


import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

lowerCamelCase		      = [(1,)]




def 			a_   (					):
       '''simple docstring'''





       _lowerCamelCase :	List[str]						=bool(os.getenv('RUN_SLOW'	,    0  )  )
       if run_slow:
              assert True
       else:
              assert False, "should have been skipped"




def 			a_   (					):
       '''simple docstring'''





       _lowerCamelCase :	str						=bool(os.getenv('RUN_SLOW'	,    0  )  )
       if run_slow and torch_device == "cuda":
              assert True
       else:
              assert False, "should have been skipped"




@require_torch
class A						(     unittest.TestCase  ):






      @slow
      @require_torch_gpu
      def  lowerCamelCase (      self		:  List[str]				)      ->		Tuple:
             """simple docstring"""




             check_slow_torch_cuda()






      @require_torch_gpu
      @slow
      def  lowerCamelCase (      self		:  Dict				)      ->		Dict:
             """simple docstring"""




             check_slow_torch_cuda()






      @parameterized.expand(lowercase_				)
      @slow
      def  lowerCamelCase (      self		:  Tuple    ,     lowercase_		:  Union[str, Any]=None				)      ->		Union[str, Any]:
             """simple docstring"""




             check_slow()




@slow
@require_torch_gpu
def 			a_   (					):
       '''simple docstring'''





       check_slow_torch_cuda()




@require_torch_gpu
@slow
def 			a_   (					):
       '''simple docstring'''





       check_slow_torch_cuda()




@slow
@pytest.mark.parametrize('param'	,    [1]  )
def 			a_   (					SCREAMING_SNAKE_CASE__      :   int  ):
       '''simple docstring'''





       check_slow()




@pytest.mark.parametrize('param'	,    [1]  )
@slow
def 			a_   (					SCREAMING_SNAKE_CASE__      :   Union[str, Any]  ):
       '''simple docstring'''





       check_slow()






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase		      = {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase		      = ['DebertaV2TokenizerFast']

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase		      = [
     'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFDebertaV2ForMaskedLM',
     'TFDebertaV2ForQuestionAnswering',
     'TFDebertaV2ForSequenceClassification',
     'TFDebertaV2ForTokenClassification',
     'TFDebertaV2Model',
     'TFDebertaV2PreTrainedModel',
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase		      = [
     'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DebertaV2ForMaskedLM',
     'DebertaV2ForMultipleChoice',
     'DebertaV2ForQuestionAnswering',
     'DebertaV2ForSequenceClassification',
     'DebertaV2ForTokenClassification',
     'DebertaV2Model',
     'DebertaV2PreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_deberta_va import (
     DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DebertaVaConfig,
     DebertaVaOnnxConfig,
 )
 from .tokenization_deberta_va import DebertaVaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta_va import (
      TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaVaForMaskedLM,
      TFDebertaVaForQuestionAnswering,
      TFDebertaVaForSequenceClassification,
      TFDebertaVaForTokenClassification,
      TFDebertaVaModel,
      TFDebertaVaPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta_va import (
      DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaVaForMaskedLM,
      DebertaVaForMultipleChoice,
      DebertaVaForQuestionAnswering,
      DebertaVaForSequenceClassification,
      DebertaVaForTokenClassification,
      DebertaVaModel,
      DebertaVaPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase		      = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


