





import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class        _a(    unittest.TestCase    ):







		@property
		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							torch.manual_seed(0				)
							_snake_case       :					Tuple				       =   UNetaDModel(
							    block_out_channels=(3_2, 6_4) ,						layers_per_block=2 ,						sample_size=3_2 ,						in_channels=3 ,						out_channels=3 ,						down_block_types=("DownBlock2D", "AttnDownBlock2D") ,						up_block_types=("AttnUpBlock2D", "UpBlock2D") ,						)
							return model







		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					List[Any]				       =   self.dummy_uncond_unet
							_snake_case       :					List[str]				       =   ScoreSdeVeScheduler()

							_snake_case       :					Optional[int]				       =   ScoreSdeVePipeline(unet=UpperCamelCase_ ,						scheduler=UpperCamelCase_				)
							sde_ve.to(UpperCamelCase_				)
							sde_ve.set_progress_bar_config(disable=UpperCamelCase_				)

							_snake_case       :					Any				       =   torch.manual_seed(0				)
							_snake_case       :					List[str]				       =   sde_ve(num_inference_steps=2 ,						output_type="numpy" ,						generator=UpperCamelCase_				).images

							_snake_case       :					str				       =   torch.manual_seed(0				)
							_snake_case       :					Union[str, Any]				       =   sde_ve(num_inference_steps=2 ,						output_type="numpy" ,						generator=UpperCamelCase_ ,						return_dict=UpperCamelCase_				)[
							    0
							]

							_snake_case       :					Optional[Any]				       =   image[0, -3:, -3:, -1]
							_snake_case       :					str				       =   image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 3_2, 3_2, 3)
							_snake_case       :					Union[str, Any]				       =   np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]				)

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






@slow
@require_torch
class        _a(    unittest.TestCase    ):







		def        lowercase       (    self				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					Optional[int]				       =   'google/ncsnpp-church-256'
							_snake_case       :					int				       =   UNetaDModel.from_pretrained(UpperCamelCase_				)

							_snake_case       :					List[Any]				       =   ScoreSdeVeScheduler.from_pretrained(UpperCamelCase_				)

							_snake_case       :					Optional[Any]				       =   ScoreSdeVePipeline(unet=UpperCamelCase_ ,						scheduler=UpperCamelCase_				)
							sde_ve.to(UpperCamelCase_				)
							sde_ve.set_progress_bar_config(disable=UpperCamelCase_				)

							_snake_case       :					int				       =   torch.manual_seed(0				)
							_snake_case       :					Any				       =   sde_ve(num_inference_steps=1_0 ,						output_type="numpy" ,						generator=UpperCamelCase_				).images

							_snake_case       :					List[str]				       =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 2_5_6, 2_5_6, 3)

							_snake_case       :					List[str]				       =   np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]				)

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






import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					List[str]				       =   1.5
					_snake_case       :					Union[str, Any]				       =   int(factor * num_class_images				)
					_snake_case       :					Dict				       =   ClipClient(
					    url="https://knn.laion.ai/knn-service"  ,  indice_name="laion_400m"  ,  num_images=UpperCAmelCase  ,  aesthetic_weight=0.1				)

					os.makedirs(F"""{class_data_dir}/images"""  ,  exist_ok=UpperCAmelCase				)
					if len(list(Path(F"""{class_data_dir}/images"""				).iterdir()				)				) >= num_class_images:
										return

					while True:
										_snake_case       :					Optional[int]				       =   client.query(text=UpperCAmelCase				)
										if len(UpperCAmelCase				) >= factor * num_class_images or num_images > 1e4:
															break
										else:
															_snake_case       :					str				       =   int(factor * num_images				)
															_snake_case       :					Union[str, Any]				       =   ClipClient(
															    url="https://knn.laion.ai/knn-service"  ,  indice_name="laion_400m"  ,  num_images=UpperCAmelCase  ,  aesthetic_weight=0.1  ,  )

					_snake_case       :					List[Any]				       =   0
					_snake_case       :					Any				       =   0
					_snake_case       :					List[Any]				       =   tqdm(desc="downloading real regularization images"  ,  total=UpperCAmelCase				)

					with open(F"""{class_data_dir}/caption.txt"""  ,  "w"				) as fa, open(F"""{class_data_dir}/urls.txt"""  ,  "w"				) as fa, open(
					    F"""{class_data_dir}/images.txt"""  ,  "w"				) as fa:
										while total < num_class_images:
															_snake_case       :					Optional[Any]				       =   class_images[count]
															count += 1
															try:
																				_snake_case       :					Tuple				       =   requests.get(images["url"]				)
																				if img.status_code == 200:
																									_snake_case       :					int				       =   Image.open(BytesIO(img.content				)				)
																									with open(F"""{class_data_dir}/images/{total}.jpg"""  ,  "wb"				) as f:
																														f.write(img.content				)
																									fa.write(images["caption"] + "\n"				)
																									fa.write(images["url"] + "\n"				)
																									fa.write(F"""{class_data_dir}/images/{total}.jpg""" + "\n"				)
																									total += 1
																									pbar.update(1				)
																				else:
																									continue
															except Exception:
																				continue
					return







def    A				(							):
					_snake_case       :					Any				       =   argparse.ArgumentParser(""  ,  add_help=UpperCAmelCase				)
					parser.add_argument("--class_prompt"  ,  help="text prompt to retrieve images"  ,  required=UpperCAmelCase  ,  type=UpperCAmelCase				)
					parser.add_argument("--class_data_dir"  ,  help="path to save images"  ,  required=UpperCAmelCase  ,  type=UpperCAmelCase				)
					parser.add_argument("--num_class_images"  ,  help="number of images to download"  ,  default=200  ,  type=UpperCAmelCase				)
					return parser.parse_args()


if __name__ == "__main__":
							__lowerCAmelCase :List[Any]     =    parse_args()
							retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)