



import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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     (	UpperCamelCase_  ,	UpperCamelCase_  ,	unittest.TestCase   ):
	lowerCAmelCase__       					=  StableDiffusionXLImgaImgPipeline
	lowerCAmelCase__       					=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
	lowerCAmelCase__       					=  PipelineTesterMixin.required_optional_params - {"""latents"""}
	lowerCAmelCase__       					=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	lowerCAmelCase__       					=  IMAGE_TO_IMAGE_IMAGE_PARAMS
	lowerCAmelCase__       					=  IMAGE_TO_IMAGE_IMAGE_PARAMS




	def        lowerCamelCase      (						self				):



				'''simple docstring'''
				torch.manual_seed(0				)
				__lowerCamelCase													=    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''')	,				attention_head_dim=(2, 4)	,				use_linear_projection=__a	,				addition_embed_type='''text_time'''	,				addition_time_embed_dim=8	,				transformer_layers_per_block=(1, 2)	,				projection_class_embeddings_input_dim=80	,				cross_attention_dim=64	,				)
				__lowerCamelCase													=    EulerDiscreteScheduler(
				    beta_start=0.00_085	,				beta_end=0.012	,				steps_offset=1	,				beta_schedule='''scaled_linear'''	,				timestep_spacing='''leading'''	,				)
				torch.manual_seed(0				)
				__lowerCamelCase													=    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	,				sample_size=128	,				)
				torch.manual_seed(0				)
				__lowerCamelCase													=    CLIPTextConfig(
				    bos_token_id=0	,				eos_token_id=2	,				hidden_size=32	,				intermediate_size=37	,				layer_norm_eps=1E-0_5	,				num_attention_heads=4	,				num_hidden_layers=5	,				pad_token_id=1	,				vocab_size=1000	,				hidden_act='''gelu'''	,				projection_dim=32	,				)
				__lowerCamelCase													=    CLIPTextModel(__a				)
				__lowerCamelCase													=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	,				local_files_only=__a				)

				__lowerCamelCase													=    CLIPTextModelWithProjection(__a				)
				__lowerCamelCase													=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	,				local_files_only=__a				)

				__lowerCamelCase													=    {
				    'unet': unet,
				    'scheduler': scheduler,
				    'vae': vae,
				    'text_encoder': text_encoder,
				    'tokenizer': tokenizer,
				    'text_encoder_2': text_encoder_a,
				    'tokenizer_2': tokenizer_a,
				    # "safety_checker": None,
				    # "feature_extractor": None,
				}
				return components




	def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase=0				):



				'''simple docstring'''
				__lowerCamelCase													=    floats_tensor((1, 3, 32, 32)	,				rng=random.Random(__a				)				).to(__a				)
				__lowerCamelCase													=    image / 2 + 0.5
				if str(__a				).startswith('''mps'''				):
							__lowerCamelCase													=    torch.manual_seed(__a				)
				else:
							__lowerCamelCase													=    torch.Generator(device=__a				).manual_seed(__a				)
				__lowerCamelCase													=    {
				    'prompt': 'A painting of a squirrel eating a burger',
				    'image': image,
				    'generator': generator,
				    'num_inference_steps': 2,
				    'guidance_scale': 5.0,
				    'output_type': 'numpy',
				    'strength': 0.75,
				}
				return inputs




	def        lowerCamelCase      (						self				):



				'''simple docstring'''
				__lowerCamelCase													=    'cpu'  # ensure determinism for the device-dependent torch.Generator
				__lowerCamelCase													=    self.get_dummy_components()
				__lowerCamelCase													=    StableDiffusionXLImgaImgPipeline(**__a				)
				__lowerCamelCase													=    sd_pipe.to(__a				)
				sd_pipe.set_progress_bar_config(disable=__a				)

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

				assert image.shape == (1, 32, 32, 3)

				__lowerCamelCase													=    np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165]				)

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




	def        lowerCamelCase      (						self				):



