import copy

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


__snake_case         =     logging.get_logger(__name__)







class       __snake_case (  lowerCamelCase__ ):
 __lowerCamelCase     : Union[str, Any] 	=   """speech-encoder-decoder"""
 __lowerCamelCase     : Any 	=   True
 def __init__(	self		, **snake_case__    )						->				Union[str, Any]:
       '''simple docstring'''





       super().__init__(**snake_case__    )
       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     :			Union[str, Any]     	=kwargs.pop('''encoder'''    )
       UpperCAmelCase     :			Dict     	=encoder_config.pop('''model_type'''    )
       UpperCAmelCase     :			Union[str, Any]     	=kwargs.pop('''decoder'''    )
       UpperCAmelCase     :			Optional[int]     	=decoder_config.pop('''model_type'''    )

       UpperCAmelCase     :			Optional[Any]     	=AutoConfig.for_model(snake_case__		, **snake_case__    )
       UpperCAmelCase     :			str     	=AutoConfig.for_model(snake_case__		, **snake_case__    )
       UpperCAmelCase     :			List[Any]     	=True
 @classmethod
 def 						UpperCAmelCase__							(	cls		, snake_case__		, snake_case__		, **snake_case__    )						->				PretrainedConfig:
       '''simple docstring'''





       logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''    )
       UpperCAmelCase     :			Tuple     	=True
       UpperCAmelCase     :			List[Any]     	=True

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






 def 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''simple docstring'''





       UpperCAmelCase     :			Any     	=copy.deepcopy(self.__dict__    )
       UpperCAmelCase     :			str     	=self.encoder.to_dict()
       UpperCAmelCase     :			Union[str, Any]     	=self.decoder.to_dict()
       UpperCAmelCase     :			Union[str, Any]     	=self.__class__.model_type
       return output

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case         =     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTJForCausalLM''',
         '''GPTJForQuestionAnswering''',
         '''GPTJForSequenceClassification''',
         '''GPTJModel''',
         '''GPTJPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''TFGPTJForCausalLM''',
         '''TFGPTJForQuestionAnswering''',
         '''TFGPTJForSequenceClassification''',
         '''TFGPTJModel''',
         '''TFGPTJPreTrainedModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''FlaxGPTJForCausalLM''',
         '''FlaxGPTJModel''',
         '''FlaxGPTJPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

     __snake_case         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
