






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:     Optional[Any]   					=					re.compile(R'''\s+''')




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




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




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Dict:
       _UpperCAmelCase							=    np.mean([c.isalnum() for c in example["""content"""]] )
       return {"alpha_frac": alpha_frac}




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Dict:
       if example["hash"] in uniques:
              uniques.remove(example["""hash"""] )
              return True
       else:
              return False




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




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




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						List[str]:
       _UpperCAmelCase							=    tokenizer(example["""content"""]   ,      truncation=__snake_case )["""input_ids"""]
       _UpperCAmelCase							=    len(example["""content"""] ) / len(__snake_case )
       return {"ratio": ratio}




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Optional[Any]:
       _UpperCAmelCase							=    {}
       results.update(get_hash(__snake_case ) )
       results.update(line_stats(__snake_case ) )
       results.update(alpha_stats(__snake_case ) )
       results.update(char_token_ratio(__snake_case ) )
       results.update(is_autogenerated(__snake_case ) )
       results.update(is_config_or_test(__snake_case ) )
       results.update(has_no_keywords(__snake_case ) )
       results.update(has_few_assignments(__snake_case ) )
       return results




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


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

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

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

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

# Deduplicate data and apply heuristics
__a:     Optional[Any]   					=					time.time()
__a:     Optional[Any]   					=					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[int]   					=					time.time()
  __a					,		__a:     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:     int   					=					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[Any]   					=					time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
  __a:     Optional[int]   					=					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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase				:  Tuple    		=      {
    'configuration_distilbert': [
        'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DistilBertConfig',
        'DistilBertOnnxConfig',
    ],
    'tokenization_distilbert': ['DistilBertTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:  str    		=      ['DistilBertTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:  List[str]    		=      [
     'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DistilBertForMaskedLM',
     'DistilBertForMultipleChoice',
     'DistilBertForQuestionAnswering',
     'DistilBertForSequenceClassification',
     'DistilBertForTokenClassification',
     'DistilBertModel',
     'DistilBertPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:  Union[str, Any]    		=      [
     'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFDistilBertForMaskedLM',
     'TFDistilBertForMultipleChoice',
     'TFDistilBertForQuestionAnswering',
     'TFDistilBertForSequenceClassification',
     'TFDistilBertForTokenClassification',
     'TFDistilBertMainLayer',
     'TFDistilBertModel',
     'TFDistilBertPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:  str    		=      [
     'FlaxDistilBertForMaskedLM',
     'FlaxDistilBertForMultipleChoice',
     'FlaxDistilBertForQuestionAnswering',
     'FlaxDistilBertForSequenceClassification',
     'FlaxDistilBertForTokenClassification',
     'FlaxDistilBertModel',
     'FlaxDistilBertPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_distilbert import (
     DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DistilBertConfig,
     DistilBertOnnxConfig,
 )
 from .tokenization_distilbert import DistilBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_distilbert_fast import DistilBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_distilbert import (
      DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DistilBertForMaskedLM,
      DistilBertForMultipleChoice,
      DistilBertForQuestionAnswering,
      DistilBertForSequenceClassification,
      DistilBertForTokenClassification,
      DistilBertModel,
      DistilBertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_distilbert import (
      TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDistilBertForMaskedLM,
      TFDistilBertForMultipleChoice,
      TFDistilBertForQuestionAnswering,
      TFDistilBertForSequenceClassification,
      TFDistilBertForTokenClassification,
      TFDistilBertMainLayer,
      TFDistilBertModel,
      TFDistilBertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_distilbert import (
      FlaxDistilBertForMaskedLM,
      FlaxDistilBertForMultipleChoice,
      FlaxDistilBertForQuestionAnswering,
      FlaxDistilBertForSequenceClassification,
      FlaxDistilBertForTokenClassification,
      FlaxDistilBertModel,
      FlaxDistilBertPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase				:  int    		=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




