


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 			__lowerCamelCase			( )					->	None:
  _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(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'''						,  UpperCAmelCase_						,  [cva.IMWRITE_JPEG_QUALITY, 85]	)
    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			( _lowerCAmelCase						,  _lowerCAmelCase	)					->	tuple[list, list]:
  _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			( _lowerCAmelCase						,  _lowerCAmelCase						,  _lowerCAmelCase = 1	)					->	tuple[list, list, list]:
  _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			( _lowerCAmelCase = 32	)					->	str:
  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 ✅")










'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class 				__snake_case     (					unittest.TestCase							):





	def   _snake_case   (		self		)      ->		Dict:
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	@property
	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   1
		snake_case__	       =   3
		snake_case__	       =   (32, 32)

		snake_case__	       =   floats_tensor((batch_size, num_channels) + sizes      ,       rng=random.Random(0		)		).to(UpperCamelCase_		)
		return image





	@property
	def   _snake_case   (		self		)      ->		Union[str, Any]:
		torch.manual_seed(0		)
		snake_case__	       =   UNetaDConditionModel(
		    block_out_channels=(32, 32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=7      ,       out_channels=4      ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')      ,       up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')      ,       cross_attention_dim=32      ,       attention_head_dim=8      ,       use_linear_projection=UpperCamelCase_      ,       only_cross_attention=(True, True, False)      ,       num_class_embeds=100      ,       )
		return model





	@property
	def   _snake_case   (		self		)      ->		Optional[Any]:
		torch.manual_seed(0		)
		snake_case__	       =   AutoencoderKL(
		    block_out_channels=[32, 32, 64]      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']      ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']      ,       latent_channels=4      ,       )
		return model





	@property
	def   _snake_case   (		self		)      ->		List[str]:
		torch.manual_seed(0		)
		snake_case__	       =   CLIPTextConfig(
		    bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-0_5      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=1000      ,       hidden_act='gelu'      ,       projection_dim=512      ,       )
		return CLIPTextModel(UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'A painting of a squirrel eating a burger'
		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       )

		snake_case__	       =   output.images

		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       return_dict=UpperCamelCase_      ,       )[0]

		snake_case__	       =   image[0, -3:, -3:, -1]
		snake_case__	       =   image_from_tuple[0, -3:, -3:, -1]

		snake_case__	       =   low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)
		snake_case__	       =   np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]		)

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





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'A painting of a squirrel eating a burger'
		snake_case__	       =   sd_pipe(
		    2 * [prompt]      ,       image=2 * [low_res_image]      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       )
		snake_case__	       =   output.images
		assert image.shape[0] == 2

		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       num_images_per_prompt=2      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       )
		snake_case__	       =   output.images
		assert image.shape[0] == 2





	@unittest.skipIf(torch_device != 'cuda'      ,       'This test requires a GPU'		)
	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# put models in fp16, except vae as it overflows in fp16
		snake_case__	       =   unet.half()
		snake_case__	       =   text_encoder.half()

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'A painting of a squirrel eating a burger'
		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       num_inference_steps=2      ,       output_type='np'      ,       ).images

		snake_case__	       =   low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)






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





	def   _snake_case   (		self		)      ->		List[str]:
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'		)
		snake_case__	       =   load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
		    '/upsampled_cat.npy'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(UpperCamelCase_		)
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing()

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       output_type='np'      ,       )
		snake_case__	       =   output.images[0]

		assert image.shape == (512, 512, 3)
		assert np.abs(expected_image - image		).max() < 1E-3





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'		)
		snake_case__	       =   load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
		    '/upsampled_cat_fp16.npy'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(
		    UpperCamelCase_      ,       torch_dtype=torch.floataa      ,       )
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing()

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       output_type='np'      ,       )
		snake_case__	       =   output.images[0]

		assert image.shape == (512, 512, 3)
		assert np.abs(expected_image - image		).max() < 5E-1





	def   _snake_case   (		self		)      ->		int:
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()

		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(
		    UpperCamelCase_      ,       torch_dtype=torch.floataa      ,       )
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing(1		)
		pipe.enable_sequential_cpu_offload()

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       num_inference_steps=5      ,       output_type='np'      ,       )

		snake_case__	       =   torch.cuda.max_memory_allocated()
		# make sure that less than 2.9 GB is allocated
		assert mem_bytes < 2.9 * 10**9



