


from math import factorial

def        lowerCAmelCase__ (			a__:		int			,  a__:		int			,  a__:		float       )  ->		float:


							'''simple docstring'''





							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(A__			,  A__       ) or not isinstance(A__			,  A__       ):
														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'       )
							_UpperCAmelCase 		=			(prob**successes) * ((1 - prob) ** (trials - successes))
							# Calculate the binomial coefficient: n! / k!(n-k)!
							_UpperCAmelCase 		=			float(factorial(A__       )       )
							coefficient /= factorial(A__       ) * 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))



from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


lowercase_									=   logging.get_logger(__name__)





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   **lowerCAmelCase					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =feature_size
        _lowercase	       =sampling_rate
        _lowercase	       =padding_value

        _lowercase	       =kwargs.pop('padding_side'							,   'right'					)
        _lowercase	       =kwargs.pop('return_attention_mask'							,   lowerCAmelCase					)

        super().__init__(**lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = True							,   lowerCAmelCase = None							,   lowerCAmelCase = False							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   )  -> BatchFeature:
        '''simple docstring'''






        if isinstance(lowerCAmelCase							,   (list, tuple)					) and isinstance(processed_features[0]							,   (dict, BatchFeature)					):
              _lowercase	       ={
                  key: [example[key] for example in processed_features] for key in processed_features[0].keys()
              }

        # The model's main input name, usually `input_values`, has be passed for padding
        if self.model_input_names[0] not in processed_features:
              raise ValueError(
                  'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
                  F''' to this method that includes {self.model_input_names[0]}, but you provided'''
                  F''' {list(processed_features.keys()					)}'''					)

        _lowercase	       =processed_features[self.model_input_names[0]]
        _lowercase	       =(
            return_attention_mask if return_attention_mask is not None else self.return_attention_mask
        )

        if len(lowerCAmelCase					) == 0:
              if return_attention_mask:
                    _lowercase	       =[]
              return processed_features

        # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
        # and rebuild them afterwards if no return_tensors is specified
        # Note that we lose the specific device the tensor may be on for PyTorch

        _lowercase	       =required_input[0]
        if isinstance(lowerCAmelCase							,   (list, tuple)					):
              # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
              _lowercase	       =0
              while len(required_input[index]					) == 0:
                    index += 1
              if index < len(lowerCAmelCase					):
                    _lowercase	       =required_input[index][0]

        if return_tensors is None:
              if is_tf_tensor(lowerCAmelCase					):
                    _lowercase	       ='tf'
              elif is_torch_tensor(lowerCAmelCase					):
                    _lowercase	       ='pt'
              elif isinstance(lowerCAmelCase							,   (int, float, list, tuple, np.ndarray)					):
                    _lowercase	       ='np'
              else:
                    raise ValueError(
                        F'''type of {first_element} unknown: {type(lowerCAmelCase					)}. '''
                        'Should be one of a python, numpy, pytorch or tensorflow object.'					)

        for key, value in processed_features.items():
              if isinstance(value[0]							,   (int, float)					):
                    _lowercase	       =to_numpy(lowerCAmelCase					)
              else:
                    _lowercase	       =[to_numpy(lowerCAmelCase					) for v in value]

        # Convert padding_strategy in PaddingStrategy
        _lowercase	       =self._get_padding_strategies(padding=lowerCAmelCase							,   max_length=lowerCAmelCase					)

        _lowercase	       =processed_features[self.model_input_names[0]]

        _lowercase	       =len(lowerCAmelCase					)
        if not all(len(lowerCAmelCase					) == batch_size for v in processed_features.values()					):
              raise ValueError('Some items in the output dictionary have a different batch size than others.'					)

        _lowercase	       =[]
        for i in range(lowerCAmelCase					):
              _lowercase	       ={k: v[i] for k, v in processed_features.items()}
              # truncation
              _lowercase	       =self._truncate(
                  lowerCAmelCase							,   max_length=lowerCAmelCase							,   pad_to_multiple_of=lowerCAmelCase							,   truncation=lowerCAmelCase							,   )
              truncated_inputs.append(lowerCAmelCase					)

        if padding_strategy == PaddingStrategy.LONGEST:
              # make sure that `max_length` cannot be longer than the longest truncated length
              _lowercase	       =max(len(input_slice[self.model_input_names[0]]					) for input_slice in truncated_inputs					)
              _lowercase	       =PaddingStrategy.MAX_LENGTH

