
import pytest

from datasets import inspect_metric, list_metrics, load_metric


@pytest.fixture
def      UpperCamelCase   (       __lowerCamelCase	:					List[Any]       ):
       monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"							,   set()       )


@pytest.fixture
def      UpperCamelCase   (       __lowerCamelCase	:					Tuple       ):






       class     UpperCAmelCase						:



          def __init__(self						:   Tuple       ,		snake_case__						:   str					)						->      Union[str, Any]:


                 '''simple docstring'''


                 snake_case			:  Any     							= metric_id






       class     UpperCAmelCase						:
          A__   :						List[Any]			=	[MetricMock(A_					) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]



          def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      Optional[Any]:


                 '''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	:					Any							,   __lowerCamelCase	:					Optional[Any]							,   __lowerCamelCase	:					str							,   __lowerCamelCase	:					List[Any]							,   __lowerCamelCase	:					Optional[Any]       ):
       if "tmp_path" in args:
              snake_case			:  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 math import factorial


def      UpperCamelCase   (       __lowerCamelCase	:					int							,   __lowerCamelCase	:					int							,   __lowerCamelCase	:					float       ):
       if successes > trials:
              raise ValueError("successes must be lower or equal to trials"       )
       if trials < 0 or successes < 0:
              raise ValueError("the function is defined for non-negative integers"       )
       if not isinstance(__lowerCamelCase							,   __lowerCamelCase       ) or not isinstance(__lowerCamelCase							,   __lowerCamelCase       ):
              raise ValueError("the function is defined for non-negative integers"       )
       if not 0 < prob < 1:
              raise ValueError("prob has to be in range of 1 - 0"       )
       snake_case			:  Tuple     							= (prob**successes) * ((1 - prob) ** (trials - successes))
       # Calculate the binomial coefficient: n! / k!(n-k)!
       snake_case			:  str     							= float(factorial(__lowerCamelCase       )       )
       coefficient /= factorial(__lowerCamelCase       ) * factorial(trials - successes       )
       return probability * coefficient


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   print("""Probability of 2 successes out of 4 trails""")
   print("""with probability of 0.75 is:""", end=""" """)
   print(binomial_distribution(2, 4, 0.75))





