





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_		     = {
    """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:
						SCREAMING_SNAKE_CASE_		     = [
						    """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """Data2VecAudioForAudioFrameClassification""",
						    """Data2VecAudioForCTC""",
						    """Data2VecAudioForSequenceClassification""",
						    """Data2VecAudioForXVector""",
						    """Data2VecAudioModel""",
						    """Data2VecAudioPreTrainedModel""",
						]
						SCREAMING_SNAKE_CASE_		     = [
						    """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """Data2VecTextForCausalLM""",
						    """Data2VecTextForMaskedLM""",
						    """Data2VecTextForMultipleChoice""",
						    """Data2VecTextForQuestionAnswering""",
						    """Data2VecTextForSequenceClassification""",
						    """Data2VecTextForTokenClassification""",
						    """Data2VecTextModel""",
						    """Data2VecTextPreTrainedModel""",
						]
						SCREAMING_SNAKE_CASE_		     = [
						    """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """Data2VecVisionForImageClassification""",
						    """Data2VecVisionForMaskedImageModeling""",
						    """Data2VecVisionForSemanticSegmentation""",
						    """Data2VecVisionModel""",
						    """Data2VecVisionPreTrainedModel""",
						]

if is_tf_available():
						SCREAMING_SNAKE_CASE_		     = [
						    """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

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









import pytest

from datasets import inspect_metric, list_metrics, load_metric



@pytest.fixture
def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     List[Any]:


      '''simple docstring'''


      monkeypatch.setattr("""datasets.utils.deprecation_utils._emitted_deprecation_warnings"""				,		set()			)



@pytest.fixture
def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Dict:


      '''simple docstring'''


      class 	UpperCamelCase__				:



           '''simple docstring'''

           def __init__(							self		:					int		,lowerCamelCase__		:					Dict )  ->	List[Any]:




                 '''simple docstring'''


                 SCREAMING_SNAKE_CASE	=		metric_id

      class 	UpperCamelCase__				:



           '''simple docstring'''

           __snake_case					:			List[str]				=    [MetricMock(lowerCAmelCase_     ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]






           def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	str:




                 '''simple docstring'''


                 return self._metrics
      monkeypatch.setattr("""datasets.inspect.huggingface_hub"""				,		HfhMock()			)



@pytest.mark.parametrize(
    """func, args"""				,		[(load_metric, ("""metrics/mse""",)), (list_metrics, ()), (inspect_metric, ("""metrics/mse""", """tmp_path"""))]			)
def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Union[str, Any]:


      '''simple docstring'''


      if "tmp_path" in args:
            SCREAMING_SNAKE_CASE	=		tuple(arg if arg != """tmp_path""" else tmp_path for arg in args			)
      with pytest.warns(_SCREAMING_SNAKE_CASE				,		match="""https://huggingface.co/docs/evaluate"""			):
            func(*_SCREAMING_SNAKE_CASE			)


