


"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						torch.FloatTensor
							__lowercase:						Optional[torch.FloatTensor]							 =      None







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=0.999			,	_SCREAMING_SNAKE_CASE="cosine"			,	)      ->			Any:
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(_SCREAMING_SNAKE_CASE			):
																					return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(_SCREAMING_SNAKE_CASE			):
																					return math.exp(t * -12.0			)

							else:
														raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""			)

							snake_case_          =						[]
							for i in range(_SCREAMING_SNAKE_CASE			):
														snake_case_          =						i / num_diffusion_timesteps
														snake_case_          =						(i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(_SCREAMING_SNAKE_CASE			) / alpha_bar_fn(_SCREAMING_SNAKE_CASE			)			,	_SCREAMING_SNAKE_CASE			)			)
							return torch.tensor(_SCREAMING_SNAKE_CASE			,	dtype=torch.floataa			)





class        __A       (snake_case__  ,						snake_case__):





							'''simple docstring'''

							__lowercase:						Union[str, Any]							 =      1



							@register_to_config
							def __init__(			self							: Dict						,    UpperCAmelCase_							: int = 1_000						,    UpperCAmelCase_							: float = 0.0_001						,    UpperCAmelCase_							: float = 0.02						,    UpperCAmelCase_							: str = "linear"						,    UpperCAmelCase_							: Optional[Union[np.ndarray, List[float]]] = None						,    UpperCAmelCase_							: bool = True						,    UpperCAmelCase_							: bool = True						,    UpperCAmelCase_							: int = 0						,    UpperCAmelCase_							: str = "epsilon"						,    UpperCAmelCase_							: float = 1.0						,    **UpperCAmelCase_							: Any						,    )   ->str:



														"""simple docstring"""



														if kwargs.get("""set_alpha_to_one"""						,    UpperCAmelCase_    ) is not None:
																					snake_case_          =						(
																					    """The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
																					)
																					deprecate("""set_alpha_to_one"""						,    """1.0.0"""						,    UpperCAmelCase_						,    standard_warn=UpperCAmelCase_    )
																					snake_case_          =						kwargs["""set_alpha_to_one"""]
														if trained_betas is not None:
																					snake_case_          =						torch.tensor(UpperCAmelCase_						,    dtype=torch.floataa    )
														elif beta_schedule == "linear":
																					snake_case_          =						torch.linspace(UpperCAmelCase_						,    UpperCAmelCase_						,    UpperCAmelCase_						,    dtype=torch.floataa    )
														elif beta_schedule == "scaled_linear":
																					# this schedule is very specific to the latent diffusion model.
																					snake_case_          =						(
																					    torch.linspace(beta_start**0.5						,    beta_end**0.5						,    UpperCAmelCase_						,    dtype=torch.floataa    ) ** 2
																					)
														elif beta_schedule == "squaredcos_cap_v2":
																					# Glide cosine schedule
																					snake_case_          =						betas_for_alpha_bar(UpperCAmelCase_    )
														else:
																					raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""    )

														snake_case_          =						1.0 - self.betas
														snake_case_          =						torch.cumprod(self.alphas						,    dim=0    )

														# At every step in inverted ddim, we are looking into the next alphas_cumprod
														# For the final step, there is no next alphas_cumprod, and the index is out of bounds
														# `set_alpha_to_zero` decides whether we set this parameter simply to zero
														# in this case, self.step() just output the predicted noise
														# or whether we use the final alpha of the "non-previous" one.
														snake_case_          =						torch.tensor(0.0    ) if set_alpha_to_zero else self.alphas_cumprod[-1]

														# standard deviation of the initial noise distribution
														snake_case_          =						1.0

														# setable values
														snake_case_          =						None
														snake_case_          =						torch.from_numpy(np.arange(0						,    UpperCAmelCase_    ).copy().astype(np.intaa    )    )



							def 						lowerCAmelCase    (			self							: int						,    UpperCAmelCase_							: torch.FloatTensor						,    UpperCAmelCase_							: Optional[int] = None    )   ->torch.FloatTensor:



