'''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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCAmelCase	:       List[str]						       =	logging.get_logger(__name__)






class 						A   (   UpperCAmelCase   ):
							a_        =      ['''pixel_values''']





							def __init__(      self				:							Optional[Any]     ,		__a				:							bool = True     ,		__a				:							Optional[Dict[str, int]] = None     ,		__a				:							PILImageResampling = PILImageResampling.BICUBIC     ,		__a				:							bool = True     ,		__a				:							bool = True     ,		__a				:							Union[int, float] = 1 / 2_5_5     ,		__a				:							Dict[str, int] = None     ,		__a				:							bool = True     ,		__a				:							Optional[Union[float, List[float]]] = None     ,		__a				:							Optional[Union[float, List[float]]] = None     ,		**__a				:							int     ,		)							->	None:
								super().__init__(**__a	)
								__UpperCAmelCase							=			size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
								__UpperCAmelCase							=			get_size_dict(__a	)
								__UpperCAmelCase							=			crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
								__UpperCAmelCase							=			get_size_dict(__a     ,		default_to_square=__a     ,		param_name='''crop_size'''	)

								__UpperCAmelCase							=			do_resize
								__UpperCAmelCase							=			do_rescale
								__UpperCAmelCase							=			do_normalize
								__UpperCAmelCase							=			do_center_crop
								__UpperCAmelCase							=			crop_size
								__UpperCAmelCase							=			size
								__UpperCAmelCase							=			resample
								__UpperCAmelCase							=			rescale_factor
								__UpperCAmelCase							=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
								__UpperCAmelCase							=			image_std if image_std is not None else IMAGENET_DEFAULT_STD





							def       snake_case__						(      self				:							Any     ,		__a				:							np.ndarray     ,		__a				:							Dict[str, int]     ,		__a				:							PILImageResampling = PILImageResampling.BILINEAR     ,		__a				:							Optional[Union[str, ChannelDimension]] = None     ,		**__a				:							Union[str, Any]     ,		)							->	np.ndarray:
								__UpperCAmelCase							=			get_size_dict(__a	)

								if "shortest_edge" in size:
									__UpperCAmelCase							=			get_resize_output_image_size(__a     ,		size=size['''shortest_edge''']     ,		default_to_square=__a	)
									# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
								elif "height" in size and "width" in size:
									__UpperCAmelCase							=			(size['''height'''], size['''width'''])
								else:
									raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""	)
								return resize(__a     ,		size=__a     ,		resample=__a     ,		data_format=__a     ,		**__a	)





							def       snake_case__						(      self				:							List[str]     ,		__a				:							np.ndarray     ,		__a				:							Dict[str, int]     ,		__a				:							Optional[Union[str, ChannelDimension]] = None     ,		**__a				:							str     ,		)							->	np.ndarray:
								__UpperCAmelCase							=			get_size_dict(__a	)
								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(__a     ,		size=(size['''height'''], size['''width'''])     ,		data_format=__a     ,		**__a	)





							def       snake_case__						(      self				:							Dict     ,		__a				:							np.ndarray     ,		__a				:							float     ,		__a				:							Optional[Union[str, ChannelDimension]] = None     ,		**__a				:							List[Any]	)							->	np.ndarray:
								return rescale(__a     ,		scale=__a     ,		data_format=__a     ,		**__a	)





							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							np.ndarray     ,		__a				:							Union[float, List[float]]     ,		__a				:							Union[float, List[float]]     ,		__a				:							Optional[Union[str, ChannelDimension]] = None     ,		**__a				:							int     ,		)							->	np.ndarray:
								return normalize(__a     ,		mean=__a     ,		std=__a     ,		data_format=__a     ,		**__a	)






							def       snake_case__						(      self				:							int     ,		__a				:							ImageInput     ,		__a				:							Optional[bool] = None     ,		__a				:							Dict[str, int] = None     ,		__a				:							PILImageResampling = None     ,		__a				:							bool = None     ,		__a				:							int = None     ,		__a				:							Optional[bool] = None     ,		__a				:							Optional[float] = None     ,		__a				:							Optional[bool] = None     ,		__a				:							Optional[Union[float, List[float]]] = None     ,		__a				:							Optional[Union[float, List[float]]] = None     ,		__a				:							Optional[Union[str, TensorType]] = None     ,		__a				:							Union[str, ChannelDimension] = ChannelDimension.FIRST     ,		**__a				:							Dict     ,		)							->	BatchFeature:
								__UpperCAmelCase							=			do_resize if do_resize is not None else self.do_resize
								__UpperCAmelCase							=			do_rescale if do_rescale is not None else self.do_rescale
								__UpperCAmelCase							=			do_normalize if do_normalize is not None else self.do_normalize
								__UpperCAmelCase							=			do_center_crop if do_center_crop is not None else self.do_center_crop
								__UpperCAmelCase							=			crop_size if crop_size is not None else self.crop_size
								__UpperCAmelCase							=			get_size_dict(__a     ,		param_name='''crop_size'''     ,		default_to_square=__a	)
								__UpperCAmelCase							=			resample if resample is not None else self.resample
								__UpperCAmelCase							=			rescale_factor if rescale_factor is not None else self.rescale_factor
								__UpperCAmelCase							=			image_mean if image_mean is not None else self.image_mean
								__UpperCAmelCase							=			image_std if image_std is not None else self.image_std

