"""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


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







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







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Dict:
  a__:					Dict      							=     [len(_SCREAMING_SNAKE_CASE       ) for line in example['content'].splitlines()]
  return {"line_mean": np.mean(_SCREAMING_SNAKE_CASE       ), "line_max": max(_SCREAMING_SNAKE_CASE       )}







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Any:
  a__:					List[Any]      							=     np.mean([c.isalnum() for c in example['content']]       )
  return {"alpha_frac": alpha_frac}







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Union[str, Any]:
  if example["hash"] in uniques:
    uniques.remove(example['hash']       )
    return True
  else:
    return False







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







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







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->str:
  a__:					Optional[Any]      							=     tokenizer(example['content']		,					truncation=_SCREAMING_SNAKE_CASE       )['input_ids']
  a__:					List[str]      							=     len(example['content']       ) / len(_SCREAMING_SNAKE_CASE       )
  return {"ratio": ratio}







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->str:
  a__:					int      							=     {}
  results.update(get_hash(_SCREAMING_SNAKE_CASE       )       )
  results.update(line_stats(_SCREAMING_SNAKE_CASE       )       )
  results.update(alpha_stats(_SCREAMING_SNAKE_CASE       )       )
  results.update(char_token_ratio(_SCREAMING_SNAKE_CASE       )       )
  results.update(is_autogenerated(_SCREAMING_SNAKE_CASE       )       )
  results.update(is_config_or_test(_SCREAMING_SNAKE_CASE       )       )
  results.update(has_no_keywords(_SCREAMING_SNAKE_CASE       )       )
  results.update(has_few_assignments(_SCREAMING_SNAKE_CASE       )       )
  return results







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


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

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

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

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

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

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

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




class        __snake_case	:



      def __init__(						self   ,      lowercase=None)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     data
        a__:					str      							=     None



      def __repr__(						self)     ->      List[str]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     []
        a__:					Union[str, Any]      							=     self
        while temp:
          string_rep.append(f'{temp.data}')
          a__:					Tuple      							=     temp.next
        return "->".join(lowercase)







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->str:
  if not elements_list:
    raise Exception('The Elements List is empty'       )

  a__:					Any      							=     Node(elements_list[0]       )
  for i in range(1		,					len(_SCREAMING_SNAKE_CASE       )       ):
    a__:					Optional[Any]      							=     Node(elements_list[i]       )
    a__:					Tuple      							=     current.next
  return head







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->None:
  if head_node is not None and isinstance(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       ):
    print_reverse(head_node.next       )
    print(head_node.data       )







def 						__a       (     )     ->Optional[Any]:
  from doctest import testmod

  testmod()

  a__:					Tuple      							=     make_linked_list([14, 52, 14, 12, 43]       )
  print('Linked List:'       )
  print(_SCREAMING_SNAKE_CASE       )
  print('Elements in Reverse:'       )
  print_reverse(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
 main()