														"""simple docstring"""



														return sample



							def 						lowerCAmelCase    (			self							: int						,    UpperCAmelCase_							: int						,    UpperCAmelCase_							: Union[str, torch.device] = None    )   ->str:



														"""simple docstring"""



														if num_inference_steps > self.config.num_train_timesteps:
																					raise ValueError(
																					    F"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
																					    F""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
																					    F""" maximal {self.config.num_train_timesteps} timesteps."""    )

														snake_case_          =						num_inference_steps
														snake_case_          =						self.config.num_train_timesteps // self.num_inference_steps
														# creates integer timesteps by multiplying by ratio
														# casting to int to avoid issues when num_inference_step is power of 3
														snake_case_          =						(np.arange(0						,    UpperCAmelCase_    ) * step_ratio).round().copy().astype(np.intaa    )
														snake_case_          =						torch.from_numpy(UpperCAmelCase_    ).to(UpperCAmelCase_    )
														self.timesteps += self.config.steps_offset



							def 						lowerCAmelCase    (			self							: Union[str, Any]						,    UpperCAmelCase_							: torch.FloatTensor						,    UpperCAmelCase_							: int						,    UpperCAmelCase_							: torch.FloatTensor						,    UpperCAmelCase_							: float = 0.0						,    UpperCAmelCase_							: bool = False						,    UpperCAmelCase_							: Optional[torch.FloatTensor] = None						,    UpperCAmelCase_							: bool = True						,    )   ->Union[DDIMSchedulerOutput, Tuple]:



														"""simple docstring"""



														snake_case_          =						timestep + self.config.num_train_timesteps // self.num_inference_steps

														# 2. compute alphas, betas
														# change original implementation to exactly match noise levels for analogous forward process
														snake_case_          =						self.alphas_cumprod[timestep]
														snake_case_          =						(
														    self.alphas_cumprod[prev_timestep]
														    if prev_timestep < self.config.num_train_timesteps
														    else self.final_alpha_cumprod
														)

														snake_case_          =						1 - alpha_prod_t

														# 3. compute predicted original sample from predicted noise also called
														# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
														if self.config.prediction_type == "epsilon":
																					snake_case_          =						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																					snake_case_          =						model_output
														elif self.config.prediction_type == "sample":
																					snake_case_          =						model_output
																					snake_case_          =						(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
														elif self.config.prediction_type == "v_prediction":
																					snake_case_          =						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																					snake_case_          =						(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
														else:
																					raise ValueError(
																					    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
																					    """ `v_prediction`"""    )

														# 4. Clip or threshold "predicted x_0"
														if self.config.clip_sample:
																					snake_case_          =						pred_original_sample.clamp(
																					    -self.config.clip_sample_range						,    self.config.clip_sample_range    )

														# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
														snake_case_          =						(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

														# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
														snake_case_          =						alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

														if not return_dict:
																					return (prev_sample, pred_original_sample)
														return DDIMSchedulerOutput(prev_sample=UpperCAmelCase_						,    pred_original_sample=UpperCAmelCase_    )



							def __len__(			self							: List[str]    )   ->Any:



														"""simple docstring"""



														return self.config.num_train_timesteps







"""simple docstring"""
from collections import namedtuple

import requests
from lxml import html  # type: ignore

__SCREAMING_SNAKE_CASE							:      List[str]						=      namedtuple('covid_data', 'cases deaths recovered')







def   _a			( _SCREAMING_SNAKE_CASE = "https://www.worldometers.info/coronavirus/"			)      ->			covid_data:
							snake_case_          =						"""//div[@class = \"maincounter-number\"]/span/text()"""
							return covid_data(*html.fromstring(requests.get(_SCREAMING_SNAKE_CASE			).content			).xpath(_SCREAMING_SNAKE_CASE			)			)


__SCREAMING_SNAKE_CASE							:      List[str]						=      'Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}'





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



