



"""simple docstring"""






import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


lowerCAmelCase__              =							logging.get_logger(__name__)






class 			__snake_case			(      _lowercase):
  snake_case__							:  Tuple         =		["input_features", "is_longer"]




  def __init__(					self	:		Optional[int]		,   __lowerCAmelCase	:		Tuple=6_4		,   __lowerCAmelCase	:		Union[str, Any]=4_8_0_0_0		,   __lowerCAmelCase	:		int=4_8_0		,   __lowerCAmelCase	:		Union[str, Any]=1_0		,   __lowerCAmelCase	:		int=1_0_2_4		,   __lowerCAmelCase	:		Optional[Any]=0.0		,   __lowerCAmelCase	:		int=False		,   __lowerCAmelCase	:		float = 0		,   __lowerCAmelCase	:		float = 1_4_0_0_0		,   __lowerCAmelCase	:		int = None		,   __lowerCAmelCase	:		str = "fusion"		,   __lowerCAmelCase	:		str = "repeatpad"		,   **__lowerCAmelCase	:		List[str]		,   ):






         """simple docstring"""






         super().__init__(
             feature_size=__lowerCAmelCase		,   sampling_rate=__lowerCAmelCase		,   padding_value=__lowerCAmelCase		,   return_attention_mask=__lowerCAmelCase		,   **__lowerCAmelCase		,   )
         _lowerCamelCase		:							str            =					top_db
         _lowerCamelCase		:							Optional[int]            =					truncation
         _lowerCamelCase		:							Tuple            =					padding
         _lowerCamelCase		:							Any            =					fft_window_size
         _lowerCamelCase		:							Tuple            =					(fft_window_size >> 1) + 1
         _lowerCamelCase		:							int            =					hop_length
         _lowerCamelCase		:							List[Any]            =					max_length_s
         _lowerCamelCase		:							Optional[Any]            =					max_length_s * sampling_rate
         _lowerCamelCase		:							List[str]            =					sampling_rate
         _lowerCamelCase		:							Tuple            =					frequency_min
         _lowerCamelCase		:							List[Any]            =					frequency_max
         _lowerCamelCase		:							str            =					mel_filter_bank(
             num_frequency_bins=self.nb_frequency_bins		,   num_mel_filters=__lowerCAmelCase		,   min_frequency=__lowerCAmelCase		,   max_frequency=__lowerCAmelCase		,   sampling_rate=__lowerCAmelCase		,   norm=__lowerCAmelCase		,   mel_scale='''htk'''		,   )
         _lowerCamelCase		:							Dict            =					mel_filter_bank(
             num_frequency_bins=self.nb_frequency_bins		,   num_mel_filters=__lowerCAmelCase		,   min_frequency=__lowerCAmelCase		,   max_frequency=__lowerCAmelCase		,   sampling_rate=__lowerCAmelCase		,   norm='''slaney'''		,   mel_scale='''slaney'''		,   )




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[Any]						):






         """simple docstring"""






         _lowerCamelCase		:							Union[str, Any]            =					copy.deepcopy(self.__dict__						)
         _lowerCamelCase		:							Optional[Any]            =					self.__class__.__name__
         if "mel_filters" in output:
                del output["mel_filters"]
         if "mel_filters_slaney" in output:
                del output["mel_filters_slaney"]
         return output




  def 		SCREAMING_SNAKE_CASE							(					self	:		Any		,   __lowerCAmelCase	:		np.array		,   __lowerCAmelCase	:		Optional[np.array] = None						):






         """simple docstring"""






         _lowerCamelCase		:							Tuple            =					spectrogram(
             __lowerCAmelCase		,   window_function(self.fft_window_size		,   '''hann'''						)		,   frame_length=self.fft_window_size		,   hop_length=self.hop_length		,   power=2.0		,   mel_filters=__lowerCAmelCase		,   log_mel='''dB'''		,   )
         return log_mel_spectrogram.T




  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]		,   __lowerCAmelCase	:		Any		,   __lowerCAmelCase	:		Any		,   __lowerCAmelCase	:		Optional[int]						):






         """simple docstring"""






         _lowerCamelCase		:							int            =					np.array_split(list(range(0		,   total_frames - chunk_frames + 1						)						)		,   3						)
         if len(ranges[1]						) == 0:
                # if the audio is too short, we just use the first chunk
                _lowerCamelCase		:							Dict            =					[0]
         if len(ranges[2]						) == 0:
                # if the audio is too short, we just use the first chunk
                _lowerCamelCase		:							Dict            =					[0]
         # randomly choose index for each part
         _lowerCamelCase		:							Dict            =					np.random.choice(ranges[0]						)
         _lowerCamelCase		:							List[str]            =					np.random.choice(ranges[1]						)
         _lowerCamelCase		:							int            =					np.random.choice(ranges[2]						)

