
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       A__      (    __lowerCamelCase,   __lowerCamelCase=0.9_99,   __lowerCamelCase="cosine",   ):





   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

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

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

   SCREAMING_SNAKE_CASE_     =	[]
   for i in range(__lowerCamelCase		):
      SCREAMING_SNAKE_CASE_     =	i / num_diffusion_timesteps
      SCREAMING_SNAKE_CASE_     =	(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 							UpperCamelCase__						(     SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_     ):






       """simple docstring"""


       UpperCAmelCase_							    =[e.name for e in KarrasDiffusionSchedulers]
       UpperCAmelCase_							    =2



       @register_to_config
       def __init__(				self      ,			_A = 1000      ,			_A = 0.0_0085      ,			_A = 0.012      ,			_A = "linear"      ,			_A = None      ,			_A = "epsilon"      ,			_A = "linspace"      ,			_A = 0      ,			)							->							List[Any]:
          if trained_betas is not None:
             SCREAMING_SNAKE_CASE_     =	torch.tensor(__UpperCAmelCase      ,			dtype=torch.floataa     )
          elif beta_schedule == "linear":
             SCREAMING_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.
             SCREAMING_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
             SCREAMING_SNAKE_CASE_     =	betas_for_alpha_bar(__UpperCAmelCase     )
          else:
             raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''     )

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

          #  set all values
          self.set_timesteps(__UpperCAmelCase      ,			__UpperCAmelCase      ,			__UpperCAmelCase     )



       def   _UpperCamelCase     (				self      ,			_A      ,			_A=None     )							->							Tuple:
          if schedule_timesteps is None:
             SCREAMING_SNAKE_CASE_     =	self.timesteps

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

          return indices[pos].item()



       @property
       def   _UpperCamelCase     (				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   _UpperCamelCase     (				self      ,			_A      ,			_A      ,			)							->							torch.FloatTensor:
          SCREAMING_SNAKE_CASE_     =	self.index_for_timestep(__UpperCAmelCase     )

          if self.state_in_first_order:
             SCREAMING_SNAKE_CASE_     =	self.sigmas[step_index]
          else:
             SCREAMING_SNAKE_CASE_     =	self.sigmas_interpol[step_index]

          SCREAMING_SNAKE_CASE_     =	sample / ((sigma**2 + 1) ** 0.5)
          return sample



       def   _UpperCamelCase     (				self      ,			_A      ,			_A = None      ,			_A = None      ,			)							->							int:
          SCREAMING_SNAKE_CASE_     =	num_inference_steps

          SCREAMING_SNAKE_CASE_     =	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":
             SCREAMING_SNAKE_CASE_     =	np.linspace(0      ,			num_train_timesteps - 1      ,			__UpperCAmelCase      ,			dtype=__UpperCAmelCase     )[::-1].copy()
          elif self.config.timestep_spacing == "leading":
             SCREAMING_SNAKE_CASE_     =	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
             SCREAMING_SNAKE_CASE_     =	(np.arange(0      ,			__UpperCAmelCase     ) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase     )
             timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
             SCREAMING_SNAKE_CASE_     =	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
             SCREAMING_SNAKE_CASE_     =	(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\'.'''     )

          SCREAMING_SNAKE_CASE_     =	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
          SCREAMING_SNAKE_CASE_     =	torch.from_numpy(np.log(__UpperCAmelCase     )     ).to(__UpperCAmelCase     )

          SCREAMING_SNAKE_CASE_     =	np.interp(__UpperCAmelCase      ,			np.arange(0      ,			len(__UpperCAmelCase     )     )      ,			__UpperCAmelCase     )
          SCREAMING_SNAKE_CASE_     =	np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )
          SCREAMING_SNAKE_CASE_     =	torch.from_numpy(__UpperCAmelCase     ).to(device=__UpperCAmelCase     )

          # interpolate sigmas
          SCREAMING_SNAKE_CASE_     =	sigmas.log().lerp(sigmas.roll(1     ).log()      ,			0.5     ).exp()

          SCREAMING_SNAKE_CASE_     =	torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2     ), sigmas[-1:]]     )
          SCREAMING_SNAKE_CASE_     =	torch.cat(
              [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2     ), sigmas_interpol[-1:]]     )

          if str(__UpperCAmelCase     ).startswith('''mps'''     ):
             # mps does not support float64
             SCREAMING_SNAKE_CASE_     =	torch.from_numpy(__UpperCAmelCase     ).to(__UpperCAmelCase      ,			dtype=torch.floataa     )
          else:
             SCREAMING_SNAKE_CASE_     =	torch.from_numpy(__UpperCAmelCase     ).to(__UpperCAmelCase     )

          # interpolate timesteps
          SCREAMING_SNAKE_CASE_     =	self.sigma_to_t(__UpperCAmelCase     ).to(__UpperCAmelCase      ,			dtype=timesteps.dtype     )
          SCREAMING_SNAKE_CASE_     =	torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])      ,			dim=-1     ).flatten()

          SCREAMING_SNAKE_CASE_     =	torch.cat([timesteps[:1], interleaved_timesteps]     )

          SCREAMING_SNAKE_CASE_     =	None

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



       def   _UpperCamelCase     (				self      ,			_A     )							->							int:
          # get log sigma
          SCREAMING_SNAKE_CASE_     =	sigma.log()

