from typing import TYPE_CHECKING

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


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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Tuple							       =		[
       '''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

   __lowerCamelCase      :			Optional[Any]							       =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

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		):




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""	,     return_dict=_lowercase				).to(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained("""google/mt5-small"""				)

     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hello there"""	,     return_tensors="""pt"""				).input_ids
     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hi I am"""	,     return_tensors="""pt"""				).input_ids

     SCREAMING_SNAKE_CASE__      							=				model(input_ids.to(_lowercase				)	,     labels=labels.to(_lowercase				)				).loss
     SCREAMING_SNAKE_CASE__      							=				-(labels.shape[-1] * loss.item())

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