





'''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 						UpperCamelCase				(							_lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					List[Any]						,       _lowerCamelCase					:					Tuple       ):
    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 == 2_00:
                    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 						UpperCamelCase				(							):
    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=2_00						,       type=UpperCamelCase__       )
    return parser.parse_args()


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










import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


a__:      List[str]									=		False




class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):
 pass




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



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



 def 				UpperCamelCase			(							self ):
        A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						'''A painting of a squirrel eating a burger '''
        A__											=						torch.manual_seed(0 )
        A__											=						pipe(
            prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=2,output_type='''numpy''' ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__lowerCamelCase )
               A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained(__lowerCamelCase )
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						generator.manual_seed(0 )
        A__											=						pipe(
            prompt=__lowerCamelCase,generator=__lowerCamelCase,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 				UpperCamelCase			(							self ):
        A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained(
            '''shi-labs/versatile-diffusion''',torch_dtype=torch.floataa )
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						'''A painting of a squirrel eating a burger '''
        A__											=						torch.manual_seed(0 )
        A__											=						pipe(
            prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=50,output_type='''numpy''' ).images

        A__											=						image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        A__											=						np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )

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





