




import copy

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


SCREAMING_SNAKE_CASE__	:				int  =						logging.get_logger(__name__)




class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    lowerCamelCase_				:     str     					=				"""speech-encoder-decoder"""
    lowerCamelCase_				:     List[str]     					=				True







    def __init__(   self					,  **UpperCamelCase__					)					->				Dict:
           super().__init__(**UpperCamelCase__					)
           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}'''					)

           lowerCamelCase       :				Dict      			= kwargs.pop("encoder"					)
           lowerCamelCase       :				Tuple      			= encoder_config.pop("model_type"					)
           lowerCamelCase       :				int      			= kwargs.pop("decoder"					)
           lowerCamelCase       :				List[Any]      			= decoder_config.pop("model_type"					)

           lowerCamelCase       :				str      			= AutoConfig.for_model(UpperCamelCase__					,  **UpperCamelCase__					)
           lowerCamelCase       :				str      			= AutoConfig.for_model(UpperCamelCase__					,  **UpperCamelCase__					)
           lowerCamelCase       :				Tuple      			= True







    @classmethod
    def    _lowercase					(   cls					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__					)					->				PretrainedConfig:
           logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"					)
           lowerCamelCase       :				Dict      			= True
           lowerCamelCase       :				Union[str, Any]      			= True

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







    def    _lowercase					(   self					)					->				Tuple:
           lowerCamelCase       :				Tuple      			= copy.deepcopy(self.__dict__					)
           lowerCamelCase       :				Optional[int]      			= self.encoder.to_dict()
           lowerCamelCase       :				List[str]      			= self.decoder.to_dict()
           lowerCamelCase       :				Dict      			= 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,
)


SCREAMING_SNAKE_CASE__	:				Optional[int]  =       {
    'configuration_gptsan_japanese': ['GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTSanJapaneseConfig'],
    'tokenization_gptsan_japanese': ['GPTSanJapaneseTokenizer'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__	:				Optional[int]  =       [
           'GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
           'GPTSanJapaneseForConditionalGeneration',
           'GPTSanJapaneseModel',
           'GPTSanJapanesePreTrainedModel',
       ]
       SCREAMING_SNAKE_CASE__	:				List[str]  =       [
           'GPTSanJapaneseTokenizer',
       ]


if TYPE_CHECKING:
       from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
       from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gptsan_japanese import (
                  GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTSanJapaneseForConditionalGeneration,
                  GPTSanJapaneseModel,
                  GPTSanJapanesePreTrainedModel,
              )
              from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
       import sys

       SCREAMING_SNAKE_CASE__	:				Optional[Any]  =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






