from __future__ import annotations





class      lowerCAmelCase						:



     def __init__(					self				,      lowerCAmelCase__=None       ):
            _A=	data
            _A=	None





     def __repr__(					self       ):
            _A=	[]
            _A=	self
            while temp:
                   string_rep.append(f"{temp.data}"       )
                   _A=	temp.next
            return "->".join(lowerCAmelCase__       )


def 							UpperCamelCase				(    lowerCAmelCase_   )		->						str:




       '''simple docstring'''






       if not elements_list:
              raise Exception('The Elements List is empty'   )

       _A=	_A=	Node(elements_list[0]   )
       for i in range(1       ,					len(lowerCAmelCase_   )   ):
              _A=	Node(elements_list[i]   )
              _A=	current.next
       return head

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						None:




       '''simple docstring'''






       if head_node is not None and isinstance(lowerCAmelCase_       ,					lowerCAmelCase_   ):
              print_reverse(head_node.next   )
              print(head_node.data   )

def 							UpperCamelCase				(    )		->						Any:




       '''simple docstring'''






       from doctest import testmod

       testmod()

       _A=	make_linked_list([14, 52, 14, 12, 43]   )
       print('Linked List:'   )
       print(lowerCAmelCase_   )
       print('Elements in Reverse:'   )
       print_reverse(lowerCAmelCase_   )


if __name__ == "__main__":
      main()
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{
    '''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						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Optional[Any]		="""speech_to_text_2"""
     _SCREAMING_SNAKE_CASE     :     str		=["""past_key_values"""]
     _SCREAMING_SNAKE_CASE     :     Tuple		={"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}



     def __init__(					self				,      lowerCAmelCase__=10000				,      lowerCAmelCase__=6				,      lowerCAmelCase__=2048				,      lowerCAmelCase__=4				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=True				,      lowerCAmelCase__="relu"				,      lowerCAmelCase__=256				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.02				,      lowerCAmelCase__=2				,      lowerCAmelCase__=True				,      lowerCAmelCase__=1				,      lowerCAmelCase__=0				,      lowerCAmelCase__=2				,      lowerCAmelCase__=1024				,      **lowerCAmelCase__				,      ):
            _A=	vocab_size
            _A=	d_model
            _A=	decoder_ffn_dim
            _A=	decoder_layers
            _A=	decoder_attention_heads
            _A=	dropout
            _A=	attention_dropout
            _A=	activation_dropout
            _A=	activation_function
            _A=	init_std
            _A=	decoder_layerdrop
            _A=	use_cache
            _A=	decoder_layers
            _A=	scale_embedding  # scale factor will be sqrt(d_model) if True
            _A=	max_target_positions

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