZ1xWQ0our1kABLS0uRL1NJXZumATft3E7H7QghsL+vrQN92xHLU9eez372s3zpS1/iC1/4AlevXuX8+fNMJpP22mZZRl3X9Pv9lswmKZwmkslqJa0wHlcURY/r169z/fp17ty5NxP50XOcdo2fv4bhgGV60UN3HFD68eSwfIoHw+LHcUXmJYSAzaQ99+A7LpqxOO8xxiKS0XglABYRgrXsNzV3NkdMwk229isuP3eBS89dYLh0RovPIpOWkRxjDSbrEVyNMRnG1BR5ydLSbHPusiyOLOs/jhzHsmiA/yKE8Mcisgz8kYj88/jdfxdC+G+7G4vIJ4G/AHwKeA74FyLysaAMIotlDifoPpgJ9e+CcfPbzE/S7vdpm+5rAh/nf7OLm3T3PcgFOcqETg8mHpZWlhmPK3Z29njjjRtcv/4GzzxzCcEyGVcMBktsb9/HEJSAN0+ZhrPdvtss0Fhg1uI8jTbDLcsSV1d85jOf4bOf/Syf+tSnlKR3T7kxxuNxe021vaJvJ1aKmij5bqAoCiXQmUyw1rK+vs6NGzfaornUCsDarPN+qkDaa9cJNc5fu/nredC17lomi6y+o2WuVuWAscwvFI/yjD2g8GSW6EcibhHQqlUx2myqbjxZgMoHdvbGcG+DssxZWlpi6dIapffUk4rQOIIIIhYxGWVhGWVjbUuRKatWcjHrZp/aNfR6H4AbEkJ4D3gvvt8Rke8Blw/Z5ReB3w4hTIA3ReQ68Dngq4f9Tnc16l7sBOClbR4VP1j0W/MK5v0SY4zS/ouoKb+xycbGBjs7O2QxkSttZ61t8YvOEZiHl7QtoZuxwkzQc7l69Sof/ehHee655xgMBu02h52niDBbK/4gnLW7u8v29i6j0Wjq9gR5pAfqZMphmZzvg4QH2auC116mo1AhEtjd3Wc0Gcd5P6vUjER3NbG65wVFocdLlroPkxgReXzEQR4SsPsI8AfAp4H/HPgPgW3gG6j1sSki/yPwtRDC/xr3+S3gn4QQ/ve5Y/0K8Cvxzx8H1oF7j3EuH6Sc58MzVvhwjffDNFb4cI33x0MIy4+687HVjYgsAf8A+M9CCNsi8reAv47iNn8d+BvAf8zideYBjRRC+DLw5c7xvxFC+DMPN/ynIx+mscKHa7wfprHCh2u8IvKNx9n/WKFTEclRRfH3Qgj/ECCEcDuE4IKiV/8z6moAvANc6ez+PHDzcQZ5KqdyKk9fjlQWoo7ubwHfCyH8zc7nlzqb/bvAt+P7rwB/QURKEXkB+DHgD5/ckE/lVE7lachx3JDPA38RuCYir8bPfh3490XkT6Muxg3gLwOEEL4jIn8f+C4aSfnVQyMhU/ny0ZucGPkwjRU+XOP9MI0VPlzjfayxPhTAeSqncir//5WTke59KqdyKidenrqyEJE/LyLfF5HrIvJrT3s8i0REbojINRF5NSHKInJWRP65iLwWX888pbH9bRG5IyLf7ny2cGyi8j/Ea/0tEXn5hIz3N0Tk3Xh9XxWRL3W++6txvN8XkX/7Ax7rFRH5PRH5noh8R0T+0/j5ibu+h4z1yV3b+aKqD/I/2hPudeBFoAC+CXzyaY7pgHHeAM7PffbfAL8W3/8a8F8/pbH9OeBl4NtHjQ34EvBP0PD2nwW+fkLG+xvAf7lg20/GOVECL8S5Yj/AsV4CXo7vl4EfxDGduOt7yFif2LV92pbF54DrIYQ3QggV8NtoBuiHQX4R+Dvx/d8B/p2nMYgQwh8AG3MfHzS2XwT+blD5GrA2F9V63+WA8R4kbTZwCOFNIGUDfyASQngvhPDH8f0OkLKXT9z1PWSsB8lDX9unrSwuA293/n6Hw0/waUkA/pmI/FHMPAV4JmgqPPH14lMb3YNy0NhO8vX+K9F0/9sdl+7EjDdmL/8k8HVO+PWdGys8oWv7tJXFsbI9T4B8PoTwMvALwK+KyJ972gN6RDmp1/tvAS8BfxqtQ/ob8fMTMd757OXDNl3w2Qc63gVjfWLX9mkriw9FtmcI4WZ8vQP8H6i5djuZmPH1ztMb4QNy0NhO5PUOJzgbeFH2Mif0+r7fmdZPW1n8a+DHROQFESnQ0vavPOUxzYiIDEVL8xGRIfBvodmqXwF+KW72S8A/ejojXCgHje0rwH8QUfs/C2wlc/ppyknNBj4oe5kTeH0/kEzrDwqtPQTF/RKK3L4O/LWnPZ4F43sRRY2/CXwnjRE4B/xL4LX4evYpje9/Q83LGl0tfvmgsaGm5/8Ur/U14M+ckPH+L3E834qT+DnVUBYAAAB6SURBVFJn+78Wx/t94Bc+4LF+ATXNvwW8Gv9/6SRe30PG+sSu7WkG56mcyqkcS562G3Iqp3IqHxI5VRanciqnciw5VRanciqnciw5VRanciqnciw5VRanciqnciw5VRanciqnciw5VRanciqnciw5VRanciqnciz5/wALAj/Xp5FT4wAAAABJRU5ErkJggg==
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<p>Before using any of the face detectors, it is standard procedure to convert the images to grayscale.  The <code>detectMultiScale</code> function executes the classifier stored in <code>face_cascade</code> and takes the grayscale image as a parameter.</p>
<p>In the above code, <code>faces</code> is a numpy array of detected faces, where each row corresponds to a detected face.  Each detected face is a 1D array with four entries that specifies the bounding box of the detected face.  The first two entries in the array (extracted in the above code as <code>x</code> and <code>y</code>) specify the horizontal and vertical positions of the top left corner of the bounding box.  The last two entries in the array (extracted here as <code>w</code> and <code>h</code>) specify the width and height of the box.</p>
<h3 id="Write-a-Human-Face-Detector">Write a Human Face Detector<a class="anchor-link" href="#Write-a-Human-Face-Detector">&#182;</a></h3><p>We can use this procedure to write a function that returns <code>True</code> if a human face is detected in an image and <code>False</code> otherwise.  This function, aptly named <code>face_detector</code>, takes a string-valued file path to an image as input and appears in the code block below.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># returns &quot;True&quot; if face is detected in image stored at img_path</span>
<span class="k">def</span> <span class="nf">face_detector</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span>
    <span class="n">gray</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2GRAY</span><span class="p">)</span>
    <span class="n">faces</span> <span class="o">=</span> <span class="n">face_cascade</span><span class="o">.</span><span class="n">detectMultiScale</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span>
    <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">faces</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
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<h3 id="(IMPLEMENTATION)-Assess-the-Human-Face-Detector">(IMPLEMENTATION) Assess the Human Face Detector<a class="anchor-link" href="#(IMPLEMENTATION)-Assess-the-Human-Face-Detector">&#182;</a></h3><p><strong>Question 1:</strong> Use the code cell below to test the performance of the <code>face_detector</code> function.</p>
<ul>
<li>What percentage of the first 100 images in <code>human_files</code> have a detected human face?  </li>
<li>What percentage of the first 100 images in <code>dog_files</code> have a detected human face? </li>
</ul>
<p>Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face.  You will see that our algorithm falls short of this goal, but still gives acceptable performance.  We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays <code>human_files_short</code> and <code>dog_files_short</code>.</p>

