




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


a      =							logging.get_logger(__name__)






class       SCREAMING_SNAKE_CASE__					(					_snake_case					):
						_a    						=						['input_values', 'padding_mask']


						def __init__(							self				:			str						,						lowerCAmelCase				:			int = 1						,						lowerCAmelCase				:			int = 2_4000						,						lowerCAmelCase				:			float = 0.0						,						lowerCAmelCase				:			float = None						,						lowerCAmelCase				:			float = None						,						**lowerCAmelCase				:			Union[str, Any]						,						):
										super().__init__(feature_size=lowerCAmelCase						,						sampling_rate=lowerCAmelCase						,						padding_value=lowerCAmelCase						,						**lowerCAmelCase					)
										lowerCAmelCase  =				chunk_length_s
										lowerCAmelCase  =				overlap


						@property
						def   __lowercase (							self				:			Tuple					):

										if self.chunk_length_s is None:
														return None
										else:
														return int(self.chunk_length_s * self.sampling_rate					)


						@property
						def   __lowercase (							self				:			Any					):

										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				:			Tuple						,						lowerCAmelCase				:			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]						,						lowerCAmelCase				:			Optional[Union[bool, str, PaddingStrategy]] = None						,						lowerCAmelCase				:			Optional[bool] = False						,						lowerCAmelCase				:			Optional[int] = None						,						lowerCAmelCase				:			Optional[Union[str, TensorType]] = None						,						lowerCAmelCase				:			Optional[int] = None						,						):

										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
														lowerCAmelCase  =				True

										lowerCAmelCase  =				bool(
										    isinstance(lowerCAmelCase						,						(list, tuple)					) and (isinstance(raw_audio[0]						,						(np.ndarray, tuple, list)					))					)

										if is_batched:
														lowerCAmelCase  =				[np.asarray(lowerCAmelCase						,						dtype=np.floataa					).T for audio in raw_audio]
										elif not is_batched and not isinstance(lowerCAmelCase						,						np.ndarray					):
														lowerCAmelCase  =				np.asarray(lowerCAmelCase						,						dtype=np.floataa					)
										elif isinstance(lowerCAmelCase						,						np.ndarray					) and raw_audio.dtype is np.dtype(np.floataa					):
														lowerCAmelCase  =				raw_audio.astype(np.floataa					)

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

										# verify inputs are valid
										for idx, example in enumerate(lowerCAmelCase					):
														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'''					)

										lowerCAmelCase  =				None
										lowerCAmelCase  =				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:
																		lowerCAmelCase  =				min(array.shape[0] for array in raw_audio					)
																		lowerCAmelCase  =				int(np.floor(max_length / self.chunk_stride					)					)
																		lowerCAmelCase  =				(nb_step - 1) * self.chunk_stride + self.chunk_length
														elif padding:
																		lowerCAmelCase  =				max(array.shape[0] for array in raw_audio					)
																		lowerCAmelCase  =				int(np.ceil(max_length / self.chunk_stride					)					)
																		lowerCAmelCase  =				(nb_step - 1) * self.chunk_stride + self.chunk_length
																		lowerCAmelCase  =				"""max_length"""
														else:
																		lowerCAmelCase  =				input_values

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

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

										lowerCAmelCase  =				input_values
										if return_tensors is not None:
														lowerCAmelCase  =				padded_inputs.convert_to_tensors(lowerCAmelCase					)

										return padded_inputs











"""simple docstring"""







from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload






class       SCREAMING_SNAKE_CASE__					:


						def __init__(							self				:			Dict						,						lowerCAmelCase				:			Collection[float] | None = None					):
										if components is None:
														lowerCAmelCase  =				[]
										lowerCAmelCase  =				list(lowerCAmelCase					)


						def __len__(							self				:			List[Any]					):
										return len(self.__components					)


						def __str__(							self				:			str					):
										return "(" + ",".join(map(lowerCAmelCase						,						self.__components					)					) + ")"


						def __add__(							self				:			Any						,						lowerCAmelCase				:			Vector					):
										lowerCAmelCase  =				len(self					)
										if size == len(lowerCAmelCase					):
														lowerCAmelCase  =				[self.__components[i] + other.component(lowerCAmelCase					) for i in range(lowerCAmelCase					)]
														return Vector(lowerCAmelCase					)
										else:
														raise Exception("""must have the same size"""					)


						def __sub__(							self				:			List[Any]						,						lowerCAmelCase				:			Vector					):
										lowerCAmelCase  =				len(self					)
										if size == len(lowerCAmelCase					):
														lowerCAmelCase  =				[self.__components[i] - other.component(lowerCAmelCase					) for i in range(lowerCAmelCase					)]
														return Vector(lowerCAmelCase					)
										else:  # error case
														raise Exception("""must have the same size"""					)


						@overload
						def __mul__(							self				:			Optional[int]						,						lowerCAmelCase				:			float					):
										...


