


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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase_ :     Any								=      logging.get_logger(__name__)


if is_vision_available():
 import PIL

class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		Union[str, Any]    							=						['''pixel_values''']


 def __init__(      self      ,      __lowercase = True      ,      __lowercase = None      ,      __lowercase = PILImageResampling.BICUBIC      ,      __lowercase = True      ,      __lowercase = None      ,      __lowercase = True      ,      __lowercase = 1 / 255      ,      __lowercase = True      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = True      ,      **__lowercase      ,      ):

  """simple docstring"""







  super().__init__(**__lowercase						)
  __A  :		Tuple       =			size if size is not None else {'shortest_edge': 224}
  __A  :		Dict       =			get_size_dict(__lowercase      ,      default_to_square=__lowercase						)
  __A  :		str       =			crop_size if crop_size is not None else {'height': 224, 'width': 224}
  __A  :		int       =			get_size_dict(__lowercase      ,      default_to_square=__lowercase      ,      param_name='crop_size'						)

  __A  :		Tuple       =			do_resize
  __A  :		Any       =			size
  __A  :		str       =			resample
  __A  :		Any       =			do_center_crop
  __A  :		Union[str, Any]       =			crop_size
  __A  :		Any       =			do_rescale
  __A  :		str       =			rescale_factor
  __A  :		str       =			do_normalize
  __A  :		Tuple       =			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
  __A  :		str       =			image_std if image_std is not None else OPENAI_CLIP_STD
  __A  :		List[Any]       =			do_convert_rgb


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = PILImageResampling.BICUBIC      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		Optional[Any]       =			get_size_dict(__lowercase      ,      default_to_square=__lowercase						)
  if "shortest_edge" not in size:
   raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""						)
  __A  :		List[Any]       =			get_resize_output_image_size(__lowercase      ,      size=size['shortest_edge']      ,      default_to_square=__lowercase						)
  return resize(__lowercase      ,      size=__lowercase      ,      resample=__lowercase      ,      data_format=__lowercase      ,      **__lowercase						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		Any       =			get_size_dict(__lowercase						)
  if "height" not in size or "width" not in size:
   raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""						)
  return center_crop(__lowercase      ,      size=(size['height'], size['width'])      ,      data_format=__lowercase      ,      **__lowercase						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







  return rescale(__lowercase      ,      scale=__lowercase      ,      data_format=__lowercase      ,      **__lowercase						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







  return normalize(__lowercase      ,      mean=__lowercase      ,      std=__lowercase      ,      data_format=__lowercase      ,      **__lowercase						)







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = ChannelDimension.FIRST      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		Dict       =			do_resize if do_resize is not None else self.do_resize
  __A  :		Dict       =			size if size is not None else self.size
  __A  :		Optional[Any]       =			get_size_dict(__lowercase      ,      param_name='size'      ,      default_to_square=__lowercase						)
  __A  :		int       =			resample if resample is not None else self.resample
  __A  :		Tuple       =			do_center_crop if do_center_crop is not None else self.do_center_crop
  __A  :		Dict       =			crop_size if crop_size is not None else self.crop_size
  __A  :		Dict       =			get_size_dict(__lowercase      ,      param_name='crop_size'      ,      default_to_square=__lowercase						)
  __A  :		Dict       =			do_rescale if do_rescale is not None else self.do_rescale
  __A  :		Union[str, Any]       =			rescale_factor if rescale_factor is not None else self.rescale_factor
  __A  :		List[Any]       =			do_normalize if do_normalize is not None else self.do_normalize
  __A  :		Any       =			image_mean if image_mean is not None else self.image_mean
  __A  :		Optional[Any]       =			image_std if image_std is not None else self.image_std
  __A  :		List[str]       =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

  __A  :		List[str]       =			make_list_of_images(__lowercase						)

  if not valid_images(__lowercase						):
   raise ValueError(
       'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
       'torch.Tensor, tf.Tensor or jax.ndarray.'						)

  if do_resize and size is None:
   raise ValueError('Size must be specified if do_resize is True.'						)

  if do_center_crop and crop_size is None:
   raise ValueError('Crop size must be specified if do_center_crop is True.'						)

  if do_rescale and rescale_factor is None:
   raise ValueError('Rescale factor must be specified if do_rescale is True.'						)

  if do_normalize and (image_mean is None or image_std is None):
   raise ValueError('Image mean and std must be specified if do_normalize is True.'						)