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<p><strong>Answer:</strong> 98% of human images with a detected face and 17% of dog images with a detected face</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">tqdm</span> <span class="k">import</span> <span class="n">tqdm</span>

<span class="n">human_files_short</span> <span class="o">=</span> <span class="n">human_files</span><span class="p">[:</span><span class="mi">100</span><span class="p">]</span>
<span class="n">dog_files_short</span> <span class="o">=</span> <span class="n">dog_files</span><span class="p">[:</span><span class="mi">100</span><span class="p">]</span>

<span class="c1">#-#-# Do NOT modify the code above this line. #-#-#</span>

<span class="c1">## TODO: Test the performance of the face_detector algorithm </span>
<span class="c1">## on the images in human_files_short and dog_files_short.</span>
<span class="k">def</span> <span class="nf">performance</span><span class="p">(</span><span class="n">files</span><span class="p">,</span> <span class="n">detector</span><span class="p">):</span>
    <span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
    <span class="n">file_length</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">files</span><span class="p">))</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">100</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">detector</span><span class="p">(</span><span class="n">files</span><span class="p">[</span><span class="n">i</span><span class="p">]):</span>
            <span class="n">count</span> <span class="o">+=</span><span class="mi">1</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;There are </span><span class="si">{:.2%}</span><span class="s2"> human faces detected&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">count</span><span class="p">)</span><span class="o">/</span><span class="n">file_length</span><span class="p">))</span>
    
<span class="n">human_performance</span> <span class="o">=</span> <span class="n">performance</span><span class="p">(</span><span class="n">human_files_short</span><span class="p">,</span><span class="n">face_detector</span><span class="p">)</span>
<span class="n">dog_performance</span> <span class="o">=</span> <span class="n">performance</span><span class="p">(</span><span class="n">dog_files_short</span><span class="p">,</span><span class="n">face_detector</span><span class="p">)</span>
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<pre>There are 98.00% human faces detected
There are 17.00% human faces detected
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<p>We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :).  Please use the code cell below to design and test your own face detection algorithm.  If you decide to pursue this <em>optional</em> task, report performance on <code>human_files_short</code> and <code>dog_files_short</code>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### (Optional) </span>
<span class="c1">### TODO: Test performance of anotherface detection algorithm.</span>
<span class="c1">### Feel free to use as many code cells as needed.</span>
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<p><a id='step2'></a></p>
<h2 id="Step-2:-Detect-Dogs">Step 2: Detect Dogs<a class="anchor-link" href="#Step-2:-Detect-Dogs">&#182;</a></h2><p>In this section, we use a <a href="http://pytorch.org/docs/master/torchvision/models.html">pre-trained model</a> to detect dogs in images.</p>
<h3 id="Obtain-Pre-trained-VGG-16-Model">Obtain Pre-trained VGG-16 Model<a class="anchor-link" href="#Obtain-Pre-trained-VGG-16-Model">&#182;</a></h3><p>The code cell below downloads the VGG-16 model, along with weights that have been trained on <a href="http://www.image-net.org/">ImageNet</a>, a very large, very popular dataset used for image classification and other vision tasks.  ImageNet contains over 10 million URLs, each linking to an image containing an object from one of <a href="https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a">1000 categories</a>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>

<span class="c1"># define VGG16 model</span>
<span class="n">VGG16</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">vgg16</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="c1"># check if CUDA is available</span>
<span class="n">use_cuda</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span>

<span class="c1"># move model to GPU if CUDA is available</span>
<span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
    <span class="n">VGG16</span> <span class="o">=</span> <span class="n">VGG16</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
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<pre>Downloading: &#34;https://download.pytorch.org/models/vgg16-397923af.pth&#34; to /root/.torch/models/vgg16-397923af.pth
100%|██████████| 553433881/553433881 [02:38&lt;00:00, 3494406.96it/s]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">image_transform</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="s1">&#39;RGB&#39;</span><span class="p">)</span>
    <span class="n">image_transform</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span>
                                          <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
                                          <span class="n">transforms</span><span class="o">.</span><span class="n">RandomHorizontalFlip</span><span class="p">(),</span>
                                          <span class="n">transforms</span><span class="o">.</span><span class="n">RandomRotation</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span>
                                          <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
                                          <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">),</span> 
                                                               <span class="p">(</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">))])</span> 
    <span class="n">image</span> <span class="o">=</span> <span class="n">image_transform</span><span class="p">(</span><span class="n">image</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
        <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>    
    <span class="k">return</span> <span class="n">image</span>
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<p>Given an image, this pre-trained VGG-16 model returns a prediction (derived from the 1000 possible categories in ImageNet) for the object that is contained in the image.</p>

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<h3 id="(IMPLEMENTATION)-Making-Predictions-with-a-Pre-trained-Model">(IMPLEMENTATION) Making Predictions with a Pre-trained Model<a class="anchor-link" href="#(IMPLEMENTATION)-Making-Predictions-with-a-Pre-trained-Model">&#182;</a></h3><p>In the next code cell, you will write a function that accepts a path to an image (such as <code>'dogImages/train/001.Affenpinscher/Affenpinscher_00001.jpg'</code>) as input and returns the index corresponding to the ImageNet class that is predicted by the pre-trained VGG-16 model.  The output should always be an integer between 0 and 999, inclusive.</p>
<p>Before writing the function, make sure that you take the time to learn  how to appropriately pre-process tensors for pre-trained models in the <a href="http://pytorch.org/docs/stable/torchvision/models.html">PyTorch documentation</a>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>

