






"""simple docstring"""
from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

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


lowerCamelCase_       			=   logging.get_logger(__name__)







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       ['''input_features''', '''attention_mask''']





	def __init__(   self      :				Dict			,			lowerCAmelCase_      :				Tuple=80			,			lowerCAmelCase_      :				Any=16_000			,			lowerCAmelCase_      :				Tuple=80			,			lowerCAmelCase_      :				Any=0.0			,			lowerCAmelCase_      :				Dict=True			,			lowerCAmelCase_      :				Dict=True			,			lowerCAmelCase_      :				Optional[int]=True			,			**lowerCAmelCase_      :				Any			,			)		->    Tuple:
				super().__init__(feature_size=lowerCAmelCase_			,			sampling_rate=lowerCAmelCase_			,			padding_value=lowerCAmelCase_			,			**lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[Any]      =      num_mel_bins
				UpperCAmelCase_  :				Any      =      do_ceptral_normalize
				UpperCAmelCase_  :				Union[str, Any]      =      normalize_means
				UpperCAmelCase_  :				int      =      normalize_vars
				UpperCAmelCase_  :				Any      =      True





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				np.ndarray			,			)		->    np.ndarray:
				UpperCAmelCase_  :				List[str]      =      waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
				UpperCAmelCase_  :				Optional[Any]      =      torch.from_numpy(lowerCAmelCase_		).unsqueeze(0		)
				UpperCAmelCase_  :				List[Any]      =      ta_kaldi.fbank(lowerCAmelCase_			,			num_mel_bins=self.num_mel_bins			,			sample_frequency=self.sampling_rate		)
				return features.numpy()





	@staticmethod
	def   _SCREAMING_SNAKE_CASE   (   lowerCAmelCase_      :				np.ndarray			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Optional[bool] = True			,			lowerCAmelCase_      :				Optional[bool] = True			,			lowerCAmelCase_      :				float = 0.0			,			)		->    np.ndarray:
				# make sure we normalize float32 arrays
				if normalize_means:
							UpperCAmelCase_  :				List[str]      =      x[:input_length].mean(axis=0		)
							UpperCAmelCase_  :				Any      =      np.subtract(lowerCAmelCase_			,			lowerCAmelCase_		)
				if normalize_vars:
							UpperCAmelCase_  :				Optional[int]      =      x[:input_length].std(axis=0		)
							UpperCAmelCase_  :				List[str]      =      np.divide(lowerCAmelCase_			,			lowerCAmelCase_		)

				if input_length < x.shape[0]:
							UpperCAmelCase_  :				List[Any]      =      padding_value

				# make sure array is in float32
				UpperCAmelCase_  :				List[Any]      =      x.astype(np.floataa		)

				return x





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				List[np.ndarray]			,			lowerCAmelCase_      :				Optional[np.ndarray] = None		)		->    List[np.ndarray]:
				UpperCAmelCase_  :				Tuple      =      attention_mask.sum(-1		) if attention_mask is not None else [x.shape[0] for x in input_features]
				return [
				    self.utterance_cmvn(lowerCAmelCase_			,			lowerCAmelCase_			,			self.normalize_means			,			self.normalize_vars			,			self.padding_value		)
				    for x, n in zip(lowerCAmelCase_			,			lowerCAmelCase_		)
				]





	def __call__(   self      :				Dict			,			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[Union[str, TensorType]] = None			,			lowerCAmelCase_      :				Optional[int] = None			,			lowerCAmelCase_      :				Optional[bool] = None			,			**lowerCAmelCase_      :				Union[str, 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."		)

				UpperCAmelCase_  :				Tuple      =      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}"""		)
				UpperCAmelCase_  :				str      =      is_batched_numpy or (
				    isinstance(lowerCAmelCase_			,			(list, tuple)		) and (isinstance(raw_speech[0]			,			(np.ndarray, tuple, list)		))
				)

				if is_batched:
							UpperCAmelCase_  :				Dict      =      [np.asarray(lowerCAmelCase_			,			dtype=np.floataa		) for speech in raw_speech]
				elif not is_batched and not isinstance(lowerCAmelCase_			,			np.ndarray		):
							UpperCAmelCase_  :				List[str]      =      np.asarray(lowerCAmelCase_			,			dtype=np.floataa		)
				elif isinstance(lowerCAmelCase_			,			np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
							UpperCAmelCase_  :				Tuple      =      raw_speech.astype(np.floataa		)

