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


_snake_case      = logging.get_logger(__name__)

_snake_case      = {
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					str       						="speech_to_text_2"
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						=["past_key_values"]
 SCREAMING_SNAKE_CASE_				:					List[Any]       						={"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}

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






      """simple docstring"""



      UpperCamelCase						    =					vocab_size
      UpperCamelCase						    =					d_model
      UpperCamelCase						    =					decoder_ffn_dim
      UpperCamelCase						    =					decoder_layers
      UpperCamelCase						    =					decoder_attention_heads
      UpperCamelCase						    =					dropout
      UpperCamelCase						    =					attention_dropout
      UpperCamelCase						    =					activation_dropout
      UpperCamelCase						    =					activation_function
      UpperCamelCase						    =					init_std
      UpperCamelCase						    =					decoder_layerdrop
      UpperCamelCase						    =					use_cache
      UpperCamelCase						    =					decoder_layers
      UpperCamelCase						    =					scale_embedding  # scale factor will be sqrt(d_model) if True
      UpperCamelCase						    =					max_target_positions

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




def  __lowerCamelCase					( _lowercase			)       ->      list:
     for i in range(len(_lowercase			) - 1						,     0						,     -1			):
          UpperCamelCase						    =					False

          for j in range(_lowercase						,     0						,     -1			):
               if unsorted[j] < unsorted[j - 1]:
                    UpperCamelCase    ,		UpperCamelCase						    =					unsorted[j - 1], unsorted[j]
                    UpperCamelCase						    =					True

          for j in range(_lowercase			):
               if unsorted[j] > unsorted[j + 1]:
                    UpperCamelCase    ,		UpperCamelCase						    =					unsorted[j + 1], unsorted[j]
                    UpperCamelCase						    =					True

          if not swapped:
               break
     return unsorted


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 _snake_case      = input('''Enter numbers separated by a comma:\n''').strip()
 _snake_case      = [int(item) for item in user_input.split(''',''')]
 print(F"{cocktail_shaker_sort(unsorted) = }")


