


'''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   __lowerCAmelCase						(unittest.TestCase     ):





      '''simple docstring'''






      @slow
      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             lowercase__      			= FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''' )
             lowercase__      			= AutoTokenizer.from_pretrained('''google/mt5-small''' )

             lowercase__      			= tokenizer('''Hello there'''   ,						return_tensors='''np''' ).input_ids
             lowercase__      			= tokenizer('''Hi I am'''   ,						return_tensors='''np''' ).input_ids

             lowercase__      			= shift_tokens_right(UpperCamelCase   ,						model.config.pad_token_id   ,						model.config.decoder_start_token_id )

             lowercase__      			= model(UpperCamelCase   ,						decoder_input_ids=UpperCamelCase ).logits
             lowercase__      			= optax.softmax_cross_entropy(UpperCamelCase   ,						onehot(UpperCamelCase   ,						logits.shape[-1] ) ).mean()

             lowercase__      			= -(labels.shape[-1] * loss.item())

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








from typing import TYPE_CHECKING

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


UpperCamelCase								=	{
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """NezhaForNextSentencePrediction""",
         """NezhaForMaskedLM""",
         """NezhaForPreTraining""",
         """NezhaForMultipleChoice""",
         """NezhaForQuestionAnswering""",
         """NezhaForSequenceClassification""",
         """NezhaForTokenClassification""",
         """NezhaModel""",
         """NezhaPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nezha import (
              NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
              NezhaForMaskedLM,
              NezhaForMultipleChoice,
              NezhaForNextSentencePrediction,
              NezhaForPreTraining,
              NezhaForQuestionAnswering,
              NezhaForSequenceClassification,
              NezhaForTokenClassification,
              NezhaModel,
              NezhaPreTrainedModel,
          )


else:
     import sys

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




