


'''simple docstring'''






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  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										=4_2
       UpperCAmelCase										=None






def lowerCamelCase__      (						__lowerCamelCase	:							Union[str, Any]	,   __lowerCamelCase	:							List[str]=0.9_99	,   __lowerCamelCase	:							List[str]="cosine"	,   ):







   '''simple docstring'''




   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(__lowerCamelCase	:							List[Any]							):
         return math.exp(t * -12.0							)

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

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






class  __magic_name__							(		lowerCAmelCase ,lowerCAmelCase    ):
       UpperCAmelCase										=1






       @register_to_config
       def __init__(      self  ,							snake_case = 1_0_0_0  ,							snake_case = 0.00_01  ,							snake_case = 0.02  ,							snake_case = "linear"  ,							snake_case = None  ,							snake_case = True  ,							snake_case = True  ,							snake_case = 0  ,							snake_case = "epsilon"  ,							snake_case = 1.0  ,							**snake_case  ,							)						->  int:




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

          _UpperCAmelCase       :       List[str]							   =1.0 - self.betas
          _UpperCAmelCase       :       Dict							   =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.
          _UpperCAmelCase       :       Tuple							   =torch.tensor(0.0) if set_alpha_to_zero else self.alphas_cumprod[-1]

          # standard deviation of the initial noise distribution
          _UpperCAmelCase       :       Optional[int]							   =1.0

          # setable values
          _UpperCAmelCase       :       Union[str, Any]							   =None
          _UpperCAmelCase       :       Tuple							   =torch.from_numpy(np.arange(0  ,							snake_case).copy().astype(np.intaa))






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case = None)						->  torch.FloatTensor:




          '''simple docstring'''
          return sample






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case = None)						->  Union[str, Any]:




          '''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.")

          _UpperCAmelCase       :       Optional[int]							   =num_inference_steps
          _UpperCAmelCase       :       Dict							   =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
          _UpperCAmelCase       :       Union[str, Any]							   =(np.arange(0  ,							snake_case) * step_ratio).round().copy().astype(np.intaa)
          _UpperCAmelCase       :       Optional[int]							   =torch.from_numpy(snake_case).to(snake_case)
          self.timesteps += self.config.steps_offset






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case = 0.0  ,							snake_case = False  ,							snake_case = None  ,							snake_case = True  ,							)						->  Union[DDIMSchedulerOutput, Tuple]:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =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
          _UpperCAmelCase       :       List[str]							   =self.alphas_cumprod[timestep]
          _UpperCAmelCase       :       List[str]							   =(
              self.alphas_cumprod[prev_timestep]
              if prev_timestep < self.config.num_train_timesteps
              else self.final_alpha_cumprod
          )

          _UpperCAmelCase       :       List[str]							   =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":
             _UpperCAmelCase       :       str							   =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
             _UpperCAmelCase       :       Tuple							   =model_output
          elif self.config.prediction_type == "sample":
             _UpperCAmelCase       :       Union[str, Any]							   =model_output
             _UpperCAmelCase       :       str							   =(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
          elif self.config.prediction_type == "v_prediction":
             _UpperCAmelCase       :       str							   =(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
             _UpperCAmelCase       :       Optional[int]							   =(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:
             _UpperCAmelCase       :       Any							   =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
          _UpperCAmelCase       :       Union[str, Any]							   =(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
          _UpperCAmelCase       :       List[str]							   =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=snake_case  ,							pred_original_sample=snake_case)






       def __len__(      self)						->  Tuple:




          '''simple docstring'''
          return self.config.num_train_timesteps





'''simple docstring'''






from string import ascii_uppercase

lowercase							={str(ord(c) - 55): c for c in ascii_uppercase}





def lowerCamelCase__      (						__lowerCamelCase	:							int	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   if isinstance(__lowerCamelCase	,   __lowerCamelCase							):
      raise TypeError('int() can\'t convert non-string with explicit base'							)
   if num < 0:
      raise ValueError('parameter must be positive int'							)
   if isinstance(__lowerCamelCase	,   __lowerCamelCase							):
      raise TypeError('\'str\' object cannot be interpreted as an integer'							)
   if isinstance(__lowerCamelCase	,   __lowerCamelCase							):
      raise TypeError('\'float\' object cannot be interpreted as an integer'							)
   if base in (0, 1):
      raise ValueError('base must be >= 2'							)
   if base > 3_6:
      raise ValueError('base must be <= 36'							)
   _UpperCAmelCase       :       Union[str, Any]							   =''
   _UpperCAmelCase       :       Optional[int]							   =0
   _UpperCAmelCase       :       str							   =0
   while div != 1:
      _UpperCAmelCase   ,	_UpperCAmelCase       :       int							   =divmod(__lowerCamelCase	,   __lowerCamelCase							)
      if base >= 1_1 and 9 < mod < 3_6:
         _UpperCAmelCase       :       str							   =ALPHABET_VALUES[str(__lowerCamelCase							)]
      else:
         _UpperCAmelCase       :       Any							   =str(__lowerCamelCase							)
      new_value += actual_value
      _UpperCAmelCase       :       Union[str, Any]							   =num // base
      _UpperCAmelCase       :       Dict							   =div
      if div == 0:
         return str(new_value[::-1]							)
      elif div == 1:
         new_value += str(__lowerCamelCase							)
         return str(new_value[::-1]							)

   return new_value[::-1]


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 for base in range(2, 37):
  for num in range(1000):
   assert int(decimal_to_any(num, base), base) == num, (
       num,
       base,
       decimal_to_any(num, base),
       int(decimal_to_any(num, base), base),
   )

