



"""simple docstring"""





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

import numpy as np

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 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					['''input_features''']



       def __init__(							self      :  Any		,    _UpperCAmelCase      :  str=80		,    _UpperCAmelCase      :  Union[str, Any]=16000		,    _UpperCAmelCase      :  Dict=160		,    _UpperCAmelCase      :  Optional[Any]=30		,    _UpperCAmelCase      :  Tuple=400		,    _UpperCAmelCase      :  Optional[int]=0.0		,    _UpperCAmelCase      :  List[Any]=False		,    **_UpperCAmelCase      :  Optional[int]		,    )			->						Tuple:


             '''simple docstring'''




             super().__init__(
                 feature_size=_UpperCAmelCase		,    sampling_rate=_UpperCAmelCase		,    padding_value=_UpperCAmelCase		,    return_attention_mask=_UpperCAmelCase		,    **_UpperCAmelCase		,    )
             UpperCAmelCase_      			=							n_fft
             UpperCAmelCase_      			=							hop_length
             UpperCAmelCase_      			=							chunk_length
             UpperCAmelCase_      			=							chunk_length * sampling_rate
             UpperCAmelCase_      			=							self.n_samples // hop_length
             UpperCAmelCase_      			=							sampling_rate
             UpperCAmelCase_      			=							mel_filter_bank(
                 num_frequency_bins=1 + n_fft // 2		,    num_mel_filters=_UpperCAmelCase		,    min_frequency=0.0		,    max_frequency=8000.0		,    sampling_rate=_UpperCAmelCase		,    norm="slaney"		,    mel_scale="slaney"		,    )



       def 					lowercase__    (							self      :  List[str]		,    _UpperCAmelCase      :  np.array   )			->						np.ndarray:


             '''simple docstring'''




             UpperCAmelCase_      			=							spectrogram(
                 _UpperCAmelCase		,    window_function(self.n_fft		,    "hann"   )		,    frame_length=self.n_fft		,    hop_length=self.hop_length		,    power=2.0		,    mel_filters=self.mel_filters		,    log_mel="log10"		,    )
             UpperCAmelCase_      			=							log_spec[:, :-1]
             UpperCAmelCase_      			=							np.maximum(_UpperCAmelCase		,    log_spec.max() - 8.0   )
             UpperCAmelCase_      			=							(log_spec + 4.0) / 4.0
             return log_spec



       @staticmethod
       # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
       def 					lowercase__    (							_UpperCAmelCase      :  List[np.ndarray]		,    _UpperCAmelCase      :  List[np.ndarray]		,    _UpperCAmelCase      :  float = 0.0   )			->						List[np.ndarray]:


             '''simple docstring'''




             if attention_mask is not None:
                   UpperCAmelCase_      			=							np.array(_UpperCAmelCase		,    np.intaa   )
                   UpperCAmelCase_      			=							[]

                   for vector, length in zip(_UpperCAmelCase		,    attention_mask.sum(-1   )   ):
                         UpperCAmelCase_      			=							(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7   )
                         if length < normed_slice.shape[0]:
                               UpperCAmelCase_      			=							padding_value

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

             return normed_input_values



       def __call__(							self      :  Dict		,    _UpperCAmelCase      :  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		,    _UpperCAmelCase      :  bool = True		,    _UpperCAmelCase      :  Optional[int] = None		,    _UpperCAmelCase      :  Optional[Union[str, TensorType]] = None		,    _UpperCAmelCase      :  Optional[bool] = None		,    _UpperCAmelCase      :  Optional[str] = "max_length"		,    _UpperCAmelCase      :  Optional[int] = None		,    _UpperCAmelCase      :  Optional[int] = None		,    _UpperCAmelCase      :  Optional[bool] = None		,    **_UpperCAmelCase      :  Optional[Any]		,    )			->						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.__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(_UpperCAmelCase		,    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(_UpperCAmelCase		,    (list, tuple)   ) and (isinstance(raw_speech[0]		,    (np.ndarray, tuple, list)   ))
             )

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

             UpperCAmelCase_      			=							BatchFeature({"input_features": raw_speech}   )

             # convert into correct format for padding

             UpperCAmelCase_      			=							self.pad(
                 _UpperCAmelCase		,    padding=_UpperCAmelCase		,    max_length=max_length if max_length else self.n_samples		,    truncation=_UpperCAmelCase		,    pad_to_multiple_of=_UpperCAmelCase		,    return_attention_mask=return_attention_mask or do_normalize		,    )

             # zero-mean and unit-variance normalization
             if do_normalize:
                   UpperCAmelCase_      			=							self.zero_mean_unit_var_norm(
                       padded_inputs["input_features"]		,    attention_mask=padded_inputs["attention_mask"]		,    padding_value=self.padding_value		,    )
                   UpperCAmelCase_      			=							np.stack(padded_inputs["input_features"]		,    axis=0   )

             # make sure list is in array format
             UpperCAmelCase_      			=							padded_inputs.get("input_features"   ).transpose(2		,    0		,    1   )