        _lowercase	       ={}
        for i in range(lowerCAmelCase					):
              # padding
              _lowercase	       =self._pad(
                  truncated_inputs[i]							,   max_length=lowerCAmelCase							,   padding_strategy=lowerCAmelCase							,   pad_to_multiple_of=lowerCAmelCase							,   return_attention_mask=lowerCAmelCase							,   )

              for key, value in outputs.items():
                    if key not in batch_outputs:
                          _lowercase	       =[]
                    if value.dtype is np.dtype(np.floataa					):
                          _lowercase	       =value.astype(np.floataa					)
                    batch_outputs[key].append(lowerCAmelCase					)

        return BatchFeature(lowerCAmelCase							,   tensor_type=lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = None							,   lowerCAmelCase = PaddingStrategy.DO_NOT_PAD							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   )  -> dict:
        '''simple docstring'''






        _lowercase	       =processed_features[self.model_input_names[0]]

        if padding_strategy == PaddingStrategy.LONGEST:
              _lowercase	       =len(lowerCAmelCase					)

        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
              _lowercase	       =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        _lowercase	       =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowerCAmelCase					) < max_length

        if return_attention_mask and "attention_mask" not in processed_features:
              _lowercase	       =np.ones(len(lowerCAmelCase					)							,   dtype=np.intaa					)

        if needs_to_be_padded:
              _lowercase	       =max_length - len(lowerCAmelCase					)
              if self.padding_side == "right":
                    if return_attention_mask:
                          _lowercase	       =np.pad(
                              processed_features['attention_mask']							,   (0, difference)					)
                    _lowercase	       =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                    _lowercase	       =np.pad(
                        lowerCAmelCase							,   lowerCAmelCase							,   'constant'							,   constant_values=self.padding_value					)
              elif self.padding_side == "left":
                    if return_attention_mask:
                          _lowercase	       =np.pad(
                              processed_features['attention_mask']							,   (difference, 0)					)
                    _lowercase	       =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                    _lowercase	       =np.pad(
                        lowerCAmelCase							,   lowerCAmelCase							,   'constant'							,   constant_values=self.padding_value					)
              else:
                    raise ValueError('Invalid padding strategy:' + str(self.padding_side					)					)

        return processed_features




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   )  -> Any:
        '''simple docstring'''






        if not truncation:
              return processed_features
        elif truncation and max_length is None:
              raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.'					)

        _lowercase	       =processed_features[self.model_input_names[0]]

        # find `max_length` that fits `pad_to_multiple_of`
        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
              _lowercase	       =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        _lowercase	       =len(lowerCAmelCase					) > max_length

        if needs_to_be_truncated:
              _lowercase	       =processed_features[self.model_input_names[0]][:max_length]
              if "attention_mask" in processed_features:
                    _lowercase	       =processed_features['attention_mask'][:max_length]

        return processed_features




  def   A__     (				self							,   lowerCAmelCase=False							,   lowerCAmelCase=None					)  -> Optional[int]:
        '''simple docstring'''






        if padding is not False:
              if padding is True:
                    _lowercase	       =PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
              elif not isinstance(lowerCAmelCase							,   lowerCAmelCase					):
                    _lowercase	       =PaddingStrategy(lowerCAmelCase					)
              elif isinstance(lowerCAmelCase							,   lowerCAmelCase					):
                    _lowercase	       =padding
        else:
              _lowercase	       =PaddingStrategy.DO_NOT_PAD

        # Set max length if needed
        if max_length is None:
              if padding_strategy == PaddingStrategy.MAX_LENGTH:
                    raise ValueError(
                        F'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''					)

        # Test if we have a padding value
        if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
              raise ValueError(
                  'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
                  ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'					)

        return padding_strategy


