




'''simple docstring'''







import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


A     =re.compile(r'\s+')





def 						snake_case_							(_a	:							List[Any]     ):
    return {"hash": hashlib.mda(re.sub(_a    ,   ''''''    ,   example['''content''']     ).encode('''utf-8'''     )     ).hexdigest()}





def 						snake_case_							(_a	:							Tuple     ):
    UpperCAmelCase			       =		[len(_a     ) for line in example['''content'''].splitlines()]
    return {"line_mean": np.mean(_a     ), "line_max": max(_a     )}





def 						snake_case_							(_a	:							Optional[int]     ):
    UpperCAmelCase			       =		np.mean([c.isalnum() for c in example['''content''']]     )
    return {"alpha_frac": alpha_frac}





def 						snake_case_							(_a	:							Any    ,   _a	:							str     ):
    if example["hash"] in uniques:
        uniques.remove(example['''hash''']     )
        return True
    else:
        return False





def 						snake_case_							(_a	:							int    ,   _a	:							List[str]=5     ):
    UpperCAmelCase			       =		['''auto-generated''', '''autogenerated''', '''automatically generated''']
    UpperCAmelCase			       =		example['''content'''].splitlines()
    for _, line in zip(range(_a     )    ,   _a     ):
        for keyword in keywords:
            if keyword in line.lower():
                return {"autogenerated": True}
    else:
        return {"autogenerated": False}





def 						snake_case_							(_a	:							Dict    ,   _a	:							int=5    ,   _a	:							Union[str, Any]=0.05     ):
    UpperCAmelCase			       =		['''unit tests''', '''test file''', '''configuration file''']
    UpperCAmelCase			       =		example['''content'''].splitlines()
    UpperCAmelCase			       =		0
    UpperCAmelCase			       =		0
    # first test
    for _, line in zip(range(_a     )    ,   _a     ):
        for keyword in keywords:
            if keyword in line.lower():
                return {"config_or_test": True}
    # second test
    UpperCAmelCase			       =		example['''content'''].count('''\n'''     )
    UpperCAmelCase			       =		int(coeff * nlines     )
    for line in lines:
        count_config += line.lower().count('''config'''     )
        count_test += line.lower().count('''test'''     )
        if count_config > threshold or count_test > threshold:
            return {"config_or_test": True}
    return {"config_or_test": False}





def 						snake_case_							(_a	:							List[Any]     ):
    UpperCAmelCase			       =		['''def ''', '''class ''', '''for ''', '''while ''']
    UpperCAmelCase			       =		example['''content'''].splitlines()
    for line in lines:
        for keyword in keywords:
            if keyword in line.lower():
                return {"has_no_keywords": False}
    return {"has_no_keywords": True}





def 						snake_case_							(_a	:							Optional[Any]    ,   _a	:							Any=4     ):
    UpperCAmelCase			       =		example['''content'''].splitlines()
    UpperCAmelCase			       =		0
    for line in lines:
        counter += line.lower().count('''='''     )
        if counter > minimum:
            return {"has_few_assignments": False}
    return {"has_few_assignments": True}





def 						snake_case_							(_a	:							List[str]     ):
    UpperCAmelCase			       =		tokenizer(example['''content''']    ,   truncation=_a     )['''input_ids''']
    UpperCAmelCase			       =		len(example['''content''']     ) / len(_a     )
    return {"ratio": ratio}





def 						snake_case_							(_a	:							str     ):
    UpperCAmelCase			       =		{}
    results.update(get_hash(_a     )     )
    results.update(line_stats(_a     )     )
    results.update(alpha_stats(_a     )     )
    results.update(char_token_ratio(_a     )     )
    results.update(is_autogenerated(_a     )     )
    results.update(is_config_or_test(_a     )     )
    results.update(has_no_keywords(_a     )     )
    results.update(has_few_assignments(_a     )     )
    return results





def 						snake_case_							(_a	:							Dict    ,   _a	:							Dict    ,   _a	:							Dict     ):
    if not check_uniques(_a    ,   _a     ):
        return False
    elif example["autogenerated"]:
        return False
    elif example["line_max"] > args.line_max:
        return False
    elif example["line_mean"] > args.line_mean:
        return False
    elif example["alpha_frac"] < args.alpha_frac:
        return False
    elif example["ratio"] < args.min_token_ratio:
        return False
    elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
        return False
    elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
        return False
    elif example["has_few_assignments"]:
        return False
    else:
        return True





def 						snake_case_							(_a	:							Optional[Any]     ):
    with open(_a    ,   '''rb'''     ) as f_in:
        with gzip.open(str(_a     ) + '''.gz'''    ,   '''wb'''    ,   compresslevel=6     ) as f_out:
            shutil.copyfileobj(_a    ,   _a     )
    os.unlink(_a     )


# Settings
A     =HfArgumentParser(PreprocessingArguments)
A     =parser.parse_args()
if args.num_workers is None:
   A     =multiprocessing.cpu_count()
A     =AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
A     =time.time()
A     =load_dataset(args.dataset_name, split='train')
print(f"""Time to load dataset: {time.time()-t_start:.2f}""")

# Run preprocessing
A     =time.time()
A     =ds.map(preprocess, num_proc=args.num_workers)
print(f"""Time to preprocess dataset: {time.time()-t_start:.2f}""")

# Deduplicate hashes
A     =set(ds.unique('hash'))
A     =len(uniques) / len(ds)
print(f"""Fraction of duplicates: {1-frac:.2%}""")

# Deduplicate data and apply heuristics
A     =time.time()
A     =ds.filter(filter, fn_kwargs={'uniques': uniques, 'args': args})
print(f"""Time to filter dataset: {time.time()-t_start:.2f}""")
print(f"""Size of filtered dataset: {len(ds_filter)}""")

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
   A     =time.time()
   A    ,      A     =deduplicate_dataset(ds_filter, args.jaccard_threshold)
   print(f"""Time to deduplicate dataset: {time.time()-t_start:.2f}""")
   print(f"""Size of deduplicate dataset: {len(ds_filter)}""")

# Save data in batches of samples_per_file
A     =Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
   with open(output_dir / 'duplicate_clusters.json', 'w') as f:
      json.dump(duplicate_clusters, f)

A     =output_dir / 'data'
data_dir.mkdir(exist_ok=True)

A     =time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
   A     =str(data_dir / f"""file-{file_number+1:012}.json""")
   A     =min(len(ds_filter), index + args.samples_per_file)
   ds_filter.select(list(range(index, end_index))).to_json(file_path)
   compress_file(file_path)







print(f"""Time to save dataset: {time.time()-t_start:.2f}""")










'''simple docstring'''







import os





def 						snake_case_							():
    UpperCAmelCase			       =		os.path.join(os.path.dirname(_a     )    ,   '''num.txt'''     )
    with open(_a     ) as file_hand:
        return str(sum(int(_a     ) for line in file_hand     )     )[:1_0]


if __name__ == "__main__":
   print(solution())




