




from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_lowerCamelCase			:	Optional[int]							 =     logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCamelCase			:	List[str]							 =     """
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_=8		)     -> Optional[int]:



       """simple docstring"""

       A__   				=					height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       A__   				=					width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_=512				,       lowercase_=512		)     -> Dict:



       """simple docstring"""

       A__   				=					pil_image.resize((w, h)				,       resample=Image.BICUBIC				,       reducing_gap=1		)
       A__   				=					np.array(pil_image.convert('''RGB'''		)		)
       A__   				=					arr.astype(np.floataa		) / 1_27.5 - 1
       A__   				=					np.transpose(lowercase_				,       [2, 0, 1]		)
       A__   				=					torch.from_numpy(lowercase_		).unsqueeze(0		)
       return image


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    def __init__(    self   :			Tuple      ,				UpperCAmelCase__   :			UNetaDConditionModel      ,				UpperCAmelCase__   :			DDPMScheduler      ,				UpperCAmelCase__   :			VQModel      ,				)		->List[str]:





           '''simple docstring'''

           super().__init__()

           self.register_modules(
               unet=UpperCAmelCase__      ,				scheduler=UpperCAmelCase__      ,				movq=UpperCAmelCase__      ,				)
           A__   				=					2 ** (len(self.movq.config.block_out_channels) - 1)


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			Tuple      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			List[str])		->List[str]:





           '''simple docstring'''

           A__   				=					min(int(num_inference_steps * strength)      ,				UpperCAmelCase__)

           A__   				=					max(num_inference_steps - init_timestep      ,				0)
           A__   				=					self.scheduler.timesteps[t_start:]

           return timesteps, num_inference_steps - t_start


    def  SCREAMING_SNAKE_CASE							(    self   :			str      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			List[Any]      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			Optional[int]=None)		->Any:





           '''simple docstring'''

           if not isinstance(UpperCAmelCase__      ,				(torch.Tensor, PIL.Image.Image, list)):
                  raise ValueError(
                      f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCAmelCase__)}""")

           A__   				=					image.to(device=UpperCAmelCase__      ,				dtype=UpperCAmelCase__)

           A__   				=					batch_size * num_images_per_prompt

           if image.shape[1] == 4:
                  A__   				=					image

           else:
                  if isinstance(UpperCAmelCase__      ,				UpperCAmelCase__) and len(UpperCAmelCase__) != batch_size:
                         raise ValueError(
                             f"""You have passed a list of generators of length {len(UpperCAmelCase__)}, but requested an effective batch"""
                             f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""")

                  elif isinstance(UpperCAmelCase__      ,				UpperCAmelCase__):
                         A__   				=					[
                             self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(UpperCAmelCase__)
                         ]
                         A__   				=					torch.cat(UpperCAmelCase__      ,				dim=0)
                  else:
                         A__   				=					self.movq.encode(UpperCAmelCase__).latent_dist.sample(UpperCAmelCase__)

                  A__   				=					self.movq.config.scaling_factor * init_latents

           A__   				=					torch.cat([init_latents]      ,				dim=0)

           A__   				=					init_latents.shape
           A__   				=					randn_tensor(UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				device=UpperCAmelCase__      ,				dtype=UpperCAmelCase__)

           # get latents
           A__   				=					self.scheduler.add_noise(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__)

           A__   				=					init_latents

           return latents


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			Tuple=0)		->Optional[int]:





           '''simple docstring'''

           if is_accelerate_available():
                  from accelerate import cpu_offload
           else:
                  raise ImportError('''Please install accelerate via `pip install accelerate`''')

           A__   				=					torch.device(f"""cuda:{gpu_id}""")

           A__   				=					[
               self.unet,
               self.movq,
           ]
           for cpu_offloaded_model in models:
                  if cpu_offloaded_model is not None:
                         cpu_offload(UpperCAmelCase__      ,				UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			Tuple=0)		->Optional[int]:





           '''simple docstring'''

           if is_accelerate_available() and is_accelerate_version('''>='''      ,				'''0.17.0.dev0'''):
                  from accelerate import cpu_offload_with_hook
           else:
                  raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''')

           A__   				=					torch.device(f"""cuda:{gpu_id}""")

           if self.device.type != "cpu":
                  self.to('''cpu'''      ,				silence_dtype_warnings=UpperCAmelCase__)
                  torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

           A__   				=					None
           for cpu_offloaded_model in [self.unet, self.movq]:
                  A__				,							A__   				=					cpu_offload_with_hook(UpperCAmelCase__      ,				UpperCAmelCase__      ,				prev_module_hook=UpperCAmelCase__)

           # We'll offload the last model manually.
           A__   				=					hook


    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->List[str]:





           '''simple docstring'''

           if not hasattr(self.unet      ,				'''_hf_hook'''):
                  return self.device
           for module in self.unet.modules():
                  if (
                      hasattr(UpperCAmelCase__      ,				'''_hf_hook''')
                      and hasattr(module._hf_hook      ,				'''execution_device''')
                      and module._hf_hook.execution_device is not None
                  ):
                         return torch.device(module._hf_hook.execution_device)
           return self.device





