





"""simple docstring"""



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  _snake_case     (    UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					Any			):
     A__										=  1.5
     A__										=  int(factor * num_class_images			)
     A__										=  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:
          A__										=  client.query(text=UpperCAmelCase_			)
          if len(UpperCAmelCase_			) >= factor * num_class_images or num_images > 1e4:
               break
          else:
               A__										=  int(factor * num_images			)
               A__										=  ClipClient(
                   url="""https://knn.laion.ai/knn-service"""					,   indice_name="""laion_400m"""					,   num_images=UpperCAmelCase_					,   aesthetic_weight=0.1					,   )

     A__										=  0
     A__										=  0
     A__										=  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:
               A__										=  class_images[count]
               count += 1
               try:
                    A__										=  requests.get(images["""url"""]			)
                    if img.status_code == 200:
                         A__										=  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  _snake_case     (    ):
     A__										=  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__":
 SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						parse_args()
 retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)









"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class 					a						(       unittest.TestCase				):



    """simple docstring"""



    @property
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         torch.manual_seed(0       )
         A__										=  UNetaDModel(
             block_out_channels=(32, 64)    ,						layers_per_block=2    ,						sample_size=32    ,						in_channels=3    ,						out_channels=3    ,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,						)
         return model





    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         A__										=  self.dummy_uncond_unet
         A__										=  PNDMScheduler()

         A__										=  PNDMPipeline(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )
         pndm.to(UpperCamelCase       )
         pndm.set_progress_bar_config(disable=UpperCamelCase       )

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""       ).images

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""    ,						return_dict=UpperCamelCase       )[0]

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

         assert image.shape == (1, 32, 32, 3)
         A__										=  np.array([1.0, 1.0, 0.0, 1.0, 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				):



    """simple docstring"""



    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         A__										=  """google/ddpm-cifar10-32"""

         A__										=  UNetaDModel.from_pretrained(UpperCamelCase       )
         A__										=  PNDMScheduler()

         A__										=  PNDMPipeline(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )
         pndm.to(UpperCamelCase       )
         pndm.set_progress_bar_config(disable=UpperCamelCase       )
         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						output_type="""numpy"""       ).images

         A__										=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A__										=  np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]       )

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