          # get distribution
          SCREAMING_SNAKE_CASE_     =	log_sigma - self.log_sigmas[:, None]

          # get sigmas range
          SCREAMING_SNAKE_CASE_     =	dists.ge(0     ).cumsum(dim=0     ).argmax(dim=0     ).clamp(max=self.log_sigmas.shape[0] - 2     )
          SCREAMING_SNAKE_CASE_     =	low_idx + 1

          SCREAMING_SNAKE_CASE_     =	self.log_sigmas[low_idx]
          SCREAMING_SNAKE_CASE_     =	self.log_sigmas[high_idx]

          # interpolate sigmas
          SCREAMING_SNAKE_CASE_     =	(low - log_sigma) / (low - high)
          SCREAMING_SNAKE_CASE_     =	w.clamp(0      ,			1     )

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



       @property
       def   _UpperCamelCase     (				self     )							->							Tuple:
          return self.sample is None



       def   _UpperCamelCase     (				self      ,			_A      ,			_A      ,			_A      ,			_A = True      ,			)							->							Union[SchedulerOutput, Tuple]:
          SCREAMING_SNAKE_CASE_     =	self.index_for_timestep(__UpperCAmelCase     )

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

          if self.state_in_first_order:
             SCREAMING_SNAKE_CASE_     =	self.sigmas[step_index]
             SCREAMING_SNAKE_CASE_     =	self.sigmas_interpol[step_index + 1]
             SCREAMING_SNAKE_CASE_     =	self.sigmas[step_index + 1]
          else:
             # 2nd order / KDPM2's method
             SCREAMING_SNAKE_CASE_     =	self.sigmas[step_index - 1]
             SCREAMING_SNAKE_CASE_     =	self.sigmas_interpol[step_index]
             SCREAMING_SNAKE_CASE_     =	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
          SCREAMING_SNAKE_CASE_     =	0
          SCREAMING_SNAKE_CASE_     =	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":
             SCREAMING_SNAKE_CASE_     =	sigma_hat if self.state_in_first_order else sigma_interpol
             SCREAMING_SNAKE_CASE_     =	sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
             SCREAMING_SNAKE_CASE_     =	sigma_hat if self.state_in_first_order else sigma_interpol
             SCREAMING_SNAKE_CASE_     =	model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
          elif self.config.prediction_type == "sample":
             raise NotImplementedError('''prediction_type not implemented yet: sample'''     )
          else:
             raise ValueError(
                 F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''     )

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

             # store for 2nd order step
             SCREAMING_SNAKE_CASE_     =	sample
          else:
             # DPM-Solver-2
             # 2. Convert to an ODE derivative for 2nd order
             SCREAMING_SNAKE_CASE_     =	(sample - pred_original_sample) / sigma_interpol

             # 3. delta timestep
             SCREAMING_SNAKE_CASE_     =	sigma_next - sigma_hat

             SCREAMING_SNAKE_CASE_     =	self.sample
             SCREAMING_SNAKE_CASE_     =	None

          SCREAMING_SNAKE_CASE_     =	sample + derivative * dt

          if not return_dict:
             return (prev_sample,)

          return SchedulerOutput(prev_sample=__UpperCAmelCase     )



       def   _UpperCamelCase     (				self      ,			_A      ,			_A      ,			_A      ,			)							->							torch.FloatTensor:
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          SCREAMING_SNAKE_CASE_     =	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
             SCREAMING_SNAKE_CASE_     =	self.timesteps.to(original_samples.device      ,			dtype=torch.floataa     )
             SCREAMING_SNAKE_CASE_     =	timesteps.to(original_samples.device      ,			dtype=torch.floataa     )
          else:
             SCREAMING_SNAKE_CASE_     =	self.timesteps.to(original_samples.device     )
             SCREAMING_SNAKE_CASE_     =	timesteps.to(original_samples.device     )

          SCREAMING_SNAKE_CASE_     =	[self.index_for_timestep(__UpperCAmelCase      ,			__UpperCAmelCase     ) for t in timesteps]

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

          SCREAMING_SNAKE_CASE_     =	original_samples + noise * sigma
          return noisy_samples




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







'''simple docstring'''





def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Tuple							      =					abs(UpperCamelCase			)
    lowerCAmelCase__ :  List[Any]							      =					0
    while n > 0:
        res += n % 10
        n //= 10
    return res
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					abs(UpperCamelCase			)
    return n if n < 10 else n % 10 + sum_of_digits(n // 10			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return sum(int(UpperCamelCase			) for c in str(abs(UpperCamelCase			)			)			)
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    from collections.abc import Callable
    from timeit import timeit

    def benchmark_a_function(UpperCamelCase							,    UpperCamelCase			) -> None:
        lowerCAmelCase__ :  str							      =					f"""{func.__name__}({value})"""
        lowerCAmelCase__ :  str							      =					timeit(f"""__main__.{call}"""							,    setup="""import __main__"""			)
        print(f"""{call:56} = {func(UpperCamelCase			)} -- {timing:.4f} seconds"""			)

    for value in (262144, 1125899906842624, 1267650600228229401496703205376):
        for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
            benchmark_a_function(UpperCamelCase							,    UpperCamelCase			)
        print()


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()


