






'''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      :       Tuple      			=      re.compile(r"""\s+""")



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[Any]    )  ->			Optional[int]:

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



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Optional[Any]    )  ->			str:

   """simple docstring"""
   __a				     =    [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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Optional[int]    )  ->			Tuple:

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



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[Any],					SCREAMING_SNAKE_CASE__:	Any    )  ->			str:

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



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Optional[Any],					SCREAMING_SNAKE_CASE__:	List[str]=5    )  ->			Dict:

   """simple docstring"""
   __a				     =    ['auto-generated', 'autogenerated', 'automatically generated']
   __a				     =    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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	str,					SCREAMING_SNAKE_CASE__:	List[str]=5,					SCREAMING_SNAKE_CASE__:	List[str]=0.0_5    )  ->			str:

   """simple docstring"""
   __a				     =    ['unit tests', 'test file', 'configuration file']
   __a				     =    example['content'].splitlines()
   __a				     =    0
   __a				     =    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				     =    example['content'].count('\n'    )
   __a				     =    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	(				SCREAMING_SNAKE_CASE__:	Union[str, Any]    )  ->			List[Any]:

   """simple docstring"""
   __a				     =    ['def ', 'class ', 'for ', 'while ']
   __a				     =    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	(				SCREAMING_SNAKE_CASE__:	Optional[int],					SCREAMING_SNAKE_CASE__:	List[str]=4    )  ->			List[str]:

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



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Any    )  ->			str:

   """simple docstring"""
   __a				     =    tokenizer(example['content'],					truncation=SCREAMING_SNAKE_CASE__    )['input_ids']
   __a				     =    len(example['content']    ) / len(SCREAMING_SNAKE_CASE__    )
   return {"ratio": ratio}



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[Any]    )  ->			Union[str, Any]:

   """simple docstring"""
   __a				     =    {}
   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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[str],					SCREAMING_SNAKE_CASE__:	Any,					SCREAMING_SNAKE_CASE__:	List[str]    )  ->			Optional[int]:

   """simple docstring"""
   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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	str    )  ->			str:

   """simple docstring"""
   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
__UpperCamelCase      :       Optional[Any]      			=      HfArgumentParser(PreprocessingArguments)
__UpperCamelCase      :       Optional[int]      			=      parser.parse_args()
if args.num_workers is None:
 __UpperCamelCase      :       Optional[Any]      			=      multiprocessing.cpu_count()
__UpperCamelCase      :       int      			=      AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
__UpperCamelCase      :       Tuple      			=      time.time()
__UpperCamelCase      :       Optional[int]      			=      load_dataset(args.dataset_name, split="""train""")
print(f"""Time to load dataset: {time.time()-t_start:.2f}""")

# Run preprocessing
__UpperCamelCase      :       str      			=      time.time()
__UpperCamelCase      :       Union[str, Any]      			=      ds.map(preprocess, num_proc=args.num_workers)
print(f"""Time to preprocess dataset: {time.time()-t_start:.2f}""")

# Deduplicate hashes
__UpperCamelCase      :       List[Any]      			=      set(ds.unique("""hash"""))
__UpperCamelCase      :       str      			=      len(uniques) / len(ds)
print(f"""Fraction of duplicates: {1-frac:.2%}""")

# Deduplicate data and apply heuristics
__UpperCamelCase      :       List[str]      			=      time.time()
__UpperCamelCase      :       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:
 __UpperCamelCase      :       Optional[int]      			=      time.time()
 __UpperCamelCase			, __UpperCamelCase      :       Optional[int]      			=      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      :       List[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)

__UpperCamelCase      :       str      			=      output_dir / """data"""
data_dir.mkdir(exist_ok=True)

__UpperCamelCase      :       Tuple      			=      time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
 __UpperCamelCase      :       Tuple      			=      str(data_dir / f"""file-{file_number+1:012}.json""")
 __UpperCamelCase      :       int      			=      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_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


__UpperCamelCase      :       Optional[int]      			=      {
    """configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2TextConfig"""],
    """processing_speech_to_text""": ["""Speech2TextProcessor"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Union[str, Any]      			=      ["""Speech2TextTokenizer"""]

try:
 if not is_speech_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Optional[Any]      			=      ["""Speech2TextFeatureExtractor"""]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       List[Any]      			=      [
     """TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFSpeech2TextForConditionalGeneration""",
     """TFSpeech2TextModel""",
     """TFSpeech2TextPreTrainedModel""",
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Tuple      			=      [
     """SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """Speech2TextForConditionalGeneration""",
     """Speech2TextModel""",
     """Speech2TextPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
 from .processing_speech_to_text import SpeechaTextProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speech_to_text import SpeechaTextTokenizer

 try:
  if not is_speech_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_speech_to_text import (
      TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFSpeechaTextForConditionalGeneration,
      TFSpeechaTextModel,
      TFSpeechaTextPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text import (
      SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextForConditionalGeneration,
      SpeechaTextModel,
      SpeechaTextPreTrainedModel,
  )

else:
 import sys

 __UpperCamelCase      :       List[str]      			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)