


import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu






class    _UpperCamelCase     (       unittest.TestCase					):

				"""simple docstring"""







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

				@property
				def   _UpperCAmelCase       (    self )						->					int:
					A             =					1
					A             =					3
					A             =					(32, 32)

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

				@property
				def   _UpperCAmelCase       (    self )						->					Any:
					torch.manual_seed(0 )
					A             =					UNetaDConditionModel(
					    block_out_channels=(32, 64)					, layers_per_block=2					, sample_size=32					, in_channels=4					, out_channels=4					, down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					, up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")					, cross_attention_dim=32					, )
					return model

				@property
				def   _UpperCAmelCase       (    self )						->					Any:
					torch.manual_seed(0 )
					A             =					AutoencoderKL(
					    block_out_channels=[32, 64]					, in_channels=3					, out_channels=3					, down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]					, up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]					, latent_channels=4					, )
					return model

				@property
				def   _UpperCAmelCase       (    self )						->					Any:
					torch.manual_seed(0 )
					A             =					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=1000					, )
					return CLIPTextModel(a__ )

				@property
				def   _UpperCAmelCase       (    self )						->					Optional[int]:
					def extract(*a__					, **a__ ):






						class    _UpperCamelCase     :

										"""simple docstring"""







										def __init__(    self )						->					Any:
											A             =					torch.ones([0] )

										def   _UpperCAmelCase       (    self					, a__ )						->					int:
											self.pixel_values.to(a__ )
											return self
						return Out()

					return extract

				def   _UpperCAmelCase       (    self )						->					Union[str, Any]:
					A             =					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
					A             =					self.dummy_cond_unet
					A             =					DDIMScheduler(
					    beta_start=0.0_00_85					, beta_end=0.0_12					, beta_schedule="""scaled_linear"""					, clip_sample=a__					, set_alpha_to_one=a__					, )

					A             =					self.dummy_vae
					A             =					self.dummy_text_encoder
					A             =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

					# make sure here that pndm scheduler skips prk
					A             =					StableDiffusionPipeline(
					    unet=a__					, scheduler=a__					, vae=a__					, text_encoder=a__					, tokenizer=a__					, safety_checker=a__					, feature_extractor=self.dummy_extractor					, )
					A             =					sd_pipe.to(a__ )
					sd_pipe.set_progress_bar_config(disable=a__ )

					A             =					"""A painting of a squirrel eating a burger"""

					A             =					torch.Generator(device=a__ ).manual_seed(0 )
					A             =					sd_pipe([prompt]					, generator=a__					, guidance_scale=6.0					, num_inference_steps=2					, output_type="""np""" )
					A             =					output.images

					A             =					torch.Generator(device=a__ ).manual_seed(0 )
					A             =					sd_pipe(
					    [prompt]					, generator=a__					, guidance_scale=6.0					, num_inference_steps=2					, output_type="""np"""					, return_dict=a__					, )[0]

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

					assert image.shape == (1, 64, 64, 3)
					A             =					np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64] )

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

				def   _UpperCAmelCase       (    self )						->					Dict:
					A             =					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
					A             =					self.dummy_cond_unet
					A             =					PNDMScheduler(skip_prk_steps=a__ )
					A             =					self.dummy_vae
					A             =					self.dummy_text_encoder
					A             =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

					# make sure here that pndm scheduler skips prk
					A             =					StableDiffusionPipeline(
					    unet=a__					, scheduler=a__					, vae=a__					, text_encoder=a__					, tokenizer=a__					, safety_checker=a__					, feature_extractor=self.dummy_extractor					, )
					A             =					sd_pipe.to(a__ )
					sd_pipe.set_progress_bar_config(disable=a__ )

					A             =					"""A painting of a squirrel eating a burger"""
					A             =					torch.Generator(device=a__ ).manual_seed(0 )
					A             =					sd_pipe([prompt]					, generator=a__					, guidance_scale=6.0					, num_inference_steps=2					, output_type="""np""" )

					A             =					output.images

					A             =					torch.Generator(device=a__ ).manual_seed(0 )
					A             =					sd_pipe(
					    [prompt]					, generator=a__					, guidance_scale=6.0					, num_inference_steps=2					, output_type="""np"""					, return_dict=a__					, )[0]

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

					assert image.shape == (1, 64, 64, 3)
					A             =					np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93] )

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

				def   _UpperCAmelCase       (    self )						->					Any:
					A             =					StableDiffusionPipeline.from_pretrained(
					    """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""					, safety_checker=a__ )
					assert isinstance(a__					, a__ )
					assert isinstance(pipe.scheduler					, a__ )
					assert pipe.safety_checker is None

					A             =					pipe("""example prompt"""					, num_inference_steps=2 ).images[0]
					assert image is not None

					# check that there's no error when saving a pipeline with one of the models being None
					with tempfile.TemporaryDirectory() as tmpdirname:
						pipe.save_pretrained(a__ )
						A             =					StableDiffusionPipeline.from_pretrained(a__ )

					# sanity check that the pipeline still works
					assert pipe.safety_checker is None
					A             =					pipe("""example prompt"""					, num_inference_steps=2 ).images[0]
					assert image is not None

				@unittest.skipIf(torch_device != """cuda"""					, """This test requires a GPU""" )
				def   _UpperCAmelCase       (    self )						->					int:
					A             =					self.dummy_cond_unet
					A             =					PNDMScheduler(skip_prk_steps=a__ )
					A             =					self.dummy_vae
					A             =					self.dummy_text_encoder
					A             =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

					# put models in fp16
					A             =					unet.half()
					A             =					vae.half()
					A             =					bert.half()

