


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


__a	 =      logging.get_logger(__name__)






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							['''input_features''', '''is_longer''']





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






         """simple docstring"""







         super().__init__(
             feature_size=__lowerCamelCase       ,							sampling_rate=__lowerCamelCase       ,							padding_value=__lowerCamelCase       ,							return_attention_mask=__lowerCamelCase       ,							**__lowerCamelCase       ,							)
         UpperCAmelCase      					=  top_db
         UpperCAmelCase      					=  truncation
         UpperCAmelCase      					=  padding
         UpperCAmelCase      					=  fft_window_size
         UpperCAmelCase      					=  (fft_window_size >> 1) + 1
         UpperCAmelCase      					=  hop_length
         UpperCAmelCase      					=  max_length_s
         UpperCAmelCase      					=  max_length_s * sampling_rate
         UpperCAmelCase      					=  sampling_rate
         UpperCAmelCase      					=  frequency_min
         UpperCAmelCase      					=  frequency_max
         UpperCAmelCase      					=  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"""       ,							)
         UpperCAmelCase      					=  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 							_lowercase					(		self    :       List[Any]							)     -> Dict[str, Any]:






         """simple docstring"""







         UpperCAmelCase      					=  copy.deepcopy(self.__dict__							)
         UpperCAmelCase      					=  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 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       np.array       ,							__lowerCamelCase    :       Optional[np.array] = None							)     -> np.ndarray:






         """simple docstring"""







         UpperCAmelCase      					=  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 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       str       ,							__lowerCamelCase    :       List[str]       ,							__lowerCamelCase    :       Optional[int]							)     -> Any:






         """simple docstring"""







         UpperCAmelCase      					=  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
            UpperCAmelCase      					=  [0]
         if len(ranges[2]							) == 0:
            # if the audio is too short, we just use the first chunk
            UpperCAmelCase      					=  [0]
         # randomly choose index for each part
         UpperCAmelCase      					=  np.random.choice(ranges[0]							)
         UpperCAmelCase      					=  np.random.choice(ranges[1]							)
         UpperCAmelCase      					=  np.random.choice(ranges[2]							)

         UpperCAmelCase      					=  mel[idx_front : idx_front + chunk_frames, :]
         UpperCAmelCase      					=  mel[idx_middle : idx_middle + chunk_frames, :]
         UpperCAmelCase      					=  mel[idx_back : idx_back + chunk_frames, :]

         UpperCAmelCase      					=  torch.tensor(mel[None, None, :]							)
         UpperCAmelCase      					=  torch.nn.functional.interpolate(
             __lowerCamelCase       ,							size=[chunk_frames, 6_4]       ,							mode="""bilinear"""       ,							align_corners=__lowerCamelCase							)
         UpperCAmelCase      					=  mel_shrink[0][0].numpy()
         UpperCAmelCase      					=  np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]       ,							axis=0							)
         return mel_fusion





      def 							_lowercase					(		self    :       str       ,							__lowerCamelCase    :       np.array       ,							__lowerCamelCase    :       Dict       ,							__lowerCamelCase    :       List[Any]       ,							__lowerCamelCase    :       Optional[Any]							)     -> np.array:






         """simple docstring"""







         if waveform.shape[0] > max_length:
            if truncation == "rand_trunc":
               UpperCAmelCase      					=  True
               # random crop to max_length (for compatibility) -> this should be handled by self.pad
               UpperCAmelCase      					=  len(__lowerCamelCase							) - max_length
               UpperCAmelCase      					=  np.random.randint(0       ,							overflow + 1							)
               UpperCAmelCase      					=  waveform[idx : idx + max_length]
               UpperCAmelCase      					=  self._np_extract_fbank_features(__lowerCamelCase       ,							self.mel_filters_slaney							)[None, :]
            elif truncation == "fusion":
               UpperCAmelCase      					=  self._np_extract_fbank_features(__lowerCamelCase       ,							self.mel_filters							)
               UpperCAmelCase      					=  max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
               UpperCAmelCase      					=  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.
                  UpperCAmelCase      					=  np.stack([mel, mel, mel, mel]       ,							axis=0							)
                  UpperCAmelCase      					=  False
               else:
                  UpperCAmelCase      					=  self._random_mel_fusion(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)
                  UpperCAmelCase      					=  True
            else:
               raise NotImplementedError(F"""data_truncating {truncation} not implemented"""							)

