






'''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 UpperCAmelCase_       (     unittest.TestCase					):




      """simple docstring"""




      @slow
      def 					A__   (			self      :		str		)	->	Optional[Any]:


       '''simple docstring'''

       lowercase       : Optional[int]						=FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''		)
       lowercase       : Optional[int]						=AutoTokenizer.from_pretrained('''google/mt5-small'''		)

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

       lowercase       : Optional[int]						=shift_tokens_right(UpperCAmelCase  ,   model.config.pad_token_id  ,   model.config.decoder_start_token_id		)

       lowercase       : Optional[int]						=model(UpperCAmelCase  ,   decoder_input_ids=UpperCAmelCase		).logits
       lowercase       : Union[str, Any]						=optax.softmax_cross_entropy(UpperCAmelCase  ,   onehot(UpperCAmelCase  ,   logits.shape[-1]		)		).mean()

       lowercase       : Tuple						=-(labels.shape[-1] * loss.item())

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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__							  =						{
    "configuration_rag": ["RagConfig"],
    "retrieval_rag": ["RagRetriever"],
    "tokenization_rag": ["RagTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "RagModel",
         "RagPreTrainedModel",
         "RagSequenceForGeneration",
         "RagTokenForGeneration",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "TFRagModel",
         "TFRagPreTrainedModel",
         "TFRagSequenceForGeneration",
         "TFRagTokenForGeneration",
     ]


if TYPE_CHECKING:
     from .configuration_rag import RagConfig
     from .retrieval_rag import RagRetriever
     from .tokenization_rag import RagTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_rag import (
              TFRagModel,
              TFRagPreTrainedModel,
              TFRagSequenceForGeneration,
              TFRagTokenForGeneration,
          )

else:
     import sys

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


