



"""simple docstring"""






import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
   import optax
   from flax.training.common_utils import onehot

   from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
   from transformers.models.ta.modeling_flax_ta import shift_tokens_right






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




  @slow
  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[int]						):






         """simple docstring"""






         _lowerCamelCase		:							Any            =					FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''						)
         _lowerCamelCase		:							Any            =					AutoTokenizer.from_pretrained('''google/mt5-small'''						)

         _lowerCamelCase		:							Union[str, Any]            =					tokenizer('''Hello there'''		,   return_tensors='''np'''						).input_ids
         _lowerCamelCase		:							Optional[int]            =					tokenizer('''Hi I am'''		,   return_tensors='''np'''						).input_ids

         _lowerCamelCase		:							List[Any]            =					shift_tokens_right(__lowerCAmelCase		,   model.config.pad_token_id		,   model.config.decoder_start_token_id						)

         _lowerCamelCase		:							int            =					model(__lowerCAmelCase		,   decoder_input_ids=__lowerCAmelCase						).logits
         _lowerCamelCase		:							Optional[Any]            =					optax.softmax_cross_entropy(__lowerCAmelCase		,   onehot(__lowerCAmelCase		,   logits.shape[-1]						)						).mean()

         _lowerCamelCase		:							Dict            =					-(labels.shape[-1] * loss.item())

         _lowerCamelCase		:							Dict            =					-84.91_27
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 1E-4						)









import copy

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


_lowerCamelCase 					=      logging.get_logger(__name__)




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       "encoder-decoder"
							lowerCamelCase_							     =       True


							def __init__(				self     :Optional[int]				,					**__A     :str		) -> int:



									"""simple docstring"""



									super().__init__(**__A		)
									assert (
									    "encoder" in kwargs and "decoder" in kwargs
									), "Config has to be initialized with encoder and decoder config"
									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"""		)

									from ..auto.configuration_auto import AutoConfig

									SCREAMING_SNAKE_CASE__								=   AutoConfig.for_model(__A				,					**__A		)
									SCREAMING_SNAKE_CASE__								=   AutoConfig.for_model(__A				,					**__A		)
									SCREAMING_SNAKE_CASE__								=   True


							@classmethod
							def   _snake_case	(				cls     :str				,					__A     :PretrainedConfig				,					__A     :PretrainedConfig				,					**__A     :List[str]		) -> PretrainedConfig:



									"""simple docstring"""



									logger.info("""Set `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   _snake_case	(				self     :str		) -> Union[str, Any]:



									"""simple docstring"""



									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