             UpperCAmelCase_      			=							[self._np_extract_fbank_features(_UpperCAmelCase   ) for waveform in input_features[0]]

             if isinstance(input_features[0]		,    _UpperCAmelCase   ):
                   UpperCAmelCase_      			=							[np.asarray(_UpperCAmelCase		,    dtype=np.floataa   ) for feature in input_features]
             else:
                   UpperCAmelCase_      			=							input_features

             if return_attention_mask:
                   # rescale from sample (48000) to feature (3000)
                   UpperCAmelCase_      			=							padded_inputs["attention_mask"][:, :: self.hop_length]

             if return_tensors is not None:
                   UpperCAmelCase_      			=							padded_inputs.convert_to_tensors(_UpperCAmelCase   )

             return padded_inputs







       def 					lowercase__    (							self      :  int   )			->						Dict[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							copy.deepcopy(self.__dict__   )
             UpperCAmelCase_      			=							self.__class__.__name__
             if "mel_filters" in output:
                   del output["mel_filters"]
             return output









"""simple docstring"""





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

import cva
lowerCamelCase       	=						""""""
lowerCamelCase       	=						""""""
lowerCamelCase       	=						""""""
lowerCamelCase       	=						1  # (0 is vertical, 1 is horizontal)
def        a__     (				):
      UpperCAmelCase_			,   UpperCAmelCase_      			=							get_dataset(lowerCAmelCase__     ,      lowerCAmelCase__				)
      print("Processing..."				)
      UpperCAmelCase_			,   UpperCAmelCase_			,   UpperCAmelCase_      			=							update_image_and_anno(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				)

      for index, image in enumerate(lowerCAmelCase__				):
            # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
            UpperCAmelCase_      			=							random_chars(32				)
            UpperCAmelCase_      			=							paths[index].split(os.sep				)[-1].rsplit("."     ,      1				)[0]
            UpperCAmelCase_      			=							f"""{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"""
            cva.imwrite(f"""/{file_root}.jpg"""     ,      lowerCAmelCase__     ,      [cva.IMWRITE_JPEG_QUALITY, 85]				)
            print(f"""Success {index+1}/{len(lowerCAmelCase__				)} with {file_name}"""				)
            UpperCAmelCase_      			=							[]
            for anno in new_annos[index]:
                  UpperCAmelCase_      			=							f"""{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"""
                  annos_list.append(lowerCAmelCase__				)
            with open(f"""/{file_root}.txt"""     ,      "w"				) as outfile:
                  outfile.write("\n".join(line for line in annos_list				)				)
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      UpperCAmelCase_      			=							[]
      UpperCAmelCase_      			=							[]
      for label_file in glob.glob(os.path.join(lowerCAmelCase__     ,      "*.txt"				)				):
            UpperCAmelCase_      			=							label_file.split(os.sep				)[-1].rsplit("."     ,      1				)[0]
            with open(lowerCAmelCase__				) as in_file:
                  UpperCAmelCase_      			=							in_file.readlines()
            UpperCAmelCase_      			=							os.path.join(lowerCAmelCase__     ,      f"""{label_name}.jpg"""				)

            UpperCAmelCase_      			=							[]
            for obj_list in obj_lists:
                  UpperCAmelCase_      			=							obj_list.rstrip("\n"				).split(" "				)
                  boxes.append(
                      [
                          int(obj[0]				),
                          float(obj[1]				),
                          float(obj[2]				),
                          float(obj[3]				),
                          float(obj[4]				),
                      ]				)
            if not boxes:
                  continue
            img_paths.append(lowerCAmelCase__				)
            labels.append(lowerCAmelCase__				)
      return img_paths, labels
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__ = 1				):
      UpperCAmelCase_      			=							[]
      UpperCAmelCase_      			=							[]
      UpperCAmelCase_      			=							[]
      for idx in range(len(lowerCAmelCase__				)				):
            UpperCAmelCase_      			=							[]
            UpperCAmelCase_      			=							img_list[idx]
            path_list.append(lowerCAmelCase__				)
            UpperCAmelCase_      			=							anno_list[idx]
            UpperCAmelCase_      			=							cva.imread(lowerCAmelCase__				)
            if flip_type == 1:
                  UpperCAmelCase_      			=							cva.flip(lowerCAmelCase__     ,      lowerCAmelCase__				)
                  for bbox in img_annos:
                        UpperCAmelCase_      			=							1 - bbox[1]
                        new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]				)
            elif flip_type == 0:
                  UpperCAmelCase_      			=							cva.flip(lowerCAmelCase__     ,      lowerCAmelCase__				)
                  for bbox in img_annos:
                        UpperCAmelCase_      			=							1 - bbox[2]
                        new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]				)
            new_annos_lists.append(lowerCAmelCase__				)
            new_imgs_list.append(lowerCAmelCase__				)
      return new_imgs_list, new_annos_lists, path_list
def        a__     (				lowerCAmelCase__ = 32				):
      assert number_char > 1, "The number of character should greater than 1"
      UpperCAmelCase_      			=							ascii_lowercase + digits
      return "".join(random.choice(lowerCAmelCase__				) for _ in range(lowerCAmelCase__				)				)


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




