





'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCAmelCase					:		str		=							{
    'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase					:		Dict		=							[
       '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					:		Optional[int]		=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









'''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					):







  @slow
  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





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

        _snake_case		:	List[str]					     =     tokenizer('''Hello there'''     ,     return_tensors='''np'''		).input_ids
        _snake_case		:	Dict					     =     tokenizer('''Hi I am'''     ,     return_tensors='''np'''		).input_ids

        _snake_case		:	Any					     =     shift_tokens_right(lowercase__     ,     model.config.pad_token_id     ,     model.config.decoder_start_token_id		)

        _snake_case		:	Any					     =     model(lowercase__     ,     decoder_input_ids=lowercase__		).logits
        _snake_case		:	Tuple					     =     optax.softmax_cross_entropy(lowercase__     ,     onehot(lowercase__     ,     logits.shape[-1]		)		).mean()

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

        _snake_case		:	Union[str, Any]					     =     -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)


