


'''simple docstring'''



import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[int] ,       UpperCAmelCase_       :							Union[str, Any]=0.999 ,       UpperCAmelCase_       :							Dict="cosine" ,       )	->						List[str]:
     if alpha_transform_type == "cosine":

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

     elif alpha_transform_type == "exp":

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

     else:
          raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'				)

     __lowerCamelCase  :      List[str]														=						[]
     for i in range(UpperCAmelCase_				):
          __lowerCamelCase  :      Any														=						i / num_diffusion_timesteps
          __lowerCamelCase  :      str														=						(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(UpperCAmelCase_				) / alpha_bar_fn(UpperCAmelCase_				) ,       UpperCAmelCase_				)				)
     return torch.tensor(UpperCAmelCase_ ,       dtype=torch.floataa				)





class   UpperCAmelCase_    (_UpperCAmelCase   ,						_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   List[Any]					=     [e.name for e in KarrasDiffusionSchedulers]
    lowerCamelCase    :   List[str]					=     2







    @register_to_config
    def __init__(				self     ,	SCREAMING_SNAKE_CASE_ = 10_00     ,	SCREAMING_SNAKE_CASE_ = 0.0_0_0_8_5     ,	SCREAMING_SNAKE_CASE_ = 0.0_1_2     ,	SCREAMING_SNAKE_CASE_ = "linear"     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = "epsilon"     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = 1.0     ,	SCREAMING_SNAKE_CASE_ = "linspace"     ,	SCREAMING_SNAKE_CASE_ = 0     ,	)							->	Dict:
         if trained_betas is not None:
              __lowerCamelCase  :      Optional[Any]														=						torch.tensor(SCREAMING_SNAKE_CASE_     ,	dtype=torch.floataa      )
         elif beta_schedule == "linear":
              __lowerCamelCase  :      Union[str, Any]														=						torch.linspace(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	dtype=torch.floataa      )
         elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              __lowerCamelCase  :      Any														=						(
                  torch.linspace(beta_start**0.5     ,	beta_end**0.5     ,	SCREAMING_SNAKE_CASE_     ,	dtype=torch.floataa      ) ** 2
              )
         elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              __lowerCamelCase  :      Optional[int]														=						betas_for_alpha_bar(SCREAMING_SNAKE_CASE_     ,	alpha_transform_type='cosine'      )
         elif beta_schedule == "exp":
              __lowerCamelCase  :      Union[str, Any]														=						betas_for_alpha_bar(SCREAMING_SNAKE_CASE_     ,	alpha_transform_type='exp'      )
         else:
              raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'      )

         __lowerCamelCase  :      Dict														=						1.0 - self.betas
         __lowerCamelCase  :      Dict														=						torch.cumprod(self.alphas     ,	dim=0      )

         #  set all values
         self.set_timesteps(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Dict														=						use_karras_sigmas







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None      )							->	List[str]:
         if schedule_timesteps is None:
              __lowerCamelCase  :      Dict														=						self.timesteps

         __lowerCamelCase  :      Any														=						(schedule_timesteps == timestep).nonzero()

         # The sigma index that is taken for the **very** first `step`
         # is always the second index (or the last index if there is only 1)
         # This way we can ensure we don't accidentally skip a sigma in
         # case we start in the middle of the denoising schedule (e.g. for image-to-image)
         if len(self._index_counter      ) == 0:
              __lowerCamelCase  :      List[str]														=						1 if len(SCREAMING_SNAKE_CASE_      ) > 1 else 0
         else:
              __lowerCamelCase  :      Optional[Any]														=						timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_      ) else timestep
              __lowerCamelCase  :      Union[str, Any]														=						self._index_counter[timestep_int]

         return indices[pos].item()







    @property
    def  lowercase_							(				self      )							->	Dict:
         # standard deviation of the initial noise distribution
         if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	)							->	torch.FloatTensor:
         __lowerCamelCase  :      Dict														=						self.index_for_timestep(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Tuple														=						self.sigmas[step_index]
         __lowerCamelCase  :      List[Any]														=						sample / ((sigma**2 + 1) ** 0.5)
         return sample







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	)							->	str:
         __lowerCamelCase  :      Optional[int]														=						num_inference_steps