<span class="k">def</span> <span class="nf">VGG16_predict</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="c1">## TODO: Complete the function.</span>
    <span class="c1">## Load and pre-process an image from the given img_path</span>
    <span class="c1">## Return the *index* of the predicted class for that image</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">image_transform</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
        <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
    <span class="n">prediction_index</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">VGG16</span><span class="p">(</span><span class="n">image</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">prediction_index</span> <span class="c1"># predicted class index    </span>
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<h3 id="(IMPLEMENTATION)-Write-a-Dog-Detector">(IMPLEMENTATION) Write a Dog Detector<a class="anchor-link" href="#(IMPLEMENTATION)-Write-a-Dog-Detector">&#182;</a></h3><p>While looking at the <a href="https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a">dictionary</a>, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from <code>'Chihuahua'</code> to <code>'Mexican hairless'</code>.  Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained VGG-16 model, we need only check if the pre-trained model predicts an index between 151 and 268 (inclusive).</p>
<p>Use these ideas to complete the <code>dog_detector</code> function below, which returns <code>True</code> if a dog is detected in an image (and <code>False</code> if not).</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### returns &quot;True&quot; if a dog is detected in the image stored at img_path</span>
<span class="k">def</span> <span class="nf">dog_detector</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">100</span><span class="p">):</span>
        <span class="n">predicted</span> <span class="o">=</span> <span class="n">VGG16_predict</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span>
        <span class="c1">#Returns Boolean if true</span>
        <span class="k">return</span> <span class="p">((</span><span class="n">predicted</span> <span class="o">&lt;=</span> <span class="mi">268</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">predicted</span> <span class="o">&gt;=</span> <span class="mi">151</span><span class="p">))</span>
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<h3 id="(IMPLEMENTATION)-Assess-the-Dog-Detector">(IMPLEMENTATION) Assess the Dog Detector<a class="anchor-link" href="#(IMPLEMENTATION)-Assess-the-Dog-Detector">&#182;</a></h3><p><strong>Question 2:</strong> Use the code cell below to test the performance of your <code>dog_detector</code> function.</p>
<ul>
<li>What percentage of the images in <code>human_files_short</code> have a detected dog?  </li>
<li>What percentage of the images in <code>dog_files_short</code> have a detected dog?</li>
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<p><strong>Answer:</strong> Based on the analysis below there are 0.00% dogs detected in human files and 98% dogs detected in dog files.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Test the performance of the dog_detector function</span>
<span class="c1">### on the images in human_files_short and dog_files_short.</span>
<span class="k">def</span> <span class="nf">dd_performance</span><span class="p">(</span><span class="n">files</span><span class="p">):</span>
    <span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="p">(</span><span class="n">files</span><span class="p">):</span>
        <span class="k">if</span><span class="p">(</span><span class="n">dog_detector</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="o">==</span> <span class="kc">True</span><span class="p">):</span>
            <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="k">return</span> <span class="p">(</span><span class="n">count</span><span class="o">/</span><span class="mi">100</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">printed_performance</span><span class="p">(</span><span class="n">human_files</span><span class="p">,</span><span class="n">dog_files</span><span class="p">):</span>
    <span class="c1">##return the dd_performance of dogs and humans</span>
    <span class="n">human</span> <span class="o">=</span> <span class="n">dd_performance</span><span class="p">(</span><span class="n">human_files</span><span class="p">)</span>
    <span class="n">dogs</span> <span class="o">=</span> <span class="n">dd_performance</span><span class="p">(</span><span class="n">dog_files</span><span class="p">)</span>
    <span class="k">return</span> <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;There are </span><span class="si">{:.2%}</span><span class="s2"> humans detected in dog files and </span><span class="si">{:.2%}</span><span class="s2"> dogs detected in dog files&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">human</span><span class="p">,</span> <span class="n">dogs</span><span class="p">))</span>

<span class="n">printed_performance</span><span class="p">(</span><span class="n">human_files_short</span><span class="p">,</span><span class="n">dog_files_short</span><span class="p">)</span>
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<pre>There are 0.00% humans detected in dog files and 99.00% dogs detected in dog files
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<p>We suggest VGG-16 as a potential network to detect dog images in your algorithm, but you are free to explore other pre-trained networks (such as <a href="http://pytorch.org/docs/master/torchvision/models.html#inception-v3">Inception-v3</a>, <a href="http://pytorch.org/docs/master/torchvision/models.html#id3">ResNet-50</a>, etc).  Please use the code cell below to test other pre-trained PyTorch models.  If you decide to pursue this <em>optional</em> task, report performance on <code>human_files_short</code> and <code>dog_files_short</code>.</p>

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<span class="c1">### TODO: Report the performance of another pre-trained network.</span>
<span class="c1">### Feel free to use as many code cells as needed.</span>
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<p><a id='step3'></a></p>
<h2 id="Step-3:-Create-a-CNN-to-Classify-Dog-Breeds-(from-Scratch)">Step 3: Create a CNN to Classify Dog Breeds (from Scratch)<a class="anchor-link" href="#Step-3:-Create-a-CNN-to-Classify-Dog-Breeds-(from-Scratch)">&#182;</a></h2><p>Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images.  In this step, you will create a CNN that classifies dog breeds.  You must create your CNN <em>from scratch</em> (so, you can't use transfer learning <em>yet</em>!), and you must attain a test accuracy of at least 10%.  In Step 4 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.</p>
<p>We mention that the task of assigning breed to dogs from images is considered exceptionally challenging.  To see why, consider that <em>even a human</em> would have trouble distinguishing between a Brittany and a Welsh Springer Spaniel.</p>
<table>
<thead><tr>
<th>Brittany</th>
<th>Welsh Springer Spaniel</th>
</tr>
</thead>
<tbody>
<tr>
<td><img src="images/Brittany_02625.jpg" width="100"></td>
<td><img src="images/Welsh_springer_spaniel_08203.jpg" width="200"></td>
</tr>
</tbody>
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<p>It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).</p>
<table>
<thead><tr>
<th>Curly-Coated Retriever</th>
<th>American Water Spaniel</th>
</tr>
</thead>
<tbody>
<tr>
<td><img src="images/Curly-coated_retriever_03896.jpg" width="200"></td>
<td><img src="images/American_water_spaniel_00648.jpg" width="200"></td>
</tr>
</tbody>
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<p>Likewise, recall that labradors come in yellow, chocolate, and black.  Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.</p>
<table>
<thead><tr>
<th>Yellow Labrador</th>
<th>Chocolate Labrador</th>
<th>Black Labrador</th>
</tr>
</thead>
<tbody>
<tr>
<td><img src="images/Labrador_retriever_06457.jpg" width="150"></td>
<td><img src="images/Labrador_retriever_06455.jpg" width="240"></td>
<td><img src="images/Labrador_retriever_06449.jpg" width="220"></td>
</tr>
</tbody>
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<p>We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.</p>
<p>Remember that the practice is far ahead of the theory in deep learning.  Experiment with many different architectures, and trust your intuition.  And, of course, have fun!</p>
<h3 id="(IMPLEMENTATION)-Specify-Data-Loaders-for-the-Dog-Dataset">(IMPLEMENTATION) Specify Data Loaders for the Dog Dataset<a class="anchor-link" href="#(IMPLEMENTATION)-Specify-Data-Loaders-for-the-Dog-Dataset">&#182;</a></h3><p>Use the code cell below to write three separate <a href="http://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader">data loaders</a> for the training, validation, and test datasets of dog images (located at <code>dog_images/train</code>, <code>dog_images/valid</code>, and <code>dog_images/test</code>, respectively).  You may find <a href="http://pytorch.org/docs/stable/torchvision/datasets.html">this documentation on custom datasets</a> to be a useful resource.  If you are interested in augmenting your training and/or validation data, check out the wide variety of <a href="http://pytorch.org/docs/stable/torchvision/transforms.html?highlight=transform">transforms</a>!</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">torchvision</span>
<span class="kn">from</span> <span class="nn">torchvision</span> <span class="k">import</span> <span class="n">datasets</span>
<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">ImageFile</span>
<span class="kn">from</span> <span class="nn">torch.utils.data.sampler</span> <span class="k">import</span> <span class="n">SubsetRandomSampler</span>
<span class="n">ImageFile</span><span class="o">.</span><span class="n">LOAD_TRUNCATED_IMAGES</span> <span class="o">=</span> <span class="kc">True</span>


