



import argparse
from collections import defaultdict

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__						,  A__	):
      lowercase__     =  F'''{file}_{class_name}_{test_name}'''
      done_test[_id] += 1

      with open(A__						,  'r'	) as f:
            lowercase__     =  f.readlines()

      lowercase__     =  F'''class {class_name}('''
      lowercase__     =  F'''{4 * ' '}def {test_name}('''
      lowercase__     =  F'''{8 * ' '}{correct_line.split()[0]}'''
      lowercase__     =  F'''{16 * ' '}{correct_line.split()[0]}'''
      lowercase__     =  False
      lowercase__     =  False
      lowercase__     =  False
      lowercase__     =  False
      lowercase__     =  0
      lowercase__     =  0

      lowercase__     =  []
      for line in lines:
            if line.startswith(A__	):
                  lowercase__     =  True
            elif in_class and line.startswith(A__	):
                  lowercase__     =  True
            elif in_class and in_func and (line.startswith(A__	) or line.startswith(A__	)):
                  lowercase__     =  len(line.split(correct_line.split()[0]	)[0]	)
                  count += 1

                  if count == done_test[_id]:
                        lowercase__     =  True

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

            if in_class and in_func and in_line and insert_line:
                  new_lines.append(F'''{spaces * ' '}{correct_line}'''	)
                  lowercase__     =  lowercase__     =  lowercase__     =  lowercase__     =  False
            else:
                  new_lines.append(A__	)

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

def   _lowerCAmelCase   (						A__						,  A__=None	):
      if fail is not None:
            with open(A__						,  'r'	) as f:
                  lowercase__     =  {l.strip() for l in f.readlines()}
      else:
            lowercase__     =  None

      with open(A__						,  'r'	) as f:
            lowercase__     =  f.readlines()

      lowercase__     =  defaultdict(A__	)
      for line in correct_lines:
            lowercase__,	lowercase__,	lowercase__,	lowercase__     =  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__":
       a__      :				int   	=			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)
       a__      :				Dict   	=			parser.parse_args()

       main(args.correct_filename, args.fail_filename)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__      :				Dict   	=			logging.get_logger(__name__)

a__      :				List[Any]   	=			{
    "facebook/s2t-small-librispeech-asr": (
        "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  int       				=    "speech_to_text"
   A    :  Optional[Any]       				=    ["past_key_values"]
   A    :  Optional[int]       				=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


   def __init__(						self  :			Optional[int]			,				lowerCAmelCase  :			Tuple=1_00_00			,				lowerCAmelCase  :			int=12			,				lowerCAmelCase  :			int=20_48			,				lowerCAmelCase  :			Union[str, Any]=4			,				lowerCAmelCase  :			str=6			,				lowerCAmelCase  :			Dict=20_48			,				lowerCAmelCase  :			Dict=4			,				lowerCAmelCase  :			Optional[int]=0.0			,				lowerCAmelCase  :			Union[str, Any]=0.0			,				lowerCAmelCase  :			int=True			,				lowerCAmelCase  :			Optional[Any]=True			,				lowerCAmelCase  :			Dict="relu"			,				lowerCAmelCase  :			Tuple=2_56			,				lowerCAmelCase  :			Dict=0.1			,				lowerCAmelCase  :			Optional[Any]=0.0			,				lowerCAmelCase  :			List[Any]=0.0			,				lowerCAmelCase  :			Any=0.02			,				lowerCAmelCase  :			List[Any]=2			,				lowerCAmelCase  :			Tuple=True			,				lowerCAmelCase  :			Tuple=1			,				lowerCAmelCase  :			List[str]=0			,				lowerCAmelCase  :			Union[str, Any]=2			,				lowerCAmelCase  :			Any=60_00			,				lowerCAmelCase  :			Optional[int]=10_24			,				lowerCAmelCase  :			Optional[Any]=2			,				lowerCAmelCase  :			Optional[Any]=(5, 5)			,				lowerCAmelCase  :			Union[str, Any]=10_24			,				lowerCAmelCase  :			List[Any]=80			,				lowerCAmelCase  :			List[str]=1			,				**lowerCAmelCase  :			List[str]			,				)	->				Dict:




         """simple docstring"""

         lowercase__     =  vocab_size
         lowercase__     =  d_model
         lowercase__     =  encoder_ffn_dim
         lowercase__     =  encoder_layers
         lowercase__     =  encoder_attention_heads
         lowercase__     =  decoder_ffn_dim
         lowercase__     =  decoder_layers
         lowercase__     =  decoder_attention_heads
         lowercase__     =  dropout
         lowercase__     =  attention_dropout
         lowercase__     =  activation_dropout
         lowercase__     =  activation_function
         lowercase__     =  init_std
         lowercase__     =  encoder_layerdrop
         lowercase__     =  decoder_layerdrop
         lowercase__     =  use_cache
         lowercase__     =  encoder_layers
         lowercase__     =  scale_embedding  # scale factor will be sqrt(d_model) if True
         lowercase__     =  max_source_positions
         lowercase__     =  max_target_positions
         lowercase__     =  num_conv_layers
         lowercase__     =  list(lowerCAmelCase)
         lowercase__     =  conv_channels
         lowercase__     =  input_feat_per_channel
         lowercase__     =  input_channels

         if len(self.conv_kernel_sizes) != self.num_conv_layers:
               raise ValueError(
                   'Configuration for convolutional module is incorrect. '
                   'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
                   f'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes)}`, '''
                   f'''`config.num_conv_layers = {self.num_conv_layers}`.''')

         super().__init__(
             pad_token_id=lowerCAmelCase			,				bos_token_id=lowerCAmelCase			,				eos_token_id=lowerCAmelCase			,				is_encoder_decoder=lowerCAmelCase			,				decoder_start_token_id=lowerCAmelCase			,				**lowerCAmelCase			,				)
