




import copy

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


UpperCAmelCase_		:					Any          =  logging.get_logger(__name__)




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """encoder-decoder"""
						UpperCamelCase      =       True

						def __init__(		self :str     ,			**__snake_case :Union[str, Any]			):
										'''simple docstring'''

										super().__init__(**__snake_case			)
										assert (
										    "encoder" in kwargs and "decoder" in kwargs
										), "Config has to be initialized with encoder and decoder config"
										__magic_name__						:						List[Any]										=kwargs.pop("""encoder"""			)
										__magic_name__						:						int										=encoder_config.pop("""model_type"""			)
										__magic_name__						:						Tuple										=kwargs.pop("""decoder"""			)
										__magic_name__						:						Dict										=decoder_config.pop("""model_type"""			)

										from ..auto.configuration_auto import AutoConfig

										__magic_name__						:						Dict										=AutoConfig.for_model(__snake_case     ,			**__snake_case			)
										__magic_name__						:						int										=AutoConfig.for_model(__snake_case     ,			**__snake_case			)
										__magic_name__						:						Tuple										=True

						@classmethod
						def      A__    (		cls :Tuple     ,			__snake_case :PretrainedConfig     ,			__snake_case :PretrainedConfig     ,			**__snake_case :List[Any]			):
										'''simple docstring'''

										logger.info("""Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""			)
										__magic_name__						:						List[Any]										=True
										__magic_name__						:						Union[str, Any]										=True

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


						def      A__    (		self :Tuple			):
										'''simple docstring'''

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











"""simple docstring"""



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






@require_torch
@require_sentencepiece
@require_tokenizers
class 						__snake_case   (						unittest.TestCase	):





 """simple docstring"""


 @slow
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Any							=	AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''   ,		return_dict=__lowerCamelCase    ).to(__lowerCamelCase    )
     __A       :		Optional[Any]							=	AutoTokenizer.from_pretrained('''google/mt5-small'''    )

     __A       :		Optional[int]							=	tokenizer('''Hello there'''   ,		return_tensors='''pt'''    ).input_ids
     __A       :		str							=	tokenizer('''Hi I am'''   ,		return_tensors='''pt'''    ).input_ids

     __A       :		Any							=	model(input_ids.to(__lowerCamelCase    )   ,		labels=labels.to(__lowerCamelCase    )    ).loss
     __A       :		Tuple							=	-(labels.shape[-1] * loss.item())

     __A       :		int							=	-8_4.9_1_2_7
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE    ) < 1e-4    )