<span class="c1">### TODO: Write data loaders for training, validation, and test sets</span>
<span class="c1">## Specify appropriate transforms, and batch_sizes</span>
<span class="n">num_workers</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">10</span>

<span class="n">data_dir</span> <span class="o">=</span> <span class="s2">&quot;/data/dog_images/&quot;</span>
<span class="n">train_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">&#39;train/&#39;</span><span class="p">)</span>
<span class="n">valid_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">&#39;valid/&#39;</span><span class="p">)</span>
<span class="n">test_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">&#39;test/&#39;</span><span class="p">)</span>

<span class="c1"># generate normalized values to stabilize gradients during backprop</span>
<span class="n">standard_norm</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span><span class="n">std</span><span class="o">=</span><span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])</span>
<span class="c1"># data transformations for train, valid, test</span>
<span class="n">data_transforms</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;train&#39;</span><span class="p">:</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span>
                                                <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
                                                <span class="n">transforms</span><span class="o">.</span><span class="n">RandomHorizontalFlip</span><span class="p">(),</span>
                                                <span class="n">transforms</span><span class="o">.</span><span class="n">RandomRotation</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span>
                                                <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
                                                <span class="n">standard_norm</span><span class="p">]),</span>
                   <span class="s1">&#39;valid&#39;</span><span class="p">:</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span>
                                                <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
                                                <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
                                                <span class="n">standard_norm</span><span class="p">]),</span>
                   <span class="s1">&#39;test&#39;</span><span class="p">:</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span>
                                               <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
                                               <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
                                               <span class="n">standard_norm</span><span class="p">])</span>
<span class="p">}</span>
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<p><strong>Outline</strong>: Below, I set num_classes equal to the number of classes associated with the dog-breeds, and I create three helper functions to load the transformed data into our training, validation, test loop.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">ImageFile</span>
<span class="n">ImageFile</span><span class="o">.</span><span class="n">LOAD_TRUNCATED_IMAGES</span> <span class="o">=</span> <span class="kc">True</span>

<span class="n">num_classes</span> <span class="o">=</span> <span class="mi">133</span> <span class="c1"># total classes of dog breeds</span>

<span class="c1"># Use dataloader function to load previous transforms into the new normalizer by calling the dictionary</span>
<span class="c1"># https://stackoverflow.com/questions/50052295/how-do-you-load-images-into-pytorch-dataloader</span>
<span class="k">def</span> <span class="nf">load_train_dataset</span><span class="p">():</span>
    <span class="n">train_dataset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span>
        <span class="n">root</span><span class="o">=</span><span class="n">train_dir</span><span class="p">,</span>
        <span class="n">transform</span><span class="o">=</span><span class="n">data_transforms</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span>
    <span class="p">)</span>
    <span class="n">train_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
        <span class="n">train_dataset</span><span class="p">,</span>
        <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
        <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">,</span>
        <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span>
    <span class="p">)</span>
    <span class="k">return</span> <span class="n">train_loader</span>

<span class="k">def</span> <span class="nf">load_test_dataset</span><span class="p">():</span>
    <span class="n">test_dataset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span>
        <span class="n">root</span><span class="o">=</span><span class="n">test_dir</span><span class="p">,</span>
        <span class="n">transform</span><span class="o">=</span><span class="n">data_transforms</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]</span>
    <span class="p">)</span>
    <span class="n">test_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
        <span class="n">test_dataset</span><span class="p">,</span>
        <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
        <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">,</span>
        <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span>
    <span class="p">)</span>
    <span class="k">return</span> <span class="n">test_loader</span>

<span class="k">def</span> <span class="nf">load_valid_dataset</span><span class="p">():</span>
    <span class="n">valid_dataset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span>
        <span class="n">root</span><span class="o">=</span><span class="n">valid_dir</span><span class="p">,</span>
        <span class="n">transform</span><span class="o">=</span><span class="n">data_transforms</span><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">]</span>
    <span class="p">)</span>
    <span class="n">valid_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
        <span class="n">valid_dataset</span><span class="p">,</span>
        <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
        <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">,</span>
        <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span>
    <span class="p">)</span>
    <span class="k">return</span> <span class="n">valid_loader</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># load the data using the load_train_dataset function</span>
<span class="n">train_loader</span><span class="o">=</span><span class="n">load_train_dataset</span><span class="p">()</span>
<span class="n">test_loader</span><span class="o">=</span><span class="n">load_test_dataset</span><span class="p">()</span>
<span class="n">valid_loader</span><span class="o">=</span><span class="n">load_valid_dataset</span><span class="p">()</span>
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<p><strong>Question 3:</strong> Describe your chosen procedure for preprocessing the data.</p>
<ul>
<li>How does your code resize the images (by cropping, stretching, etc)?  What size did you pick for the input tensor, and why?</li>
<li>Did you decide to augment the dataset?  If so, how (through translations, flips, rotations, etc)?  If not, why not?</li>
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<p><strong>Answer</strong>: The goal was to develop the simplest architecture that meets the core objective while minimizing computation cost. Data was processed using the transforms pipeline with the goal of ensuring the model learns an invariant representation of the image. First, pipeline resized images to 256. This ensures the model is scale invariant. The pipeline uses RandomCrop, RandomHorizontalFlip and RandomRotation of 10% to ensure translation and rotation invariance using data augmentation. The pipeline transforms the data to a sensor using ToTensor and then normalizes the data. Data normalization is an important step. First, subtracting the mean serves to center the data, and dividing my sigma normalizes the features to a z-score.  This important to maintain stability stability of gradients when backpropagating through the network in the training step.</p>

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<h3 id="(IMPLEMENTATION)-Model-Architecture">(IMPLEMENTATION) Model Architecture<a class="anchor-link" href="#(IMPLEMENTATION)-Model-Architecture">&#182;</a></h3><p>Create a CNN to classify dog breed.  Use the template in the code cell below.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>

