





import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 			__A(    __UpperCAmelCase    ,      unittest.TestCase			):
     __A							=							RePaintPipeline
     __A							=							IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
     __A							=							PipelineTesterMixin.required_optional_params - {
         "latents",
         "num_images_per_prompt",
         "callback",
         "callback_steps",
     }
     __A							=							IMAGE_INPAINTING_BATCH_PARAMS
     __A							=							False

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            torch.manual_seed(0   )
            torch.manual_seed(0   )
            _UpperCamelCase														=    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'''),							)
            _UpperCamelCase														=    RePaintScheduler()
            _UpperCamelCase														=    {'''unet''': unet, '''scheduler''': scheduler}
            return components

     def 						_UpperCamelCase  (   self,							A,							A=0   ):





            """simple docstring"""

            if str(A   ).startswith('''mps'''   ):
                   _UpperCamelCase														=    torch.manual_seed(A   )
            else:
                   _UpperCamelCase														=    torch.Generator(device=A   ).manual_seed(A   )
            _UpperCamelCase														=    np.random.RandomState(A   ).standard_normal((1, 3, 32, 32)   )
            _UpperCamelCase														=    torch.from_numpy(A   ).to(device=A,							dtype=torch.floataa   )
            _UpperCamelCase														=    (image > 0).to(device=A,							dtype=torch.floataa   )
            _UpperCamelCase														=    {
                '''image''': image,
                '''mask_image''': mask,
                '''generator''': generator,
                '''num_inference_steps''': 5,
                '''eta''': 0.0,
                '''jump_length''': 2,
                '''jump_n_sample''': 2,
                '''output_type''': '''numpy''',
            }
            return inputs

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
            _UpperCamelCase														=    self.get_dummy_components()
            _UpperCamelCase														=    RePaintPipeline(**A   )
            _UpperCamelCase														=    sd_pipe.to(A   )
            sd_pipe.set_progress_bar_config(disable=A   )

            _UpperCamelCase														=    self.get_dummy_inputs(A   )
            _UpperCamelCase														=    sd_pipe(**A   ).images
            _UpperCamelCase														=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            _UpperCamelCase														=    np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]   )

            assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3

     @skip_mps
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return super().test_save_load_local()

     @unittest.skip('''non-deterministic pipeline'''   )
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return super().test_inference_batch_single_identical()

     @skip_mps
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return super().test_dict_tuple_outputs_equivalent()

     @skip_mps
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return super().test_save_load_optional_components()



     @skip_mps
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return super().test_attention_slicing_forward_pass()






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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    load_image(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
                '''repaint/celeba_hq_256.png'''   )
            _UpperCamelCase														=    load_image(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''   )
            _UpperCamelCase														=    load_numpy(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
                '''repaint/celeba_hq_256_result.npy'''   )

            _UpperCamelCase														=    '''google/ddpm-ema-celebahq-256'''
            _UpperCamelCase														=    UNetaDModel.from_pretrained(A   )
            _UpperCamelCase														=    RePaintScheduler.from_pretrained(A   )

            _UpperCamelCase														=    RePaintPipeline(unet=A,							scheduler=A   ).to(A   )
            repaint.set_progress_bar_config(disable=A   )
            repaint.enable_attention_slicing()

            _UpperCamelCase														=    torch.manual_seed(0   )
            _UpperCamelCase														=    repaint(
                A,							A,							num_inference_steps=250,							eta=0.0,							jump_length=10,							jump_n_sample=10,							generator=A,							output_type='''np''',							)
            _UpperCamelCase														=    output.images[0]

            assert image.shape == (256, 256, 3)
            assert np.abs(expected_image - image   ).mean() < 1E-2













import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowercase     :			Optional[int]     						=	{"""UserAgent""": UserAgent().random}
lowercase     :			Union[str, Any]     						=	"""https://www.mywaifulist.moe/random"""


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    requests.get(lowerCAmelCase     ,							headers=lowerCAmelCase       )
       with open(lowerCAmelCase     ,							'''wb'''       ) as file:
              file.write(image.content       )


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    BeautifulSoup(requests.get(lowerCAmelCase     ,							headers=lowerCAmelCase       ).text     ,							'''html.parser'''       )
       _UpperCamelCase														=    soup.find('''meta'''     ,							attrs={'''property''': '''og:title'''}       ).attrs['''content''']
       _UpperCamelCase														=    soup.find('''meta'''     ,							attrs={'''property''': '''og:image'''}       ).attrs['''content''']
       _UpperCamelCase														=    soup.find('''p'''     ,							id='''description'''       ).get_text()
       _UpperCamelCase							,		_UpperCamelCase														=    os.path.splitext(os.path.basename(lowerCAmelCase       )       )
       _UpperCamelCase														=    title.strip().replace(''' '''     ,							'''_'''       )
       _UpperCamelCase														=    f'''{image_title}{image_extension}'''
       save_image(lowerCAmelCase     ,							lowerCAmelCase       )
       return (title, description, image_title)


if __name__ == "__main__":
       lowercase						,lowercase						,lowercase     :			str     						=	random_anime_character()
       print(F'''{title}\n\n{desc}\n\nImage saved : {image_title}''')






