




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 :    Union[str, Any]      						=      logging.get_logger(__name__)


class  lowerCamelCase     (		_lowerCamelCase    ):






			'''simple docstring'''





			UpperCamelCase__										=['''input_values''', '''attention_mask''']

			def __init__(			self      : List[Any]							,    lowerCamelCase_      : List[Any]=1							,    lowerCamelCase_      : Union[str, Any]=16000							,    lowerCamelCase_      : Optional[int]=0.0							,    lowerCamelCase_      : Any=False							,    lowerCamelCase_      : Optional[int]=True							,    **lowerCamelCase_      : List[Any]							,    )    -> Any:
								super().__init__(feature_size=lowerCamelCase_							,    sampling_rate=lowerCamelCase_							,    padding_value=lowerCamelCase_							,    **lowerCamelCase_	)
								__magic_name__				:						Any     				=				return_attention_mask
								__magic_name__				:						str     				=				do_normalize

			@staticmethod
			def 				UpperCAmelCase__							(			lowerCamelCase_      : List[np.ndarray]							,    lowerCamelCase_      : List[np.ndarray]							,    lowerCamelCase_      : float = 0.0	)    -> List[np.ndarray]:
								if attention_mask is not None:
													__magic_name__				:						Union[str, Any]     				=				np.array(lowerCamelCase_							,    np.intaa	)
													__magic_name__				:						str     				=				[]

													for vector, length in zip(lowerCamelCase_							,    attention_mask.sum(-1	)	):
																		__magic_name__				:						Optional[int]     				=				(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7	)
																		if length < normed_slice.shape[0]:
																							__magic_name__				:						Any     				=				padding_value

																		normed_input_values.append(lowerCamelCase_	)
								else:
													__magic_name__				:						Tuple     				=				[(x - x.mean()) / np.sqrt(x.var() + 1E-7	) for x in input_values]

								return normed_input_values


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

								__magic_name__				:						Dict     				=				isinstance(lowerCamelCase_							,    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}'''	)
								__magic_name__				:						int     				=				is_batched_numpy or (
								    isinstance(lowerCamelCase_							,    (list, tuple)	) and (isinstance(raw_speech[0]							,    (np.ndarray, tuple, list)	))
								)

								# always return batch
								if not is_batched:
													__magic_name__				:						Optional[Any]     				=				[raw_speech]

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

								__magic_name__				:						Union[str, Any]     				=				self.pad(
								    lowerCamelCase_							,    padding=lowerCamelCase_							,    max_length=lowerCamelCase_							,    truncation=lowerCamelCase_							,    pad_to_multiple_of=lowerCamelCase_							,    return_attention_mask=lowerCamelCase_							,    )

								# convert input values to correct format
								__magic_name__				:						Union[str, Any]     				=				padded_inputs['''input_values''']
								if not isinstance(input_values[0]							,    np.ndarray	):
													__magic_name__				:						Dict     				=				[np.asarray(lowerCamelCase_							,    dtype=np.floataa	) for array in input_values]
								elif (
								    not isinstance(lowerCamelCase_							,    np.ndarray	)
								    and isinstance(input_values[0]							,    np.ndarray	)
								    and input_values[0].dtype is np.dtype(np.floataa	)
								):
													__magic_name__				:						Union[str, Any]     				=				[array.astype(np.floataa	) for array in input_values]
								elif isinstance(lowerCamelCase_							,    np.ndarray	) and input_values.dtype is np.dtype(np.floataa	):
													__magic_name__				:						Tuple     				=				input_values.astype(np.floataa	)

								# convert attention_mask to correct format
								__magic_name__				:						Tuple     				=				padded_inputs.get('''attention_mask'''	)
								if attention_mask is not None:
													__magic_name__				:						Any     				=				[np.asarray(lowerCamelCase_							,    dtype=np.intaa	) for array in attention_mask]

								# zero-mean and unit-variance normalization
								if self.do_normalize:
													__magic_name__				:						List[str]     				=				(
													    attention_mask
													    if self._get_padding_strategies(lowerCamelCase_							,    max_length=lowerCamelCase_	) is not PaddingStrategy.DO_NOT_PAD
													    else None
													)
													__magic_name__				:						Any     				=				self.zero_mean_unit_var_norm(
													    padded_inputs['''input_values''']							,    attention_mask=lowerCamelCase_							,    padding_value=self.padding_value	)

								if return_tensors is not None:
													__magic_name__				:						Dict     				=				padded_inputs.convert_to_tensors(lowerCamelCase_	)

								return padded_inputs









def 		lowercase__ (		__A:						list[list[int]]					):





					'''simple docstring'''






					if len(__A					) == 0 or not all(len(__A					) == 2 for pair in coordinates					):
										raise ValueError('''The program cannot work out a fitting polynomial.'''					)

					if len({tuple(__A					) for pair in coordinates}					) != len(__A					):
										raise ValueError('''The program cannot work out a fitting polynomial.'''					)

					__magic_name__				:						Union[str, Any]     				=				{x for x, _ in coordinates}
					if len(__A					) == 1:
										return F'''x={coordinates[0][0]}'''

					if len(__A					) != len(__A					):
										raise ValueError('''The program cannot work out a fitting polynomial.'''					)

					__magic_name__				:						List[Any]     				=				len(__A					)

					__magic_name__				:						int     				=				0
					__magic_name__				:						list[list[float]]     				=				[]
					# put the x and x to the power values in a matrix
					while count_of_line < x:
										__magic_name__				:						List[Any]     				=				0
										__magic_name__				:						List[str]     				=				coordinates[count_of_line][0]
										__magic_name__				:						list[float]     				=				[]
										while count_in_line < x:
															count_line.append(a ** (x - (count_in_line + 1))					)
															count_in_line += 1
										matrix.append(__A					)
										count_of_line += 1

					__magic_name__				:						Union[str, Any]     				=				0
					# put the y values into a vector
					__magic_name__				:						list[float]     				=				[]
					while count_of_line < x:
										vector.append(coordinates[count_of_line][1]					)
										count_of_line += 1

					__magic_name__				:						int     				=				0

					while count < x:
										__magic_name__				:						int     				=				0
										while zahlen < x:
															if count == zahlen:
																				zahlen += 1
															if zahlen == x:
																				break
															__magic_name__				:						Tuple     				=				matrix[zahlen][count] / matrix[count][count]
															for counting_columns, item in enumerate(matrix[count]					):
																				# manipulating all the values in the matrix
																				matrix[zahlen][counting_columns] -= item * bruch
															# manipulating the values in the vector
															vector[zahlen] -= vector[count] * bruch
															zahlen += 1
										count += 1

					__magic_name__				:						str     				=				0
					# make solutions
					__magic_name__				:						list[str]     				=				[]
					while count < x:
										solution.append(str(vector[count] / matrix[count][count]					)					)
										count += 1

					__magic_name__				:						List[Any]     				=				0
					__magic_name__				:						Union[str, Any]     				=				'''f(x)='''

					while count < x:
										__magic_name__				:						list[str]     				=				solution[count].split('''E'''					)
										if len(__A					) > 1:
															__magic_name__				:						Union[str, Any]     				=				F'''{remove_e[0]}*10^{remove_e[1]}'''
										solved += F'''x^{x - (count + 1)}*{solution[count]}'''
										if count + 1 != x:
															solved += "+"
										count += 1

					return solved


if __name__ == "__main__":
	print(points_to_polynomial([]))
	print(points_to_polynomial([[]]))
	print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
	print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
	print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
	print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
	print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
	print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
	print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
	print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))



