




from math import factorial







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      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(snake_case_,snake_case_     ) or not isinstance(snake_case_,snake_case_     ):
            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"""     )
      _A							:     Union[str, Any]			=						(prob**successes) * ((1 - prob) ** (trials - successes))
      # Calculate the binomial coefficient: n! / k!(n-k)!
      _A							:     Any			=						float(factorial(snake_case_     )     )
      coefficient /= factorial(snake_case_     ) * 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, 0.7_5))






'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}






class __A			(  A   ,   A						):



 '''simple docstring'''







 __lowerCamelCase    :       Tuple          =       'resnet'
 __lowerCamelCase    :       Any          =       ['basic', 'bottleneck']



 def __init__(self ,   A=3 ,   A=64 ,   A=[256, 512, 1_024, 2_048] ,   A=[3, 4, 6, 3] ,   A="bottleneck" ,   A="relu" ,   A=False ,   A=None ,   A=None ,   **A ,   )    ->     Dict:


      """simple docstring"""



      super().__init__(**A       )
      if layer_type not in self.layer_types:
           raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types       )}'''       )
      _a      =  num_channels
      _a      =  embedding_size
      _a      =  hidden_sizes
      _a      =  depths
      _a      =  layer_type
      _a      =  hidden_act
      _a      =  downsample_in_first_stage
      _a      =  ['''stem'''] + [f'''stage{idx}''' for idx in range(1 ,   len(A       ) + 1       )]
      _a  ,    _a      =  get_aligned_output_features_output_indices(
          out_features=A ,   out_indices=A ,   stage_names=self.stage_names       )






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       version.parse('1.11'						)



 @property
 def 					a__  (self       )    ->     Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      return OrderedDict(
          [
              ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
          ]       )



 @property
 def 					a__  (self       )    ->     float:


      """simple docstring"""



      return 1E-3



