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

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


lowerCAmelCase					:    int           = logging.get_logger(__name__)


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							["""input_features""", """attention_mask"""]







				def __init__(       self						,				A_=80						,				A_=16000						,				A_=0.0						,				A_=10						,				A_=25						,				A_="hamming_window"						,				A_=32_768.0						,				A_=0.97						,				A_=1.0						,				A_=True						,				A_=True						,				A_=False						,				**A_						,				)->		List[str]:







											'''simple docstring'''






											super().__init__(feature_size=A_						,				sampling_rate=A_						,				padding_value=A_						,				**A_     )

											UpperCamelCase   			=     feature_size
											UpperCamelCase   			=     sampling_rate
											UpperCamelCase   			=     padding_value
											UpperCamelCase   			=     hop_length
											UpperCamelCase   			=     win_length
											UpperCamelCase   			=     frame_signal_scale
											UpperCamelCase   			=     preemphasis_coeff
											UpperCamelCase   			=     mel_floor
											UpperCamelCase   			=     normalize_means
											UpperCamelCase   			=     normalize_vars
											UpperCamelCase   			=     win_function
											UpperCamelCase   			=     return_attention_mask

											UpperCamelCase   			=     win_length * sampling_rate // 1000
											UpperCamelCase   			=     hop_length * sampling_rate // 1000

											UpperCamelCase   			=     optimal_fft_length(self.sample_size     )
											UpperCamelCase   			=     (self.n_fft // 2) + 1







				def 						UpperCAmelCase_    (       self						,				A_     )->		np.ndarray:







											'''simple docstring'''






											if self.win_function == "hamming_window":
																		UpperCamelCase   			=     window_function(window_length=self.sample_size						,				name=self.win_function						,				periodic=A_     )
											else:
																		UpperCamelCase   			=     window_function(window_length=self.sample_size						,				name=self.win_function     )

											UpperCamelCase   			=     mel_filter_bank(
											    num_frequency_bins=self.n_freqs						,				num_mel_filters=self.feature_size						,				min_frequency=0.0						,				max_frequency=self.sampling_rate / 2.0						,				sampling_rate=self.sampling_rate						,				)

											UpperCamelCase   			=     spectrogram(
											    one_waveform * self.frame_signal_scale						,				window=A_						,				frame_length=self.sample_size						,				hop_length=self.sample_stride						,				fft_length=self.n_fft						,				center=A_						,				preemphasis=self.preemphasis_coeff						,				mel_filters=A_						,				mel_floor=self.mel_floor						,				log_mel='log'						,				)
											return msfc_features.T







				def 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_     )->		Dict:







											'''simple docstring'''






											if self.normalize_means:
																		UpperCamelCase   			=     x[:input_length].mean(axis=0     )
																		UpperCamelCase   			=     np.subtract(A_						,				A_     )
											if self.normalize_vars:
																		UpperCamelCase   			=     x[:input_length].std(axis=0     )
																		UpperCamelCase   			=     np.divide(A_						,				A_     )

											if input_length < x.shape[0]:
																		UpperCamelCase   			=     padding_value

											# make sure array is in float32
											UpperCamelCase   			=     x.astype(np.floataa     )

											return x







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None     )->		List[np.ndarray]:







											'''simple docstring'''






											UpperCamelCase   			=     attention_mask.sum(-1     ) if attention_mask is not None else [x.shape[0] for x in input_features]
											return [self._normalize_one(A_						,				A_						,				self.padding_value     ) for x, n in zip(A_						,				A_     )]







				def __call__(       self						,				A_						,				A_ = False						,				A_ = None						,				A_ = False						,				A_ = None						,				A_ = None						,				A_ = None						,				A_ = None						,				**A_						,				)->		BatchFeature:







											'''simple docstring'''






