



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 				__A					( A_      ):





			def __init__(self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    **__magic_name__				):
						lowerCamelCase__ :      Dict								=		feature_size
						lowerCamelCase__ :      Optional[Any]								=		sampling_rate
						lowerCamelCase__ :      List[Any]								=		padding_value

						lowerCamelCase__ :      Optional[int]								=		kwargs.pop("""padding_side"""							,    """right"""				)
						lowerCamelCase__ :      int								=		kwargs.pop("""return_attention_mask"""							,    __magic_name__				)

						super().__init__(**__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__ = True							,    __magic_name__ = None							,    __magic_name__ = False							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    ):
						# 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(__magic_name__							,    (list, tuple)				) and isinstance(processed_features[0]							,    (dict, BatchFeature)				):
									lowerCamelCase__ :      Optional[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__ :      Tuple								=		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(__magic_name__				) == 0:
									if return_attention_mask:
												lowerCamelCase__ :      str								=		[]
									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__ :      str								=		required_input[0]
						if isinstance(__magic_name__							,    (list, tuple)				):
									# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
									lowerCamelCase__ :      Any								=		0
									while len(required_input[index]				) == 0:
												index += 1
									if index < len(__magic_name__				):
												lowerCamelCase__ :      Optional[Any]								=		required_input[index][0]

						if return_tensors is None:
									if is_tf_tensor(__magic_name__				):
												lowerCamelCase__ :      Dict								=		"""tf"""
									elif is_torch_tensor(__magic_name__				):
												lowerCamelCase__ :      int								=		"""pt"""
									elif isinstance(__magic_name__							,    (int, float, list, tuple, np.ndarray)				):
												lowerCamelCase__ :      Any								=		"""np"""
									else:
												raise ValueError(
												    f"type of {first_element} unknown: {type(__magic_name__				)}. "
												    """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__ :      Any								=		to_numpy(__magic_name__				)
									else:
												lowerCamelCase__ :      Optional[int]								=		[to_numpy(__magic_name__				) for v in value]

        # Convert padding_strategy in PaddingStrategy
						lowerCamelCase__ :      Union[str, Any]								=		self._get_padding_strategies(padding=__magic_name__							,    max_length=__magic_name__				)

						lowerCamelCase__ :      List[str]								=		processed_features[self.model_input_names[0]]

						lowerCamelCase__ :      Union[str, Any]								=		len(__magic_name__				)
						if not all(len(__magic_name__				) == batch_size for v in processed_features.values()				):
									raise ValueError("""Some items in the output dictionary have a different batch size than others."""				)

						lowerCamelCase__ :      str								=		[]
						for i in range(__magic_name__				):
									lowerCamelCase__ :      Union[str, Any]								=		{k: v[i] for k, v in processed_features.items()}
									# truncation
									lowerCamelCase__ :      Any								=		self._truncate(
									    __magic_name__							,    max_length=__magic_name__							,    pad_to_multiple_of=__magic_name__							,    truncation=__magic_name__							,    )
									truncated_inputs.append(__magic_name__				)

						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__ :      List[str]								=		PaddingStrategy.MAX_LENGTH

						lowerCamelCase__ :      int								=		{}
						for i in range(__magic_name__				):
									# padding
									lowerCamelCase__ :      Optional[Any]								=		self._pad(
									    truncated_inputs[i]							,    max_length=__magic_name__							,    padding_strategy=__magic_name__							,    pad_to_multiple_of=__magic_name__							,    return_attention_mask=__magic_name__							,    )

									for key, value in outputs.items():
												if key not in batch_outputs:
															lowerCamelCase__ :      Tuple								=		[]
												if value.dtype is np.dtype(np.floataa				):
															lowerCamelCase__ :      Any								=		value.astype(np.floataa				)
												batch_outputs[key].append(__magic_name__				)

						return BatchFeature(__magic_name__							,    tensor_type=__magic_name__				)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__ = PaddingStrategy.DO_NOT_PAD							,    __magic_name__ = None							,    __magic_name__ = None							,    ):
						lowerCamelCase__ :      str								=		processed_features[self.model_input_names[0]]

						if padding_strategy == PaddingStrategy.LONGEST:
									lowerCamelCase__ :      Union[str, Any]								=		len(__magic_name__				)

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

						lowerCamelCase__ :      Optional[Any]								=		padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__magic_name__				) < max_length

						if return_attention_mask and "attention_mask" not in processed_features:
									lowerCamelCase__ :      Union[str, Any]								=		np.ones(len(__magic_name__				)							,    dtype=np.intaa				)

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

						return processed_features





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    ):
						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__ :      Dict								=		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__ :      Union[str, Any]								=		((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						lowerCamelCase__ :      Any								=		len(__magic_name__				) > max_length

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

						return processed_features





			def        _snake_case    (self							,    __magic_name__=False							,    __magic_name__=None				):

						# Get padding strategy
						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(__magic_name__							,    __magic_name__				):
												lowerCamelCase__ :      Union[str, Any]								=		PaddingStrategy(__magic_name__				)
									elif isinstance(__magic_name__							,    __magic_name__				):
												lowerCamelCase__ :      Optional[int]								=		padding
						else:
									lowerCamelCase__ :      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








from math import factorial, pi






def      _A				(UpperCamelCase		:      float      ,			UpperCamelCase		:      int = 30 )  ->float:



			'''simple docstring'''







			if not isinstance(UpperCamelCase      ,			(int, float) ):
						raise ValueError("""maclaurin_sin() requires either an int or float for theta""" )

			if not isinstance(UpperCamelCase      ,			UpperCamelCase ) or accuracy <= 0:
						raise ValueError("""maclaurin_sin() requires a positive int for accuracy""" )

			lowerCamelCase__ :      Dict								=		float(UpperCamelCase )
			lowerCamelCase__ :      int								=		theta // (2 * pi)
			theta -= 2 * div * pi
			return sum(
			    (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1 ) for r in range(UpperCamelCase ) )






def      _A				(UpperCamelCase		:      float      ,			UpperCamelCase		:      int = 30 )  ->float:



			'''simple docstring'''







			if not isinstance(UpperCamelCase      ,			(int, float) ):
						raise ValueError("""maclaurin_cos() requires either an int or float for theta""" )

			if not isinstance(UpperCamelCase      ,			UpperCamelCase ) or accuracy <= 0:
						raise ValueError("""maclaurin_cos() requires a positive int for accuracy""" )

			lowerCamelCase__ :      int								=		float(UpperCamelCase )
			lowerCamelCase__ :      Optional[int]								=		theta // (2 * pi)
			theta -= 2 * div * pi
			return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r ) for r in range(UpperCamelCase ) )


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				print(maclaurin_sin(10))
				print(maclaurin_sin(-10))
				print(maclaurin_sin(10, 15))
				print(maclaurin_sin(-10, 15))

				print(maclaurin_cos(5))
				print(maclaurin_cos(-5))
				print(maclaurin_cos(10, 15))
				print(maclaurin_cos(-10, 15))



