






'''simple docstring'''





from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

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


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

__lowerCamelCase	:		Optional[int]										=   """
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/Kandinsky-2-1-prior\")
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")
        >>> pipe.to(\"cuda\")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

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







def 			A__							(      _a  :					int		,  _a  :					int		,  _a  :					Optional[Any]=8	):





 '''simple docstring'''







 snake_case__  :    Optional[int]							       =h // scale_factor**2
 if h % scale_factor**2 != 0:
  new_h += 1
 snake_case__  :    Any							       =w // scale_factor**2
 if w % scale_factor**2 != 0:
  new_w += 1
 return new_h * scale_factor, new_w * scale_factor







class  _lowercase   (   _A						):
      def __init__(     self				,			a				,			a				,			a				,			a				,			a				,			):
       super().__init__()

       self.register_modules(
           text_encoder=a				,			tokenizer=a				,			unet=a				,			scheduler=a				,			movq=a				,			)
       snake_case__  :    Dict							       =2 ** (len(self.movq.config.block_out_channels   ) - 1)
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a				,			a				,			a   ):
       if latents is None:
        snake_case__  :    int							       =randn_tensor(a				,			generator=a				,			device=a				,			dtype=a   )
       else:
        if latents.shape != shape:
         raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"   )
        snake_case__  :    Optional[Any]							       =latents.to(a   )

       snake_case__  :    str							       =latents * scheduler.init_noise_sigma
       return latents
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a				,			a=None				,			):
       snake_case__  :    int							       =len(a   ) if isinstance(a				,			a   ) else 1
       # get prompt text embeddings
       snake_case__  :    Optional[Any]							       =self.tokenizer(
           a				,			padding="""max_length"""				,			truncation=a				,			max_length=7_7				,			return_attention_mask=a				,			add_special_tokens=a				,			return_tensors="""pt"""				,			)

       snake_case__  :    Dict							       =text_inputs.input_ids
       snake_case__  :    Union[str, Any]							       =self.tokenizer(a				,			padding="""longest"""				,			return_tensors="""pt"""   ).input_ids

       if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(a				,			a   ):
        snake_case__  :    Any							       =self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]   )
        logger.warning(
            """The following part of your input was truncated because CLIP can only handle sequences up to"""
            F" {self.tokenizer.model_max_length} tokens: {removed_text}"   )

       snake_case__  :    Any							       =text_input_ids.to(a   )
       snake_case__  :    Optional[int]							       =text_inputs.attention_mask.to(a   )

       snake_case__  :    Tuple							       =self.text_encoder(
           input_ids=a				,			attention_mask=a   )

       snake_case__  :    Any							       =prompt_embeds.repeat_interleave(a				,			dim=0   )
       snake_case__  :    Union[str, Any]							       =text_encoder_hidden_states.repeat_interleave(a				,			dim=0   )
       snake_case__  :    List[Any]							       =text_mask.repeat_interleave(a				,			dim=0   )

       if do_classifier_free_guidance:
        snake_case__  :    List[str]
        if negative_prompt is None:
         snake_case__  :    List[str]							       =[""""""] * batch_size
        elif type(a   ) is not type(a   ):
         raise TypeError(
             F"`negative_prompt` should be the same type to `prompt`, but got {type(a   )} !="
             F" {type(a   )}."   )
        elif isinstance(a				,			a   ):
         snake_case__  :    List[Any]							       =[negative_prompt]
        elif batch_size != len(a   ):
         raise ValueError(
             F"`negative_prompt`: {negative_prompt} has batch size {len(a   )}, but `prompt`:"
             F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
             """ the batch size of `prompt`."""   )
        else:
         snake_case__  :    List[str]							       =negative_prompt

        snake_case__  :    List[str]							       =self.tokenizer(
            a				,			padding="""max_length"""				,			max_length=7_7				,			truncation=a				,			return_attention_mask=a				,			add_special_tokens=a				,			return_tensors="""pt"""				,			)
        snake_case__  :    Dict							       =uncond_input.input_ids.to(a   )
        snake_case__  :    Optional[Any]							       =uncond_input.attention_mask.to(a   )

        snake_case__  :    str							       =self.text_encoder(
            input_ids=a				,			attention_mask=a   )

        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

        snake_case__  :    Union[str, Any]							       =negative_prompt_embeds.shape[1]
        snake_case__  :    Optional[int]							       =negative_prompt_embeds.repeat(1				,			a   )
        snake_case__  :    Optional[int]							       =negative_prompt_embeds.view(batch_size * num_images_per_prompt				,			a   )

        snake_case__  :    List[str]							       =uncond_text_encoder_hidden_states.shape[1]
        snake_case__  :    Tuple							       =uncond_text_encoder_hidden_states.repeat(1				,			a				,			1   )
        snake_case__  :    Union[str, Any]							       =uncond_text_encoder_hidden_states.view(
            batch_size * num_images_per_prompt				,			a				,			-1   )
        snake_case__  :    List[Any]							       =uncond_text_mask.repeat_interleave(a				,			dim=0   )

        # done duplicates

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
        snake_case__  :    Tuple							       =torch.cat([negative_prompt_embeds, prompt_embeds]   )
        snake_case__  :    int							       =torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]   )

        snake_case__  :    Tuple							       =torch.cat([uncond_text_mask, text_mask]   )

       return prompt_embeds, text_encoder_hidden_states, text_mask
      def 						lowercase__					(     self				,			a=0   ):
       if is_accelerate_available():
        from accelerate import cpu_offload
       else:
        raise ImportError("""Please install accelerate via `pip install accelerate`"""   )

       snake_case__  :    Optional[int]							       =torch.device(F"cuda:{gpu_id}"   )

       snake_case__  :    int							       =[
           self.unet,
           self.text_encoder,
           self.movq,
       ]
       for cpu_offloaded_model in models:
        if cpu_offloaded_model is not None:
         cpu_offload(a				,			a   )
      def 						lowercase__					(     self				,			a=0   ):
       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."""   )

       snake_case__  :    int							       =torch.device(F"cuda:{gpu_id}"   )