         else:
            UpperCAmelCase      					=  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":
                  UpperCAmelCase      					=  int(max_length / len(__lowerCamelCase							)							)
                  UpperCAmelCase      					=  np.stack(np.tile(__lowerCamelCase       ,							n_repeat + 1							)							)[:max_length]
               if padding == "repeatpad":
                  UpperCAmelCase      					=  int(max_length / len(__lowerCamelCase							)							)
                  UpperCAmelCase      					=  np.stack(np.tile(__lowerCamelCase       ,							__lowerCamelCase							)							)
               UpperCAmelCase      					=  np.pad(__lowerCamelCase       ,							(0, max_length - waveform.shape[0])       ,							mode="""constant"""       ,							constant_values=0							)

            if truncation == "fusion":
               UpperCAmelCase      					=  self._np_extract_fbank_features(__lowerCamelCase       ,							self.mel_filters							)
               UpperCAmelCase      					=  np.stack([input_mel, input_mel, input_mel, input_mel]       ,							axis=0							)
            else:
               UpperCAmelCase      					=  self._np_extract_fbank_features(__lowerCamelCase       ,							self.mel_filters_slaney							)[None, :]

         return input_mel, longer





      def __call__(		self    :       int       ,							__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    :       int       ,							)     -> BatchFeature:






         """simple docstring"""







         UpperCAmelCase      					=  truncation if truncation is not None else self.truncation
         UpperCAmelCase      					=  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."""							)

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

         if is_batched:
            UpperCAmelCase      					=  [np.asarray(__lowerCamelCase       ,							dtype=np.floataa							) for speech in raw_speech]
         elif not is_batched and not isinstance(__lowerCamelCase       ,							np.ndarray							):
            UpperCAmelCase      					=  np.asarray(__lowerCamelCase       ,							dtype=np.floataa							)
         elif isinstance(__lowerCamelCase       ,							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      					=  [np.asarray(__lowerCamelCase							)]

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

         UpperCAmelCase      					=  []
         UpperCAmelCase      					=  []
         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
            UpperCAmelCase      					=  np.random.randint(0       ,							len(__lowerCamelCase							)							)
            UpperCAmelCase      					=  True

         if isinstance(input_mel[0]       ,							__lowerCamelCase							):
            UpperCAmelCase      					=  [np.asarray(__lowerCamelCase       ,							dtype=np.floataa							) for feature in input_mel]

         # is_longer is a list of bool
         UpperCAmelCase      					=  [[longer] for longer in is_longer]

         UpperCAmelCase      					=  {"""input_features""": input_mel, """is_longer""": is_longer}
         UpperCAmelCase      					=  BatchFeature(__lowerCamelCase							)

         if return_tensors is not None:
            UpperCAmelCase      					=  input_features.convert_to_tensors(__lowerCamelCase							)

         return input_features











import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__a:     List[Any]   					=					(720, 1280)  # Height, Width
__a:     str   					=					(0.4, 0.6)  # if height or width lower than this scale, drop it.
__a:     Optional[Any]   					=					1 / 100
__a:     Optional[Any]   					=					''''''
__a:     Tuple   					=					''''''
__a:     List[Any]   					=					''''''
__a:     Union[str, Any]   					=					250




def       _SCREAMING_SNAKE_CASE (							)				->						None:
       _UpperCAmelCase	,			_UpperCAmelCase							=    get_dataset(__snake_case   ,      __snake_case )
       for index in range(__snake_case ):
              _UpperCAmelCase							=    random.sample(range(len(__snake_case ) )   ,      4 )
              _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    update_image_and_anno(
                  __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      filter_scale=__snake_case   ,      )

              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              _UpperCAmelCase							=    random_chars(3_2 )
              _UpperCAmelCase							=    path.split(os.sep )[-1].rsplit("""."""   ,      1 )[0]
              _UpperCAmelCase							=    f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
              cva.imwrite(f"""{file_root}.jpg"""   ,      __snake_case   ,      [cva.IMWRITE_JPEG_QUALITY, 8_5] )
              print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}""" )
              _UpperCAmelCase							=    []
              for anno in new_annos:
                     _UpperCAmelCase							=    anno[3] - anno[1]
                     _UpperCAmelCase							=    anno[4] - anno[2]
                     _UpperCAmelCase							=    anno[1] + width / 2
                     _UpperCAmelCase							=    anno[2] + height / 2
                     _UpperCAmelCase							=    f"""{anno[0]} {x_center} {y_center} {width} {height}"""
                     annos_list.append(__snake_case )
              with open(f"""{file_root}.txt"""   ,      """w""" ) as outfile:
                     outfile.write("""\n""".join(line for line in annos_list ) )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						tuple[list, list]:
       _UpperCAmelCase							=    []
       _UpperCAmelCase							=    []
       for label_file in glob.glob(os.path.join(__snake_case   ,      """*.txt""" ) ):
              _UpperCAmelCase							=    label_file.split(os.sep )[-1].rsplit("""."""   ,      1 )[0]
              with open(__snake_case ) as in_file:
                     _UpperCAmelCase							=    in_file.readlines()
              _UpperCAmelCase							=    os.path.join(__snake_case   ,      f"""{label_name}.jpg""" )

