


# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput





@dataclass
class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							42
       _a   							=							42







class   SCREAMING_SNAKE_CASE_			(a__						,       a__						):




       '''simple docstring'''



       _a   							=							1



       @register_to_config
       def __init__(						self				:				Dict		,				__a				:				int = 2_000		,				__a				:				float = 0.15		,				__a				:				float = 0.01		,				__a				:				float = 1_348.0		,				__a				:				float = 1e-5		,				__a				:				int = 1		,				)					->List[str]:
          # standard deviation of the initial noise distribution
          lowerCamelCase_						:   Any		= sigma_max

          # setable values
          lowerCamelCase_						:   Any		= None

          self.set_sigmas(__a		,				__a		,				__a		,				__a							)



       def 				_lowerCAmelCase							(						self				:				Any		,				__a				:				torch.FloatTensor		,				__a				:				Optional[int] = None							)					->torch.FloatTensor:
          return sample



       def 				_lowerCAmelCase							(						self				:				Dict		,				__a				:				int		,				__a				:				float = None		,				__a				:				Union[str, torch.device] = None							)					->List[str]:
          lowerCamelCase_						:   int		= sampling_eps if sampling_eps is not None else self.config.sampling_eps

          lowerCamelCase_						:   Any		= torch.linspace(1		,				__a		,				__a		,				device=__a							)



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				int		,				__a				:				float = None		,				__a				:				float = None		,				__a				:				float = None							)					->Any:
          lowerCamelCase_						:   Union[str, Any]		= sigma_min if sigma_min is not None else self.config.sigma_min
          lowerCamelCase_						:   Optional[Any]		= sigma_max if sigma_max is not None else self.config.sigma_max
          lowerCamelCase_						:   Dict		= sampling_eps if sampling_eps is not None else self.config.sampling_eps
          if self.timesteps is None:
             self.set_timesteps(__a		,				__a							)

          lowerCamelCase_						:   Optional[Any]		= sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
          lowerCamelCase_						:   Optional[Any]		= torch.exp(torch.linspace(math.log(__a							)		,				math.log(__a							)		,				__a							)							)
          lowerCamelCase_						:   Optional[Any]		= torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]							)



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				__a				:				Optional[Any]		,				__a				:				Any							)					->Any:
          return torch.where(
              timesteps == 0		,				torch.zeros_like(t.to(timesteps.device							)							)		,				self.discrete_sigmas[timesteps - 1].to(timesteps.device							)		,				)



       def 				_lowerCAmelCase							(						self				:				Tuple		,				__a				:				torch.FloatTensor		,				__a				:				int		,				__a				:				torch.FloatTensor		,				__a				:				Optional[torch.Generator] = None		,				__a				:				bool = True		,				)					->Union[SdeVeOutput, Tuple]:
          if self.timesteps is None:
             raise ValueError(
                 """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""							)

          lowerCamelCase_						:   Dict		= timestep * torch.ones(
              sample.shape[0]		,				device=sample.device							)  # torch.repeat_interleave(timestep, sample.shape[0])
          lowerCamelCase_						:   List[str]		= (timestep * (len(self.timesteps							) - 1)).long()

          # mps requires indices to be in the same device, so we use cpu as is the default with cuda
          lowerCamelCase_						:   Optional[Any]		= timesteps.to(self.discrete_sigmas.device							)

          lowerCamelCase_						:   Tuple		= self.discrete_sigmas[timesteps].to(sample.device							)
          lowerCamelCase_						:   int		= self.get_adjacent_sigma(__a		,				__a							).to(sample.device							)
          lowerCamelCase_						:   int		= torch.zeros_like(__a							)
          lowerCamelCase_						:   Any		= (sigma**2 - adjacent_sigma**2) ** 0.5

          # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
          # also equation 47 shows the analog from SDE models to ancestral sampling methods
          lowerCamelCase_						:   List[str]		= diffusion.flatten()
          while len(diffusion.shape							) < len(sample.shape							):
             lowerCamelCase_						:   Dict		= diffusion.unsqueeze(-1							)
          lowerCamelCase_						:   Dict		= drift - diffusion**2 * model_output

          #  equation 6: sample noise for the diffusion term of
          lowerCamelCase_						:   Dict		= randn_tensor(
              sample.shape		,				layout=sample.layout		,				generator=__a		,				device=sample.device		,				dtype=sample.dtype							)
          lowerCamelCase_						:   Any		= sample - drift  # subtract because `dt` is a small negative timestep
          # TODO is the variable diffusion the correct scaling term for the noise?
          lowerCamelCase_						:   List[str]		= prev_sample_mean + diffusion * noise  # add impact of diffusion field g

          if not return_dict:
             return (prev_sample, prev_sample_mean)

          return SdeVeOutput(prev_sample=__a		,				prev_sample_mean=__a							)



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				torch.FloatTensor		,				__a				:				torch.FloatTensor		,				__a				:				Optional[torch.Generator] = None		,				__a				:				bool = True		,				)					->Union[SchedulerOutput, Tuple]:
          if self.timesteps is None:
             raise ValueError(
                 """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""							)

          # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
          # sample noise for correction
          lowerCamelCase_						:   Union[str, Any]		= randn_tensor(sample.shape		,				layout=sample.layout		,				generator=__a							).to(sample.device							)

          # compute step size from the model_output, the noise, and the snr
          lowerCamelCase_						:   Optional[int]		= torch.norm(model_output.reshape(model_output.shape[0]		,				-1							)		,				dim=-1							).mean()
          lowerCamelCase_						:   List[str]		= torch.norm(noise.reshape(noise.shape[0]		,				-1							)		,				dim=-1							).mean()
          lowerCamelCase_						:   Any		= (self.config.snr * noise_norm / grad_norm) ** 2 * 2
          lowerCamelCase_						:   Optional[int]		= step_size * torch.ones(sample.shape[0]							).to(sample.device							)
          # self.repeat_scalar(step_size, sample.shape[0])

          # compute corrected sample: model_output term and noise term
          lowerCamelCase_						:   Union[str, Any]		= step_size.flatten()
          while len(step_size.shape							) < len(sample.shape							):
             lowerCamelCase_						:   List[Any]		= step_size.unsqueeze(-1							)
          lowerCamelCase_						:   Optional[int]		= sample + step_size * model_output
          lowerCamelCase_						:   Optional[int]		= prev_sample_mean + ((step_size * 2) ** 0.5) * noise

          if not return_dict:
             return (prev_sample,)

          return SchedulerOutput(prev_sample=__a							)



       def 				_lowerCAmelCase							(						self				:				int		,				__a				:				torch.FloatTensor		,				__a				:				torch.FloatTensor		,				__a				:				torch.FloatTensor		,				)					->torch.FloatTensor:
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          lowerCamelCase_						:   List[Any]		= timesteps.to(original_samples.device							)
          lowerCamelCase_						:   Optional[Any]		= self.discrete_sigmas.to(original_samples.device							)[timesteps]
          lowerCamelCase_						:   Optional[int]		= (
              noise * sigmas[:, None, None, None]
              if noise is not None
              else torch.randn_like(__a							) * sigmas[:, None, None, None]
          )
          lowerCamelCase_						:   Optional[Any]		= noise + original_samples
          return noisy_samples




       def __len__(						self				:				int							)					->Any:
          return self.config.num_train_timesteps









import requests
from bsa import BeautifulSoup


def    __lowerCamelCase   (      A__						: str = "https://www.worldometers.info/coronavirus"					)     ->    dict:
   lowerCamelCase_						:   List[str]		= BeautifulSoup(requests.get(A__					).text						,						"""html.parser"""					)
   lowerCamelCase_						:   Tuple		= soup.findAll("""h1"""					)
   lowerCamelCase_						:   Tuple		= 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')





