


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_					(							__snake_case     ,						__snake_case=0.9_99     ,						__snake_case="cosine"     ,						)     ->			Union[str, Any]:







 """simple docstring"""


 if alpha_transform_type == "cosine":

  def alpha_bar_fn(__snake_case						):
   return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2						) ** 2

 elif alpha_transform_type == "exp":

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

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

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







class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			[e.name for e in KarrasDiffusionSchedulers]
     SCREAMING_SNAKE_CASE__      			=			2





     @register_to_config
     def __init__(self		,     UpperCAmelCase = 1_0_0_0		,     UpperCAmelCase = 0.0_0085		,     UpperCAmelCase = 0.012		,     UpperCAmelCase = "linear"		,     UpperCAmelCase = None		,     UpperCAmelCase = "epsilon"		,     UpperCAmelCase = False		,     UpperCAmelCase = False		,     UpperCAmelCase = 1.0		,     UpperCAmelCase = "linspace"		,     UpperCAmelCase = 0		,     )     ->   List[str]:
      if trained_betas is not None:
       _lowercase							      =torch.tensor(UpperCAmelCase		,     dtype=torch.floataa       )
      elif beta_schedule == "linear":
       _lowercase							      =torch.linspace(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     dtype=torch.floataa       )
      elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       _lowercase							      =(
           torch.linspace(beta_start**0.5		,     beta_end**0.5		,     UpperCAmelCase		,     dtype=torch.floataa       ) ** 2
       )
      elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       _lowercase							      =betas_for_alpha_bar(UpperCAmelCase		,     alpha_transform_type='''cosine'''       )
      elif beta_schedule == "exp":
       _lowercase							      =betas_for_alpha_bar(UpperCAmelCase		,     alpha_transform_type='''exp'''       )
      else:
       raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"       )

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

      #  set all values
      self.set_timesteps(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )
      _lowercase							      =use_karras_sigmas





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase=None       )     ->   Dict:
      if schedule_timesteps is None:
       _lowercase							      =self.timesteps

      _lowercase							      =(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:
       _lowercase							      =1 if len(UpperCAmelCase       ) > 1 else 0
      else:
       _lowercase							      =timestep.cpu().item() if torch.is_tensor(UpperCAmelCase       ) else timestep
       _lowercase							      =self._index_counter[timestep_int]

      return indices[pos].item()





     @property
     def      __A					(self       )     ->   str:
      # 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      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     )     ->   torch.FloatTensor:
      _lowercase							      =self.index_for_timestep(UpperCAmelCase       )

      _lowercase							      =self.sigmas[step_index]
      _lowercase							      =sample / ((sigma**2 + 1) ** 0.5)
      return sample





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     )     ->   int:
      _lowercase							      =num_inference_steps

      _lowercase							      =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":
       _lowercase							      =np.linspace(0		,     num_train_timesteps - 1		,     UpperCAmelCase		,     dtype=UpperCAmelCase       )[::-1].copy()
      elif self.config.timestep_spacing == "leading":
       _lowercase							      =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
       _lowercase							      =(np.arange(0		,     UpperCAmelCase       ) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase       )
       timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
       _lowercase							      =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
       _lowercase							      =(np.arange(UpperCAmelCase		,     0		,     -step_ratio       )).round().copy().astype(UpperCAmelCase       )
       timesteps -= 1
      else:
       raise ValueError(
           f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."       )

      _lowercase							      =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
      _lowercase							      =np.log(UpperCAmelCase       )
      _lowercase							      =np.interp(UpperCAmelCase		,     np.arange(0		,     len(UpperCAmelCase       )       )		,     UpperCAmelCase       )

      if self.config.use_karras_sigmas:
       _lowercase							      =self._convert_to_karras(in_sigmas=UpperCAmelCase		,     num_inference_steps=self.num_inference_steps       )
       _lowercase							      =np.array([self._sigma_to_t(UpperCAmelCase		,     UpperCAmelCase       ) for sigma in sigmas]       )

      _lowercase							      =np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
      _lowercase							      =torch.from_numpy(UpperCAmelCase       ).to(device=UpperCAmelCase       )
      _lowercase							      =torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2       ), sigmas[-1:]]       )

      _lowercase							      =torch.from_numpy(UpperCAmelCase       )
      _lowercase							      =torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2       )]       )

      if str(UpperCAmelCase       ).startswith('''mps'''       ):
       # mps does not support float64
       _lowercase							      =timesteps.to(UpperCAmelCase		,     dtype=torch.floataa       )
      else:
       _lowercase							      =timesteps.to(device=UpperCAmelCase       )