              _UpperCAmelCase							=    []
              for obj_list in obj_lists:
                     _UpperCAmelCase							=    obj_list.rstrip("""\n""" ).split(""" """ )
                     _UpperCAmelCase							=    float(obj[1] ) - float(obj[3] ) / 2
                     _UpperCAmelCase							=    float(obj[2] ) - float(obj[4] ) / 2
                     _UpperCAmelCase							=    float(obj[1] ) + float(obj[3] ) / 2
                     _UpperCAmelCase							=    float(obj[2] ) + float(obj[4] ) / 2

                     boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
              if not boxes:
                     continue
              img_paths.append(__snake_case )
              labels.append(__snake_case )
       return img_paths, labels




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case = 0.0   ,      )				->						tuple[list, list, str]:
       _UpperCAmelCase							=    np.zeros([output_size[0], output_size[1], 3]   ,      dtype=np.uinta )
       _UpperCAmelCase							=    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       _UpperCAmelCase							=    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       _UpperCAmelCase							=    int(scale_x * output_size[1] )
       _UpperCAmelCase							=    int(scale_y * output_size[0] )

       _UpperCAmelCase							=    []
       _UpperCAmelCase							=    []
       for i, index in enumerate(__snake_case ):
              _UpperCAmelCase							=    all_img_list[index]
              path_list.append(__snake_case )
              _UpperCAmelCase							=    all_annos[index]
              _UpperCAmelCase							=    cva.imread(__snake_case )
              if i == 0:  # top-left
                     _UpperCAmelCase							=    cva.resize(__snake_case   ,      (divid_point_x, divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    bbox[1] * scale_x
                            _UpperCAmelCase							=    bbox[2] * scale_y
                            _UpperCAmelCase							=    bbox[3] * scale_x
                            _UpperCAmelCase							=    bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
              elif i == 1:  # top-right
                     _UpperCAmelCase							=    cva.resize(__snake_case   ,      (output_size[1] - divid_point_x, divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    scale_x + bbox[1] * (1 - scale_x)
                            _UpperCAmelCase							=    bbox[2] * scale_y
                            _UpperCAmelCase							=    scale_x + bbox[3] * (1 - scale_x)
                            _UpperCAmelCase							=    bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
              elif i == 2:  # bottom-left
                     _UpperCAmelCase							=    cva.resize(__snake_case   ,      (divid_point_x, output_size[0] - divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    bbox[1] * scale_x
                            _UpperCAmelCase							=    scale_y + bbox[2] * (1 - scale_y)
                            _UpperCAmelCase							=    bbox[3] * scale_x
                            _UpperCAmelCase							=    scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
              else:  # bottom-right
                     _UpperCAmelCase							=    cva.resize(
                         __snake_case   ,      (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
                     _UpperCAmelCase							=    img
                     for bbox in img_annos:
                            _UpperCAmelCase							=    scale_x + bbox[1] * (1 - scale_x)
                            _UpperCAmelCase							=    scale_y + bbox[2] * (1 - scale_y)
                            _UpperCAmelCase							=    scale_x + bbox[3] * (1 - scale_x)
                            _UpperCAmelCase							=    scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )

    # Remove bounding box small than scale of filter
       if filter_scale > 0:
              _UpperCAmelCase							=    [
                  anno
                  for anno in new_anno
                  if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
              ]

       return output_img, new_anno, path_list[0]




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						str:
       assert number_char > 1, "The number of character should greater than 1"
       _UpperCAmelCase							=    ascii_lowercase + digits
       return "".join(random.choice(__snake_case ) for _ in range(__snake_case ) )


if __name__ == "__main__":
  main()
  print('''DONE ✅''')