




from typing import TYPE_CHECKING

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


__magic_name__  :  List[str]					=       {
    '''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
    '''configuration_data2vec_text''': [
        '''DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecTextConfig''',
        '''Data2VecTextOnnxConfig''',
    ],
    '''configuration_data2vec_vision''': [
        '''DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecVisionConfig''',
        '''Data2VecVisionOnnxConfig''',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  List[str]					=       [
          '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''Data2VecAudioForAudioFrameClassification''',
          '''Data2VecAudioForCTC''',
          '''Data2VecAudioForSequenceClassification''',
          '''Data2VecAudioForXVector''',
          '''Data2VecAudioModel''',
          '''Data2VecAudioPreTrainedModel''',
      ]
      __magic_name__  :  int					=       [
          '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''Data2VecTextForCausalLM''',
          '''Data2VecTextForMaskedLM''',
          '''Data2VecTextForMultipleChoice''',
          '''Data2VecTextForQuestionAnswering''',
          '''Data2VecTextForSequenceClassification''',
          '''Data2VecTextForTokenClassification''',
          '''Data2VecTextModel''',
          '''Data2VecTextPreTrainedModel''',
      ]
      __magic_name__  :  str					=       [
          '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''Data2VecVisionForImageClassification''',
          '''Data2VecVisionForMaskedImageModeling''',
          '''Data2VecVisionForSemanticSegmentation''',
          '''Data2VecVisionModel''',
          '''Data2VecVisionPreTrainedModel''',
      ]

if is_tf_available():
      __magic_name__  :  Optional[int]					=       [
          '''TFData2VecVisionForImageClassification''',
          '''TFData2VecVisionForSemanticSegmentation''',
          '''TFData2VecVisionModel''',
          '''TFData2VecVisionPreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
      from .configuration_dataavec_text import (
          DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecTextConfig,
          DataaVecTextOnnxConfig,
      )
      from .configuration_dataavec_vision import (
          DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecVisionConfig,
          DataaVecVisionOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dataavec_audio import (
                DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecAudioForAudioFrameClassification,
                DataaVecAudioForCTC,
                DataaVecAudioForSequenceClassification,
                DataaVecAudioForXVector,
                DataaVecAudioModel,
                DataaVecAudioPreTrainedModel,
            )
            from .modeling_dataavec_text import (
                DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecTextForCausalLM,
                DataaVecTextForMaskedLM,
                DataaVecTextForMultipleChoice,
                DataaVecTextForQuestionAnswering,
                DataaVecTextForSequenceClassification,
                DataaVecTextForTokenClassification,
                DataaVecTextModel,
                DataaVecTextPreTrainedModel,
            )
            from .modeling_dataavec_vision import (
                DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecVisionForImageClassification,
                DataaVecVisionForMaskedImageModeling,
                DataaVecVisionForSemanticSegmentation,
                DataaVecVisionModel,
                DataaVecVisionPreTrainedModel,
            )
      if is_tf_available():
            from .modeling_tf_dataavec_vision import (
                TFDataaVecVisionForImageClassification,
                TFDataaVecVisionForSemanticSegmentation,
                TFDataaVecVisionModel,
                TFDataaVecVisionPreTrainedModel,
            )

else:
      import sys

      __magic_name__  :  Union[str, Any]					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


'''simple docstring'''






import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline





@pytest.mark.integration
def  _SCREAMING_SNAKE_CASE		(      ):
     with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT				):
          with pytest.raises(__snake_case				):
               requests.request('GET'   ,		'https://huggingface.co'				)
          with pytest.raises(requests.exceptions.ConnectTimeout				):
               requests.request('GET'   ,		'https://huggingface.co'   ,		timeout=1.0				)





@pytest.mark.integration
def  _SCREAMING_SNAKE_CASE		(      ):
     with offline(OfflineSimulationMode.CONNECTION_FAILS				):
          with pytest.raises(requests.exceptions.ConnectionError				):
               requests.request('GET'   ,		'https://huggingface.co'				)





def  _SCREAMING_SNAKE_CASE		(      ):
     with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1				):
          with pytest.raises(__snake_case				):
               http_head('https://huggingface.co'				)



