
from typing import TYPE_CHECKING

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


__UpperCAmelCase   						=     {
    "configuration_led": ["LED_PRETRAINED_CONFIG_ARCHIVE_MAP", "LEDConfig"],
    "tokenization_led": ["LEDTokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase   						=     ["LEDTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase   						=     [
       "LED_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LEDForConditionalGeneration",
       "LEDForQuestionAnswering",
       "LEDForSequenceClassification",
       "LEDModel",
       "LEDPreTrainedModel",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase   						=     ["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]


if TYPE_CHECKING:
   from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
   from .tokenization_led import LEDTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_led_fast import LEDTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_led import (
          LED_PRETRAINED_MODEL_ARCHIVE_LIST,
          LEDForConditionalGeneration,
          LEDForQuestionAnswering,
          LEDForSequenceClassification,
          LEDModel,
          LEDPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
   import sys

   __UpperCAmelCase   						=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




import copy

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


__UpperCAmelCase   						=     logging.get_logger(__name__)





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       ="speech-encoder-decoder"
       UpperCAmelCase_				       =True



       def __init__(							self ,     **_A	)				->				Dict:
          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}'''	)

          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(_A ,     **_A	)
          SCREAMING_SNAKE_CASE_	=      AutoConfig.for_model(_A ,     **_A	)
          SCREAMING_SNAKE_CASE_	=      True



       @classmethod
       def     _UpperCamelCase	(							cls ,     _A ,     _A ,     **_A	)				->				PretrainedConfig:
          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() ,     **_A	)




       def     _UpperCamelCase	(							self	)				->				Union[str, 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