						@overload
						def __mul__(							self				:			List[Any]						,						lowerCAmelCase				:			Vector					):
										...


						def __mul__(							self				:			Dict						,						lowerCAmelCase				:			float | Vector					):
										if isinstance(lowerCAmelCase						,						(float, int)					):
														lowerCAmelCase  =				[c * other for c in self.__components]
														return Vector(lowerCAmelCase					)
										elif isinstance(lowerCAmelCase						,						lowerCAmelCase					) and len(self					) == len(lowerCAmelCase					):
														lowerCAmelCase  =				len(self					)
														lowerCAmelCase  =				[self.__components[i] * other.component(lowerCAmelCase					) for i in range(lowerCAmelCase					)]
														return sum(lowerCAmelCase					)
										else:  # error case
														raise Exception("""invalid operand!"""					)


						def   __lowercase (							self				:			Tuple					):
										return Vector(self.__components					)


						def   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			int					):
										if isinstance(lowerCAmelCase						,						lowerCAmelCase					) and -len(self.__components					) <= i < len(self.__components					):
														return self.__components[i]
										else:
														raise Exception("""index out of range"""					)


						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			float					):
										assert -len(self.__components					) <= pos < len(self.__components					)
										lowerCAmelCase  =				value


						def   __lowercase (							self				:			Optional[int]					):
										if len(self.__components					) == 0:
														raise Exception("""Vector is empty"""					)
										lowerCAmelCase  =				[c**2 for c in self.__components]
										return math.sqrt(sum(lowerCAmelCase					)					)




						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			Vector						,						lowerCAmelCase				:			bool = False					):
										lowerCAmelCase  =				self * other
										lowerCAmelCase  =				self.euclidean_length() * other.euclidean_length()
										if deg:
														return math.degrees(math.acos(num / den					)					)
										else:
														return math.acos(num / den					)





def 							lowercase					(snake_case__   : int )     ->  Vector:


				'''simple docstring'''






				assert isinstance(snake_case__      ,       snake_case__ )
				return Vector([0] * dimension )





def 							lowercase					(snake_case__   : int      ,       snake_case__   : int )     ->  Vector:


				'''simple docstring'''






				assert isinstance(snake_case__      ,       snake_case__ ) and (isinstance(snake_case__      ,       snake_case__ ))
				lowerCAmelCase  =				[0] * dimension
				lowerCAmelCase  =				1
				return Vector(snake_case__ )





def 							lowercase					(snake_case__   : float      ,       snake_case__   : Vector      ,       snake_case__   : Vector )     ->  Vector:


				'''simple docstring'''






				assert (
				    isinstance(snake_case__      ,       snake_case__ )
				    and isinstance(snake_case__      ,       snake_case__ )
				    and (isinstance(snake_case__      ,       (int, float) ))
				)
				return x * scalar + y





def 							lowercase					(snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int )     ->  Vector:


				'''simple docstring'''






				random.seed(snake_case__ )
				lowerCAmelCase  =				[random.randint(snake_case__      ,       snake_case__ ) for _ in range(snake_case__ )]
				return Vector(snake_case__ )






class       SCREAMING_SNAKE_CASE__					:


						def __init__(							self				:			Any						,						lowerCAmelCase				:			list[list[float]]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				matrix
										lowerCAmelCase  =				w
										lowerCAmelCase  =				h


						def __str__(							self				:			List[str]					):
										lowerCAmelCase  =				""""""
										for i in range(self.__height					):
														ans += "|"
														for j in range(self.__width					):
																		if j < self.__width - 1:
																						ans += str(self.__matrix[i][j]					) + ","
																		else:
																						ans += str(self.__matrix[i][j]					) + "|\n"
										return ans


						def __add__(							self				:			Optional[Any]						,						lowerCAmelCase				:			Matrix					):
										if self.__width == other.width() and self.__height == other.height():
														lowerCAmelCase  =				[]
														for i in range(self.__height					):
																		lowerCAmelCase  =				[
																		    self.__matrix[i][j] + other.component(lowerCAmelCase						,						lowerCAmelCase					)
																		    for j in range(self.__width					)
																		]
																		matrix.append(lowerCAmelCase					)
														return Matrix(lowerCAmelCase						,						self.__width						,						self.__height					)
										else:
														raise Exception("""matrix must have the same dimension!"""					)


						def __sub__(							self				:			Union[str, Any]						,						lowerCAmelCase				:			Matrix					):
										if self.__width == other.width() and self.__height == other.height():
														lowerCAmelCase  =				[]
														for i in range(self.__height					):
																		lowerCAmelCase  =				[
																		    self.__matrix[i][j] - other.component(lowerCAmelCase						,						lowerCAmelCase					)
																		    for j in range(self.__width					)
																		]
																		matrix.append(lowerCAmelCase					)
														return Matrix(lowerCAmelCase						,						self.__width						,						self.__height					)
										else:
														raise Exception("""matrices must have the same dimension!"""					)


						@overload
						def __mul__(							self				:			Any						,						lowerCAmelCase				:			float					):
										...


						@overload
						def __mul__(							self				:			Optional[int]						,						lowerCAmelCase				:			Vector					):
										...


						def __mul__(							self				:			Dict						,						lowerCAmelCase				:			float | Vector					):
										if isinstance(lowerCAmelCase						,						lowerCAmelCase					):  # matrix-vector
														if len(lowerCAmelCase					) == self.__width:
																		lowerCAmelCase  =				zero_vector(self.__height					)
																		for i in range(self.__height					):
																						lowerCAmelCase  =				[
																						    self.__matrix[i][j] * other.component(lowerCAmelCase					)
																						    for j in range(self.__width					)
																						]
																						ans.change_component(lowerCAmelCase						,						sum(lowerCAmelCase					)					)
																		return ans
														else:
																		raise Exception(
																		    """vector must have the same size as the """
																		    """number of columns of the matrix!"""					)
										elif isinstance(lowerCAmelCase						,						(int, float)					):  # matrix-scalar
														lowerCAmelCase  =				[
														    [self.__matrix[i][j] * other for j in range(self.__width					)]
														    for i in range(self.__height					)
														]
														return Matrix(lowerCAmelCase						,						self.__width						,						self.__height					)
										return None


						def   __lowercase (							self				:			Any					):
										return self.__height


						def   __lowercase (							self				:			int					):
										return self.__width


						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										if 0 <= x < self.__height and 0 <= y < self.__width:
														return self.__matrix[x][y]
										else:
														raise Exception("""change_component: indices out of bounds"""					)


						def   __lowercase (							self				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			float					):
										if 0 <= x < self.__height and 0 <= y < self.__width:
														lowerCAmelCase  =				value
										else:
														raise Exception("""change_component: indices out of bounds"""					)


						def   __lowercase (							self				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										if self.__height != self.__width:
														raise Exception("""Matrix is not square"""					)
										lowerCAmelCase  =				self.__matrix[:x] + self.__matrix[x + 1 :]
										for i in range(len(lowerCAmelCase					)					):
														lowerCAmelCase  =				minor[i][:y] + minor[i][y + 1 :]
										return Matrix(lowerCAmelCase						,						self.__width - 1						,						self.__height - 1					).determinant()


						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										if self.__height != self.__width:
														raise Exception("""Matrix is not square"""					)
										if 0 <= x < self.__height and 0 <= y < self.__width:
														return (-1) ** (x + y) * self.minor(lowerCAmelCase						,						lowerCAmelCase					)
										else:
														raise Exception("""Indices out of bounds"""					)




						def   __lowercase (							self				:			List[Any]					):
										if self.__height != self.__width:
														raise Exception("""Matrix is not square"""					)
										if self.__height < 1:
														raise Exception("""Matrix has no element"""					)
										elif self.__height == 1:
														return self.__matrix[0][0]
										elif self.__height == 2:
														return (
														    self.__matrix[0][0] * self.__matrix[1][1]
														    - self.__matrix[0][1] * self.__matrix[1][0]
														)
										else:
														lowerCAmelCase  =				[
														    self.__matrix[0][y] * self.cofactor(0						,						lowerCAmelCase					) for y in range(self.__width					)
														]
														return sum(lowerCAmelCase					)





def 							lowercase					(snake_case__   : int )     ->  Matrix:


				'''simple docstring'''






				lowerCAmelCase  =				[[0] * n for _ in range(snake_case__ )]
				return Matrix(snake_case__      ,       snake_case__      ,       snake_case__ )






def 							lowercase					(snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int )     ->  Matrix:


				'''simple docstring'''






				random.seed(snake_case__ )
				lowerCAmelCase  =				[
				    [random.randint(snake_case__      ,       snake_case__ ) for _ in range(snake_case__ )] for _ in range(snake_case__ )
				]
				return Matrix(snake_case__      ,       snake_case__      ,       snake_case__ )