<span class="c1"># define the CNN architecture</span>
<span class="c1"># Source: https://discuss.pytorch.org/t/using-custom-dataset-for-training-a-classifier-runtimeerror-at-outputs-net-variable-images/6443</span>

<span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="c1">### TODO: choose an architecture, and complete the class</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="c1">## Define layers of a CNN</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">7</span><span class="o">*</span><span class="mi">7</span><span class="o">*</span><span class="mi">64</span><span class="p">,</span> <span class="mi">800</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">800</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span> 
        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.15</span><span class="p">)</span>
    
    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="c1">## Define forward behavior</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv3</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">7</span><span class="o">*</span><span class="mi">7</span><span class="o">*</span><span class="mi">64</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">x</span>

<span class="c1">#-#-# You so NOT have to modify the code below this line. #-#-#</span>

<span class="c1"># instantiate the CNN</span>
<span class="n">model_scratch</span> <span class="o">=</span> <span class="n">Net</span><span class="p">()</span>

<span class="c1"># move tensors to GPU if CUDA is available</span>
<span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
    <span class="n">model_scratch</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
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<p><strong>Question 4:</strong> Outline the steps you took to get to your final CNN architecture and your reasoning at each step.</p>

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<p><strong>Answer:</strong> I defined the convolutional layer "conv1" to take in an input image size of 28x28, and output 32 feature maps. Three-2D convolutional layers were selected. Each layer doubles the depth of the output layer until we get to a layer having a depth of 64. I wanted the convolutional layer to be roughly 1/2 the size of the input image so a stride of two is used for the first two convolutional layers. Padding was added so that the filter stays inside the image bounds during the stride. Padding allows the model to capture information about all regions of the image. I added a drop-out layer which reduces model overfitting. Finally, I have two fully-connected layers (fc) which take-in the final downsized stack of feature maps. In the forward funtion, I apply a pooling layer after each convolutional layer which reduces the image size. I have the first fc produce 800 outputs and those outputs are shared with fc2 which produces 133 classes (num of dog image classes). Please note, I add a sequence of convolutional and pooling layers. I am passing the input image into the first convolutional layer, applying an activation function (relu) and then a pooling layer. I repeat this for the next convolutional layers. Next, I flatten the input shape which necessary to pass it into the first fc layer. In between the flattened layer and the next fc layer I add dropout layers which prevent overfitting.</p>

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<h3 id="(IMPLEMENTATION)-Specify-Loss-Function-and-Optimizer">(IMPLEMENTATION) Specify Loss Function and Optimizer<a class="anchor-link" href="#(IMPLEMENTATION)-Specify-Loss-Function-and-Optimizer">&#182;</a></h3><p>Use the next code cell to specify a <a href="http://pytorch.org/docs/stable/nn.html#loss-functions">loss function</a> and <a href="http://pytorch.org/docs/stable/optim.html">optimizer</a>.  Save the chosen loss function as <code>criterion_scratch</code>, and the optimizer as <code>optimizer_scratch</code> below.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>

<span class="c1">### TODO: select loss function</span>
<span class="n">criterion_scratch</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>

<span class="c1">### TODO: select optimizer</span>
<span class="n">optimizer_scratch</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">model_scratch</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.001</span><span class="p">)</span>
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<h3 id="(IMPLEMENTATION)-Train-and-Validate-the-Model">(IMPLEMENTATION) Train and Validate the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Train-and-Validate-the-Model">&#182;</a></h3><p>Train and validate your model in the code cell below.  <a href="http://pytorch.org/docs/master/notes/serialization.html">Save the final model parameters</a> at filepath <code>'model_scratch.pt'</code>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">,</span> <span class="n">save_path</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;returns trained model&quot;&quot;&quot;</span>
    <span class="n">valid_loss_min</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">Inf</span>
    
    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_epochs</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
        <span class="c1"># initialize variables to monitor training and validation loss</span>
        <span class="n">train_loss</span> <span class="o">=</span> <span class="mf">0.0</span>
        <span class="n">valid_loss</span> <span class="o">=</span> <span class="mf">0.0</span>
        
        <span class="c1">###################</span>
        <span class="c1"># train the model #</span>
        <span class="c1">###################</span>
        <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
        <span class="k">for</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">load_train_dataset</span><span class="p">()):</span>
            <span class="c1"># move to GPU</span>
            <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
                <span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span> <span class="n">target</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
            <span class="c1">## find the loss and update the model parameters accordingly</span>
            <span class="c1">## record the average training loss, using something like</span>
            <span class="c1">## train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data - train_loss))</span>

            <span class="c1"># initialize weights to zero</span>
            <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
            
            <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
            
            <span class="c1"># calculate loss</span>
            <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
            
            <span class="c1"># back prop</span>
            <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
            
            <span class="c1"># grad</span>
            <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
            
            <span class="n">train_loss</span> <span class="o">=</span> <span class="n">train_loss</span> <span class="o">+</span> <span class="p">((</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">batch_idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">data</span> <span class="o">-</span> <span class="n">train_loss</span><span class="p">))</span>
            
            
        <span class="c1">######################    </span>
        <span class="c1"># validate the model #</span>
        <span class="c1">######################</span>
        <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
        <span class="k">for</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">load_valid_dataset</span><span class="p">()):</span>
            <span class="c1"># move to GPU</span>
            <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
                <span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span> <span class="n">target</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
            <span class="c1">## update the average validation loss</span>
            <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
            <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
            <span class="n">valid_loss</span> <span class="o">=</span> <span class="n">valid_loss</span> <span class="o">+</span> <span class="p">((</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">batch_idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">data</span> <span class="o">-</span> <span class="n">valid_loss</span><span class="p">))</span>

            
        <span class="c1"># print training/validation statistics </span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch: </span><span class="si">{}</span><span class="s1"> </span><span class="se">\t</span><span class="s1">Training Loss: </span><span class="si">{:.6f}</span><span class="s1"> </span><span class="se">\t</span><span class="s1">Validation Loss: </span><span class="si">{:.6f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
            <span class="n">epoch</span><span class="p">,</span> 
            <span class="n">train_loss</span><span class="p">,</span>
            <span class="n">valid_loss</span>
            <span class="p">))</span>
        
        <span class="c1">## TODO: save the model if validation loss has decreased</span>
        <span class="k">if</span> <span class="n">valid_loss</span> <span class="o">&lt;</span> <span class="n">valid_loss_min</span><span class="p">:</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">save_path</span><span class="p">)</span>
            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Validation loss decreased (</span><span class="si">{:.6f}</span><span class="s1"> --&gt; </span><span class="si">{:.6f}</span><span class="s1">).  Saving model ...&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
            <span class="n">valid_loss_min</span><span class="p">,</span>
            <span class="n">valid_loss</span><span class="p">))</span>
            <span class="n">valid_loss_min</span> <span class="o">=</span> <span class="n">valid_loss</span>
            
