





'''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__			:					Tuple       						=						re.compile(R'\s+')
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Union[str, Any] )							->     Dict:

       """simple docstring"""


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

       """simple docstring"""


       UpperCAmelCase  =						[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       __snake_case		( SCREAMING_SNAKE_CASE_ :			Any )							->     List[str]:

       """simple docstring"""


       UpperCAmelCase  =						np.mean([c.isalnum() for c in example['''content''']] )
       return {"alpha_frac": alpha_frac}
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Optional[Any]			,	SCREAMING_SNAKE_CASE_ :			Any )							->     Any:

       """simple docstring"""


       if example["hash"] in uniques:
              uniques.remove(example['''hash'''] )
              return True
       else:
              return False
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Union[str, Any]			,	SCREAMING_SNAKE_CASE_ :			Any=5 )							->     Optional[int]:

       """simple docstring"""


       UpperCAmelCase  =						['''auto-generated''', '''autogenerated''', '''automatically generated''']
       UpperCAmelCase  =						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       __snake_case		( SCREAMING_SNAKE_CASE_ :			List[Any]			,	SCREAMING_SNAKE_CASE_ :			str=5			,	SCREAMING_SNAKE_CASE_ :			Union[str, Any]=0.05 )							->     List[Any]:

       """simple docstring"""


       UpperCAmelCase  =						['''unit tests''', '''test file''', '''configuration file''']
       UpperCAmelCase  =						example['''content'''].splitlines()
       UpperCAmelCase  =						0
       UpperCAmelCase  =						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
       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       __snake_case		( SCREAMING_SNAKE_CASE_ :			str )							->     Optional[Any]:

       """simple docstring"""


       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       __snake_case		( SCREAMING_SNAKE_CASE_ :			str			,	SCREAMING_SNAKE_CASE_ :			Any=4 )							->     int:

       """simple docstring"""


       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       __snake_case		( SCREAMING_SNAKE_CASE_ :			str )							->     Dict:

       """simple docstring"""


       UpperCAmelCase  =						tokenizer(example['''content''']			,	truncation=SCREAMING_SNAKE_CASE_ )['''input_ids''']
       UpperCAmelCase  =						len(example['''content'''] ) / len(SCREAMING_SNAKE_CASE_ )
       return {"ratio": ratio}
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			str )							->     int:

       """simple docstring"""


       UpperCAmelCase  =						{}
       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       __snake_case		( SCREAMING_SNAKE_CASE_ :			Any			,	SCREAMING_SNAKE_CASE_ :			Tuple			,	SCREAMING_SNAKE_CASE_ :			Optional[Any] )							->     Union[str, Any]:

       """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       __snake_case		( SCREAMING_SNAKE_CASE_ :			List[Any] )							->     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
a__			:					List[Any]       						=						HfArgumentParser(PreprocessingArguments)
a__			:					Tuple       						=						parser.parse_args()
if args.num_workers is None:
    a__			:					Optional[Any]       						=						multiprocessing.cpu_count()
a__			:					Dict       						=						AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

# Run preprocessing
a__			:					Any       						=						time.time()
a__			:					Union[str, 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__			:					Any       						=						len(uniques) / len(ds)
print(F"""Fraction of duplicates: {1-frac:.2%}""")

# Deduplicate data and apply heuristics
a__			:					str       						=						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__			:					List[str]       						=						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__			:					Tuple       						=						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__			:					int       						=						output_dir / 'data'
data_dir.mkdir(exist_ok=True)

a__			:					Tuple       						=						time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
    a__			:					Any       						=						str(data_dir / F"""file-{file_number+1:012}.json""")
    a__			:					Dict       						=						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_torch_available, is_vision_available


_A     :			Optional[Any]  						=			{
    """configuration_bridgetower""": [
        """BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BridgeTowerConfig""",
        """BridgeTowerTextConfig""",
        """BridgeTowerVisionConfig""",
    ],
    """processing_bridgetower""": ["""BridgeTowerProcessor"""],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			Any  						=			["""BridgeTowerImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			List[Any]  						=			[
       """BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BridgeTowerForContrastiveLearning""",
       """BridgeTowerForImageAndTextRetrieval""",
       """BridgeTowerForMaskedLM""",
       """BridgeTowerModel""",
       """BridgeTowerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_bridgetower import (
       BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BridgeTowerConfig,
       BridgeTowerTextConfig,
       BridgeTowerVisionConfig,
   )
   from .processing_bridgetower import BridgeTowerProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_bridgetower import BridgeTowerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bridgetower import (
          BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
          BridgeTowerForContrastiveLearning,
          BridgeTowerForImageAndTextRetrieval,
          BridgeTowerForMaskedLM,
          BridgeTowerModel,
          BridgeTowerPreTrainedModel,
      )


else:
   import sys

   _A     :			Any  						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure)


