


'''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__		:					Tuple												=  re.compile(R"""\s+""")



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Dict							)     ->		str:
     return {"hash": hashlib.mda(re.sub(UpperCAmelCase_      ,    ''      ,    example['content']							).encode('utf-8'							)							).hexdigest()}



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]							)     ->		Dict:
     __lowerCamelCase   :	Tuple								=		[len(UpperCAmelCase_							) for line in example['content'].splitlines()]
     return {"line_mean": np.mean(UpperCAmelCase_							), "line_max": max(UpperCAmelCase_							)}



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				List[str]							)     ->		Union[str, Any]:
     __lowerCamelCase   :	Optional[int]								=		np.mean([c.isalnum() for c in example['content']]							)
     return {"alpha_frac": alpha_frac}



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Any      ,    UpperCAmelCase_    :				List[str]							)     ->		Dict:
     if example["hash"] in uniques:
          uniques.remove(example['hash']							)
          return True
     else:
          return False



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				Optional[int]=5							)     ->		Any:
     __lowerCamelCase   :	str								=		['auto-generated', 'autogenerated', 'automatically generated']
     __lowerCamelCase   :	Tuple								=		example['content'].splitlines()
     for _, line in zip(range(UpperCAmelCase_							)      ,    UpperCAmelCase_							):
          for keyword in keywords:
               if keyword in line.lower():
                    return {"autogenerated": True}
     else:
          return {"autogenerated": False}



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				Any=5      ,    UpperCAmelCase_    :				Dict=0.05							)     ->		str:
     __lowerCamelCase   :	Dict								=		['unit tests', 'test file', 'configuration file']
     __lowerCamelCase   :	int								=		example['content'].splitlines()
     __lowerCamelCase   :	Optional[Any]								=		0
     __lowerCamelCase   :	str								=		0
     # first test
     for _, line in zip(range(UpperCAmelCase_							)      ,    UpperCAmelCase_							):
          for keyword in keywords:
               if keyword in line.lower():
                    return {"config_or_test": True}
    # second test
     __lowerCamelCase   :	Union[str, Any]								=		example['content'].count('\n'							)
     __lowerCamelCase   :	List[str]								=		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 			UpperCAmelCase__					(	UpperCAmelCase_    :				Optional[Any]							)     ->		Optional[Any]:
     __lowerCamelCase   :	Union[str, Any]								=		['def ', 'class ', 'for ', 'while ']
     __lowerCamelCase   :	Union[str, Any]								=		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 			UpperCAmelCase__					(	UpperCAmelCase_    :				List[str]      ,    UpperCAmelCase_    :				List[str]=4							)     ->		List[str]:
     __lowerCamelCase   :	Optional[int]								=		example['content'].splitlines()
     __lowerCamelCase   :	Dict								=		0
     for line in lines:
          counter += line.lower().count('='							)
          if counter > minimum:
               return {"has_few_assignments": False}
     return {"has_few_assignments": True}



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Optional[int]							)     ->		List[Any]:
     __lowerCamelCase   :	int								=		tokenizer(example['content']      ,    truncation=UpperCAmelCase_							)['input_ids']
     __lowerCamelCase   :	Optional[Any]								=		len(example['content']							) / len(UpperCAmelCase_							)
     return {"ratio": ratio}



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]							)     ->		List[Any]:
     __lowerCamelCase   :	Any								=		{}
     results.update(get_hash(UpperCAmelCase_							)							)
     results.update(line_stats(UpperCAmelCase_							)							)
     results.update(alpha_stats(UpperCAmelCase_							)							)
     results.update(char_token_ratio(UpperCAmelCase_							)							)
     results.update(is_autogenerated(UpperCAmelCase_							)							)
     results.update(is_config_or_test(UpperCAmelCase_							)							)
     results.update(has_no_keywords(UpperCAmelCase_							)							)
     results.update(has_few_assignments(UpperCAmelCase_							)							)
     return results



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				List[Any]							)     ->		Union[str, Any]:
     if not check_uniques(UpperCAmelCase_      ,    UpperCAmelCase_							):
          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 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]							)     ->		int:
     with open(UpperCAmelCase_      ,    'rb'							) as f_in:
          with gzip.open(str(UpperCAmelCase_							) + '.gz'      ,    'wb'      ,    compresslevel=6							) as f_out:
               shutil.copyfileobj(UpperCAmelCase_      ,    UpperCAmelCase_							)
     os.unlink(UpperCAmelCase_							)


# Settings
A__		:					Union[str, Any]												=  HfArgumentParser(PreprocessingArguments)
A__		:					Optional[Any]												=  parser.parse_args()
if args.num_workers is None:
      A__		:					Any												=  multiprocessing.cpu_count()
A__		:					List[str]												=  AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
A__		:					List[Any]												=  time.time()
A__		:					str												=  load_dataset(args.dataset_name, split="""train""")
print(f'''Time to load dataset: {time.time()-t_start:.2f}''')

# Run preprocessing
A__		:					List[str]												=  time.time()
A__		:					Dict												=  ds.map(preprocess, num_proc=args.num_workers)
print(f'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

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

# Deduplicate data and apply heuristics
A__		:					List[str]												=  time.time()
A__		:					str												=  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__		:					List[str]												=  time.time()
      A__						,					A__		:					List[str]												=  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__		:					Tuple												=  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__		:					Optional[int]												=  output_dir / """data"""
data_dir.mkdir(exist_ok=True)

A__		:					str												=  time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
      A__		:					Tuple												=  str(data_dir / f'''file-{file_number+1:012}.json''')
      A__		:					List[str]												=  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'''



from math import isqrt



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int							)     ->		bool:
     return all(number % divisor != 0 for divisor in range(2      ,    isqrt(UpperCAmelCase_							) + 1							)							)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int = 10**6							)     ->		int:
     __lowerCamelCase   :	Optional[Any]								=		0
     __lowerCamelCase   :	Optional[int]								=		1
     __lowerCamelCase   :	List[str]								=		7
     while prime_candidate < max_prime:
          primes_count += is_prime(UpperCAmelCase_							)

          cube_index += 1
          prime_candidate += 6 * cube_index

     return primes_count


if __name__ == "__main__":
      print(f'''{solution() = }''')