    <span class="c1"># return trained model</span>
    <span class="k">return</span> <span class="n">model</span>

<span class="n">model_scratch</span> <span class="o">=</span> <span class="n">train</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">model_scratch</span><span class="p">,</span> <span class="n">optimizer_scratch</span><span class="p">,</span> <span class="n">criterion_scratch</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">,</span> <span class="s1">&#39;model_scratch.pt&#39;</span><span class="p">)</span>
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<pre>Epoch: 1 	Training Loss: 4.836179 	Validation Loss: 4.688531
Validation loss decreased (inf --&gt; 4.688531).  Saving model ...
Epoch: 2 	Training Loss: 4.545269 	Validation Loss: 4.412127
Validation loss decreased (4.688531 --&gt; 4.412127).  Saving model ...
Epoch: 3 	Training Loss: 4.246764 	Validation Loss: 4.207706
Validation loss decreased (4.412127 --&gt; 4.207706).  Saving model ...
Epoch: 4 	Training Loss: 4.048598 	Validation Loss: 4.144358
Validation loss decreased (4.207706 --&gt; 4.144358).  Saving model ...
Epoch: 5 	Training Loss: 3.845702 	Validation Loss: 4.058660
Validation loss decreased (4.144358 --&gt; 4.058660).  Saving model ...
Epoch: 6 	Training Loss: 3.654207 	Validation Loss: 3.970448
Validation loss decreased (4.058660 --&gt; 3.970448).  Saving model ...
Epoch: 7 	Training Loss: 3.449458 	Validation Loss: 3.854552
Validation loss decreased (3.970448 --&gt; 3.854552).  Saving model ...
Epoch: 8 	Training Loss: 3.213185 	Validation Loss: 3.998009
Epoch: 9 	Training Loss: 2.979814 	Validation Loss: 4.054300
Epoch: 10 	Training Loss: 2.707078 	Validation Loss: 4.200097
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<h3 id="(IMPLEMENTATION)-Test-the-Model">(IMPLEMENTATION) Test the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Test-the-Model">&#182;</a></h3><p>Try out your model on the test dataset of dog images.  Use the code cell below to calculate and print the test loss and accuracy.  Ensure that your test accuracy is greater than 10%.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">test</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">):</span>

    <span class="c1"># monitor test loss and accuracy</span>
    <span class="n">test_loss</span> <span class="o">=</span> <span class="mf">0.</span>
    <span class="n">correct</span> <span class="o">=</span> <span class="mf">0.</span>
    <span class="n">total</span> <span class="o">=</span> <span class="mf">0.</span>

    <span class="k">for</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">load_test_dataset</span><span class="p">()):</span>
        <span class="c1"># move to GPU</span>
        <span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
            <span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span> <span class="n">target</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
        <span class="c1"># forward pass: compute predicted outputs by passing inputs to the model</span>
        <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
        <span class="c1"># calculate the loss</span>
        <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
        <span class="c1"># update average test loss </span>
        <span class="n">test_loss</span> <span class="o">=</span> <span class="n">test_loss</span> <span class="o">+</span> <span class="p">((</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">batch_idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">data</span> <span class="o">-</span> <span class="n">test_loss</span><span class="p">))</span>
        <span class="c1"># convert output probabilities to predicted class</span>
        <span class="n">pred</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
        <span class="c1"># compare predictions to true label</span>
        <span class="n">correct</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">target</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">view_as</span><span class="p">(</span><span class="n">pred</span><span class="p">)))</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
        <span class="n">total</span> <span class="o">+=</span> <span class="n">data</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
            
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test Loss: </span><span class="si">{:.6f}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_loss</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Test Accuracy: </span><span class="si">%2d%%</span><span class="s1"> (</span><span class="si">%2d</span><span class="s1">/</span><span class="si">%2d</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="p">(</span>
        <span class="mf">100.</span> <span class="o">*</span> <span class="n">correct</span> <span class="o">/</span> <span class="n">total</span><span class="p">,</span> <span class="n">correct</span><span class="p">,</span> <span class="n">total</span><span class="p">))</span>

<span class="c1"># call test function    </span>
<span class="n">test</span><span class="p">(</span><span class="n">model_scratch</span><span class="p">,</span> <span class="n">criterion_scratch</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">)</span>
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<pre>Test Loss: 4.185776


Test Accuracy: 12% (101/836)
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<p><a id='step4'></a></p>
<h2 id="Step-4:-Create-a-CNN-to-Classify-Dog-Breeds-(using-Transfer-Learning)">Step 4: Create a CNN to Classify Dog Breeds (using Transfer Learning)<a class="anchor-link" href="#Step-4:-Create-a-CNN-to-Classify-Dog-Breeds-(using-Transfer-Learning)">&#182;</a></h2><p>You will now use transfer learning to create a CNN that can identify dog breed from images.  Your CNN must attain at least 60% accuracy on the test set.</p>
<h3 id="(IMPLEMENTATION)-Specify-Data-Loaders-for-the-Dog-Dataset">(IMPLEMENTATION) Specify Data Loaders for the Dog Dataset<a class="anchor-link" href="#(IMPLEMENTATION)-Specify-Data-Loaders-for-the-Dog-Dataset">&#182;</a></h3><p>Use the code cell below to write three separate <a href="http://pytorch.org/docs/master/data.html#torch.utils.data.DataLoader">data loaders</a> for the training, validation, and test datasets of dog images (located at <code>dogImages/train</code>, <code>dogImages/valid</code>, and <code>dogImages/test</code>, respectively).</p>
<p>If you like, <strong>you are welcome to use the same data loaders from the previous step</strong>, when you created a CNN from scratch.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## TODO: Specify data loaders</span>
<span class="n">num_workers</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">15</span>

<span class="n">train_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">&#39;train/&#39;</span><span class="p">)</span>
<span class="n">valid_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">&#39;valid/&#39;</span><span class="p">)</span>
<span class="n">test_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">&#39;test/&#39;</span><span class="p">)</span>

<span class="n">train_loader</span><span class="o">=</span><span class="n">load_train_dataset</span><span class="p">()</span>
<span class="n">test_loader</span><span class="o">=</span><span class="n">load_test_dataset</span><span class="p">()</span>
<span class="n">valid_loader</span><span class="o">=</span><span class="n">load_valid_dataset</span><span class="p">()</span>
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<h3 id="(IMPLEMENTATION)-Model-Architecture">(IMPLEMENTATION) Model Architecture<a class="anchor-link" href="#(IMPLEMENTATION)-Model-Architecture">&#182;</a></h3><p>Use transfer learning to create a CNN to classify dog breed.  Use the code cell below, and save your initialized model as the variable <code>model_transfer</code>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>

<span class="c1">## TODO: Specify model architecture </span>
<span class="c1">#model_transfer = models.vgg16(pretrained=True)</span>
<span class="n">model_transfer</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">alexnet</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="k">if</span> <span class="n">use_cuda</span><span class="p">:</span>
    <span class="n">model_transfer</span> <span class="o">=</span> <span class="n">model_transfer</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
    
