





"""simple docstring"""
import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class       a							(   tf.keras.layers.Layer  ):



 def __init__(     self				,       _snake_case				,       _snake_case				,       _snake_case = None				,       _snake_case = None     ):







  """simple docstring"""






  super().__init__()
  lowerCAmelCase    				= pad_token_id
  lowerCAmelCase    				= max_length
  lowerCAmelCase    				= vocab
  lowerCAmelCase    				= merges
  lowerCAmelCase    				= BytePairTokenizer(_snake_case				,       _snake_case				,       sequence_length=_snake_case     )



 @classmethod
 def    UpperCamelCase__  (     cls				,       _snake_case				,       *_snake_case				,       **_snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= [' '.join(_snake_case     ) for m in tokenizer.bpe_ranks.keys()]
  lowerCAmelCase    				= tokenizer.get_vocab()
  return cls(_snake_case				,       _snake_case				,       *_snake_case				,       **_snake_case     )



 @classmethod
 def    UpperCamelCase__  (     cls				,       _snake_case				,       *_snake_case				,       **_snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= GPTaTokenizer.from_pretrained(_snake_case				,       *_snake_case				,       **_snake_case     )
  return cls.from_tokenizer(_snake_case				,       *_snake_case				,       **_snake_case     )



 @classmethod
 def    UpperCamelCase__  (     cls				,       _snake_case     ):







  """simple docstring"""






  return cls(**_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return {
      "vocab": self.vocab,
      "merges": self.merges,
      "max_length": self.max_length,
      "pad_token_id": self.pad_token_id,
  }



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = None     ):







  """simple docstring"""






  lowerCAmelCase    				= self.tf_tokenizer(_snake_case     )
  lowerCAmelCase    				= tf.ones_like(_snake_case     )

  if self.pad_token_id is not None:
   # pad the tokens up to max length
   lowerCAmelCase    				= max_length if max_length is not None else self.max_length

   if max_length is not None:
    lowerCAmelCase				,lowerCAmelCase    				= pad_model_inputs(
        _snake_case				,       max_seq_length=_snake_case				,       pad_value=self.pad_token_id     )

  return {"attention_mask": attention_mask, "input_ids": input_ids}













'''simple docstring'''



import argparse
from collections import defaultdict


def 				lowercase_					(     __A			:							Optional[Any]       ,   __A			:							Optional[int]       ,   __A			:							str       ,   __A			:							Tuple       ,   __A			:							int      )							->   Dict:



 """simple docstring"""




 lowercase       : int						=F'{file}_{class_name}_{test_name}'
 done_test[_id] += 1

 with open(__A       ,   '''r'''      ) as f:
  lowercase       : List[Any]						=f.readlines()

 lowercase       : Tuple						=F'class {class_name}('
 lowercase       : Optional[Any]						=F'{4 * " "}def {test_name}('
 lowercase       : Union[str, Any]						=F'{8 * " "}{correct_line.split()[0]}'
 lowercase       : List[Any]						=F'{1_6 * " "}{correct_line.split()[0]}'
 lowercase       : int						=False
 lowercase       : Dict						=False
 lowercase       : Optional[Any]						=False
 lowercase       : str						=False
 lowercase       : str						=0
 lowercase       : Optional[Any]						=0

 lowercase       : List[Any]						=[]
 for line in lines:
  if line.startswith(__A      ):
   lowercase       : List[str]						=True
  elif in_class and line.startswith(__A      ):
   lowercase       : int						=True
  elif in_class and in_func and (line.startswith(__A      ) or line.startswith(__A      )):
   lowercase       : str						=len(line.split(correct_line.split()[0]      )[0]      )
   count += 1

   if count == done_test[_id]:
    lowercase       : Dict						=True

  if in_class and in_func and in_line:
   if ")" not in line:
    continue
   else:
    lowercase       : Any						=True

  if in_class and in_func and in_line and insert_line:
   new_lines.append(F'{spaces * " "}{correct_line}'      )
   lowercase       : List[str]						=False
  else:
   new_lines.append(__A      )

 with open(__A       ,   '''w'''      ) as f:
  for line in new_lines:
   f.write(__A      )


def 				lowercase_					(     __A			:							Any       ,   __A			:							List[Any]=None      )							->   str:



 """simple docstring"""




 if fail is not None:
  with open(__A       ,   '''r'''      ) as f:
   lowercase       : Optional[Any]						={l.strip() for l in f.readlines()}
 else:
  lowercase       : int						=None

 with open(__A       ,   '''r'''      ) as f:
  lowercase       : Union[str, Any]						=f.readlines()

 lowercase       : Union[str, Any]						=defaultdict(__A      )
 for line in correct_lines:
  lowercase	,		lowercase	,		lowercase	,		lowercase       : Optional[int]						=line.split(''';'''      )
  if test_failures is None or "::".join([file, class_name, test_name]      ) in test_failures:
   overwrite_file(__A       ,   __A       ,   __A       ,   __A       ,   __A      )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=						argparse.ArgumentParser()
      parser.add_argument('--correct_filename', help='filename of tests with expected result')
      parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
      SCREAMING_SNAKE_CASE							=						parser.parse_args()

      main(args.correct_filename, args.fail_filename)