         _lowerCamelCase		:							Tuple            =					mel[idx_front : idx_front + chunk_frames, :]
         _lowerCamelCase		:							int            =					mel[idx_middle : idx_middle + chunk_frames, :]
         _lowerCamelCase		:							Dict            =					mel[idx_back : idx_back + chunk_frames, :]

         _lowerCamelCase		:							str            =					torch.tensor(mel[None, None, :]						)
         _lowerCamelCase		:							List[Any]            =					torch.nn.functional.interpolate(
             __lowerCAmelCase		,   size=[chunk_frames, 6_4]		,   mode='''bilinear'''		,   align_corners=__lowerCAmelCase						)
         _lowerCamelCase		:							Dict            =					mel_shrink[0][0].numpy()
         _lowerCamelCase		:							Optional[Any]            =					np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]		,   axis=0						)
         return mel_fusion




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[int]		,   __lowerCAmelCase	:		np.array		,   __lowerCAmelCase	:		List[str]		,   __lowerCAmelCase	:		Union[str, Any]		,   __lowerCAmelCase	:		Optional[Any]						):






         """simple docstring"""






         if waveform.shape[0] > max_length:
                if truncation == "rand_trunc":
                       _lowerCamelCase		:							Any            =					True
                       # random crop to max_length (for compatibility) -> this should be handled by self.pad
                       _lowerCamelCase		:							int            =					len(__lowerCAmelCase						) - max_length
                       _lowerCamelCase		:							str            =					np.random.randint(0		,   overflow + 1						)
                       _lowerCamelCase		:							List[str]            =					waveform[idx : idx + max_length]
                       _lowerCamelCase		:							List[Any]            =					self._np_extract_fbank_features(__lowerCAmelCase		,   self.mel_filters_slaney						)[None, :]
                elif truncation == "fusion":
                       _lowerCamelCase		:							Any            =					self._np_extract_fbank_features(__lowerCAmelCase		,   self.mel_filters						)
                       _lowerCamelCase		:							Tuple            =					max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                       _lowerCamelCase		:							int            =					mel.shape[0]
                       if chunk_frames == total_frames:
                              # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                              # In this case, we just use the whole audio.
                              _lowerCamelCase		:							str            =					np.stack([mel, mel, mel, mel]		,   axis=0						)
                              _lowerCamelCase		:							Any            =					False
                       else:
                              _lowerCamelCase		:							Any            =					self._random_mel_fusion(__lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase						)
                              _lowerCamelCase		:							Optional[int]            =					True
                else:
                       raise NotImplementedError(f'''data_truncating {truncation} not implemented'''						)

         else:
                _lowerCamelCase		:							Optional[Any]            =					False
                # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
                if waveform.shape[0] < max_length:
                       if padding == "repeat":
                              _lowerCamelCase		:							Tuple            =					int(max_length / len(__lowerCAmelCase						)						)
                              _lowerCamelCase		:							Dict            =					np.stack(np.tile(__lowerCAmelCase		,   n_repeat + 1						)						)[:max_length]
                       if padding == "repeatpad":
                              _lowerCamelCase		:							Optional[Any]            =					int(max_length / len(__lowerCAmelCase						)						)
                              _lowerCamelCase		:							int            =					np.stack(np.tile(__lowerCAmelCase		,   __lowerCAmelCase						)						)
                       _lowerCamelCase		:							Tuple            =					np.pad(__lowerCAmelCase		,   (0, max_length - waveform.shape[0])		,   mode='''constant'''		,   constant_values=0						)

                if truncation == "fusion":
                       _lowerCamelCase		:							Tuple            =					self._np_extract_fbank_features(__lowerCAmelCase		,   self.mel_filters						)
                       _lowerCamelCase		:							Tuple            =					np.stack([input_mel, input_mel, input_mel, input_mel]		,   axis=0						)
                else:
                       _lowerCamelCase		:							List[Any]            =					self._np_extract_fbank_features(__lowerCAmelCase		,   self.mel_filters_slaney						)[None, :]

         return input_mel, longer




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






         """simple docstring"""






         _lowerCamelCase		:							List[str]            =					truncation if truncation is not None else self.truncation
         _lowerCamelCase		:							Optional[Any]            =					padding if padding else self.padding

         if sampling_rate is not None:
                if sampling_rate != self.sampling_rate:
                       raise ValueError(
                           f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
                           f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
                           f''' was sampled with {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(__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}'''						)
         _lowerCamelCase		:							str            =					is_batched_numpy or (
             isinstance(__lowerCAmelCase		,   (list, tuple)						) and (isinstance(raw_speech[0]		,   (np.ndarray, tuple, list)						))
         )

         if is_batched:
                _lowerCamelCase		:							str            =					[np.asarray(__lowerCAmelCase		,   dtype=np.floataa						) for speech in raw_speech]
         elif not is_batched and not isinstance(__lowerCAmelCase		,   np.ndarray						):
                _lowerCamelCase		:							int            =					np.asarray(__lowerCAmelCase		,   dtype=np.floataa						)
         elif isinstance(__lowerCAmelCase		,   np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
                _lowerCamelCase		:							Union[str, Any]            =					raw_speech.astype(np.floataa						)

