






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


__UpperCAmelCase      =				logging.get_logger(__name__)
class 	a_(		lowercase__	):



     """simple docstring"""



     __snake_case		:				Union[str, Any]  					='''speech-encoder-decoder'''
     __snake_case		:				List[Any]  					=True





     def __init__(		self					:	Any  ,  **lowerCAmelCase__					:	List[Any])							->	Tuple:

      """simple docstring"""
      super().__init__(**lowerCAmelCase__)
      if "encoder" not in kwargs or "decoder" not in kwargs:
       raise ValueError(
           f'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
           f''' `decoder` sub-configurations are passed, but only {kwargs}''')

      SCREAMING_SNAKE_CASE				    =		kwargs.pop('encoder')
      SCREAMING_SNAKE_CASE				    =		encoder_config.pop('model_type')
      SCREAMING_SNAKE_CASE				    =		kwargs.pop('decoder')
      SCREAMING_SNAKE_CASE				    =		decoder_config.pop('model_type')

      SCREAMING_SNAKE_CASE				    =		AutoConfig.for_model(lowerCAmelCase__  ,  **lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		AutoConfig.for_model(lowerCAmelCase__  ,  **lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		True





     @classmethod
     def     __UpperCamelCase						(		cls					:	str  ,  lowerCAmelCase__					:	PretrainedConfig  ,  lowerCAmelCase__					:	PretrainedConfig  ,  **lowerCAmelCase__					:	List[str])							->	PretrainedConfig:

      """simple docstring"""
      logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config')
      SCREAMING_SNAKE_CASE				    =		True
      SCREAMING_SNAKE_CASE				    =		True

      return cls(encoder=encoder_config.to_dict()  ,  decoder=decoder_config.to_dict()  ,  **lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	str)							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		copy.deepcopy(self.__dict__)
      SCREAMING_SNAKE_CASE				    =		self.encoder.to_dict()
      SCREAMING_SNAKE_CASE				    =		self.decoder.to_dict()
      SCREAMING_SNAKE_CASE				    =		self.__class__.model_type
      return output










"""simple docstring"""





_lowercase						   =     {0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

_lowercase						   =     {0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       Any				      =       True
						snake_case__							:       int				      =       []

						for neighbour in graph[vert]:
												if not visited[neighbour]:
																		connected_verts += dfs(lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase		)

						return [vert, *connected_verts]

def 		lowerCAmelCase				(			lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       Optional[int]				      =       len(lowerCamelCase		)
						snake_case__							:       str				      =       graph_size * [False]
						snake_case__							:       List[str]				      =       []

						for i in range(lowerCamelCase		):
												if not visited[i]:
																		snake_case__							:       Dict				      =       dfs(lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase		)
																		components_list.append(lowerCamelCase		)

						return components_list


if __name__ == "__main__":
							import doctest

							doctest.testmod()


