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

import cva
UpperCamelCase_   	=	""
UpperCamelCase_   	=	""
UpperCamelCase_   	=	""
UpperCamelCase_   	=	1  # (0 is vertical, 1 is horizontal)






def 		_UpperCAmelCase   (	):






	"""simple docstring"""


	__lowerCAmelCase   ,						__lowerCAmelCase         =							get_dataset(UpperCamelCase			,							UpperCamelCase  )
	print("Processing..."  )
	__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							update_image_and_anno(UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase  )

	for index, image in enumerate(UpperCamelCase  ):
		# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
		__lowerCAmelCase         =							random_chars(3_2  )
		__lowerCAmelCase         =							paths[index].split(os.sep  )[-1].rsplit("."			,							1  )[0]
		__lowerCAmelCase         =							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}"  )
		__lowerCAmelCase         =							[]
		for anno in new_annos[index]:
			__lowerCAmelCase         =							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 		_UpperCAmelCase   (	UpperCamelCase:     str			,							UpperCamelCase:     str  ):






	"""simple docstring"""


	__lowerCAmelCase         =							[]
	__lowerCAmelCase         =							[]
	for label_file in glob.glob(os.path.join(UpperCamelCase			,							"*.txt"  )  ):
		__lowerCAmelCase         =							label_file.split(os.sep  )[-1].rsplit("."			,							1  )[0]
		with open(UpperCamelCase  ) as in_file:
			__lowerCAmelCase         =							in_file.readlines()
		__lowerCAmelCase         =							os.path.join(UpperCamelCase			,							F"{label_name}.jpg"  )

		__lowerCAmelCase         =							[]
		for obj_list in obj_lists:
			__lowerCAmelCase         =							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 		_UpperCAmelCase   (	UpperCamelCase:     list			,							UpperCamelCase:     list			,							UpperCamelCase:     int = 1  ):






	"""simple docstring"""


	__lowerCAmelCase         =							[]
	__lowerCAmelCase         =							[]
	__lowerCAmelCase         =							[]
	for idx in range(len(UpperCamelCase  )  ):
		__lowerCAmelCase         =							[]
		__lowerCAmelCase         =							img_list[idx]
		path_list.append(UpperCamelCase  )
		__lowerCAmelCase         =							anno_list[idx]
		__lowerCAmelCase         =							cva.imread(UpperCamelCase  )
		if flip_type == 1:
			__lowerCAmelCase         =							cva.flip(UpperCamelCase			,							UpperCamelCase  )
			for bbox in img_annos:
				__lowerCAmelCase         =							1 - bbox[1]
				new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]  )
		elif flip_type == 0:
			__lowerCAmelCase         =							cva.flip(UpperCamelCase			,							UpperCamelCase  )
			for bbox in img_annos:
				__lowerCAmelCase         =							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 		_UpperCAmelCase   (	UpperCamelCase:     int = 3_2  ):






	"""simple docstring"""


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


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








import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


UpperCamelCase_   	=	False

class       a		(   unittest.TestCase					):
							pass

@slow
@require_torch_gpu
class       a		(   unittest.TestCase					):







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion" )
								pipe.to(snake_case__ )
								pipe.set_progress_bar_config(disable=snake_case__ )

								__lowerCAmelCase         =							load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
								__lowerCAmelCase         =							torch.manual_seed(0 )
								__lowerCAmelCase         =							pipe(
								    image=snake_case__      ,				generator=snake_case__      ,				guidance_scale=7.5      ,				num_inference_steps=50      ,				output_type="numpy"      ,				).images

								__lowerCAmelCase         =							image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								__lowerCAmelCase         =							np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5] )

								assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2






