






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


__magic_name__:					Optional[Any]							=     logging.get_logger(__name__)





class 	snake_case__ (							_lowerCAmelCase				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				List[str]:
							__magic_name__      :       List[Any]       						= feature_size
							__magic_name__      :       Tuple       						= sampling_rate
							__magic_name__      :       int       						= padding_value

							__magic_name__      :       str       						= kwargs.pop("""padding_side"""      ,       """right"""			)
							__magic_name__      :       Dict       						= kwargs.pop("""return_attention_mask"""      ,       lowerCAmelCase__			)

							super().__init__(**lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = True      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None      ,       )							->				BatchFeature:
							# If we have a list of dicts, let's convert it in a dict of lists
							# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
							if isinstance(lowerCAmelCase__      ,       (list, tuple)			) and isinstance(processed_features[0]      ,       (dict, BatchFeature)			):
													__magic_name__      :       Union[str, 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()			)}'			)

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

							if len(lowerCAmelCase__			) == 0:
													if return_attention_mask:
																			__magic_name__      :       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

							__magic_name__      :       Dict       						= 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.
													__magic_name__      :       Any       						= 0
													while len(required_input[index]			) == 0:
																			index += 1
													if index < len(lowerCAmelCase__			):
																			__magic_name__      :       Tuple       						= required_input[index][0]

							if return_tensors is None:
													if is_tf_tensor(lowerCAmelCase__			):
																			__magic_name__      :       Optional[int]       						= """tf"""
													elif is_torch_tensor(lowerCAmelCase__			):
																			__magic_name__      :       Dict       						= """pt"""
													elif isinstance(lowerCAmelCase__      ,       (int, float, list, tuple, np.ndarray)			):
																			__magic_name__      :       List[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)			):
																			__magic_name__      :       Tuple       						= to_numpy(lowerCAmelCase__			)
													else:
																			__magic_name__      :       int       						= [to_numpy(lowerCAmelCase__			) for v in value]

        # Convert padding_strategy in PaddingStrategy
							__magic_name__      :       List[str]       						= self._get_padding_strategies(padding=lowerCAmelCase__      ,       max_length=lowerCAmelCase__			)

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

							__magic_name__      :       int       						= 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."""			)

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

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

							return BatchFeature(lowerCAmelCase__      ,       tensor_type=lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = PaddingStrategy.DO_NOT_PAD      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None      ,       )							->				dict:
							__magic_name__      :       Any       						= processed_features[self.model_input_names[0]]

							if padding_strategy == PaddingStrategy.LONGEST:
													__magic_name__      :       Optional[Any]       						= len(lowerCAmelCase__			)

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

							__magic_name__      :       Union[str, Any]       						= padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowerCAmelCase__			) < max_length

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

							if needs_to_be_padded:
													__magic_name__      :       Optional[int]       						= max_length - len(lowerCAmelCase__			)
													if self.padding_side == "right":
																			if return_attention_mask:
																									__magic_name__      :       Any       						= np.pad(
																									    processed_features["""attention_mask"""]      ,       (0, difference)			)
																			__magic_name__      :       Tuple       						= ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																			__magic_name__      :       Dict       						= np.pad(
																			    lowerCAmelCase__      ,       lowerCAmelCase__      ,       """constant"""      ,       constant_values=self.padding_value			)
													elif self.padding_side == "left":
																			if return_attention_mask:
																									__magic_name__      :       Any       						= np.pad(
																									    processed_features["""attention_mask"""]      ,       (difference, 0)			)
																			__magic_name__      :       Tuple       						= ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																			__magic_name__      :       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       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None      ,       )							->				Union[str, Any]:
							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."""			)

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

							__magic_name__      :       Optional[Any]       						= len(lowerCAmelCase__			) > max_length

							if needs_to_be_truncated:
													__magic_name__      :       Optional[int]       						= processed_features[self.model_input_names[0]][:max_length]
													if "attention_mask" in processed_features:
																			__magic_name__      :       List[Any]       						= processed_features["""attention_mask"""][:max_length]

							return processed_features







	def       __magic_name__    (    self      ,       lowerCAmelCase__=False      ,       lowerCAmelCase__=None			)							->				List[str]:

							# Get padding strategy
							if padding is not False:
													if padding is True:
																			__magic_name__      :       Any       						= PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
													elif not isinstance(lowerCAmelCase__      ,       lowerCAmelCase__			):
																			__magic_name__      :       Dict       						= PaddingStrategy(lowerCAmelCase__			)
													elif isinstance(lowerCAmelCase__      ,       lowerCAmelCase__			):
																			__magic_name__      :       Optional[int]       						= padding
							else:
													__magic_name__      :       List[Any]       						= 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








def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						if not isinstance(_A,   _A			) or number < 0:
												raise ValueError("""Input must be a non-negative integer"""			)

						__magic_name__      :       str       						= 0
						while number:
												# This way we arrive at next set bit (next 1) instead of looping
												# through each bit and checking for 1s hence the
												# loop won't run 32 times it will only run the number of `1` times
												number &= number - 1
												count += 1
						return count


if __name__ == "__main__":
		import doctest

		doctest.testmod()