         # always return batch
         if not is_batched:
                _lowerCamelCase		:							Dict            =					[np.asarray(__lowerCAmelCase						)]

         # convert to mel spectrogram, truncate and pad if needed.
         _lowerCamelCase		:							Dict            =					[
             self._get_input_mel(__lowerCAmelCase		,   max_length if max_length else self.nb_max_samples		,   __lowerCAmelCase		,   __lowerCAmelCase						)
             for waveform in raw_speech
         ]

         _lowerCamelCase		:							Any            =					[]
         _lowerCamelCase		:							Optional[int]            =					[]
         for mel, longer in padded_inputs:
                input_mel.append(__lowerCAmelCase						)
                is_longer.append(__lowerCAmelCase						)

         if truncation == "fusion" and sum(__lowerCAmelCase						) == 0:
                # if no audio is longer than 10s, then randomly select one audio to be longer
                _lowerCamelCase		:							List[Any]            =					np.random.randint(0		,   len(__lowerCAmelCase						)						)
                _lowerCamelCase		:							Union[str, Any]            =					True

         if isinstance(input_mel[0]		,   __lowerCAmelCase						):
                _lowerCamelCase		:							Optional[int]            =					[np.asarray(__lowerCAmelCase		,   dtype=np.floataa						) for feature in input_mel]

         # is_longer is a list of bool
         _lowerCamelCase		:							Optional[Any]            =					[[longer] for longer in is_longer]

         _lowerCamelCase		:							Tuple            =					{'''input_features''': input_mel, '''is_longer''': is_longer}
         _lowerCamelCase		:							Union[str, Any]            =					BatchFeature(__lowerCAmelCase						)

         if return_tensors is not None:
                _lowerCamelCase		:							List[str]            =					input_features.convert_to_tensors(__lowerCAmelCase						)

         return input_features









'''simple docstring'''






import cva
import numpy as np




class  _A :

		def __init__(   self					:   Any  ,      __magic_name__					:   float  ,      __magic_name__					:   int			)					->    Optional[int]:


					"""simple docstring"""

					if k in (0.04, 0.06):
								__snake_case					:      List[str]							      =     k
								__snake_case					:      int							      =     window_size
					else:
								raise ValueError("""invalid k value"""			)

		def __str__(   self					:   Union[str, Any]			)					->    str:


					"""simple docstring"""

					return str(self.k			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   str			)					->    tuple[cva.Mat, list[list[int]]]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     cva.imread(__magic_name__  ,      0			)
					__snake_case      ,     __snake_case					:      List[str]							      =     img.shape
					__snake_case					:      list[list[int]]							      =     []
					__snake_case					:      str							      =     img.copy()
					__snake_case					:      Tuple							      =     cva.cvtColor(__magic_name__  ,      cva.COLOR_GRAY2RGB			)
					__snake_case      ,     __snake_case					:      List[Any]							      =     np.gradient(__magic_name__			)
					__snake_case					:      Optional[Any]							      =     dx**2
					__snake_case					:      Tuple							      =     dy**2
					__snake_case					:      List[Any]							      =     dx * dy
					__snake_case					:      List[Any]							      =     0.04
					__snake_case					:      Tuple							      =     self.window_size // 2
					for y in range(__magic_name__  ,      h - offset			):
								for x in range(__magic_name__  ,      w - offset			):
											__snake_case					:      Dict							      =     ixx[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()
											__snake_case					:      Optional[int]							      =     iyy[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()
											__snake_case					:      str							      =     ixy[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()

											__snake_case					:      List[str]							      =     (wxx * wyy) - (wxy**2)
											__snake_case					:      Dict							      =     wxx + wyy
											__snake_case					:      List[str]							      =     det - k * (trace**2)
											# Can change the value
											if r > 0.5:
														corner_list.append([x, y, r]			)
														color_img.itemset((y, x, 0)  ,      0			)
														color_img.itemset((y, x, 1)  ,      0			)
														color_img.itemset((y, x, 2)  ,      2_55			)
					return color_img, corner_list


if __name__ == "__main__":
	__UpperCamelCase					    =	HarrisCorner(0.04, 3)
	__UpperCamelCase    ,		__UpperCamelCase					    =	edge_detect.detect("path_to_image")
	cva.imwrite("detect.png", color_img)


