
'''simple docstring'''







import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import BeitImageProcessor


class 		__UpperCAmelCase   (      unittest.TestCase		):


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=7					,     lowerCAmelCase_=3					,     lowerCAmelCase_=18					,     lowerCAmelCase_=30					,     lowerCAmelCase_=4_00					,     lowerCAmelCase_=True					,     lowerCAmelCase_=None					,     lowerCAmelCase_=True					,     lowerCAmelCase_=None					,     lowerCAmelCase_=True					,     lowerCAmelCase_=[0.5, 0.5, 0.5]					,     lowerCAmelCase_=[0.5, 0.5, 0.5]					,     lowerCAmelCase_=False					,     ):





								"""simple docstring"""






								_snake_case     		=	size if size is not None else {'height': 20, 'width': 20}
								_snake_case     		=	crop_size if crop_size is not None else {'height': 18, 'width': 18}
								_snake_case     		=	parent
								_snake_case     		=	batch_size
								_snake_case     		=	num_channels
								_snake_case     		=	image_size
								_snake_case     		=	min_resolution
								_snake_case     		=	max_resolution
								_snake_case     		=	do_resize
								_snake_case     		=	size
								_snake_case     		=	do_center_crop
								_snake_case     		=	crop_size
								_snake_case     		=	do_normalize
								_snake_case     		=	image_mean
								_snake_case     		=	image_std
								_snake_case     		=	do_reduce_labels




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_reduce_labels": self.do_reduce_labels,
								}







def        SCREAMING_SNAKE_CASE__			(     ) ->			Union[str, Any]:
	_snake_case     		=	load_dataset('hf-internal-testing/fixtures_ade20k'    ,       split='test'			)

	_snake_case     		=	Image.open(dataset[0]['file']			)
	_snake_case     		=	Image.open(dataset[1]['file']			)

	return image, map







def        SCREAMING_SNAKE_CASE__			(     ) ->			Union[str, Any]:
	_snake_case     		=	load_dataset('hf-internal-testing/fixtures_ade20k'    ,       split='test'			)

	_snake_case     		=	Image.open(ds[0]['file']			)
	_snake_case     		=	Image.open(ds[1]['file']			)
	_snake_case     		=	Image.open(ds[2]['file']			)
	_snake_case     		=	Image.open(ds[3]['file']			)

	return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class 		__UpperCAmelCase   (      _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= BeitImageProcessor if is_vision_available() else None


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	BeitImageProcessingTester(self						)


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return self.image_processor_tester.prepare_image_processor_dict()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'do_resize'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'size'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'do_center_crop'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'center_crop'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'do_normalize'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'image_mean'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'image_std'						)						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class.from_dict(self.image_processor_dict						)
								self.assertEqual(image_processor.size					,     {'height': 20, 'width': 20}						)
								self.assertEqual(image_processor.crop_size					,     {'height': 18, 'width': 18}						)
								self.assertEqual(image_processor.do_reduce_labels					,     lowerCAmelCase_						)

								_snake_case     		=	self.image_processing_class.from_dict(
								    self.image_processor_dict					,     size=42					,     crop_size=84					,     reduce_labels=lowerCAmelCase_						)
								self.assertEqual(image_processor.size					,     {'height': 42, 'width': 42}						)
								self.assertEqual(image_processor.crop_size					,     {'height': 84, 'width': 84}						)
								self.assertEqual(image_processor.do_reduce_labels					,     lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								pass


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								_snake_case     		=	prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase_						)
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_					,     Image.Image						)

								# Test not batched input
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertEqual(
								    encoded_images.shape					,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )					,     )

								# Test batched
								_snake_case     		=	image_processing(lowerCAmelCase_					,     return_tensors='pt'						).pixel_values
								self.assertEqual(
								    encoded_images.shape					,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )					,     )


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								# create random numpy tensors
								_snake_case     		=	prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase_					,     numpify=lowerCAmelCase_						)
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_					,     np.ndarray						)

								# Test not batched input
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertEqual(
								    encoded_images.shape					,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )					,     )

								# Test batched
								_snake_case     		=	image_processing(lowerCAmelCase_					,     return_tensors='pt'						).pixel_values
								self.assertEqual(
								    encoded_images.shape					,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )					,     )


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								_snake_case     		=	prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase_					,     torchify=lowerCAmelCase_						)
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_					,     torch.Tensor						)

								# Test not batched input
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertEqual(
								    encoded_images.shape					,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )					,     )

								# Test batched
								_snake_case     		=	image_processing(lowerCAmelCase_					,     return_tensors='pt'						).pixel_values
								self.assertEqual(
								    encoded_images.shape					,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )					,     )


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								_snake_case     		=	prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase_					,     torchify=lowerCAmelCase_						)
								_snake_case     		=	[]
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_					,     torch.Tensor						)
									maps.append(torch.zeros(image.shape[-2:]						).long()						)

