

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_(_UpperCamelCase					)							->     str:



  """simple docstring"""






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



def 				snake_case_(_UpperCamelCase					)							->     List[str]:



  """simple docstring"""






  _snake_case						=							[len(_UpperCamelCase					) for line in example['''content'''].splitlines()]
  return {"line_mean": np.mean(_UpperCamelCase					), "line_max": max(_UpperCamelCase					)}



def 				snake_case_(_UpperCamelCase					)							->     List[str]:



  """simple docstring"""






  _snake_case						=							np.mean([c.isalnum() for c in example['''content''']]					)
  return {"alpha_frac": alpha_frac}



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     Optional[Any]:



  """simple docstring"""






  if example["hash"] in uniques:
    uniques.remove(example['''hash''']					)
    return True
  else:
    return False



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase=5					)							->     Union[str, Any]:



  """simple docstring"""






  _snake_case						=							['''auto-generated''', '''autogenerated''', '''automatically generated''']
  _snake_case						=							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 				snake_case_(_UpperCamelCase			,	_UpperCamelCase=5			,	_UpperCamelCase=0.05					)							->     Any:



  """simple docstring"""






  _snake_case						=							['''unit tests''', '''test file''', '''configuration file''']
  _snake_case						=							example['''content'''].splitlines()
  _snake_case						=							0
  _snake_case						=							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
  _snake_case						=							example['''content'''].count('''\n'''					)
  _snake_case						=							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_(_UpperCamelCase					)							->     Optional[int]:



  """simple docstring"""






  _snake_case						=							['''def ''', '''class ''', '''for ''', '''while ''']
  _snake_case						=							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_(_UpperCamelCase			,	_UpperCamelCase=4					)							->     str:



  """simple docstring"""






  _snake_case						=							example['''content'''].splitlines()
  _snake_case						=							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_(_UpperCamelCase					)							->     int:



  """simple docstring"""






  _snake_case						=							tokenizer(example['''content''']			,	truncation=_UpperCamelCase					)['''input_ids''']
  _snake_case						=							len(example['''content''']					) / len(_UpperCamelCase					)
  return {"ratio": ratio}



def 				snake_case_(_UpperCamelCase					)							->     List[Any]:



  """simple docstring"""






  _snake_case						=							{}
  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 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Optional[int]:



  """simple docstring"""






  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 				snake_case_(_UpperCamelCase					)							->     Tuple:



  """simple docstring"""






  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      		=     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}''')




from math import factorial



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     int:



  """simple docstring"""






  if n < k or k < 0:
    raise ValueError('''Please enter positive integers for n and k where n >= k'''					)
  return factorial(_UpperCamelCase					) // (factorial(_UpperCamelCase					) * factorial(n - k					))


if __name__ == "__main__":
 print(
     '''The number of five-card hands possible from a standard''',
     f'''fifty-two card deck is: {combinations(52, 5)}\n''',
 )

 print(
     '''If a class of 40 students must be arranged into groups of''',
     f'''4 for group projects, there are {combinations(40, 4)} ways''',
     '''to arrange them.\n''',
 )

 print(
     '''If 10 teams are competing in a Formula One race, there''',
     f'''are {combinations(10, 3)} ways that first, second and''',
     '''third place can be awarded.''',
 )