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

       snake_case__  :    List[Any]							       =None
       for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
        snake_case__  :    Dict							       =cpu_offload_with_hook(a				,			a				,			prev_module_hook=a   )

       if self.safety_checker is not None:
        snake_case__  :    List[Any]							       =cpu_offload_with_hook(self.safety_checker				,			a				,			prev_module_hook=a   )

       # We'll offload the last model manually.
       snake_case__  :    List[str]							       =hook
      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 						lowercase__					(     self   ):
       if not hasattr(self.unet				,			"""_hf_hook"""   ):
        return self.device
       for module in self.unet.modules():
        if (
            hasattr(a				,			"""_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(a   )
      def __call__(     self				,			a				,			a				,			a				,			a = None				,			a = 5_1_2				,			a = 5_1_2				,			a = 1_0_0				,			a = 4.0				,			a = 1				,			a = None				,			a = None				,			a = "pil"				,			a = True				,			):

       if isinstance(a				,			a   ):
        snake_case__  :    Any							       =1
       elif isinstance(a				,			a   ):
        snake_case__  :    Optional[int]							       =len(a   )
       else:
        raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(a   )}"   )

       snake_case__  :    Any							       =self._execution_device

       snake_case__  :    str							       =batch_size * num_images_per_prompt
       snake_case__  :    str							       =guidance_scale > 1.0

       snake_case__  :    Tuple							       =self._encode_prompt(
           a				,			a				,			a				,			a				,			a   )

       if isinstance(a				,			a   ):
        snake_case__  :    Union[str, Any]							       =torch.cat(a				,			dim=0   )
       if isinstance(a				,			a   ):
        snake_case__  :    Union[str, Any]							       =torch.cat(a				,			dim=0   )

       if do_classifier_free_guidance:
        snake_case__  :    Tuple							       =image_embeds.repeat_interleave(a				,			dim=0   )
        snake_case__  :    Optional[Any]							       =negative_image_embeds.repeat_interleave(a				,			dim=0   )

       snake_case__  :    List[Any]							       =torch.cat([negative_image_embeds, image_embeds]				,			dim=0   ).to(
           dtype=prompt_embeds.dtype				,			device=a   )

       self.scheduler.set_timesteps(a				,			device=a   )
       snake_case__  :    List[str]							       =self.scheduler.timesteps

       snake_case__  :    Optional[int]							       =self.unet.config.in_channels

       snake_case__  :    Union[str, Any]							       =get_new_h_w(a				,			a				,			self.movq_scale_factor   )

       # create initial latent
       snake_case__  :    str							       =self.prepare_latents(
           (batch_size, num_channels_latents, height, width)				,			text_encoder_hidden_states.dtype				,			a				,			a				,			a				,			self.scheduler				,			)

       for i, t in enumerate(self.progress_bar(a   )   ):
        # expand the latents if we are doing classifier free guidance
        snake_case__  :    str							       =torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

        snake_case__  :    Optional[int]							       ={"""text_embeds""": prompt_embeds, """image_embeds""": image_embeds}
        snake_case__  :    List[str]							       =self.unet(
            sample=a				,			timestep=a				,			encoder_hidden_states=a				,			added_cond_kwargs=a				,			return_dict=a				,			)[0]

        if do_classifier_free_guidance:
         snake_case__  :    Union[str, Any]							       =noise_pred.split(latents.shape[1]				,			dim=1   )
         snake_case__  :    Any							       =noise_pred.chunk(2   )
         snake_case__  :    Union[str, Any]							       =variance_pred.chunk(2   )
         snake_case__  :    Union[str, Any]							       =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
         snake_case__  :    Union[str, Any]							       =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"]
        ):
         snake_case__  :    Dict							       =noise_pred.split(latents.shape[1]				,			dim=1   )

        # compute the previous noisy sample x_t -> x_t-1
        snake_case__  :    str							       =self.scheduler.step(
            a				,			a				,			a				,			generator=a				,			).prev_sample
       # post-processing
       snake_case__  :    List[str]							       =self.movq.decode(a				,			force_not_quantize=a   )["""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"]:
        snake_case__  :    List[Any]							       =image * 0.5 + 0.5
        snake_case__  :    List[str]							       =image.clamp(0				,			1   )
        snake_case__  :    int							       =image.cpu().permute(0				,			2				,			3				,			1   ).float().numpy()

       if output_type == "pil":
        snake_case__  :    Tuple							       =self.numpy_to_pil(a   )

       if not return_dict:
        return (image,)

       return ImagePipelineOutput(images=a   )













from collections import namedtuple

import requests
from lxml import html  # type: ignore

__lowerCamelCase	:		Any										=   namedtuple("""covid_data""", """cases deaths recovered""")







def 			A__							(      _a  :					str = "https://www.worldometers.info/coronavirus/"	):





 '''simple docstring'''







 snake_case__  :    Any							       ="""//div[@class = \"maincounter-number\"]/span/text()"""
 return covid_data(*html.fromstring(requests.get(_a	).content	).xpath(_a	)	)


__lowerCamelCase	:		str										=   """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()))