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

				# extract fbank features
				UpperCAmelCase_  :				Dict      =      [self._extract_fbank_features(lowerCAmelCase_		) for waveform in raw_speech]

				# convert into correct format for padding
				UpperCAmelCase_  :				Any      =      BatchFeature({"input_features": features}		)

				UpperCAmelCase_  :				List[str]      =      self.pad(
				    lowerCAmelCase_			,			padding=lowerCAmelCase_			,			max_length=lowerCAmelCase_			,			truncation=lowerCAmelCase_			,			pad_to_multiple_of=lowerCAmelCase_			,			return_attention_mask=lowerCAmelCase_			,			**lowerCAmelCase_			,			)

				# make sure list is in array format
				UpperCAmelCase_  :				List[Any]      =      padded_inputs.get("input_features"		)
				if isinstance(input_features[0]			,			lowerCAmelCase_		):
							UpperCAmelCase_  :				List[Any]      =      [np.asarray(lowerCAmelCase_			,			dtype=np.floataa		) for feature in input_features]

				UpperCAmelCase_  :				int      =      padded_inputs.get("attention_mask"		)
				if attention_mask is not None:
							UpperCAmelCase_  :				Tuple      =      [np.asarray(lowerCAmelCase_			,			dtype=np.intaa		) for array in attention_mask]

				# Utterance-level cepstral mean and variance normalization
				if self.do_ceptral_normalize:
							UpperCAmelCase_  :				Any      =      (
							    np.array(lowerCAmelCase_			,			dtype=np.intaa		)
							    if self._get_padding_strategies(lowerCAmelCase_			,			max_length=lowerCAmelCase_		) is not PaddingStrategy.DO_NOT_PAD
							    else None
							)
							UpperCAmelCase_  :				Dict      =      self.normalize(
							    padded_inputs["input_features"]			,			attention_mask=lowerCAmelCase_		)

				if return_tensors is not None:
							UpperCAmelCase_  :				Optional[int]      =      padded_inputs.convert_to_tensors(lowerCAmelCase_		)

				return padded_inputs











"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey

def        snake_case   (     A__			,A__			,A__			,A__			,A__			,A__		):

			# prepare kernel
			# the kernel size have to be odd
			if (ksize % 2) == 0:
						UpperCAmelCase_  :				List[Any]      =      ksize + 1
			UpperCAmelCase_  :				Optional[Any]      =      np.zeros((ksize, ksize)			,dtype=np.floataa		)

			# each value
			for y in range(A__		):
						for x in range(A__		):
									# distance from center
									UpperCAmelCase_  :				Tuple      =      x - ksize // 2
									UpperCAmelCase_  :				Any      =      y - ksize // 2

									# degree to radiant
									UpperCAmelCase_  :				int      =      theta / 1_80 * np.pi
									UpperCAmelCase_  :				Optional[int]      =      np.cos(_theta		)
									UpperCAmelCase_  :				Union[str, Any]      =      np.sin(_theta		)

									# get kernel x
									UpperCAmelCase_  :				Tuple      =      cos_theta * px + sin_theta * py

									# get kernel y
									UpperCAmelCase_  :				List[str]      =      -sin_theta * px + cos_theta * py

									# fill kernel
									UpperCAmelCase_  :				Dict      =      np.exp(
									    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)		) * np.cos(2 * np.pi * _x / lambd + psi		)

			return gabor


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			# read original image
			lowerCamelCase_       			=   imread('''../image_data/lena.jpg''')
			# turn image in gray scale value
			lowerCamelCase_       			=   cvtColor(img, COLOR_BGR2GRAY)

			# Apply multiple Kernel to detect edges
			lowerCamelCase_       			=   np.zeros(gray.shape[:2])
			for theta in [0, 30, 60, 90, 120, 150]:
						lowerCamelCase_       			=   gabor_filter_kernel(10, 8, theta, 10, 0, 0)
						out += filteraD(gray, CV_8UC3, kernel_aa)
			lowerCamelCase_       			=   out / out.max() * 255
			lowerCamelCase_       			=   out.astype(np.uinta)

			imshow('''Original''', gray)
			imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

			waitKey(0)



