





'''simple docstring'''


from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
    a__			:					int       						=						input('Enter image url: ').strip()
    print(F"""Downloading image from {url} ...""")
    a__			:					Dict       						=						BeautifulSoup(requests.get(url).content, 'html.parser')
    # The image URL is in the content field of the first meta tag with property og:image
    a__			:					List[str]       						=						soup.find('meta', {'property': 'og:image'})['content']
    a__			:					Optional[int]       						=						requests.get(image_url).content
    a__			:					Tuple       						=						F"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
    with open(file_name, 'wb') as fp:
        fp.write(image_data)
    print(F"""Done. Image saved to disk as {file_name}.""")






import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				List[Any]:
  snake_case__						= [tensor.shape for tensor in tensor_list]
  return all(shape == shapes[0] for shape in shapes[1:]				)
class 		__magic_name__						(snake_case_   ,snake_case_   ,snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							Dict	   =       StableDiffusionLatentUpscalePipeline
     __lowercase				:							List[str]	   =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         'height',
         'width',
         'cross_attention_kwargs',
         'negative_prompt_embeds',
         'prompt_embeds',
     }
     __lowercase				:							List[Any]	   =       PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
     __lowercase				:							Any	   =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     __lowercase				:							int	   =       frozenset(
         []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     __lowercase				:							List[Any]	   =       frozenset([]				)

     __lowercase				:							Any	   =       True

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       snake_case__						= 1
       snake_case__						= 4
       snake_case__						= (16, 16)

       snake_case__						= floats_tensor((batch_size, num_channels) + sizes     ,		rng=random.Random(0   )   ).to(_a   )
       return image

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       torch.manual_seed(0   )
       snake_case__						= UNetaDConditionModel(
           act_fn='''gelu'''     ,		attention_head_dim=8     ,		norm_num_groups=_a     ,		block_out_channels=[32, 32, 64, 64]     ,		time_cond_proj_dim=1_60     ,		conv_in_kernel=1     ,		conv_out_kernel=1     ,		cross_attention_dim=32     ,		down_block_types=(
               '''KDownBlock2D''',
               '''KCrossAttnDownBlock2D''',
               '''KCrossAttnDownBlock2D''',
               '''KCrossAttnDownBlock2D''',
           )     ,		in_channels=8     ,		mid_block_type=_a     ,		only_cross_attention=_a     ,		out_channels=5     ,		resnet_time_scale_shift='''scale_shift'''     ,		time_embedding_type='''fourier'''     ,		timestep_post_act='''gelu'''     ,		up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')     ,		)
       snake_case__						= AutoencoderKL(
           block_out_channels=[32, 32, 64, 64]     ,		in_channels=3     ,		out_channels=3     ,		down_block_types=[
               '''DownEncoderBlock2D''',
               '''DownEncoderBlock2D''',
               '''DownEncoderBlock2D''',
               '''DownEncoderBlock2D''',
           ]     ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,		latent_channels=4     ,		)
       snake_case__						= EulerDiscreteScheduler(prediction_type='''sample'''   )
       snake_case__						= 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=10_00     ,		hidden_act='''quick_gelu'''     ,		projection_dim=5_12     ,		)
       snake_case__						= CLIPTextModel(_a   )
       snake_case__						= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

       snake_case__						= {
           '''unet''': model.eval(),
           '''vae''': vae.eval(),
           '''scheduler''': scheduler,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
       }

       return components

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:Optional[Any]     ,		_a:List[str]=0   ):
       if str(_a   ).startswith('''mps'''   ):
         snake_case__						= torch.manual_seed(_a   )
       else:
         snake_case__						= torch.Generator(device=_a   ).manual_seed(_a   )
       snake_case__						= {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': self.dummy_image.cpu(),
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }
       return inputs

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= '''cpu'''

       snake_case__						= self.get_dummy_components()
       snake_case__						= self.pipeline_class(**_a   )
       pipe.to(_a   )
       pipe.set_progress_bar_config(disable=_a   )

       snake_case__						= self.get_dummy_inputs(_a   )
       snake_case__						= pipe(**_a   ).images
       snake_case__						= image[0, -3:, -3:, -1]

       self.assertEqual(image.shape     ,		(1, 2_56, 2_56, 3)   )
       snake_case__						= np.array(
           [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]   )
       snake_case__						= np.abs(image_slice.flatten() - expected_slice   ).max()
       self.assertLessEqual(_a     ,		1e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       super().test_attention_slicing_forward_pass(expected_max_diff=7e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       super().test_cpu_offload_forward_pass(expected_max_diff=3e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       super().test_inference_batch_single_identical(expected_max_diff=7e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       super().test_save_load_local(expected_max_difference=3e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       super().test_save_load_optional_components(expected_max_difference=3e-3   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= [
           '''DDIMScheduler''',
           '''DDPMScheduler''',
           '''PNDMScheduler''',
           '''HeunDiscreteScheduler''',
           '''EulerAncestralDiscreteScheduler''',
           '''KDPM2DiscreteScheduler''',
           '''KDPM2AncestralDiscreteScheduler''',
           '''DPMSolverSDEScheduler''',
       ]
       snake_case__						= self.get_dummy_components()
       snake_case__						= self.pipeline_class(**_a   )

       # make sure that PNDM does not need warm-up
       pipe.scheduler.register_to_config(skip_prk_steps=_a   )

       pipe.to(_a   )
       pipe.set_progress_bar_config(disable=_a   )
       snake_case__						= self.get_dummy_inputs(_a   )
       snake_case__						= 2

       snake_case__						= []
       for scheduler_enum in KarrasDiffusionSchedulers:
         if scheduler_enum.name in skip_schedulers:
           # no sigma schedulers are not supported
           # no schedulers
           continue

         snake_case__						= getattr(_a     ,		scheduler_enum.name   )
         snake_case__						= scheduler_cls.from_config(pipe.scheduler.config   )
         snake_case__						= pipe(**_a   )[0]
         outputs.append(_a   )

       assert check_same_shape(_a   )




@require_torch_gpu
@slow
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= torch.manual_seed(33   )

       snake_case__						= StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''     ,		torch_dtype=torch.floataa   )
       pipe.to('''cuda'''   )

       snake_case__						= StableDiffusionLatentUpscalePipeline.from_pretrained(
           '''stabilityai/sd-x2-latent-upscaler'''     ,		torch_dtype=torch.floataa   )
       upscaler.to('''cuda'''   )

       snake_case__						= '''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

       snake_case__						= pipe(_a     ,		generator=_a     ,		output_type='''latent'''   ).images

       snake_case__						= upscaler(
           prompt=_a     ,		image=_a     ,		num_inference_steps=20     ,		guidance_scale=0     ,		generator=_a     ,		output_type='''np'''     ,		).images[0]

       snake_case__						= load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'''   )
       assert np.abs((expected_image - image).mean()   ) < 5e-2







     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= torch.manual_seed(33   )

       snake_case__						= StableDiffusionLatentUpscalePipeline.from_pretrained(
           '''stabilityai/sd-x2-latent-upscaler'''     ,		torch_dtype=torch.floataa   )
       upscaler.to('''cuda'''   )

       snake_case__						= '''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

       snake_case__						= load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'''   )

       snake_case__						= upscaler(
           prompt=_a     ,		image=_a     ,		num_inference_steps=20     ,		guidance_scale=0     ,		generator=_a     ,		output_type='''np'''     ,		).images[0]

       snake_case__						= load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'''   )
       assert np.abs((expected_image - image).max()   ) < 5e-2

