


import pytest

from datasets import inspect_metric, list_metrics, load_metric



@pytest.fixture
def        UpperCamelCase_(   lowerCamelCase_				)					->		List[str]:
    monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings'							,				set()				)



@pytest.fixture
def        UpperCamelCase_(   lowerCamelCase_				)					->		str:






    class _lowerCamelCase:
           def __init__(      self,   lowerCamelCase) ->     Tuple:




               """simple docstring"""

               _lowercase :							Any   	=	metric_id






    class _lowerCamelCase:
           lowercase_				:			Any									=    [MetricMock(_a     ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]
           def     UpperCamelCase  (      self) ->     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        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		Any:
    if "tmp_path" in args:
        _lowercase :							List[Any]   	=	tuple(arg if arg != 'tmp_path' else tmp_path for arg in args				)
    with pytest.warns(lowerCamelCase_							,				match='https://huggingface.co/docs/evaluate'				):
        func(*lowerCamelCase_				)






from typing import Dict

from .base import GenericTensor, Pipeline






class _lowerCamelCase(		_a     ):
       def     UpperCamelCase  (      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=None,   **lowerCamelCase) ->     int:




           """simple docstring"""

           if tokenize_kwargs is None:
               _lowercase :							Any   	=	{}

           if truncation is not None:
               if "truncation" in tokenize_kwargs:
                   raise ValueError(
                       'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)')
               _lowercase :							List[Any]   	=	truncation

           _lowercase :							Optional[Any]   	=	tokenize_kwargs

           _lowercase :							Tuple   	=	{}
           if return_tensors is not None:
               _lowercase :							Union[str, Any]   	=	return_tensors

           return preprocess_params, {}, postprocess_params
       def     UpperCamelCase  (      self,   lowerCamelCase,   **lowerCamelCase) ->     Dict[str, GenericTensor]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	self.framework
           _lowercase :							Dict   	=	self.tokenizer(lowerCamelCase,   return_tensors=lowerCamelCase,   **lowerCamelCase)
           return model_inputs
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							str   	=	self.model(**lowerCamelCase)
           return model_outputs
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase=False) ->     Dict:




           """simple docstring"""

           if return_tensors:
               return model_outputs[0]
           if self.framework == "pt":
               return model_outputs[0].tolist()
           elif self.framework == "tf":
               return model_outputs[0].numpy().tolist()




       def __call__(      self,   *lowerCamelCase,   **lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           return super().__call__(*lowerCamelCase,   **lowerCamelCase)