<span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">model_transfer</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
    <span class="n">param</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="kc">True</span>
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<pre>Downloading: &#34;https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth&#34; to /root/.torch/models/alexnet-owt-4df8aa71.pth
100%|██████████| 244418560/244418560 [01:00&lt;00:00, 4047351.23it/s]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">model_transfer</span><span class="p">)</span>
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<pre>AlexNet(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
    (1): ReLU(inplace)
    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (4): ReLU(inplace)
    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (7): ReLU(inplace)
    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (9): ReLU(inplace)
    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace)
    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (classifier): Sequential(
    (0): Dropout(p=0.5)
    (1): Linear(in_features=9216, out_features=4096, bias=True)
    (2): ReLU(inplace)
    (3): Dropout(p=0.5)
    (4): Linear(in_features=4096, out_features=4096, bias=True)
    (5): ReLU(inplace)
    (6): Linear(in_features=4096, out_features=1000, bias=True)
  )
)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># source: https://discuss.pytorch.org/t/using-custom-dataset-for-training-a-classifier-runtimeerror-at-outputs-net-variable-images/6443</span>
<span class="n">im_datasets</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span>
                                          <span class="n">data_transforms</span><span class="p">[</span><span class="n">x</span><span class="p">])</span>
                  <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="s1">&#39;valid&#39;</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">]}</span>
<span class="n">class_names</span> <span class="o">=</span> <span class="n">im_datasets</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">classes</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">class_names</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n_inputs</span> <span class="o">=</span> <span class="n">model_transfer</span><span class="o">.</span><span class="n">classifier</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span><span class="o">.</span><span class="n">in_features</span>
<span class="n">last_layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
<span class="n">model_transfer</span><span class="o">.</span><span class="n">classifier</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span> <span class="o">=</span> <span class="n">last_layer</span>

<span class="n">train_on_gpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span>

<span class="c1"># after completing your model, if GPU is available, move the model to GPU</span>
<span class="k">if</span> <span class="n">train_on_gpu</span><span class="p">:</span>
    <span class="n">model_transfer</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">model_transfer</span><span class="p">)</span>
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<pre>AlexNet(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
    (1): ReLU(inplace)
    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (4): ReLU(inplace)
    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (7): ReLU(inplace)
    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (9): ReLU(inplace)
    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace)
    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (classifier): Sequential(
    (0): Dropout(p=0.5)
    (1): Linear(in_features=9216, out_features=4096, bias=True)
    (2): ReLU(inplace)
    (3): Dropout(p=0.5)
    (4): Linear(in_features=4096, out_features=4096, bias=True)
    (5): ReLU(inplace)
    (6): Linear(in_features=4096, out_features=133, bias=True)
  )
)
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<p><strong>Question 5:</strong> Outline the steps you took to get to your final CNN architecture and your reasoning at each step.  Describe why you think the architecture is suitable for the current problem.</p>

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<p><strong>Answer:</strong> I used the pretrained vgg16 architecture as a feature extractor as it has 4096 in features that have learned a variety of shapes and edges in images. In fact the convolutional filters in a trained CNN are arranged in a sort of hierarchy in which "general" features are identified in first layers and complex features are identified in later layers. These features can be applied to almost any image dataset. I dropped filters in the final layers that are specific to the training set but I kept the earlier layers, froze the gradients and replaced the out_features to equal 133.</p>

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<h3 id="(IMPLEMENTATION)-Specify-Loss-Function-and-Optimizer">(IMPLEMENTATION) Specify Loss Function and Optimizer<a class="anchor-link" href="#(IMPLEMENTATION)-Specify-Loss-Function-and-Optimizer">&#182;</a></h3><p>Use the next code cell to specify a <a href="http://pytorch.org/docs/master/nn.html#loss-functions">loss function</a> and <a href="http://pytorch.org/docs/master/optim.html">optimizer</a>.  Save the chosen loss function as <code>criterion_transfer</code>, and the optimizer as <code>optimizer_transfer</code> below.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">criterion_transfer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
<span class="n">optimizer_transfer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">model_transfer</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
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<h3 id="(IMPLEMENTATION)-Train-and-Validate-the-Model">(IMPLEMENTATION) Train and Validate the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Train-and-Validate-the-Model">&#182;</a></h3><p>Train and validate your model in the code cell below.  <a href="http://pytorch.org/docs/master/notes/serialization.html">Save the final model parameters</a> at filepath <code>'model_transfer.pt'</code>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># train the model</span>
<span class="n">n_epochs</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">model_transfer</span> <span class="o">=</span> <span class="n">train</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">,</span> <span class="n">model_transfer</span><span class="p">,</span> <span class="n">optimizer_transfer</span><span class="p">,</span> <span class="n">criterion_transfer</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">,</span> <span class="s1">&#39;model_transfer.pt&#39;</span><span class="p">)</span>

<span class="c1"># load the model that got the best validation accuracy (uncomment the line below)</span>
<span class="n">model_transfer</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;model_transfer.pt&#39;</span><span class="p">))</span>
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<pre>Epoch: 1 	Training Loss: 2.376309 	Validation Loss: 1.618646
Validation loss decreased (inf --&gt; 1.618646).  Saving model ...
Epoch: 2 	Training Loss: 1.376804 	Validation Loss: 1.420344
Validation loss decreased (1.618646 --&gt; 1.420344).  Saving model ...
Epoch: 3 	Training Loss: 1.082263 	Validation Loss: 1.437729
Epoch: 4 	Training Loss: 0.886989 	Validation Loss: 1.309155
Validation loss decreased (1.420344 --&gt; 1.309155).  Saving model ...
Epoch: 5 	Training Loss: 0.738039 	Validation Loss: 1.392767
Epoch: 6 	Training Loss: 0.606553 	Validation Loss: 1.304799
Validation loss decreased (1.309155 --&gt; 1.304799).  Saving model ...
Epoch: 7 	Training Loss: 0.497771 	Validation Loss: 1.718183
Epoch: 8 	Training Loss: 0.459839 	Validation Loss: 1.381003
Epoch: 9 	Training Loss: 0.399025 	Validation Loss: 1.502898
Epoch: 10 	Training Loss: 0.350196 	Validation Loss: 1.572753
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<h3 id="(IMPLEMENTATION)-Test-the-Model">(IMPLEMENTATION) Test the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Test-the-Model">&#182;</a></h3><p>Try out your model on the test dataset of dog images. Use the code cell below to calculate and print the test loss and accuracy.  Ensure that your test accuracy is greater than 60%.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test</span><span class="p">(</span><span class="n">model_transfer</span><span class="p">,</span> <span class="n">criterion_transfer</span><span class="p">,</span> <span class="n">use_cuda</span><span class="p">)</span>
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<pre>Test Loss: 1.272505