								# Test not batched input
								_snake_case     		=	image_processing(image_inputs[0]					,     maps[0]					,     return_tensors='pt'						)
								self.assertEqual(
								    encoding['pixel_values'].shape					,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )					,     )
								self.assertEqual(
								    encoding['labels'].shape					,     (
								        1,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )					,     )
								self.assertEqual(encoding['labels'].dtype					,     torch.long						)
								self.assertTrue(encoding['labels'].min().item() >= 0						)
								self.assertTrue(encoding['labels'].max().item() <= 2_55						)

								# Test batched
								_snake_case     		=	image_processing(lowerCAmelCase_					,     lowerCAmelCase_					,     return_tensors='pt'						)
								self.assertEqual(
								    encoding['pixel_values'].shape					,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )					,     )
								self.assertEqual(
								    encoding['labels'].shape					,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )					,     )
								self.assertEqual(encoding['labels'].dtype					,     torch.long						)
								self.assertTrue(encoding['labels'].min().item() >= 0						)
								self.assertTrue(encoding['labels'].max().item() <= 2_55						)

								# Test not batched input (PIL images)
								_snake_case		,				_snake_case     		=	prepare_semantic_single_inputs()

								_snake_case     		=	image_processing(lowerCAmelCase_					,     lowerCAmelCase_					,     return_tensors='pt'						)
								self.assertEqual(
								    encoding['pixel_values'].shape					,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )					,     )
								self.assertEqual(
								    encoding['labels'].shape					,     (
								        1,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )					,     )
								self.assertEqual(encoding['labels'].dtype					,     torch.long						)
								self.assertTrue(encoding['labels'].min().item() >= 0						)
								self.assertTrue(encoding['labels'].max().item() <= 2_55						)

								# Test batched input (PIL images)
								_snake_case		,				_snake_case     		=	prepare_semantic_batch_inputs()

								_snake_case     		=	image_processing(lowerCAmelCase_					,     lowerCAmelCase_					,     return_tensors='pt'						)
								self.assertEqual(
								    encoding['pixel_values'].shape					,     (
								        2,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )					,     )
								self.assertEqual(
								    encoding['labels'].shape					,     (
								        2,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )					,     )
								self.assertEqual(encoding['labels'].dtype					,     torch.long						)
								self.assertTrue(encoding['labels'].min().item() >= 0						)
								self.assertTrue(encoding['labels'].max().item() <= 2_55						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)

								# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
								_snake_case		,				_snake_case     		=	prepare_semantic_single_inputs()
								_snake_case     		=	image_processing(lowerCAmelCase_					,     lowerCAmelCase_					,     return_tensors='pt'						)
								self.assertTrue(encoding['labels'].min().item() >= 0						)
								self.assertTrue(encoding['labels'].max().item() <= 1_50						)

								_snake_case     		=	True
								_snake_case     		=	image_processing(lowerCAmelCase_					,     lowerCAmelCase_					,     return_tensors='pt'						)
								self.assertTrue(encoding['labels'].min().item() >= 0						)
								self.assertTrue(encoding['labels'].max().item() <= 2_55						)



'''simple docstring'''







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

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







def        SCREAMING_SNAKE_CASE__			(     ) ->			None:
	_snake_case		,				_snake_case     		=	get_dataset(__A    ,       __A			)
	print('Processing...'			)
	_snake_case		,				_snake_case		,				_snake_case     		=	update_image_and_anno(__A    ,       __A    ,       __A			)

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







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			tuple[list, list]:
	_snake_case     		=	[]
	_snake_case     		=	[]
	for label_file in glob.glob(os.path.join(__A    ,       '*.txt'			)			):
		_snake_case     		=	label_file.split(os.sep			)[-1].rsplit('.'    ,       1			)[0]
		with open(__A			) as in_file:
			_snake_case     		=	in_file.readlines()
		_snake_case     		=	os.path.join(__A    ,       F'{label_name}.jpg'			)

		_snake_case     		=	[]
		for obj_list in obj_lists:
			_snake_case     		=	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(__A			)
		labels.append(__A			)
	return img_paths, labels







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A = 1			) ->			tuple[list, list, list]:
	_snake_case     		=	[]
	_snake_case     		=	[]
	_snake_case     		=	[]
	for idx in range(len(__A			)			):
		_snake_case     		=	[]
		_snake_case     		=	img_list[idx]
		path_list.append(__A			)
		_snake_case     		=	anno_list[idx]
		_snake_case     		=	cva.imread(__A			)
		if flip_type == 1:
			_snake_case     		=	cva.flip(__A    ,       __A			)
			for bbox in img_annos:
				_snake_case     		=	1 - bbox[1]
				new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
		elif flip_type == 0:
			_snake_case     		=	cva.flip(__A    ,       __A			)
			for bbox in img_annos:
				_snake_case     		=	1 - bbox[2]
				new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
		new_annos_lists.append(__A			)
		new_imgs_list.append(__A			)
	return new_imgs_list, new_annos_lists, path_list







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


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

