






import copy

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


a_		:    Optional[Any]   							=     logging.get_logger(__name__)

class 		_snake_case	(	A__       ):
			_lowercase		:     int        =				'''encoder-decoder'''
			_lowercase		:     Dict        =				True






			def __init__(							self		,       **a)   ->							List[Any]:
						super().__init__(**a)
						assert (
						    "encoder" in kwargs and "decoder" in kwargs
						), "Config has to be initialized with encoder and decoder config"
						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')

						from ..auto.configuration_auto import AutoConfig

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






			@classmethod
			def  SCREAMING_SNAKE_CASE__     (							cls		,       a		,       a		,       **a)   ->							PretrainedConfig:
						logger.info('Set `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()		,       **a)







			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[Any]:
						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







from collections import deque





def      A__					(			__A  :						Optional[Any]			)					->Tuple:
      __A    =len(__A			)
      __A    =deque()
      __A    =[False for _ in range(__A			)]
      __A    =[-1 for _ in range(__A			)]
      __A    =index_of[:]

      def strong_connect(__A  :						Union[str, Any]     ,				__A  :						int     ,				__A  :						Optional[int]			):
            __A    =index  # the number when this node is seen
            __A    =index  # lowest rank node reachable from here
            index += 1
            stack.append(__A			)
            __A    =True

            for w in g[v]:
                  if index_of[w] == -1:
                        __A    =strong_connect(__A     ,				__A     ,				__A			)
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )
                  elif on_stack[w]:
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )

            if lowlink_of[v] == index_of[v]:
                  __A    =[]
                  __A    =stack.pop()
                  __A    =False
                  component.append(__A			)
                  while w != v:
                        __A    =stack.pop()
                        __A    =False
                        component.append(__A			)
                  components.append(__A			)
            return index

      __A    =[]
      for v in range(__A			):
            if index_of[v] == -1:
                  strong_connect(__A     ,				0     ,				__A			)

      return components





def      A__					(			__A  :						List[Any]     ,				__A  :						Optional[int]			)					->Tuple:
      __A    =[[] for _ in range(__A			)]
      for u, v in edges:
            g[u].append(__A			)
      return g


if __name__ == "__main__":
   # Test
   _lowerCamelCase      :					int					    =      7
   _lowerCamelCase      :					Tuple					    =      [0, 0, 1, 2, 3, 3, 4, 4, 6]
   _lowerCamelCase      :					Dict					    =      [1, 3, 2, 0, 1, 4, 5, 6, 5]
   _lowerCamelCase      :					Optional[Any]					    =      [(u, v) for u, v in zip(source, target)]
   _lowerCamelCase      :					Dict					    =      create_graph(n_vertices, edges)

   assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


