Remove blobfile dep, load directly from URL

drop_eval.py

@@ -10,8 +10,8 @@
 import re
 import string
 from typing import Any, Dict, List, Optional, Set, Tuple, Union
+import urllib
 
-import blobfile as bf
 import numpy as np
 from scipy.optimize import linear_sum_assignment
 
@@ -247,9 +247,9 @@ def __init__(self, num_examples: int | None = None, train_samples_per_prompt: in
         self.test_jsonl = (
             "https://openaipublic.blob.core.windows.net/simple-evals/drop_v0_dev.jsonl.gz"
         )
-        with gzip.GzipFile(fileobj=bf.BlobFile(self.train_jsonl, "rb"), mode="rb") as f:
+        with gzip.GzipFile(fileobj=urllib.request.urlopen(self.train_jsonl), mode="rb") as f:
             self.train_samples = list(map(json.loads, f.readlines()))
-        with gzip.GzipFile(fileobj=bf.BlobFile(self.test_jsonl, "rb"), mode="rb") as f:
+        with gzip.GzipFile(fileobj=urllib.request.urlopen(self.test_jsonl), mode="rb") as f:
             self.test_samples = list(map(json.loads, f.readlines()))
             if self._num_examples:
                 self.test_samples = random.Random(self.seed).sample(

gpqa_eval.py

@@ -7,7 +7,6 @@
 import random
 import re
 
-import blobfile as bf
 import pandas
 
 from . import common
@@ -27,9 +26,7 @@ def __init__(
         num_examples: int | None = None,  # restrict to a subset of the data for debugging
     ):
         df = pandas.read_csv(
-            bf.BlobFile(
-                f"https://openaipublic.blob.core.windows.net/simple-evals/gpqa_{variant}.csv"
-            )
+            f"https://openaipublic.blob.core.windows.net/simple-evals/gpqa_{variant}.csv"
         )
         examples = [row.to_dict() for _, row in df.iterrows()]
         rng = random.Random(0)

humaneval_eval.py

@@ -14,7 +14,6 @@
 from io import BytesIO
 from typing import Any, Tuple
 
-import blobfile as bf
 import tqdm
 from human_eval.data import HUMAN_EVAL, read_problems
 from human_eval.evaluation import estimate_pass_at_k

math_eval.py

@@ -7,7 +7,6 @@
 import random
 import re
 
-import blobfile as bf
 import pandas
 
 from . import common
@@ -92,7 +91,7 @@ def check_equality(sampler: SamplerBase, expr1: str, expr2: str):
 
 class MathEval(Eval):
     def __init__(self, equality_checker: SamplerBase, num_examples: int | None = None):
-        df = pandas.read_csv(bf.BlobFile("https://openaipublic.blob.core.windows.net/simple-evals/math_test.csv"))
+        df = pandas.read_csv("https://openaipublic.blob.core.windows.net/simple-evals/math_test.csv")
         examples = [row.to_dict() for _, row in df.iterrows()]
         if num_examples:
             examples = random.Random(0).sample(examples, num_examples)

mgsm_eval.py

@@ -7,8 +7,7 @@
 
 import re
 from typing import Optional
-
-import blobfile as bf
+import urllib
 
 from . import common
 from .mmlu_eval import HTML_JINJA
@@ -109,8 +108,8 @@ def score_mgsm(target: str, prediction: str) -> bool:
 def get_lang_examples(lang: str) -> list[dict[str, str]]:
     fpath = LANG_TO_FPATH[lang]
     examples = []
-    with bf.BlobFile(fpath, "r") as f:
-        for line in f:
+    with urllib.request.urlopen(fpath) as f:
+        for line in f.read().decode("utf-8").splitlines():
             inputs, targets = line.strip().split("\t")
             if "." in targets:
                 raise ValueError(f"targets {targets} contains a decimal point.")

mmlu_eval.py

@@ -7,7 +7,6 @@
 import random
 import re
 
-import blobfile as bf
 import pandas
 
 from . import common
@@ -95,7 +94,7 @@ def format_question(row):
 class MMLUEval(Eval):
     def __init__(self, num_examples: int | None = None):
         df = pandas.read_csv(
-            bf.BlobFile("https://openaipublic.blob.core.windows.net/simple-evals/mmlu.csv")
+            "https://openaipublic.blob.core.windows.net/simple-evals/mmlu.csv"
         )
         examples = [row.to_dict() for _, row in df.iterrows()]
         if num_examples: