
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


lowerCamelCase_  :       str					      =     logging.get_logger(__name__)

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			__UpperCamelCase					:  Optional[Any]    					=       ["""input_values""", """attention_mask"""]

			def __init__(				self       :				Tuple  ,		snake_case_       :				Any=1  ,		snake_case_       :				str=1_6000  ,		snake_case_       :				Optional[Any]=0.0  ,		snake_case_       :				str=False  ,		snake_case_       :				List[str]=True  ,		**snake_case_       :				List[Any]  ,		):
							super().__init__(feature_size=snake_case_  ,		sampling_rate=snake_case_  ,		padding_value=snake_case_  ,		**snake_case_					)
							UpperCamelCase_:							Optional[Any]		=  return_attention_mask
							UpperCamelCase_:							Tuple		=  do_normalize

			@staticmethod
			def 					lowerCAmelCase__		(				snake_case_       :				List[np.ndarray]  ,		snake_case_       :				List[np.ndarray]  ,		snake_case_       :				float = 0.0					):
							if attention_mask is not None:
											UpperCamelCase_:							Tuple		=  np.array(snake_case_  ,		np.intaa					)
											UpperCamelCase_:							Tuple		=  []

											for vector, length in zip(snake_case_  ,		attention_mask.sum(-1					)					):
															UpperCamelCase_:							List[str]		=  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7					)
															if length < normed_slice.shape[0]:
																			UpperCamelCase_:							str		=  padding_value

															normed_input_values.append(snake_case_					)
							else:
											UpperCamelCase_:							str		=  [(x - x.mean()) / np.sqrt(x.var() + 1e-7					) for x in input_values]

							return normed_input_values




			def __call__(				self       :				Union[str, Any]  ,		snake_case_       :				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]  ,		snake_case_       :				Union[bool, str, PaddingStrategy] = False  ,		snake_case_       :				Optional[int] = None  ,		snake_case_       :				bool = False  ,		snake_case_       :				Optional[int] = None  ,		snake_case_       :				Optional[bool] = None  ,		snake_case_       :				Optional[Union[str, TensorType]] = None  ,		snake_case_       :				Optional[int] = None  ,		**snake_case_       :				int  ,		):

							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 `raw_speech` 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."""					)

							UpperCamelCase_:							Optional[Any]		=  isinstance(snake_case_  ,		np.ndarray					) and len(raw_speech.shape					) > 1
							if is_batched_numpy and len(raw_speech.shape					) > 2:
											raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''					)
							UpperCamelCase_:							Optional[int]		=  is_batched_numpy or (
							    isinstance(snake_case_  ,		(list, tuple)					) and (isinstance(raw_speech[0]  ,		(np.ndarray, tuple, list)					))
							)

							# always return batch
							if not is_batched:
											UpperCamelCase_:							List[str]		=  [raw_speech]

							# convert into correct format for padding
							UpperCamelCase_:							List[Any]		=  BatchFeature({"""input_values""": raw_speech}					)

							UpperCamelCase_:							Optional[int]		=  self.pad(
							    snake_case_  ,		padding=snake_case_  ,		max_length=snake_case_  ,		truncation=snake_case_  ,		pad_to_multiple_of=snake_case_  ,		return_attention_mask=snake_case_  ,		)

							# convert input values to correct format
							UpperCamelCase_:							Union[str, Any]		=  padded_inputs["""input_values"""]
							if not isinstance(input_values[0]  ,		np.ndarray					):
											UpperCamelCase_:							Dict		=  [np.asarray(snake_case_  ,		dtype=np.floataa					) for array in input_values]
							elif (
							    not isinstance(snake_case_  ,		np.ndarray					)
							    and isinstance(input_values[0]  ,		np.ndarray					)
							    and input_values[0].dtype is np.dtype(np.floataa					)
							):
											UpperCamelCase_:							List[str]		=  [array.astype(np.floataa					) for array in input_values]
							elif isinstance(snake_case_  ,		np.ndarray					) and input_values.dtype is np.dtype(np.floataa					):
											UpperCamelCase_:							Optional[int]		=  input_values.astype(np.floataa					)

							# convert attention_mask to correct format
							UpperCamelCase_:							Union[str, Any]		=  padded_inputs.get("""attention_mask"""					)
							if attention_mask is not None:
											UpperCamelCase_:							Dict		=  [np.asarray(snake_case_  ,		dtype=np.intaa					) for array in attention_mask]

							# zero-mean and unit-variance normalization
							if self.do_normalize:
											UpperCamelCase_:							Dict		=  (
											    attention_mask
											    if self._get_padding_strategies(snake_case_  ,		max_length=snake_case_					) is not PaddingStrategy.DO_NOT_PAD
											    else None
											)
											UpperCamelCase_:							str		=  self.zero_mean_unit_var_norm(
											    padded_inputs["""input_values"""]  ,		attention_mask=snake_case_  ,		padding_value=self.padding_value					)

							if return_tensors is not None:
											UpperCamelCase_:							int		=  padded_inputs.convert_to_tensors(snake_case_					)

							return padded_inputs








from math import cos, sin







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_:							int		=  float(lowerCamelCase  )
				return [[scaling_factor * int(x == y  ) for x in range(2  )] for y in range(2  )]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							Union[str, Any]		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				return [[c, -s], [s, c]]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							Any		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				UpperCamelCase_:							List[Any]		=  c * s
				return [[c * c, cs], [cs, s * s]]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							List[str]		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				UpperCamelCase_:							int		=  c * s
				return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F"""    {scaling(5) = }""")
print(F"""  {rotation(45) = }""")
print(F"""{projection(45) = }""")





print(F"""{reflection(45) = }""")






