



'''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


_UpperCamelCase											=		re.compile(R'\s+')

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

def 			a_	(				_lowerCAmelCase			) -> List[str]:
  __lowerCamelCase			:			List[str]       			=					[len(_lowerCAmelCase			) for line in example['content'].splitlines()]
  return {"line_mean": np.mean(_lowerCAmelCase			), "line_max": max(_lowerCAmelCase			)}

def 			a_	(				_lowerCAmelCase			) -> Optional[Any]:
  __lowerCamelCase			:			Optional[int]       			=					np.mean([c.isalnum() for c in example['content']]			)
  return {"alpha_frac": alpha_frac}

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> str:
  if example["hash"] in uniques:
    uniques.remove(example['hash']			)
    return True
  else:
    return False

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

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

def 			a_	(				_lowerCAmelCase			) -> Any:
  __lowerCamelCase			:			List[Any]       			=					tokenizer(example['content']			,truncation=_lowerCAmelCase			)['input_ids']
  __lowerCamelCase			:			List[str]       			=					len(example['content']			) / len(_lowerCAmelCase			)
  return {"ratio": ratio}

def 			a_	(				_lowerCAmelCase			) -> List[Any]:
  __lowerCamelCase			:			int       			=					{}
  results.update(get_hash(_lowerCAmelCase			)			)
  results.update(line_stats(_lowerCAmelCase			)			)
  results.update(alpha_stats(_lowerCAmelCase			)			)
  results.update(char_token_ratio(_lowerCAmelCase			)			)
  results.update(is_autogenerated(_lowerCAmelCase			)			)
  results.update(is_config_or_test(_lowerCAmelCase			)			)
  results.update(has_no_keywords(_lowerCAmelCase			)			)
  results.update(has_few_assignments(_lowerCAmelCase			)			)
  return results

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


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

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

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

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

# Deduplicate data and apply heuristics
_UpperCamelCase											=		time.time()
_UpperCamelCase											=		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:
   _UpperCamelCase											=		time.time()
   _UpperCamelCase							,							_UpperCamelCase											=		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
_UpperCamelCase											=		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)

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

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





def 			a_	(				_lowerCAmelCase			) -> int:

  if not isinstance(_lowerCAmelCase			,_lowerCAmelCase			):
    raise TypeError('only integers accepted as input'			)
  else:
    __lowerCamelCase			:			Any       			=					str(abs(_lowerCAmelCase			)			)
    __lowerCamelCase			:			Tuple       			=					[list(_lowerCAmelCase			) for char in range(len(_lowerCAmelCase			)			)]
    for index in range(len(_lowerCAmelCase			)			):
      num_transpositions[index].pop(_lowerCAmelCase			)
    return max(
        int(''.join(list(_lowerCAmelCase			)			)			) for transposition in num_transpositions			)


if __name__ == "__main__":
   __import__('doctest').testmod()