         __lowerCamelCase  :      List[Any]														=						num_train_timesteps or self.config.num_train_timesteps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
              __lowerCamelCase  :      List[Any]														=						np.linspace(0     ,	num_train_timesteps - 1     ,	SCREAMING_SNAKE_CASE_     ,	dtype=SCREAMING_SNAKE_CASE_      )[::-1].copy()
         elif self.config.timestep_spacing == "leading":
              __lowerCamelCase  :      str														=						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
              __lowerCamelCase  :      Optional[Any]														=						(np.arange(0     ,	SCREAMING_SNAKE_CASE_      ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_      )
              timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
              __lowerCamelCase  :      Any														=						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
              __lowerCamelCase  :      List[str]														=						(np.arange(SCREAMING_SNAKE_CASE_     ,	0     ,	-step_ratio      )).round().copy().astype(SCREAMING_SNAKE_CASE_      )
              timesteps -= 1
         else:
              raise ValueError(
                  f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'      )

         __lowerCamelCase  :      Optional[int]														=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
         __lowerCamelCase  :      Dict														=						np.log(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      List[Any]														=						np.interp(SCREAMING_SNAKE_CASE_     ,	np.arange(0     ,	len(SCREAMING_SNAKE_CASE_      )      )     ,	SCREAMING_SNAKE_CASE_      )

         if self.config.use_karras_sigmas:
              __lowerCamelCase  :      Any														=						self._convert_to_karras(in_sigmas=SCREAMING_SNAKE_CASE_     ,	num_inference_steps=self.num_inference_steps      )
              __lowerCamelCase  :      List[str]														=						np.array([self._sigma_to_t(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ) for sigma in sigmas]      )

         __lowerCamelCase  :      List[Any]														=						np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
         __lowerCamelCase  :      Any														=						torch.from_numpy(SCREAMING_SNAKE_CASE_      ).to(device=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      List[str]														=						torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2      ), sigmas[-1:]]      )

         __lowerCamelCase  :      str														=						torch.from_numpy(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Optional[int]														=						torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2      )]      )

         if str(SCREAMING_SNAKE_CASE_      ).startswith('mps'      ):
              # mps does not support float64
              __lowerCamelCase  :      int														=						timesteps.to(SCREAMING_SNAKE_CASE_     ,	dtype=torch.floataa      )
         else:
              __lowerCamelCase  :      Optional[int]														=						timesteps.to(device=SCREAMING_SNAKE_CASE_      )

         # empty dt and derivative
         __lowerCamelCase  :      Tuple														=						None
         __lowerCamelCase  :      str														=						None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         __lowerCamelCase  :      int														=						defaultdict(SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	List[Any]:
         # get log sigma
         __lowerCamelCase  :      str														=						np.log(SCREAMING_SNAKE_CASE_      )

         # get distribution
         __lowerCamelCase  :      Optional[Any]														=						log_sigma - log_sigmas[:, np.newaxis]

         # get sigmas range
         __lowerCamelCase  :      int														=						np.cumsum((dists >= 0)     ,	axis=0      ).argmax(axis=0      ).clip(max=log_sigmas.shape[0] - 2      )
         __lowerCamelCase  :      Optional[Any]														=						low_idx + 1

         __lowerCamelCase  :      Optional[Any]														=						log_sigmas[low_idx]
         __lowerCamelCase  :      Optional[int]														=						log_sigmas[high_idx]

         # interpolate sigmas
         __lowerCamelCase  :      Union[str, Any]														=						(low - log_sigma) / (low - high)
         __lowerCamelCase  :      Optional[Any]														=						np.clip(SCREAMING_SNAKE_CASE_     ,	0     ,	1      )

         # transform interpolation to time range
         __lowerCamelCase  :      List[str]														=						(1 - w) * low_idx + w * high_idx
         __lowerCamelCase  :      str														=						t.reshape(sigma.shape      )
         return t







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	torch.FloatTensor:
         __lowerCamelCase  :      float														=						in_sigmas[-1].item()
         __lowerCamelCase  :      float														=						in_sigmas[0].item()

         __lowerCamelCase  :      int														=						7.0  # 7.0 is the value used in the paper
         __lowerCamelCase  :      int														=						np.linspace(0     ,	1     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      int														=						sigma_min ** (1 / rho)
         __lowerCamelCase  :      List[Any]														=						sigma_max ** (1 / rho)
         __lowerCamelCase  :      Union[str, Any]														=						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas







    @property
    def  lowercase_							(				self      )							->	Any:
         return self.dt is None