					# make sure here that pndm scheduler skips prk
					A             =					StableDiffusionPipeline(
					    unet=a__					, scheduler=a__					, vae=a__					, text_encoder=a__					, tokenizer=a__					, safety_checker=a__					, feature_extractor=self.dummy_extractor					, )
					A             =					sd_pipe.to(a__ )
					sd_pipe.set_progress_bar_config(disable=a__ )

					A             =					"""A painting of a squirrel eating a burger"""
					A             =					sd_pipe([prompt]					, num_inference_steps=2					, output_type="""np""" ).images

					assert image.shape == (1, 64, 64, 3)






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

				"""simple docstring"""







				def   _UpperCAmelCase       (    self )						->					Union[str, Any]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()

				def   _UpperCAmelCase       (    self )						->					Tuple:
					A             =					StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""					, safety_checker=a__ )
					A             =					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
					A             =					sd_pipe.to(a__ )
					sd_pipe.set_progress_bar_config(disable=a__ )

					A             =					(
					    """portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
					    """ coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
					    """ anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
					    """ children from bahnhof zoo, detailed """
					)
					A             =					40_0366_0346
					A             =					7

					# without safety guidance (sld_guidance_scale = 0)
					A             =					torch.manual_seed(a__ )
					A             =					sd_pipe(
					    [prompt]					, generator=a__					, guidance_scale=a__					, num_inference_steps=50					, output_type="""np"""					, width=512					, height=512					, sld_guidance_scale=0					, )

					A             =					output.images
					A             =					image[0, -3:, -3:, -1]
					A             =					[0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

					assert image.shape == (1, 512, 512, 3)

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

					# without safety guidance (strong configuration)
					A             =					torch.manual_seed(a__ )
					A             =					sd_pipe(
					    [prompt]					, generator=a__					, guidance_scale=a__					, num_inference_steps=50					, output_type="""np"""					, width=512					, height=512					, sld_guidance_scale=2000					, sld_warmup_steps=7					, sld_threshold=0.0_25					, sld_momentum_scale=0.5					, sld_mom_beta=0.7					, )

					A             =					output.images
					A             =					image[0, -3:, -3:, -1]
					A             =					[0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

					assert image.shape == (1, 512, 512, 3)

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

				def   _UpperCAmelCase       (    self )						->					Optional[int]:
					A             =					StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""					, safety_checker=a__ )
					A             =					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
					A             =					sd_pipe.to(a__ )
					sd_pipe.set_progress_bar_config(disable=a__ )

					A             =					"""padme amidala taking a bath artwork, safe for work, no nudity"""
					A             =					27_3497_1755
					A             =					7

					A             =					torch.manual_seed(a__ )
					A             =					sd_pipe(
					    [prompt]					, generator=a__					, guidance_scale=a__					, num_inference_steps=50					, output_type="""np"""					, width=512					, height=512					, sld_guidance_scale=0					, )

					A             =					output.images
					A             =					image[0, -3:, -3:, -1]
					A             =					[0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

					assert image.shape == (1, 512, 512, 3)

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

					A             =					torch.manual_seed(a__ )
					A             =					sd_pipe(
					    [prompt]					, generator=a__					, guidance_scale=a__					, num_inference_steps=50					, output_type="""np"""					, width=512					, height=512					, sld_guidance_scale=2000					, sld_warmup_steps=7					, sld_threshold=0.0_25					, sld_momentum_scale=0.5					, sld_mom_beta=0.7					, )

					A             =					output.images
					A             =					image[0, -3:, -3:, -1]
					A             =					[0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

					assert image.shape == (1, 512, 512, 3)

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

				def   _UpperCAmelCase       (    self )						->					Any:
					A             =					StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" )
					A             =					sd_pipe.to(a__ )
					sd_pipe.set_progress_bar_config(disable=a__ )

					A             =					(
					    """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
					    """ leyendecker"""
					)
					A             =					10_4435_5234
					A             =					12

					A             =					torch.manual_seed(a__ )
					A             =					sd_pipe(
					    [prompt]					, generator=a__					, guidance_scale=a__					, num_inference_steps=50					, output_type="""np"""					, width=512					, height=512					, sld_guidance_scale=0					, )

					A             =					output.images
					A             =					image[0, -3:, -3:, -1]
					A             =					np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )

					assert image.shape == (1, 512, 512, 3)

					assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-7

					A             =					torch.manual_seed(a__ )
					A             =					sd_pipe(
					    [prompt]					, generator=a__					, guidance_scale=a__					, num_inference_steps=50					, output_type="""np"""					, width=512					, height=512					, sld_guidance_scale=2000					, sld_warmup_steps=7					, sld_threshold=0.0_25					, sld_momentum_scale=0.5					, sld_mom_beta=0.7					, )

					A             =					output.images
					A             =					image[0, -3:, -3:, -1]
					A             =					np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61] )
					assert image.shape == (1, 512, 512, 3)

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







import requests
from bsa import BeautifulSoup





def 	_lowerCAmelCase					( UpperCamelCase__:  str = "https://www.worldometers.info/coronavirus"    )			-> dict:





	"""simple docstring"""







	A             =					BeautifulSoup(requests.get(UpperCamelCase__    ).text				,       """html.parser"""    )
	A             =					soup.findAll("""h1"""    )
	A             =					soup.findAll("""div"""				,       {"""class""": """maincounter-number"""}    )
	keys += soup.findAll("""span"""				,       {"""class""": """panel-title"""}    )
	values += soup.findAll("""div"""				,       {"""class""": """number-table-main"""}    )
	return {key.text.strip(): value.text.strip() for key, value in zip(UpperCamelCase__				,       UpperCamelCase__    )}


if __name__ == "__main__":
				print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
				for key, value in world_covidaa_stats().items():
								print(f'''{key}\n{value}\n''')



