'''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    :     Union[str, Any]         =       re.compile(r'''\s+''')





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





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]				):
     _A    		=  [len(a_				) for line in example["""content"""].splitlines()]
     return {"line_mean": np.mean(a_				), "line_max": max(a_				)}





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





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[str]   ,		__snake_case							:			Any				):

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





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[Any]   ,		__snake_case							:			Any=5				):
     _A    		=  ["""auto-generated""", """autogenerated""", """automatically generated"""]
     _A    		=  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  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Any   ,		__snake_case							:			List[Any]=5   ,		__snake_case							:			Any=0.05				):
     _A    		=  ["""unit tests""", """test file""", """configuration file"""]
     _A    		=  example["""content"""].splitlines()
     _A    		=  0
     _A    		=  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
     _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  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int				):
     _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  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str   ,		__snake_case							:			Optional[int]=4				):
     _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  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int				):
     _A    		=  tokenizer(example['content']   ,		truncation=a_				)["""input_ids"""]
     _A    		=  len(example['content']				) / len(a_				)
     return {"ratio": ratio}





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]				):
     _A    		=  {}
     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  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[Any]   ,		__snake_case							:			List[str]   ,		__snake_case							:			List[str]				):

     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  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[int]				):

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

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

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

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

# Deduplicate data and apply heuristics
_UpperCAmelCase    :     Dict         =       time.time()
_UpperCAmelCase    :     List[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    :     int         =       time.time()
   _UpperCAmelCase       ,  _UpperCAmelCase    :     Optional[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
_UpperCAmelCase    :     Any         =       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    :     Optional[Any]         =       output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

_UpperCAmelCase    :     Optional[Any]         =       time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
   _UpperCAmelCase    :     Union[str, Any]         =       str(data_dir / F'''file-{file_number+1:012}.json''')
   _UpperCAmelCase    :     Any         =       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 transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest




@require_tokenizers
class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    Optional[int]			  =    DistilBertTokenizer
 __a      :    Union[str, Any]			  =    DistilBertTokenizerFast
 __a      :    Any			  =    True
 @slow
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				DistilBertTokenizer.from_pretrained("""distilbert-base-uncased"""			)

       UpperCamelCase_					: Dict            =				tokenizer.encode("""sequence builders"""      ,  add_special_tokens=__lowerCAmelCase			)
       UpperCamelCase_					: int            =				tokenizer.encode("""multi-sequence build"""      ,  add_special_tokens=__lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase			)
       UpperCamelCase_					: Dict            =				tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase      ,  __lowerCAmelCase			)

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
           tokenizer.sep_token_id
       ]






