





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_				=      logging.get_logger(__name__)
class 							snake_case_		(      _A):
		lowerCamelCase     :Optional[int]							=  ["input_values", "attention_mask"]







		def __init__(		self      ,  __lowercase=1      ,  __lowercase=1_6_0_0_0      ,  __lowercase=0.0      ,  __lowercase=False      ,  __lowercase=True      ,  **__lowercase      ,  )					-> Any:
				super().__init__(feature_size=__lowercase      ,  sampling_rate=__lowercase      ,  padding_value=__lowercase      ,  **__lowercase )
				lowerCamelCase :			List[str]    		=return_attention_mask
				lowerCamelCase :			Tuple    		=do_normalize







		@staticmethod
		def       __lowercase (		__lowercase      ,  __lowercase      ,  __lowercase = 0.0 )					-> List[np.ndarray]:
				if attention_mask is not None:
						lowerCamelCase :			int    		=np.array(__lowercase      ,  np.intaa )
						lowerCamelCase :			Any    		=[]

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

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

				return normed_input_values







		def __call__(		self      ,  __lowercase      ,  __lowercase = False      ,  __lowercase = None      ,  __lowercase = False      ,  __lowercase = None      ,  __lowercase = None      ,  __lowercase = None      ,  __lowercase = None      ,  **__lowercase      ,  )					-> 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.''' )

				lowerCamelCase :			List[str]    		=isinstance(__lowercase      ,  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}" )
				lowerCamelCase :			Any    		=is_batched_numpy or (
				    isinstance(__lowercase      ,  (list, tuple) ) and (isinstance(raw_speech[0]      ,  (np.ndarray, tuple, list) ))
				)

				# always return batch
				if not is_batched:
						lowerCamelCase :			Tuple    		=[raw_speech]

				# convert into correct format for padding
				lowerCamelCase :			Union[str, Any]    		=BatchFeature({'''input_values''': raw_speech} )

				lowerCamelCase :			Dict    		=self.pad(
				    __lowercase      ,  padding=__lowercase      ,  max_length=__lowercase      ,  truncation=__lowercase      ,  pad_to_multiple_of=__lowercase      ,  return_attention_mask=__lowercase      ,  )

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

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

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

				if return_tensors is not None:
						lowerCamelCase :			Any    		=padded_inputs.convert_to_tensors(__lowercase )

				return padded_inputs








def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Union[str, Any]:
		if n < 0:
				return 0
		# table[i] represents the number of ways to get to amount i
		lowerCamelCase :			int    		=[0] * (n + 1)

		# There is exactly 1 way to get to zero(You pick no coins).
		lowerCamelCase :			List[Any]    		=1

		# Pick all coins one by one and update table[] values
		# after the index greater than or equal to the value of the
		# picked coin
		for coin_val in s:
				for j in range(SCREAMING_SNAKE_CASE_				,							n + 1		):
						table[j] += table[j - coin_val]

		return table[n]


if __name__ == "__main__":
				import doctest

				doctest.testmod()

