


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







def 							UpperCamelCase_     (       A__   :  Optional[Any]   ):
  '''simple docstring'''





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







def 							UpperCamelCase_     (       A__   :  Any   ):
  '''simple docstring'''





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







def 							UpperCamelCase_     (       A__   :  Any   ):
  '''simple docstring'''





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







def 							UpperCamelCase_     (       A__   :  str				,       A__   :  Optional[int]   ):
  '''simple docstring'''





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







def 							UpperCamelCase_     (       A__   :  str				,       A__   :  List[str]=5   ):
  '''simple docstring'''





  lowerCAmelCase_     : Any     =  ["""auto-generated""", """autogenerated""", """automatically generated"""]
  lowerCAmelCase_     : Any     =  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 							UpperCamelCase_     (       A__   :  List[Any]				,       A__   :  Dict=5				,       A__   :  List[str]=0.05   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[int]     =  ["""unit tests""", """test file""", """configuration file"""]
  lowerCAmelCase_     : str     =  example["""content"""].splitlines()
  lowerCAmelCase_     : Dict     =  0
  lowerCAmelCase_     : str     =  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
  lowerCAmelCase_     : Tuple     =  example["""content"""].count("""\n"""   )
  lowerCAmelCase_     : Union[str, 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 							UpperCamelCase_     (       A__   :  Tuple   ):
  '''simple docstring'''





  lowerCAmelCase_     : Union[str, Any]     =  ["""def """, """class """, """for """, """while """]
  lowerCAmelCase_     : Tuple     =  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_     (       A__   :  List[Any]				,       A__   :  Optional[int]=4   ):
  '''simple docstring'''





  lowerCAmelCase_     : Any     =  example["""content"""].splitlines()
  lowerCAmelCase_     : Optional[Any]     =  0
  for line in lines:
    counter += line.lower().count("""="""   )
    if counter > minimum:
      return {"has_few_assignments": False}
  return {"has_few_assignments": True}







def 							UpperCamelCase_     (       A__   :  Tuple   ):
  '''simple docstring'''





  lowerCAmelCase_     : List[str]     =  tokenizer(example["""content"""]				,       truncation=A__   )["""input_ids"""]
  lowerCAmelCase_     : int     =  len(example["""content"""]   ) / len(A__   )
  return {"ratio": ratio}







def 							UpperCamelCase_     (       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : str     =  {}
  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 							UpperCamelCase_     (       A__   :  Optional[Any]				,       A__   :  int				,       A__   :  List[Any]   ):
  '''simple docstring'''





  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 							UpperCamelCase_     (       A__   :  int   ):
  '''simple docstring'''





  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   :    List[Any]											= HfArgumentParser(PreprocessingArguments)
__A   :    Tuple											= parser.parse_args()
if args.num_workers is None:
  __A   :    Optional[Any]											= multiprocessing.cpu_count()
__A   :    List[Any]											= AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

# Run preprocessing
__A   :    int											= time.time()
__A   :    Optional[Any]											= ds.map(preprocess, num_proc=args.num_workers)
print(F'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

# Deduplicate hashes
__A   :    Tuple											= set(ds.unique("hash"))
__A   :    Optional[Any]											= len(uniques) / len(ds)
print(F'''Fraction of duplicates: {1-frac:.2%}''')

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

__A   :    List[str]											= time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
  __A   :    int											= str(data_dir / F'''file-{file_number+1:012}.json''')
  __A   :    Tuple											= 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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__A   :    str											= logging.get_logger(__name__)

__A   :    int											= {
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   'roc_bert'

    def __init__(  self       :    str       ,		lowerCamelCase       :    Optional[Any]=3_05_22       ,		lowerCamelCase       :    List[Any]=7_68       ,		lowerCamelCase       :    int=12       ,		lowerCamelCase       :    str=12       ,		lowerCamelCase       :    Any=30_72       ,		lowerCamelCase       :    List[Any]="gelu"       ,		lowerCamelCase       :    Optional[int]=0.1       ,		lowerCamelCase       :    List[str]=0.1       ,		lowerCamelCase       :    int=5_12       ,		lowerCamelCase       :    List[Any]=2       ,		lowerCamelCase       :    int=0.02       ,		lowerCamelCase       :    str=1E-12       ,		lowerCamelCase       :    int=True       ,		lowerCamelCase       :    Optional[Any]=0       ,		lowerCamelCase       :    List[Any]="absolute"       ,		lowerCamelCase       :    str=None       ,		lowerCamelCase       :    str=True       ,		lowerCamelCase       :    Tuple=True       ,		lowerCamelCase       :    str=7_68       ,		lowerCamelCase       :    str=9_10       ,		lowerCamelCase       :    Tuple=5_12       ,		lowerCamelCase       :    List[str]=2_48_58       ,		lowerCamelCase       :    Dict=True       ,		**lowerCamelCase       :    Tuple       ,		)  ->    Optional[int]:
      lowerCAmelCase_     : Optional[Any]     =  vocab_size
      lowerCAmelCase_     : Any     =  max_position_embeddings
      lowerCAmelCase_     : Any     =  hidden_size
      lowerCAmelCase_     : int     =  num_hidden_layers
      lowerCAmelCase_     : Tuple     =  num_attention_heads
      lowerCAmelCase_     : List[Any]     =  intermediate_size
      lowerCAmelCase_     : Tuple     =  hidden_act
      lowerCAmelCase_     : List[Any]     =  hidden_dropout_prob
      lowerCAmelCase_     : Dict     =  attention_probs_dropout_prob
      lowerCAmelCase_     : Any     =  initializer_range
      lowerCAmelCase_     : Any     =  type_vocab_size
      lowerCAmelCase_     : Optional[int]     =  layer_norm_eps
      lowerCAmelCase_     : Tuple     =  use_cache
      lowerCAmelCase_     : List[Any]     =  enable_pronunciation
      lowerCAmelCase_     : List[Any]     =  enable_shape
      lowerCAmelCase_     : List[str]     =  pronunciation_embed_dim
      lowerCAmelCase_     : List[str]     =  pronunciation_vocab_size
      lowerCAmelCase_     : Dict     =  shape_embed_dim
      lowerCAmelCase_     : Optional[Any]     =  shape_vocab_size
      lowerCAmelCase_     : List[Any]     =  concat_input
      lowerCAmelCase_     : Any     =  position_embedding_type
      lowerCAmelCase_     : List[str]     =  classifier_dropout
      super().__init__(pad_token_id=lowerCamelCase       ,		**lowerCamelCase						)