      # empty dt and derivative
      _lowercase							      =None
      _lowercase							      =None

      # for exp beta schedules, such as the one for `pipeline_shap_e.py`
      # we need an index counter
      _lowercase							      =defaultdict(UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Optional[int]:
      # get log sigma
      _lowercase							      =np.log(UpperCAmelCase       )

      # get distribution
      _lowercase							      =log_sigma - log_sigmas[:, np.newaxis]

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

      _lowercase							      =log_sigmas[low_idx]
      _lowercase							      =log_sigmas[high_idx]

      # interpolate sigmas
      _lowercase							      =(low - log_sigma) / (low - high)
      _lowercase							      =np.clip(UpperCAmelCase		,     0		,     1       )

      # transform interpolation to time range
      _lowercase							      =(1 - w) * low_idx + w * high_idx
      _lowercase							      =t.reshape(sigma.shape       )
      return t





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   torch.FloatTensor:
      _lowercase							      =in_sigmas[-1].item()
      _lowercase							      =in_sigmas[0].item()

      _lowercase							      =7.0  # 7.0 is the value used in the paper
      _lowercase							      =np.linspace(0		,     1		,     UpperCAmelCase       )
      _lowercase							      =sigma_min ** (1 / rho)
      _lowercase							      =sigma_max ** (1 / rho)
      _lowercase							      =(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
      return sigmas





     @property
     def      __A					(self       )     ->   List[Any]:
      return self.dt is None





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase = True		,     )     ->   Union[SchedulerOutput, Tuple]:
      _lowercase							      =self.index_for_timestep(UpperCAmelCase       )

      # advance index counter by 1
      _lowercase							      =timestep.cpu().item() if torch.is_tensor(UpperCAmelCase       ) else timestep
      self._index_counter[timestep_int] += 1

      if self.state_in_first_order:
       _lowercase							      =self.sigmas[step_index]
       _lowercase							      =self.sigmas[step_index + 1]
      else:
       # 2nd order / Heun's method
       _lowercase							      =self.sigmas[step_index - 1]
       _lowercase							      =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
      _lowercase							      =0
      _lowercase							      =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":
       _lowercase							      =sigma_hat if self.state_in_first_order else sigma_next
       _lowercase							      =sample - sigma_input * model_output
      elif self.config.prediction_type == "v_prediction":
       _lowercase							      =sigma_hat if self.state_in_first_order else sigma_next
       _lowercase							      =model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
           sample / (sigma_input**2 + 1)
       )
      elif self.config.prediction_type == "sample":
       _lowercase							      =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:
       _lowercase							      =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
       _lowercase							      =(sample - pred_original_sample) / sigma_hat
       # 3. delta timestep
       _lowercase							      =sigma_next - sigma_hat

       # store for 2nd order step
       _lowercase							      =derivative
       _lowercase							      =dt
       _lowercase							      =sample
      else:
       # 2. 2nd order / Heun's method
       _lowercase							      =(sample - pred_original_sample) / sigma_next
       _lowercase							      =(self.prev_derivative + derivative) / 2

       # 3. take prev timestep & sample
       _lowercase							      =self.dt
       _lowercase							      =self.sample

       # free dt and derivative
       # Note, this puts the scheduler in "first order mode"
       _lowercase							      =None
       _lowercase							      =None
       _lowercase							      =None

      _lowercase							      =sample + derivative * dt

      if not return_dict:
       return (prev_sample,)

      return SchedulerOutput(prev_sample=UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     )     ->   torch.FloatTensor:
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      _lowercase							      =self.sigmas.to(device=original_samples.device		,     dtype=original_samples.dtype       )
      if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase       ):
       # mps does not support float64
       _lowercase							      =self.timesteps.to(original_samples.device		,     dtype=torch.floataa       )
       _lowercase							      =timesteps.to(original_samples.device		,     dtype=torch.floataa       )
      else:
       _lowercase							      =self.timesteps.to(original_samples.device       )
       _lowercase							      =timesteps.to(original_samples.device       )

      _lowercase							      =[self.index_for_timestep(UpperCAmelCase		,     UpperCAmelCase       ) for t in timesteps]

      _lowercase							      =sigmas[step_indices].flatten()
      while len(sigma.shape       ) < len(original_samples.shape       ):
       _lowercase							      =sigma.unsqueeze(-1       )

      _lowercase							      =original_samples + noise * sigma
      return noisy_samples





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





import requests
from bsa import BeautifulSoup
def 				UpperCAmelCase_					(							__snake_case = "https://www.worldometers.info/coronavirus"						)     ->			dict:







 """simple docstring"""


 _lowercase							      =BeautifulSoup(requests.get(__snake_case						).text     ,						'''html.parser'''						)
 _lowercase							      =soup.findAll('''h1'''						)
 _lowercase							      =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(__snake_case     ,						__snake_case						)}


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''')