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = True     ,	)							->	Union[SchedulerOutput, Tuple]:
         __lowerCamelCase  :      Union[str, Any]														=						self.index_for_timestep(SCREAMING_SNAKE_CASE_      )

         # advance index counter by 1
         __lowerCamelCase  :      Any														=						timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_      ) else timestep
         self._index_counter[timestep_int] += 1

         if self.state_in_first_order:
              __lowerCamelCase  :      List[Any]														=						self.sigmas[step_index]
              __lowerCamelCase  :      Optional[int]														=						self.sigmas[step_index + 1]
         else:
              # 2nd order / Heun's method
              __lowerCamelCase  :      str														=						self.sigmas[step_index - 1]
              __lowerCamelCase  :      int														=						self.sigmas[step_index]

         # currently only gamma=0 is supported. This usually works best anyways.
         # We can support gamma in the future but then need to scale the timestep before
         # passing it to the model which requires a change in API
         __lowerCamelCase  :      List[str]														=						0
         __lowerCamelCase  :      Any														=						sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
              __lowerCamelCase  :      List[str]														=						sigma_hat if self.state_in_first_order else sigma_next
              __lowerCamelCase  :      Optional[int]														=						sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
              __lowerCamelCase  :      List[str]														=						sigma_hat if self.state_in_first_order else sigma_next
              __lowerCamelCase  :      Optional[int]														=						model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                  sample / (sigma_input**2 + 1)
              )
         elif self.config.prediction_type == "sample":
              __lowerCamelCase  :      Any														=						model_output
         else:
              raise ValueError(
                  f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'      )

         if self.config.clip_sample:
              __lowerCamelCase  :      Any														=						pred_original_sample.clamp(
                  -self.config.clip_sample_range     ,	self.config.clip_sample_range      )

         if self.state_in_first_order:
              # 2. Convert to an ODE derivative for 1st order
              __lowerCamelCase  :      str														=						(sample - pred_original_sample) / sigma_hat
              # 3. delta timestep
              __lowerCamelCase  :      str														=						sigma_next - sigma_hat

              # store for 2nd order step
              __lowerCamelCase  :      str														=						derivative
              __lowerCamelCase  :      Any														=						dt
              __lowerCamelCase  :      Any														=						sample
         else:
              # 2. 2nd order / Heun's method
              __lowerCamelCase  :      Optional[Any]														=						(sample - pred_original_sample) / sigma_next
              __lowerCamelCase  :      int														=						(self.prev_derivative + derivative) / 2

              # 3. take prev timestep & sample
              __lowerCamelCase  :      Optional[int]														=						self.dt
              __lowerCamelCase  :      Dict														=						self.sample

              # free dt and derivative
              # Note, this puts the scheduler in "first order mode"
              __lowerCamelCase  :      Dict														=						None
              __lowerCamelCase  :      Any														=						None
              __lowerCamelCase  :      Optional[Any]														=						None

         __lowerCamelCase  :      int														=						sample + derivative * dt

         if not return_dict:
              return (prev_sample,)

         return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	)							->	torch.FloatTensor:
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         __lowerCamelCase  :      Optional[int]														=						self.sigmas.to(device=original_samples.device     ,	dtype=original_samples.dtype      )
         if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_      ):
              # mps does not support float64
              __lowerCamelCase  :      str														=						self.timesteps.to(original_samples.device     ,	dtype=torch.floataa      )
              __lowerCamelCase  :      Union[str, Any]														=						timesteps.to(original_samples.device     ,	dtype=torch.floataa      )
         else:
              __lowerCamelCase  :      Dict														=						self.timesteps.to(original_samples.device      )
              __lowerCamelCase  :      Any														=						timesteps.to(original_samples.device      )

         __lowerCamelCase  :      int														=						[self.index_for_timestep(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ) for t in timesteps]

         __lowerCamelCase  :      Optional[int]														=						sigmas[step_indices].flatten()
         while len(sigma.shape      ) < len(original_samples.shape      ):
              __lowerCamelCase  :      Dict														=						sigma.unsqueeze(-1      )

         __lowerCamelCase  :      int														=						original_samples + noise * sigma
         return noisy_samples







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










'''simple docstring'''



from collections import namedtuple

import requests
from lxml import html  # type: ignore

A__						:   Tuple				=    namedtuple("""covid_data""", """cases deaths recovered""")



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


A__						:   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()))