Test Accuracy: 65% (546/836)
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<h3 id="(IMPLEMENTATION)-Predict-Dog-Breed-with-the-Model">(IMPLEMENTATION) Predict Dog Breed with the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Predict-Dog-Breed-with-the-Model">&#182;</a></h3><p>Write a function that takes an image path as input and returns the dog breed (<code>Affenpinscher</code>, <code>Afghan hound</code>, etc) that is predicted by your model.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Write a function that takes a path to an image as input</span>
<span class="c1">### and returns the dog breed that is predicted by the model.</span>

<span class="c1"># list of class names by index, i.e. a name can be accessed like class_names[0]</span>
<span class="c1">#class_names = [item[4:].replace(&quot;_&quot;, &quot; &quot;) for item in model_transfer[&#39;train&#39;].classes]</span>

<span class="k">def</span> <span class="nf">predict_breed_transfer</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="c1"># load the image and return the predicted breed</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">image_transform</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span>
    <span class="n">pred_index</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">model_transfer</span><span class="p">(</span><span class="n">image</span><span class="p">))</span>
    <span class="n">dog_breed</span> <span class="o">=</span> <span class="n">class_names</span><span class="p">[</span><span class="n">pred_index</span><span class="p">]</span>
    <span class="k">return</span> <span class="n">dog_breed</span>
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<hr>
<p><a id='step5'></a></p>
<h2 id="Step-5:-Write-your-Algorithm">Step 5: Write your Algorithm<a class="anchor-link" href="#Step-5:-Write-your-Algorithm">&#182;</a></h2><p>Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither.  Then,</p>
<ul>
<li>if a <strong>dog</strong> is detected in the image, return the predicted breed.</li>
<li>if a <strong>human</strong> is detected in the image, return the resembling dog breed.</li>
<li>if <strong>neither</strong> is detected in the image, provide output that indicates an error.</li>
</ul>
<p>You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the <code>face_detector</code> and <code>human_detector</code> functions developed above.  You are <strong>required</strong> to use your CNN from Step 4 to predict dog breed.</p>
<p>Some sample output for our algorithm is provided below, but feel free to design your own user experience!</p>
<p><img src="images/sample_human_output.png" alt="Sample Human Output"></p>
<h3 id="(IMPLEMENTATION)-Write-your-Algorithm">(IMPLEMENTATION) Write your Algorithm<a class="anchor-link" href="#(IMPLEMENTATION)-Write-your-Algorithm">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Write your algorithm.</span>
<span class="c1">### Feel free to use as many code cells as needed.</span>

<span class="k">def</span> <span class="nf">run_app</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="c1">## Handle cases for a human face, dog, and neither</span>
    <span class="k">if</span> <span class="n">face_detector</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>      
        <span class="n">pred_index</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">model_transfer</span><span class="p">(</span><span class="n">image_transform</span><span class="p">(</span><span class="n">img_path</span><span class="p">)))</span>
        <span class="n">dog_name</span> <span class="o">=</span> <span class="n">class_names</span><span class="p">[</span><span class="n">pred_index</span><span class="p">]</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Hello! You are human, but you resemble a </span><span class="si">{0}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dog_name</span><span class="p">))</span>
    <span class="k">elif</span> <span class="n">dog_detector</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
        <span class="n">pred_index</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">model_transfer</span><span class="p">(</span><span class="n">image_transform</span><span class="p">(</span><span class="n">img_path</span><span class="p">)))</span>
        <span class="n">dog_name</span> <span class="o">=</span> <span class="n">class_names</span><span class="p">[</span><span class="n">pred_index</span><span class="p">]</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Hey! You&#39;re a dog, and your breed is </span><span class="si">{0}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dog_name</span><span class="p">))</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The breed was not determined!&quot;</span><span class="p">)</span>    
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<p><a id='step6'></a></p>
<h2 id="Step-6:-Test-Your-Algorithm">Step 6: Test Your Algorithm<a class="anchor-link" href="#Step-6:-Test-Your-Algorithm">&#182;</a></h2><p>In this section, you will take your new algorithm for a spin!  What kind of dog does the algorithm think that <em>you</em> look like?  If you have a dog, does it predict your dog's breed accurately?  If you have a cat, does it mistakenly think that your cat is a dog?</p>
<h3 id="(IMPLEMENTATION)-Test-Your-Algorithm-on-Sample-Images!">(IMPLEMENTATION) Test Your Algorithm on Sample Images!<a class="anchor-link" href="#(IMPLEMENTATION)-Test-Your-Algorithm-on-Sample-Images!">&#182;</a></h3><p>Test your algorithm at least six images on your computer.  Feel free to use any images you like.  Use at least two human and two dog images.</p>
<p><strong>Question 6:</strong> Is the output better than you expected :) ?  Or worse :( ?  Provide at least three possible points of improvement for your algorithm.</p>

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<p><strong>Answer:</strong> (Three possible points for improvement), First the model could be more ambitious.  I believe it could be easily modified to determine other factors like age, weight, eye color of the humans and dogs in the image. Second, we only used six or so images from the computer so, perhaps I could use an image api to get a better understanding of the ability of the model to generalize well. Third, perhaps we should determine the location or landmark information in the image? Overall, given the scope of the project, the model does it's job well.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## TODO: Execute your algorithm from Step 6 on</span>
<span class="c1">## at least 6 images on your computer.</span>
<span class="c1">## Feel free to use as many code cells as needed.</span>

<span class="c1">## suggested code, below</span>
<span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">human_files</span><span class="p">[:</span><span class="mi">10</span><span class="p">],</span> <span class="n">dog_files</span><span class="p">[:</span><span class="mi">10</span><span class="p">])):</span>
    <span class="n">run_app</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
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<pre>Hello! You are human, but you resemble a 048.Chihuahua
Hello! You are human, but you resemble a 127.Silky_terrier
Hello! You are human, but you resemble a 004.Akita
Hello! You are human, but you resemble a 047.Chesapeake_bay_retriever
Hello! You are human, but you resemble a 004.Akita
Hello! You are human, but you resemble a 120.Pharaoh_hound
Hello! You are human, but you resemble a 072.German_shorthaired_pointer
Hello! You are human, but you resemble a 127.Silky_terrier
Hello! You are human, but you resemble a 007.American_foxhound
Hello! You are human, but you resemble a 004.Akita
Hey! You&#39;re a dog, and your breed is 103.Mastiff
Hey! You&#39;re a dog, and your breed is 103.Mastiff
Hey! You&#39;re a dog, and your breed is 103.Mastiff
Hey! You&#39;re a dog, and your breed is 103.Mastiff
Hey! You&#39;re a dog, and your breed is 103.Mastiff
Hey! You&#39;re a dog, and your breed is 103.Mastiff
Hey! You&#39;re a dog, and your breed is 103.Mastiff
Hey! You&#39;re a dog, and your breed is 103.Mastiff
Hey! You&#39;re a dog, and your breed is 103.Mastiff
Hey! You&#39;re a dog, and your breed is 121.Plott
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