'''simple docstring'''




import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

__SCREAMING_SNAKE_CASE :  Dict  =						[(1,)]

def 			UpperCamelCase_							(      )					->					List[Any]:


       """simple docstring"""







       _UpperCAmelCase  :						Union[str, Any]							   =					bool(os.getenv("RUN_SLOW"    ,			0					)					)
       if run_slow:
              assert True
       else:
              assert False, "should have been skipped"

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


       """simple docstring"""







       _UpperCAmelCase  :						Any							   =					bool(os.getenv("RUN_SLOW"    ,			0					)					)
       if run_slow and torch_device == "cuda":
              assert True
       else:
              assert False, "should have been skipped"

@require_torch
class 			lowerCamelCase_			(unittest.TestCase					):
      '''simple docstring'''

      @slow
      @require_torch_gpu
      def   _A			( self		:       Optional[Any] ):
             check_slow_torch_cuda()




      @require_torch_gpu
      @slow
      def   _A			( self		:       Optional[Any] ):
             check_slow_torch_cuda()





      @parameterized.expand(A )
      @slow
      def   _A			( self		:       str		,      A		:       List[str]=None ):
             check_slow()

@slow
@require_torch_gpu
def 			UpperCamelCase_							(      )					->					int:


       """simple docstring"""







       check_slow_torch_cuda()

@require_torch_gpu
@slow
def 			UpperCamelCase_							(      )					->					Any:


       """simple docstring"""







       check_slow_torch_cuda()

@slow
@pytest.mark.parametrize("param"    ,			[1]					)
def 			UpperCamelCase_							(      _UpperCAmelCase						:   List[Any]					)					->					str:


       """simple docstring"""







       check_slow()


@pytest.mark.parametrize("param"    ,			[1]					)
@slow
def 			UpperCamelCase_							(      _UpperCAmelCase						:   Tuple					)					->					Union[str, Any]:


       """simple docstring"""







       check_slow()


'''simple docstring'''




import copy

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


__SCREAMING_SNAKE_CASE :  Optional[int]  =						logging.get_logger(__name__)

class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						List[Any]      			=		"speech-encoder-decoder"
      __UpperCamelCase:						Optional[int]      			=		True




      def __init__( self		:       Optional[int]		,      **A		:       Tuple ):
             super().__init__(**A )
             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}""" )

             _UpperCAmelCase  :						Dict							   =					kwargs.pop("encoder" )
             _UpperCAmelCase  :						Any							   =					encoder_config.pop("model_type" )
             _UpperCAmelCase  :						str							   =					kwargs.pop("decoder" )
             _UpperCAmelCase  :						int							   =					decoder_config.pop("model_type" )

             _UpperCAmelCase  :						List[Any]							   =					AutoConfig.for_model(A		,      **A )
             _UpperCAmelCase  :						str							   =					AutoConfig.for_model(A		,      **A )
             _UpperCAmelCase  :						Optional[int]							   =					True




      @classmethod
      def   _A			( cls		:       int		,      A		:       PretrainedConfig		,      A		:       PretrainedConfig		,      **A		:       Dict ):
             logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config" )
             _UpperCAmelCase  :						Any							   =					True
             _UpperCAmelCase  :						Union[str, Any]							   =					True

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





      def   _A			( self		:       Tuple ):
             _UpperCAmelCase  :						List[str]							   =					copy.deepcopy(self.__dict__ )
             _UpperCAmelCase  :						Any							   =					self.encoder.to_dict()
             _UpperCAmelCase  :						Union[str, Any]							   =					self.decoder.to_dict()
             _UpperCAmelCase  :						Union[str, Any]							   =					self.__class__.model_type
             return output