    @torch.no_grad()
    @replace_example_docstring(UpperCAmelCase__)
    def __call__(    self   :			Any      ,				UpperCAmelCase__   :			Union[torch.FloatTensor, List[torch.FloatTensor]]      ,				UpperCAmelCase__   :			Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]      ,				UpperCAmelCase__   :			Union[torch.FloatTensor, List[torch.FloatTensor]]      ,				UpperCAmelCase__   :			int = 512      ,				UpperCAmelCase__   :			int = 512      ,				UpperCAmelCase__   :			int = 100      ,				UpperCAmelCase__   :			float = 4.0      ,				UpperCAmelCase__   :			float = 0.3      ,				UpperCAmelCase__   :			int = 1      ,				UpperCAmelCase__   :			Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,				UpperCAmelCase__   :			Optional[str] = "pil"      ,				UpperCAmelCase__   :			bool = True      ,				)		->Dict:





           '''simple docstring'''

           A__   				=					self._execution_device

           A__   				=					guidance_scale > 1.0

           if isinstance(UpperCAmelCase__      ,				UpperCAmelCase__):
                  A__   				=					torch.cat(UpperCAmelCase__      ,				dim=0)
           A__   				=					image_embeds.shape[0]
           if isinstance(UpperCAmelCase__      ,				UpperCAmelCase__):
                  A__   				=					torch.cat(UpperCAmelCase__      ,				dim=0)

           if do_classifier_free_guidance:
                  A__   				=					image_embeds.repeat_interleave(UpperCAmelCase__      ,				dim=0)
                  A__   				=					negative_image_embeds.repeat_interleave(UpperCAmelCase__      ,				dim=0)

           A__   				=					torch.cat([negative_image_embeds, image_embeds]      ,				dim=0).to(dtype=self.unet.dtype      ,				device=UpperCAmelCase__)

           if not isinstance(UpperCAmelCase__      ,				UpperCAmelCase__):
                  A__   				=					[image]
           if not all(isinstance(UpperCAmelCase__      ,				(PIL.Image.Image, torch.Tensor)) for i in image):
                  raise ValueError(
                      f"""Input is in incorrect format: {[type(UpperCAmelCase__) for i in image]}. Currently, we only support  PIL image and pytorch tensor""")

           A__   				=					torch.cat([prepare_image(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__) for i in image]      ,				dim=0)
           A__   				=					image.to(dtype=image_embeds.dtype      ,				device=UpperCAmelCase__)

           A__   				=					self.movq.encode(UpperCAmelCase__)['''latents''']
           A__   				=					latents.repeat_interleave(UpperCAmelCase__      ,				dim=0)
           self.scheduler.set_timesteps(UpperCAmelCase__      ,				device=UpperCAmelCase__)
           A__				,							A__   				=					self.get_timesteps(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__)
           A__   				=					timesteps[:1].repeat(batch_size * num_images_per_prompt)
           A__				,							A__   				=					downscale_height_and_width(UpperCAmelCase__      ,				UpperCAmelCase__      ,				self.movq_scale_factor)
           A__   				=					self.prepare_latents(
               UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__      ,				image_embeds.dtype      ,				UpperCAmelCase__      ,				UpperCAmelCase__)
           for i, t in enumerate(self.progress_bar(UpperCAmelCase__)):
                  # expand the latents if we are doing classifier free guidance
                  A__   				=					torch.cat([latents] * 2) if do_classifier_free_guidance else latents

                  A__   				=					{'''image_embeds''': image_embeds}
                  A__   				=					self.unet(
                      sample=UpperCAmelCase__      ,				timestep=UpperCAmelCase__      ,				encoder_hidden_states=UpperCAmelCase__      ,				added_cond_kwargs=UpperCAmelCase__      ,				return_dict=UpperCAmelCase__      ,				)[0]

                  if do_classifier_free_guidance:
                         A__				,							A__   				=					noise_pred.split(latents.shape[1]      ,				dim=1)
                         A__				,							A__   				=					noise_pred.chunk(2)
                         A__				,							A__   				=					variance_pred.chunk(2)
                         A__   				=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                         A__   				=					torch.cat([noise_pred, variance_pred_text]      ,				dim=1)

                  if not (
                      hasattr(self.scheduler.config      ,				'''variance_type''')
                      and self.scheduler.config.variance_type in ["learned", "learned_range"]
                  ):
                         A__				,							A__   				=					noise_pred.split(latents.shape[1]      ,				dim=1)

                  # compute the previous noisy sample x_t -> x_t-1
                  A__   				=					self.scheduler.step(
                      UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__      ,				generator=UpperCAmelCase__      ,				)[0]

           # post-processing
           A__   				=					self.movq.decode(UpperCAmelCase__      ,				force_not_quantize=UpperCAmelCase__)['''sample''']

           if output_type not in ["pt", "np", "pil"]:
                  raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}""")

           if output_type in ["np", "pil"]:
                  A__   				=					image * 0.5 + 0.5
                  A__   				=					image.clamp(0      ,				1)
                  A__   				=					image.cpu().permute(0      ,				2      ,				3      ,				1).float().numpy()

           if output_type == "pil":
                  A__   				=					self.numpy_to_pil(UpperCAmelCase__)

           if not return_dict:
                  return (image,)

           return ImagePipelineOutput(images=UpperCAmelCase__)








from collections import namedtuple

import requests
from lxml import html  # type: ignore

_lowerCamelCase			:	Optional[int]							 =     namedtuple("""covid_data""", """cases deaths recovered""")







def SCREAMING_SNAKE_CASE       ( lowercase_ = "https://www.worldometers.info/coronavirus/"		)     -> covid_data:



       """simple docstring"""

       A__   				=					'''//div[@class = "maincounter-number"]/span/text()'''
       return covid_data(*html.fromstring(requests.get(lowercase_		).content		).xpath(lowercase_		)		)


_lowerCamelCase			:	Optional[int]							 =     """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()))