											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   			=     isinstance(A_						,				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   			=     is_batched_numpy or (
											    isinstance(A_						,				(list, tuple)     ) and (isinstance(raw_speech[0]						,				(np.ndarray, tuple, list)     ))
											)

											if is_batched:
																		UpperCamelCase   			=     [np.asarray(A_						,				dtype=np.floataa     ) for speech in raw_speech]
											elif not is_batched and not isinstance(A_						,				np.ndarray     ):
																		UpperCamelCase   			=     np.asarray(A_						,				dtype=np.floataa     )
											elif isinstance(A_						,				np.ndarray     ) and raw_speech.dtype is np.dtype(np.floataa     ):
																		UpperCamelCase   			=     raw_speech.astype(np.floataa     )

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

											# extract fbank features
											UpperCamelCase   			=     [self._extract_mfsc_features(A_     ) for one_waveform in raw_speech]

											# convert into correct format for padding
											UpperCamelCase   			=     BatchFeature({'input_features': features}     )

											UpperCamelCase   			=     self.pad(
											    A_						,				padding=A_						,				max_length=A_						,				truncation=A_						,				pad_to_multiple_of=A_						,				return_attention_mask=A_						,				**A_						,				)
											# make sure list is in array format
											UpperCamelCase   			=     padded_inputs.get('input_features'     )
											if isinstance(input_features[0]						,				A_     ):
																		UpperCamelCase   			=     [np.asarray(A_						,				dtype=np.floataa     ) for feature in input_features]

											UpperCamelCase   			=     padded_inputs.get('attention_mask'     )
											if attention_mask is not None:
																		UpperCamelCase   			=     [np.asarray(A_						,				dtype=np.intaa     ) for array in attention_mask]

											if self.normalize_means or self.normalize_vars:
																		UpperCamelCase   			=     (
																		    np.array(A_						,				dtype=np.intaa     )
																		    if self._get_padding_strategies(A_						,				max_length=A_     ) is not PaddingStrategy.DO_NOT_PAD
																		    and padding
																		    else None
																		)
																		UpperCamelCase   			=     self.normalize(
																		    padded_inputs['input_features']						,				attention_mask=A_     )

											if return_tensors is not None:
																		UpperCamelCase   			=     padded_inputs.convert_to_tensors(A_     )

											return padded_inputs






'''simple docstring'''
import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt


class 			SCREAMING_SNAKE_CASE__      :







				def __init__(       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     ''
											UpperCamelCase   			=     ''
											UpperCamelCase   			=     []
											UpperCamelCase   			=     0
											UpperCamelCase   			=     256
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0







				def 						UpperCAmelCase_    (       self						,				A_     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     cva.imread(A_						,				0     )
											UpperCamelCase   			=     copy.deepcopy(self.img     )
											UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase   			=     plt.hist(self.img.ravel()						,				256						,				[0, 256]						,				label='x'     )
											UpperCamelCase   			=     np.sum(A_     )
											for i in range(len(A_     )     ):
																		UpperCamelCase   			=     x[i] / self.k
																		self.sk += prk
																		UpperCamelCase   			=     (self.L - 1) * self.sk
																		if self.rem != 0:
																									UpperCamelCase   			=     int(last % last     )
																		UpperCamelCase   			=     int(last + 1 if self.rem >= 0.5 else last     )
																		self.last_list.append(A_     )
																		UpperCamelCase   			=     int(np.ma.count(self.img     ) / self.img[1].size     )
																		UpperCamelCase   			=     self.img[1].size
											for i in range(self.number_of_cols     ):
																		for j in range(self.number_of_rows     ):
																									UpperCamelCase   			=     self.img[j][i]
																									if num != self.last_list[num]:
																																UpperCamelCase   			=     self.last_list[num]
											cva.imwrite('output_data/output.jpg'						,				self.img     )







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											plt.hist(self.img.ravel()						,				256						,				[0, 256]     )







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											cva.imshow('Output-Image'						,				self.img     )
											cva.imshow('Input-Image'						,				self.original_image     )
											cva.waitKey(5000     )
											cva.destroyAllWindows()


if __name__ == "__main__":
						lowerCAmelCase					:    Union[str, Any]           = os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
						lowerCAmelCase					:    str           = ConstantStretch()
						stretcher.stretch(file_path)
						stretcher.plot_histogram()
						stretcher.show_image()




