

import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Any       				=  KandinskyVaaPipeline
	_lowercase  :   List[Any]       				=  [
	    "image_embeds",
	    "negative_image_embeds",
	]
	_lowercase  :   str       				=  ["image_embeds", "negative_image_embeds"]
	_lowercase  :   Union[str, Any]       				=  [
	    "generator",
	    "height",
	    "width",
	    "latents",
	    "guidance_scale",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	_lowercase  :   List[Any]       				=  False







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						return 32







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						return 32







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						return self.time_input_dim







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						return self.time_input_dim * 4







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						return 1_00







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						torch.manual_seed(0      )

						__magic_name__							=    {
						    'in_channels': 4,
						    # Out channels is double in channels because predicts mean and variance
						    'out_channels': 8,
						    'addition_embed_type': 'image',
						    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
						    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
						    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
						    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
						    'layers_per_block': 1,
						    'encoder_hid_dim': self.text_embedder_hidden_size,
						    'encoder_hid_dim_type': 'image_proj',
						    'cross_attention_dim': self.cross_attention_dim,
						    'attention_head_dim': 4,
						    'resnet_time_scale_shift': 'scale_shift',
						    'class_embed_type': None,
						}

						__magic_name__							=    UNetaDConditionModel(**__UpperCamelCase      )
						return model







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						return {
						    "block_out_channels": [32, 64],
						    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
						    "in_channels": 3,
						    "latent_channels": 4,
						    "layers_per_block": 1,
						    "norm_num_groups": 8,
						    "norm_type": "spatial",
						    "num_vq_embeddings": 12,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						torch.manual_seed(0      )
						__magic_name__							=    VQModel(**self.dummy_movq_kwargs      )
						return model







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    self.dummy_unet
						__magic_name__							=    self.dummy_movq

						__magic_name__							=    DDIMScheduler(
						    num_train_timesteps=10_00  , beta_schedule='linear'  , beta_start=0.00085  , beta_end=0.012  , clip_sample=__UpperCamelCase  , set_alpha_to_one=__UpperCamelCase  , steps_offset=1  , prediction_type='epsilon'  , thresholding=__UpperCamelCase  , )

						__magic_name__							=    {
						    'unet': unet,
						    'scheduler': scheduler,
						    'movq': movq,
						}
						return components







	def 	_SCREAMING_SNAKE_CASE							(   self: str  , __UpperCamelCase: List[Any]  , __UpperCamelCase: List[str]=0      ):

						'''simple docstring'''





						__magic_name__							=    floats_tensor((1, self.text_embedder_hidden_size)  , rng=random.Random(__UpperCamelCase      )      ).to(__UpperCamelCase      )
						__magic_name__							=    floats_tensor((1, self.text_embedder_hidden_size)  , rng=random.Random(seed + 1      )      ).to(
						    __UpperCamelCase      )
						if str(__UpperCamelCase      ).startswith('mps'      ):
											__magic_name__							=    torch.manual_seed(__UpperCamelCase      )
						else:
											__magic_name__							=    torch.Generator(device=__UpperCamelCase      ).manual_seed(__UpperCamelCase      )
						__magic_name__							=    {
						    'image_embeds': image_embeds,
						    'negative_image_embeds': negative_image_embeds,
						    'generator': generator,
						    'height': 64,
						    'width': 64,
						    'guidance_scale': 4.0,
						    'num_inference_steps': 2,
						    'output_type': 'np',
						}
						return inputs







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    'cpu'

						__magic_name__							=    self.get_dummy_components()

						__magic_name__							=    self.pipeline_class(**__UpperCamelCase      )
						__magic_name__							=    pipe.to(__UpperCamelCase      )

						pipe.set_progress_bar_config(disable=__UpperCamelCase      )

						__magic_name__							=    pipe(**self.get_dummy_inputs(__UpperCamelCase      )      )
						__magic_name__							=    output.images

						__magic_name__							=    pipe(
						    **self.get_dummy_inputs(__UpperCamelCase      )  , return_dict=__UpperCamelCase  , )[0]

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

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

						__magic_name__							=    np.array(
						    [0.6237976, 1.0, 0.36441332, 1.0, 0.70639634, 0.29877186, 0.85652125, 0.5216843, 0.54454046]      )

						assert (
						    np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
						), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'

						assert (
						    np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2
						), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'




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







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						__magic_name__							=    load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'      )

						__magic_name__							=    KandinskyVaaPriorPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-prior'  , torch_dtype=torch.floataa      )
						pipe_prior.to(__UpperCamelCase      )

						__magic_name__							=    KandinskyVaaPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-decoder'  , torch_dtype=torch.floataa      )
						__magic_name__							=    pipeline.to(__UpperCamelCase      )
						pipeline.set_progress_bar_config(disable=__UpperCamelCase      )

						__magic_name__							=    'red cat, 4k photo'

						__magic_name__							=    torch.Generator(device='cuda'      ).manual_seed(0      )
						__magic_name__,	__magic_name__							=    pipe_prior(
						    __UpperCamelCase  , generator=__UpperCamelCase  , num_inference_steps=5  , negative_prompt=''  , ).to_tuple()

						__magic_name__							=    torch.Generator(device='cuda'      ).manual_seed(0      )
						__magic_name__							=    pipeline(
						    image_embeds=__UpperCamelCase  , negative_image_embeds=__UpperCamelCase  , generator=__UpperCamelCase  , num_inference_steps=1_00  , output_type='np'  , )

						__magic_name__							=    output.images[0]

						assert image.shape == (5_12, 5_12, 3)

						assert_mean_pixel_difference(__UpperCamelCase  , __UpperCamelCase      )







import requests
from bsa import BeautifulSoup




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




					'''simple docstring'''







					__magic_name__							=    BeautifulSoup(requests.get(a_				).text					,'html.parser'				)
					__magic_name__							=    soup.findAll('h1'				)
					__magic_name__							=    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(a_					,a_				)}


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''')




