


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

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 						_lowerCamelCase					(    snake_case_    ,		snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		int    							=						StableDiffusionSAGPipeline
 __lowercase				:		List[str]    							=						TEXT_TO_IMAGE_PARAMS
 __lowercase				:		Optional[int]    							=						TEXT_TO_IMAGE_BATCH_PARAMS
 __lowercase				:		List[Any]    							=						TEXT_TO_IMAGE_IMAGE_PARAMS
 __lowercase				:		List[Any]    							=						TEXT_TO_IMAGE_IMAGE_PARAMS
 __lowercase				:		Union[str, Any]    							=						False


 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		List[Any]       =			UNetaDConditionModel(
      block_out_channels=(32, 64)      ,      layers_per_block=2      ,      sample_size=32      ,      in_channels=4      ,      out_channels=4      ,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,      cross_attention_dim=32      ,      )
  __A  :		Union[str, Any]       =			DDIMScheduler(
      beta_start=0.0_0_0_8_5      ,      beta_end=0.0_1_2      ,      beta_schedule='scaled_linear'      ,      clip_sample=__lowercase      ,      set_alpha_to_one=__lowercase      ,      )
  torch.manual_seed(0						)
  __A  :		List[Any]       =			AutoencoderKL(
      block_out_channels=[32, 64]      ,      in_channels=3      ,      out_channels=3      ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,      latent_channels=4      ,      )
  torch.manual_seed(0						)
  __A  :		List[str]       =			CLIPTextConfig(
      bos_token_id=0      ,      eos_token_id=2      ,      hidden_size=32      ,      intermediate_size=37      ,      layer_norm_eps=1E-05      ,      num_attention_heads=4      ,      num_hidden_layers=5      ,      pad_token_id=1      ,      vocab_size=1_000      ,      )
  __A  :		str       =			CLIPTextModel(__lowercase						)
  __A  :		List[Any]       =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

  __A  :		Dict       =			{
      'unet': unet,
      'scheduler': scheduler,
      'vae': vae,
      'text_encoder': text_encoder,
      'tokenizer': tokenizer,
      'safety_checker': None,
      'feature_extractor': None,
  }
  return components


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase=0						):

  """simple docstring"""







  if str(__lowercase						).startswith('mps'						):
   __A  :		int       =			torch.manual_seed(__lowercase						)
  else:
   __A  :		Any       =			torch.Generator(device=__lowercase						).manual_seed(__lowercase						)
  __A  :		Union[str, Any]       =			{
      'prompt': '.',
      'generator': generator,
      'num_inference_steps': 2,
      'guidance_scale': 1.0,
      'sag_scale': 1.0,
      'output_type': 'numpy',
  }
  return inputs







 def  snake_case__   (      self						):

  """simple docstring"""







  super().test_inference_batch_single_identical(expected_max_diff=3E-3						)


@slow
@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  :		int       =			StableDiffusionSAGPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'						)
  __A  :		Optional[Any]       =			sag_pipe.to(__lowercase						)
  sag_pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		List[Any]       =			'.'
  __A  :		List[Any]       =			torch.manual_seed(0						)
  __A  :		Tuple       =			sag_pipe(
      [prompt]      ,      generator=__lowercase      ,      guidance_scale=7.5      ,      sag_scale=1.0      ,      num_inference_steps=20      ,      output_type='np'						)

  __A  :		int       =			output.images

  __A  :		Tuple       =			image[0, -3:, -3:, -1]

  assert image.shape == (1, 512, 512, 3)
  __A  :		List[str]       =			np.array([0.1_5_6_8, 0.1_7_3_8, 0.1_6_9_5, 0.1_6_9_3, 0.1_5_0_7, 0.1_7_0_5, 0.1_5_4_7, 0.1_7_5_1, 0.1_9_4_9]						)

  assert np.abs(image_slice.flatten() - expected_slice						).max() < 5E-2


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
  __A  :		Dict       =			sag_pipe.to(__lowercase						)
  sag_pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		int       =			'.'
  __A  :		List[str]       =			torch.manual_seed(0						)
  __A  :		Dict       =			sag_pipe(
      [prompt]      ,      generator=__lowercase      ,      guidance_scale=7.5      ,      sag_scale=1.0      ,      num_inference_steps=20      ,      output_type='np'						)

  __A  :		List[Any]       =			output.images

  __A  :		Union[str, Any]       =			image[0, -3:, -3:, -1]

  assert image.shape == (1, 512, 512, 3)
  __A  :		Dict       =			np.array([0.3_4_5_9, 0.2_8_7_6, 0.2_5_3_7, 0.3_0_0_2, 0.2_6_7_1, 0.2_1_6_0, 0.3_0_2_6, 0.2_2_6_2, 0.2_3_7_1]						)

  assert np.abs(image_slice.flatten() - expected_slice						).max() < 5E-2







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
  __A  :		int       =			sag_pipe.to(__lowercase						)
  sag_pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		Any       =			'.'
  __A  :		List[Any]       =			torch.manual_seed(0						)
  __A  :		List[Any]       =			sag_pipe(
      [prompt]      ,      width=768      ,      height=512      ,      generator=__lowercase      ,      guidance_scale=7.5      ,      sag_scale=1.0      ,      num_inference_steps=20      ,      output_type='np'      ,      )

  __A  :		List[Any]       =			output.images

  assert image.shape == (1, 512, 768, 3)










'''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 ✅')






