1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
|
import os
import re
import gzip
import json
import itertools
import numpy as np
from tqdm import tqdm
from collections import defaultdict
from typing import Dict, List, Union, Iterable, Callable, Literal
from concurrent.futures import ThreadPoolExecutor, as_completed
def refine_text(text: str, add_new_line: bool = True) -> str:
text = text.replace("\t", " ")
text = text.replace("\r\n", "\n").replace("\r", "\n")
if add_new_line:
return text.strip() + "\n"
else:
return text.strip()
def multi_process_function(function: Callable,
parameters: List,
num_workers: int = 1,
desc: str = "Completing tasks"):
if num_workers > len(parameters) or num_workers > os.cpu_count():
num_workers = min(os.cpu_count(), len(parameters))
with ThreadPoolExecutor(num_workers) as executor:
futures = []
for param in parameters:
future = executor.submit(function, param)
futures.append(future)
results = []
for future in tqdm(as_completed(futures), total=len(futures), desc=desc):
result = future.result()
results.append(result)
return results
def markdown_extract(text: str, mode: Literal["first", "last", "all"] = "all") -> str:
"""
Extract content enclosed by triple backticks (```) in a Markdown text.
Args:
text (str): The Markdown text to extract from.
mode (Literal["first", "last", "all"]):
- "first": Extract the first block of content.
- "last": Extract the last block of content.
- "all": Extract all blocks of content and join them with double newlines.
Returns:
str: Extracted content based on the specified mode.
"""
# Match content inside triple backticks, ignoring the ``` lines
pattern = r"```[ \t]*[\r\n]+[ \t]*(.*?)[ \t]*[\r\n]+[ \t]*```"
matches = re.findall(pattern, text, re.DOTALL)
if matches:
if mode == "first":
return matches[0]
elif mode == "last":
return matches[-1]
elif mode == "all":
return "\n\n".join(matches)
else:
return ""
def program_extract(
text: str,
program: str = "python",
mode: Literal["first", "last", "all"] = "all"
) -> str:
program_pattern = rf"```{program}[ \t]*[\r\n]+[ \t]*(.*?)[ \t]*[\r\n]+[ \t]*```"
program_re = re.compile(program_pattern, re.DOTALL | re.IGNORECASE)
matches = program_re.findall(text)
if matches:
if mode == "first":
return matches[0]
elif mode == "last":
return matches[-1]
elif mode == "all":
return "\n\n".join(matches)
else:
return ""
def stream_jsonl(filename: str) -> Iterable[Dict]:
"""
Parses each jsonl line and yields it as a dictionary
"""
if filename.endswith(".gz"):
with open(filename, "rb") as gzfp:
with gzip.open(gzfp, 'rt') as fp:
for line in fp:
if any(not x.isspace() for x in line):
yield json.loads(line)
else:
with open(filename, "r", encoding="utf-8") as fp:
for line in fp:
if any(not x.isspace() for x in line):
yield json.loads(line)
def write_jsonl(filename: str, data: Iterable[Dict], append: bool = False):
"""
Writes an iterable of dictionaries to jsonl
"""
if append:
mode = 'ab'
else:
mode = 'wb'
filename = os.path.expanduser(filename)
if filename.endswith(".gz"):
with open(filename, mode) as fp:
with gzip.GzipFile(fileobj=fp, mode='wb') as gzfp:
for x in data:
gzfp.write((json.dumps(x) + "\n").encode('utf-8'))
else:
with open(filename, mode) as fp:
for x in data:
fp.write((json.dumps(x) + "\n").encode('utf-8'))
def estimate_pass_at_k(
num_samples: Union[int, List[int], np.ndarray],
num_correct: Union[List[int], np.ndarray],
k: int
) -> np.ndarray:
"""
Estimates pass@k of each problem and returns them in an array.
"""
def estimator(n: int, c: int, k: int) -> float:
"""
Calculates 1 - comb(n - c, k) / comb(n, k).
"""
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1))
if isinstance(num_samples, int):
num_samples_it = itertools.repeat(num_samples, len(num_correct))
else:
assert len(num_samples) == len(num_correct)
num_samples_it = iter(num_samples)
return np.array([estimator(int(n), int(c), k) for n, c in zip(num_samples_it, num_correct)])
def calculate_pass_at_k(
evaluations: List[Dict],
num_samples: int,
list_k: List[int]
) -> List[Dict]:
task_results = defaultdict(int)
for evaluation in evaluations:
task_results[evaluation['task_id']] += evaluation['passed']
task_results = list(task_results.values())
benchmark_results = []
for k in list_k:
pass_rate = float(np.mean(estimate_pass_at_k(num_samples = num_samples, num_correct = task_results, k = k)))
benchmark_results.append({f"Pass@{k}": pass_rate})
return benchmark_results
|