								__UpperCAmelCase							=			size if size is not None else self.size
								__UpperCAmelCase							=			get_size_dict(__a	)

								if not is_batched(__a	):
									__UpperCAmelCase							=			[images]

								if not valid_images(__a	):
									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.'''	)

								# All transformations expect numpy arrays.
								__UpperCAmelCase							=			[to_numpy_array(__a	) for image in images]

								if do_resize:
									__UpperCAmelCase							=			[self.resize(image=__a     ,		size=__a     ,		resample=__a	) for image in images]

								if do_center_crop:
									__UpperCAmelCase							=			[self.center_crop(image=__a     ,		size=__a	) for image in images]

								if do_rescale:
									__UpperCAmelCase							=			[self.rescale(image=__a     ,		scale=__a	) for image in images]

								if do_normalize:
									__UpperCAmelCase							=			[self.normalize(image=__a     ,		mean=__a     ,		std=__a	) for image in images]

								__UpperCAmelCase							=			[to_channel_dimension_format(__a     ,		__a	) for image in images]

								__UpperCAmelCase							=			{'''pixel_values''': images}
								return BatchFeature(data=__a     ,		tensor_type=__a	)

'''simple docstring'''


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

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





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase				,			__UpperCAmelCase							=			get_dataset(UpperCamelCase__    ,					UpperCamelCase__       )
	print('''Processing...'''       )
	__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			update_image_and_anno(UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__       )

	for index, image in enumerate(UpperCamelCase__       ):
		# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
		__UpperCAmelCase							=			random_chars(3_2       )
		__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"""    ,					UpperCamelCase__    ,					[cva.IMWRITE_JPEG_QUALITY, 8_5]       )
		print(f"""Success {index+1}/{len(UpperCamelCase__       )} 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(UpperCamelCase__       )
		with open(f"""/{file_root}.txt"""    ,					'''w'''       ) as outfile:
			outfile.write('''\n'''.join(line for line in annos_list       )       )





def       lowerCAmelCase					(		UpperCamelCase__				:   str    ,					UpperCamelCase__				:   str       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[]
	__UpperCAmelCase							=			[]
	for label_file in glob.glob(os.path.join(UpperCamelCase__    ,					'''*.txt'''       )       ):
		__UpperCAmelCase							=			label_file.split(os.sep       )[-1].rsplit('''.'''    ,					1       )[0]
		with open(UpperCamelCase__       ) as in_file:
			__UpperCAmelCase							=			in_file.readlines()
		__UpperCAmelCase							=			os.path.join(UpperCamelCase__    ,					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(UpperCamelCase__       )
		labels.append(UpperCamelCase__       )
	return img_paths, labels





def       lowerCAmelCase					(		UpperCamelCase__				:   list    ,					UpperCamelCase__				:   list    ,					UpperCamelCase__				:   int = 1       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[]
	__UpperCAmelCase							=			[]
	__UpperCAmelCase							=			[]
	for idx in range(len(UpperCamelCase__       )       ):
		__UpperCAmelCase							=			[]
		__UpperCAmelCase							=			img_list[idx]
		path_list.append(UpperCamelCase__       )
		__UpperCAmelCase							=			anno_list[idx]
		__UpperCAmelCase							=			cva.imread(UpperCamelCase__       )
		if flip_type == 1:
			__UpperCAmelCase							=			cva.flip(UpperCamelCase__    ,					UpperCamelCase__       )
			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(UpperCamelCase__    ,					UpperCamelCase__       )
			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(UpperCamelCase__       )
		new_imgs_list.append(UpperCamelCase__       )
	return new_imgs_list, new_annos_lists, path_list





def       lowerCAmelCase					(		UpperCamelCase__				:   int = 3_2       ):


	"""simple docstring"""
	assert number_char > 1, "The number of character should greater than 1"
	__UpperCAmelCase							=			ascii_lowercase + digits
	return "".join(random.choice(UpperCamelCase__       ) for _ in range(UpperCamelCase__       )       )


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