  # PIL RGBA images are converted to RGB
  if do_convert_rgb:
   __A  :		Optional[Any]       =			[convert_to_rgb(__lowercase						) for image in images]

  # All transformations expect numpy arrays.
  __A  :		Any       =			[to_numpy_array(__lowercase						) for image in images]

  if do_resize:
   __A  :		str       =			[self.resize(image=__lowercase      ,      size=__lowercase      ,      resample=__lowercase						) for image in images]

  if do_center_crop:
   __A  :		List[Any]       =			[self.center_crop(image=__lowercase      ,      size=__lowercase						) for image in images]

  if do_rescale:
   __A  :		List[str]       =			[self.rescale(image=__lowercase      ,      scale=__lowercase						) for image in images]

  if do_normalize:
   __A  :		List[str]       =			[self.normalize(image=__lowercase      ,      mean=__lowercase      ,      std=__lowercase						) for image in images]

  __A  :		str       =			[to_channel_dimension_format(__lowercase      ,      __lowercase						) for image in images]

  __A  :		List[str]       =			{'pixel_values': images}
  return BatchFeature(data=__lowercase      ,      tensor_type=__lowercase						)










'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits

import cva
UpperCAmelCase_ :     int								=      ''
UpperCAmelCase_ :     Union[str, Any]								=      ''
UpperCAmelCase_ :     Any								=      ''
UpperCAmelCase_ :     int								=      1  # (0 is vertical, 1 is horizontal)


def 			_lowercase       (				):
 __A     ,__A  :		Optional[int]       =			get_dataset(UpperCamelCase__,				UpperCamelCase__			)
 print('Processing...'			)
 __A     ,__A     ,__A  :		Any       =			update_image_and_anno(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

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


def 			_lowercase       (				UpperCamelCase__       :					str,				UpperCamelCase__       :					str			):
 __A  :		Tuple       =			[]
 __A  :		int       =			[]
 for label_file in glob.glob(os.path.join(UpperCamelCase__,				'*.txt'			)			):
  __A  :		Optional[int]       =			label_file.split(os.sep			)[-1].rsplit('.',				1			)[0]
  with open(UpperCamelCase__			) as in_file:
   __A  :		Optional[Any]       =			in_file.readlines()
  __A  :		int       =			os.path.join(UpperCamelCase__,				f"""{label_name}.jpg"""			)

  __A  :		Optional[int]       =			[]
  for obj_list in obj_lists:
   __A  :		str       =			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(UpperCamelCase__			)
  labels.append(UpperCamelCase__			)
 return img_paths, labels


def 			_lowercase       (				UpperCamelCase__       :					list,				UpperCamelCase__       :					list,				UpperCamelCase__       :					int = 1			):
 __A  :		int       =			[]
 __A  :		Optional[Any]       =			[]
 __A  :		str       =			[]
 for idx in range(len(UpperCamelCase__			)			):
  __A  :		List[Any]       =			[]
  __A  :		List[str]       =			img_list[idx]
  path_list.append(UpperCamelCase__			)
  __A  :		Optional[Any]       =			anno_list[idx]
  __A  :		Union[str, Any]       =			cva.imread(UpperCamelCase__			)
  if flip_type == 1:
   __A  :		int       =			cva.flip(UpperCamelCase__,				UpperCamelCase__			)
   for bbox in img_annos:
    __A  :		Union[str, Any]       =			1 - bbox[1]
    new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
  elif flip_type == 0:
   __A  :		Tuple       =			cva.flip(UpperCamelCase__,				UpperCamelCase__			)
   for bbox in img_annos:
    __A  :		Dict       =			1 - bbox[2]
    new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
  new_annos_lists.append(UpperCamelCase__			)
  new_imgs_list.append(UpperCamelCase__			)
 return new_imgs_list, new_annos_lists, path_list


def 			_lowercase       (				UpperCamelCase__       :					int = 32			):
 assert number_char > 1, "The number of character should greater than 1"
 __A  :		int       =			ascii_lowercase + digits
 return "".join(random.choice(UpperCamelCase__			) for _ in range(UpperCamelCase__			)			)


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