				'''simple docstring'''
				super().test_attention_slicing_forward_pass(expected_max_diff=3E-3				)




	def        lowerCamelCase      (						self				):



				'''simple docstring'''
				super().test_inference_batch_single_identical(expected_max_diff=3E-3				)




	def        lowerCamelCase      (						self				):



				'''simple docstring'''
				pass




	def        lowerCamelCase      (						self				):



				'''simple docstring'''
				__lowerCamelCase													=    self.get_dummy_components()
				__lowerCamelCase													=    StableDiffusionXLImgaImgPipeline(**__a				)
				__lowerCamelCase													=    sd_pipe.to(__a				)
				__lowerCamelCase													=    sd_pipe.to(__a				)
				sd_pipe.set_progress_bar_config(disable=__a				)

				# forward without prompt embeds
				__lowerCamelCase													=    self.get_dummy_inputs(__a				)
				__lowerCamelCase													=    3 * ['this is a negative prompt']
				__lowerCamelCase													=    negative_prompt
				__lowerCamelCase													=    3 * [inputs['prompt']]

				__lowerCamelCase													=    sd_pipe(**__a				)
				__lowerCamelCase													=    output.images[0, -3:, -3:, -1]

				# forward with prompt embeds
				__lowerCamelCase													=    self.get_dummy_inputs(__a				)
				__lowerCamelCase													=    3 * ['this is a negative prompt']
				__lowerCamelCase													=    3 * [inputs.pop('''prompt'''				)]

				(
				    __lowerCamelCase
				)													=    sd_pipe.encode_prompt(__a	,				negative_prompt=__a				)

				__lowerCamelCase													=    sd_pipe(
				    **__a	,				prompt_embeds=__a	,				negative_prompt_embeds=__a	,				pooled_prompt_embeds=__a	,				negative_pooled_prompt_embeds=__a	,				)
				__lowerCamelCase													=    output.images[0, -3:, -3:, -1]

				# make sure that it's equal
				assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()				).max() < 1E-4



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




	def        lowerCamelCase      (						self				):



				'''simple docstring'''
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()




	def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase="cpu"	,				__UpperCAmelCase=torch.floataa	,				__UpperCAmelCase=0				):



				'''simple docstring'''
				__lowerCamelCase													=    torch.Generator(device=__a				).manual_seed(__a				)
				__lowerCamelCase													=    np.random.RandomState(__a				).standard_normal((1, 4, 64, 64)				)
				__lowerCamelCase													=    torch.from_numpy(__a				).to(device=__a	,				dtype=__a				)
				__lowerCamelCase													=    {
				    'prompt': 'a photograph of an astronaut riding a horse',
				    'latents': latents,
				    'generator': generator,
				    'num_inference_steps': 3,
				    'guidance_scale': 7.5,
				    'output_type': 'numpy',
				}
				return inputs




	def        lowerCamelCase      (						self				):



				'''simple docstring'''
				__lowerCamelCase													=    DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''				)
				pipe.to(__a				)
				pipe.set_progress_bar_config(disable=__a				)

				__lowerCamelCase													=    self.get_inputs(__a				)
				__lowerCamelCase													=    pipe(**__a				).images
				__lowerCamelCase													=    image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 512, 512, 3)
				__lowerCamelCase													=    np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]				)
				assert np.abs(image_slice - expected_slice				).max() < 7E-3











from collections import namedtuple

import requests
from lxml import html  # type: ignore

a_			=					namedtuple("""covid_data""", """cases deaths recovered""")





def 		a__			(		_UpperCamelCase			: str = "https://www.worldometers.info/coronavirus/"							):
   __lowerCamelCase													=    '''//div[@class = "maincounter-number"]/span/text()'''
   return covid_data(*html.fromstring(requests.get(_UpperCamelCase							).content							).xpath(_UpperCamelCase							)							)


a_			=					"""Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""





print(fmt.format(*covid_stats()))






