




import copy

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


snake_case__			:    Any			     =      logging.get_logger(__name__)





class      snake_case	(						_snake_case				):


   '''simple docstring'''







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




   def __init__(       self  :						int    ,     **lowerCamelCase_  :						Any      )       ->List[Any]:
     '''simple docstring'''







     super().__init__(**lowerCamelCase_      )
     assert (
         "encoder" in kwargs and "decoder" in kwargs
     ), "Config has to be initialized with encoder and decoder config"
     UpperCAmelCase__         = kwargs.pop("""encoder"""      )
     UpperCAmelCase__         = encoder_config.pop("""model_type"""      )
     UpperCAmelCase__         = kwargs.pop("""decoder"""      )
     UpperCAmelCase__         = decoder_config.pop("""model_type"""      )

     from ..auto.configuration_auto import AutoConfig

     UpperCAmelCase__         = AutoConfig.for_model(lowerCamelCase_    ,     **lowerCamelCase_      )
     UpperCAmelCase__         = AutoConfig.for_model(lowerCamelCase_    ,     **lowerCamelCase_      )
     UpperCAmelCase__         = True




   @classmethod
   def 						UpperCAmelCase							(       cls  :						Optional[Any]    ,     lowerCamelCase_  :						PretrainedConfig    ,     lowerCamelCase_  :						PretrainedConfig    ,     **lowerCamelCase_  :						int      )       ->PretrainedConfig:
     '''simple docstring'''







     logger.info("""Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""      )
     UpperCAmelCase__         = True
     UpperCAmelCase__         = True

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







   def 						UpperCAmelCase							(       self  :						Tuple      )       ->Optional[int]:
     '''simple docstring'''







     UpperCAmelCase__         = copy.deepcopy(self.__dict__      )
     UpperCAmelCase__         = self.encoder.to_dict()
     UpperCAmelCase__         = self.decoder.to_dict()
     UpperCAmelCase__         = self.__class__.model_type
     return output












import os
import sys

__a:     Union[str, Any]   					=					os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


__a:     Union[str, Any]   					=					[
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]




@add_start_docstrings(AutoConfig.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Union[str, Any]:
       return AutoConfig.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoTokenizer.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Any:
       return AutoTokenizer.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModel.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Tuple:
       return AutoModel.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Tuple:
       return AutoModelForCausalLM.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Optional[Any]:
       return AutoModelForMaskedLM.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						List[str]:
       return AutoModelForSequenceClassification.from_pretrained(*__snake_case   ,      **__snake_case )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						List[Any]:
       return AutoModelForQuestionAnswering.from_pretrained(*__snake_case   ,      **__snake_case )