


'''simple docstring'''
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCAmelCase_ :     Optional[int]								=      False

class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''
 pass


@nightly
@require_torch_gpu
class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[Any]       =			VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'      ,      torch_dtype=torch.floataa						)
  pipe.to(UpperCamelCase_						)
  pipe.set_progress_bar_config(disable=UpperCamelCase_						)

  __A  :		Optional[int]       =			load_image(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)

  __A  :		List[Any]       =			torch.manual_seed(0						)
  __A  :		Dict       =			pipe.dual_guided(
      prompt='first prompt'      ,      image=UpperCamelCase_      ,      text_to_image_strength=0.7_5      ,      generator=UpperCamelCase_      ,      guidance_scale=7.5      ,      num_inference_steps=2      ,      output_type='numpy'      ,      ).images

  with tempfile.TemporaryDirectory() as tmpdirname:
   pipe.save_pretrained(UpperCamelCase_						)
   __A  :		Union[str, Any]       =			VersatileDiffusionPipeline.from_pretrained(UpperCamelCase_      ,      torch_dtype=torch.floataa						)
  pipe.to(UpperCamelCase_						)
  pipe.set_progress_bar_config(disable=UpperCamelCase_						)

  __A  :		List[str]       =			generator.manual_seed(0						)
  __A  :		str       =			pipe.dual_guided(
      prompt='first prompt'      ,      image=UpperCamelCase_      ,      text_to_image_strength=0.7_5      ,      generator=UpperCamelCase_      ,      guidance_scale=7.5      ,      num_inference_steps=2      ,      output_type='numpy'      ,      ).images

  assert np.abs(image - new_image						).sum() < 1E-5, "Models don't have the same forward pass"







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'      ,      torch_dtype=torch.floataa						)
  pipe.to(UpperCamelCase_						)
  pipe.set_progress_bar_config(disable=UpperCamelCase_						)

  __A  :		int       =			"cyberpunk 2077"
  __A  :		Tuple       =			load_image(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)
  __A  :		List[str]       =			torch.manual_seed(0						)
  __A  :		Any       =			pipe.dual_guided(
      prompt=UpperCamelCase_      ,      image=UpperCamelCase_      ,      text_to_image_strength=0.7_5      ,      generator=UpperCamelCase_      ,      guidance_scale=7.5      ,      num_inference_steps=50      ,      output_type='numpy'      ,      ).images

  __A  :		Union[str, Any]       =			image[0, 253:256, 253:256, -1]

  assert image.shape == (1, 512, 512, 3)
  __A  :		List[Any]       =			np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]						)

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

  __A  :		List[str]       =			"A painting of a squirrel eating a burger "
  __A  :		int       =			torch.manual_seed(0						)
  __A  :		Any       =			pipe.text_to_image(
      prompt=UpperCamelCase_      ,      generator=UpperCamelCase_      ,      guidance_scale=7.5      ,      num_inference_steps=50      ,      output_type='numpy'						).images

  __A  :		List[str]       =			image[0, 253:256, 253:256, -1]

  assert image.shape == (1, 512, 512, 3)
  __A  :		Optional[Any]       =			np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]						)

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

  __A  :		Optional[int]       =			pipe.image_variation(UpperCamelCase_      ,      generator=UpperCamelCase_      ,      output_type='numpy'						).images

  __A  :		Union[str, Any]       =			image[0, 253:256, 253:256, -1]

  assert image.shape == (1, 512, 512, 3)
  __A  :		Optional[Any]       =			np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]						)

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










'''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 ✅')






