"""simple docstring"""
import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 					lowerCAmelCase_      (     lowerCAmelCase       ,      lowerCAmelCase       ,      unittest.TestCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      str     					=  CycleDiffusionPipeline
				_lowerCAmelCase       :      Union[str, Any]     					=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    """negative_prompt""",
				    """height""",
				    """width""",
				    """negative_prompt_embeds""",
				}
				_lowerCAmelCase       :      List[Any]     					=  PipelineTesterMixin.required_optional_params - {"""latents"""}
				_lowerCAmelCase       :      Optional[int]     					=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}   )
				_lowerCAmelCase       :      Tuple     					=  IMAGE_TO_IMAGE_IMAGE_PARAMS
				_lowerCAmelCase       :      Any     					=  IMAGE_TO_IMAGE_IMAGE_PARAMS

				def 						snake_case	(			self      ):






										"""simple docstring"""

										torch.manual_seed(0      )
										snake_case        =     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						,      )
										snake_case        =     DDIMScheduler(
										    beta_start=0.0_00_85						,      beta_end=0.0_12						,      beta_schedule='scaled_linear'						,      num_train_timesteps=10_00						,      clip_sample=lowerCAmelCase						,      set_alpha_to_one=lowerCAmelCase						,      )
										torch.manual_seed(0      )
										snake_case        =     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						,      )
										torch.manual_seed(0      )
										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						,      )
										snake_case        =     CLIPTextModel(lowerCAmelCase      )
										snake_case        =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

										snake_case        =     {
										    'unet': unet,
										    'scheduler': scheduler,
										    'vae': vae,
										    'text_encoder': text_encoder,
										    'tokenizer': tokenizer,
										    'safety_checker': None,
										    'feature_extractor': None,
										}
										return components

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase=0      ):






										"""simple docstring"""

										snake_case        =     floats_tensor((1, 3, 32, 32)						,      rng=random.Random(lowerCAmelCase      )      ).to(lowerCAmelCase      )
										snake_case        =     image / 2 + 0.5
										if str(lowerCAmelCase      ).startswith('mps'      ):
																snake_case        =     torch.manual_seed(lowerCAmelCase      )
										else:
																snake_case        =     torch.Generator(device=lowerCAmelCase      ).manual_seed(lowerCAmelCase      )
										snake_case        =     {
										    'prompt': 'An astronaut riding an elephant',
										    'source_prompt': 'An astronaut riding a horse',
										    'image': image,
										    'generator': generator,
										    'num_inference_steps': 2,
										    'eta': 0.1,
										    'strength': 0.8,
										    'guidance_scale': 3,
										    'source_guidance_scale': 1,
										    'output_type': 'numpy',
										}
										return inputs

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     'cpu'  # ensure determinism for the device-dependent torch.Generator

										snake_case        =     self.get_dummy_components()
										snake_case        =     CycleDiffusionPipeline(**lowerCAmelCase      )
										snake_case        =     pipe.to(lowerCAmelCase      )
										pipe.set_progress_bar_config(disable=lowerCAmelCase      )

										snake_case        =     self.get_dummy_inputs(lowerCAmelCase      )
										snake_case        =     pipe(**lowerCAmelCase      )
										snake_case        =     output.images

										snake_case        =     images[0, -3:, -3:, -1]

										assert images.shape == (1, 32, 32, 3)
										snake_case        =     np.array([0.44_59, 0.49_43, 0.45_44, 0.66_43, 0.54_74, 0.43_27, 0.57_01, 0.59_59, 0.51_79]      )

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

				@unittest.skipIf(torch_device != 'cuda'						,      'This test requires a GPU'      )
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.get_dummy_components()
										for name, module in components.items():
																if hasattr(lowerCAmelCase						,      'half'      ):
																						snake_case        =     module.half()
										snake_case        =     CycleDiffusionPipeline(**lowerCAmelCase      )
										snake_case        =     pipe.to(lowerCAmelCase      )
										pipe.set_progress_bar_config(disable=lowerCAmelCase      )

										snake_case        =     self.get_dummy_inputs(lowerCAmelCase      )
										snake_case        =     pipe(**lowerCAmelCase      )
										snake_case        =     output.images

										snake_case        =     images[0, -3:, -3:, -1]

										assert images.shape == (1, 32, 32, 3)
										snake_case        =     np.array([0.35_06, 0.45_43, 0.4_46, 0.45_75, 0.51_95, 0.41_55, 0.52_73, 0.5_18, 0.41_16]      )

