
'''simple docstring'''






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


A			      =      logging.get_logger(__name__)




class     __snake_case		(   __UpperCAmelCase):
					def __init__(     self,    A,    A,    A,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= feature_size
									lowerCamelCase					:			str									= sampling_rate
									lowerCamelCase					:			Tuple									= padding_value

									lowerCamelCase					:			Dict									= kwargs.pop('padding_side',    'right'      )
									lowerCamelCase					:			Union[str, Any]									= kwargs.pop('return_attention_mask',    lowerCAmelCase_      )

									super().__init__(**lowerCAmelCase_      )
					def     UpperCAmelCase_							(     self,    A,    A = True,    A = None,    A = False,    A = None,    A = None,    A = None,    ):

									"""simple docstring"""


									if isinstance(lowerCAmelCase_,    (list, tuple)      ) and isinstance(processed_features[0],    (dict, BatchFeature)      ):
													lowerCamelCase					:			List[Any]									= {
													    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()      )}'''      )

									lowerCamelCase					:			Any									= processed_features[self.model_input_names[0]]
									lowerCamelCase					:			Optional[int]									= (
									    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
									)

									if len(lowerCAmelCase_      ) == 0:
													if return_attention_mask:
																	lowerCamelCase					:			int									= []
													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

									lowerCamelCase					:			Optional[int]									= 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.
													lowerCamelCase					:			Tuple									= 0
													while len(required_input[index]      ) == 0:
																	index += 1
													if index < len(lowerCAmelCase_      ):
																	lowerCamelCase					:			List[Any]									= required_input[index][0]

									if return_tensors is None:
													if is_tf_tensor(lowerCAmelCase_      ):
																	lowerCamelCase					:			Optional[int]									= 'tf'
													elif is_torch_tensor(lowerCAmelCase_      ):
																	lowerCamelCase					:			Dict									= 'pt'
													elif isinstance(lowerCAmelCase_,    (int, float, list, tuple, np.ndarray)      ):
																	lowerCamelCase					:			str									= '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)      ):
																	lowerCamelCase					:			Union[str, Any]									= to_numpy(lowerCAmelCase_      )
													else:
																	lowerCamelCase					:			Union[str, Any]									= [to_numpy(lowerCAmelCase_      ) for v in value]

													# Convert padding_strategy in PaddingStrategy
									lowerCamelCase					:			int									= self._get_padding_strategies(padding=lowerCAmelCase_,    max_length=lowerCAmelCase_      )

									lowerCamelCase					:			Optional[Any]									= processed_features[self.model_input_names[0]]

									lowerCamelCase					:			Optional[Any]									= 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.'      )

									lowerCamelCase					:			List[str]									= []
									for i in range(lowerCAmelCase_      ):
													lowerCamelCase					:			Optional[Any]									= {k: v[i] for k, v in processed_features.items()}
													# truncation
													lowerCamelCase					:			Tuple									= 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
													lowerCamelCase					:			Optional[int]									= max(len(input_slice[self.model_input_names[0]]      ) for input_slice in truncated_inputs      )
													lowerCamelCase					:			Optional[Any]									= PaddingStrategy.MAX_LENGTH

									lowerCamelCase					:			List[str]									= {}
									for i in range(lowerCAmelCase_      ):
													# padding
													lowerCamelCase					:			str									= 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:
																					lowerCamelCase					:			List[str]									= []
																	if value.dtype is np.dtype(np.floataa      ):
																					lowerCamelCase					:			Union[str, Any]									= value.astype(np.floataa      )
																	batch_outputs[key].append(lowerCAmelCase_      )

									return BatchFeature(lowerCAmelCase_,    tensor_type=lowerCAmelCase_      )
					def     UpperCAmelCase_							(     self,    A,    A = None,    A = PaddingStrategy.DO_NOT_PAD,    A = None,    A = None,    ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= processed_features[self.model_input_names[0]]

									if padding_strategy == PaddingStrategy.LONGEST:
													lowerCamelCase					:			List[str]									= len(lowerCAmelCase_      )

									if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
													lowerCamelCase					:			Optional[int]									= ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

									lowerCamelCase					:			str									= padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowerCAmelCase_      ) < max_length

									if return_attention_mask and "attention_mask" not in processed_features:
													lowerCamelCase					:			Any									= np.ones(len(lowerCAmelCase_      ),    dtype=np.intaa      )

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

									return processed_features
					def     UpperCAmelCase_							(     self,    A,    A = None,    A = None,    A = None,    ):

									"""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.'      )

									lowerCamelCase					:			Tuple									= 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):
													lowerCamelCase					:			int									= ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

									lowerCamelCase					:			str									= len(lowerCAmelCase_      ) > max_length

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

									return processed_features




					def     UpperCAmelCase_							(     self,    A=False,    A=None      ):

									"""simple docstring"""


									if padding is not False:
													if padding is True:
																	lowerCamelCase					:			Tuple									= PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
													elif not isinstance(lowerCAmelCase_,    lowerCAmelCase_      ):
																	lowerCamelCase					:			str									= PaddingStrategy(lowerCAmelCase_      )
													elif isinstance(lowerCAmelCase_,    lowerCAmelCase_      ):
																	lowerCamelCase					:			List[Any]									= padding
									else:
													lowerCamelCase					:			str									= 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







'''simple docstring'''






def    UpperCAmelCase							(  UpperCAmelCase__							:							int = 10_00):
				lowerCamelCase					:			Optional[Any]									= 2**power
				lowerCamelCase					:			str									= str(UpperCAmelCase__)
				lowerCamelCase					:			Union[str, Any]									= list(UpperCAmelCase__)
				lowerCamelCase					:			Optional[Any]									= 0

				for i in list_num:
								sum_of_num += int(UpperCAmelCase__)

				return sum_of_num


if __name__ == "__main__":
					A			      =      int(input('Enter the power of 2: ').strip())
					print('2 ^ ', power, ' = ', 2**power)
					A			      =      solution(power)
					print('Sum of the digits is: ', result)
