


from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_snake_case  :	Dict			      =       logging.get_logger(__name__)

class        a	(UpperCamelCase__   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Dict  						=						["input_values", "padding_mask"]

							def __init__(							self :						Tuple					,			lowerCamelCase :						Dict = 1					,			lowerCamelCase :						str = 24000					,			lowerCamelCase :						Optional[Any] = 0.0					,			lowerCamelCase :						Tuple = None					,			lowerCamelCase :						Tuple = None					,			**lowerCamelCase :						Dict					,			)			->					Optional[int]:
									super().__init__(feature_size=_a					,			sampling_rate=_a					,			padding_value=_a					,			**_a       )
									__snake_case	:    List[Any]											=     chunk_length_s
									__snake_case	:    Union[str, Any]											=     overlap

							@property
							def 					__snake_case      (							self :						Dict       )			->					Optional[int]:
									if self.chunk_length_s is None:
											return None
									else:
											return int(self.chunk_length_s * self.sampling_rate       )

							@property
							def 					__snake_case      (							self :						Tuple       )			->					Optional[int]:
									if self.chunk_length_s is None or self.overlap is None:
											return None
									else:
											return max(1					,			int((1.0 - self.overlap) * self.chunk_length       )       )






							def __call__(							self :						List[str]					,			lowerCamelCase :						Dict					,			lowerCamelCase :						Optional[Any] = None					,			lowerCamelCase :						Any = False					,			lowerCamelCase :						int = None					,			lowerCamelCase :						Optional[int] = None					,			lowerCamelCase :						Tuple = None					,			)			->					BatchFeature:
									if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
													raise ValueError(
													    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
													    F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
													    F' {self.sampling_rate} and not {sampling_rate}.'       )
									else:
											logger.warning(
											    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
											    "Failing to do so can result in silent errors that might be hard to debug."       )

									if padding and truncation:
											raise ValueError("Both padding and truncation were set. Make sure you only set one."       )
									elif padding is None:
											# by default let's pad the inputs
											__snake_case	:    Dict											=     True

									__snake_case	:    Dict											=     bool(
									    isinstance(_a					,			(list, tuple)       ) and (isinstance(raw_audio[0]					,			(np.ndarray, tuple, list)       ))       )

									if is_batched:
											__snake_case	:    Optional[int]											=     [np.asarray(_a					,			dtype=np.floataa       ).T for audio in raw_audio]
									elif not is_batched and not isinstance(_a					,			np.ndarray       ):
											__snake_case	:    List[str]											=     np.asarray(_a					,			dtype=np.floataa       )
									elif isinstance(_a					,			np.ndarray       ) and raw_audio.dtype is np.dtype(np.floataa       ):
											__snake_case	:    Union[str, Any]											=     raw_audio.astype(np.floataa       )

									# always return batch
									if not is_batched:
											__snake_case	:    int											=     [np.asarray(_a       ).T]

									# verify inputs are valid
									for idx, example in enumerate(_a       ):
											if example.ndim > 2:
													raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'       )
											if self.feature_size == 1 and example.ndim != 1:
													raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'       )
											if self.feature_size == 2 and example.shape[-1] != 2:
													raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'       )

									__snake_case	:    Any											=     None
									__snake_case	:    Optional[int]											=     BatchFeature({"input_values": raw_audio}       )
									if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
											if truncation:
													__snake_case	:    Optional[int]											=     min(array.shape[0] for array in raw_audio       )
													__snake_case	:    str											=     int(np.floor(max_length / self.chunk_stride       )       )
													__snake_case	:    Union[str, Any]											=     (nb_step - 1) * self.chunk_stride + self.chunk_length
											elif padding:
													__snake_case	:    Tuple											=     max(array.shape[0] for array in raw_audio       )
													__snake_case	:    Dict											=     int(np.ceil(max_length / self.chunk_stride       )       )
													__snake_case	:    Union[str, Any]											=     (nb_step - 1) * self.chunk_stride + self.chunk_length
													__snake_case	:    Tuple											=     """max_length"""
											else:
													__snake_case	:    Optional[int]											=     input_values

        # normal padding on batch
									if padded_inputs is None:
											__snake_case	:    Tuple											=     self.pad(
											    _a					,			max_length=_a					,			truncation=_a					,			padding=_a					,			return_attention_mask=_a					,			)
											if padding:
													__snake_case	:    Dict											=     padded_inputs.pop("attention_mask"       )

									__snake_case	:    Any											=     []
									for example in padded_inputs.pop("input_values"       ):
											if self.feature_size == 1:
													__snake_case	:    str											=     example[..., None]
											input_values.append(example.T       )

									__snake_case	:    List[str]											=     input_values
									if return_tensors is not None:
											__snake_case	:    List[str]											=     padded_inputs.convert_to_tensors(_a       )

									return padded_inputs





import math







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):

							if initial_intensity < 0:
														raise ValueError("The value of intensity cannot be negative"			)
														# handling of negative values of initial intensity
							if angle < 0 or angle > 3_6_0:
														raise ValueError("In Malus Law, the angle is in the range 0-360 degrees"			)
														# handling of values out of allowed range
							return initial_intensity * (math.cos(math.radians(__lowerCamelCase			)			) ** 2)


if __name__ == "__main__":
			import doctest

			doctest.testmod(name="malus_law")