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

				@skip_mps
				def 						snake_case	(			self      ):






										"""simple docstring"""

										return super().test_save_load_local()

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






										"""simple docstring"""

										return super().test_inference_batch_single_identical()

				@skip_mps
				def 						snake_case	(			self      ):






										"""simple docstring"""

										return super().test_dict_tuple_outputs_equivalent()

				@skip_mps
				def 						snake_case	(			self      ):






										"""simple docstring"""

										return super().test_save_load_optional_components()

				@skip_mps
				def 						snake_case	(			self      ):






										"""simple docstring"""

										return super().test_attention_slicing_forward_pass()





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



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/cycle-diffusion/black_colored_car.png'      )
										snake_case        =     load_numpy(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'      )
										snake_case        =     init_image.resize((5_12, 5_12)      )

										snake_case        =     'CompVis/stable-diffusion-v1-4'
										snake_case        =     DDIMScheduler.from_pretrained(lowerCAmelCase						,      subfolder='scheduler'      )
										snake_case        =     CycleDiffusionPipeline.from_pretrained(
										    lowerCAmelCase						,      scheduler=lowerCAmelCase						,      safety_checker=lowerCAmelCase						,      torch_dtype=torch.floataa						,      revision='fp16'      )

										pipe.to(lowerCAmelCase      )
										pipe.set_progress_bar_config(disable=lowerCAmelCase      )
										pipe.enable_attention_slicing()

										snake_case        =     'A black colored car'
										snake_case        =     'A blue colored car'

										snake_case        =     torch.manual_seed(0      )
										snake_case        =     pipe(
										    prompt=lowerCAmelCase						,      source_prompt=lowerCAmelCase						,      image=lowerCAmelCase						,      num_inference_steps=1_00						,      eta=0.1						,      strength=0.85						,      guidance_scale=3						,      source_guidance_scale=1						,      generator=lowerCAmelCase						,      output_type='np'						,      )
										snake_case        =     output.images

										# the values aren't exactly equal, but the images look the same visually
										assert np.abs(image - expected_image      ).max() < 5E-1

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/cycle-diffusion/black_colored_car.png'      )
										snake_case        =     load_numpy(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'      )
										snake_case        =     init_image.resize((5_12, 5_12)      )

										snake_case        =     'CompVis/stable-diffusion-v1-4'
										snake_case        =     DDIMScheduler.from_pretrained(lowerCAmelCase						,      subfolder='scheduler'      )
										snake_case        =     CycleDiffusionPipeline.from_pretrained(lowerCAmelCase						,      scheduler=lowerCAmelCase						,      safety_checker=lowerCAmelCase      )

										pipe.to(lowerCAmelCase      )
										pipe.set_progress_bar_config(disable=lowerCAmelCase      )
										pipe.enable_attention_slicing()

										snake_case        =     'A black colored car'
										snake_case        =     'A blue colored car'

										snake_case        =     torch.manual_seed(0      )
										snake_case        =     pipe(
										    prompt=lowerCAmelCase						,      source_prompt=lowerCAmelCase						,      image=lowerCAmelCase						,      num_inference_steps=1_00						,      eta=0.1						,      strength=0.85						,      guidance_scale=3						,      source_guidance_scale=1						,      generator=lowerCAmelCase						,      output_type='np'						,      )
										snake_case        =     output.images

										assert np.abs(image - expected_image      ).max() < 2E-2



"""simple docstring"""
import requests
from bsa import BeautifulSoup





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





						"""simple docstring"""






						snake_case        =     BeautifulSoup(requests.get(_UpperCamelCase   ).text	,     'html.parser'   )
						snake_case        =     soup.findAll('h1'   )
						snake_case        =     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""")


