


# 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     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      42
     _lowerCamelCase   =      None







def  a   (lowerCAmelCase__		,				lowerCAmelCase__=0.9_9_9		,				lowerCAmelCase__="cosine"		,				):
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(lowerCAmelCase__   ):
         return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2   ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(lowerCAmelCase__   ):
         return math.exp(t * -1_2.0   )

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

   __a					=  []
   for i in range(lowerCAmelCase__   ):
      __a					=  i / num_diffusion_timesteps
      __a					=  (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							(       __A ,					__A							):


     """simple docstring"""
     _lowerCamelCase   =      1


     @register_to_config
     def __init__(	self  ,       __A = 1000  ,       __A = 0.0001  ,       __A = 0.02  ,       __A = "linear"  ,       __A = None  ,       __A = True  ,       __A = True  ,       __A = 0  ,       __A = "epsilon"  ,       __A = 1.0  ,       **__A  ,       ):
        if kwargs.get("""set_alpha_to_one"""  ,       __A    ) is not None:
           __a					=  (
               """The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
           )
           deprecate("""set_alpha_to_one"""  ,       """1.0.0"""  ,       __A  ,       standard_warn=__A    )
           __a					=  kwargs["""set_alpha_to_one"""]
        if trained_betas is not None:
           __a					=  torch.tensor(__A  ,       dtype=torch.floataa    )
        elif beta_schedule == "linear":
           __a					=  torch.linspace(__A  ,       __A  ,       __A  ,       dtype=torch.floataa    )
        elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           __a					=  (
               torch.linspace(beta_start**0.5  ,       beta_end**0.5  ,       __A  ,       dtype=torch.floataa    ) ** 2
           )
        elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           __a					=  betas_for_alpha_bar(__A    )
        else:
           raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''    )

        __a					=  1.0 - self.betas
        __a					=  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.
        __a					=  torch.tensor(0.0    ) if set_alpha_to_zero else self.alphas_cumprod[-1]

        # standard deviation of the initial noise distribution
        __a					=  1.0

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


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        return sample


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):

        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.'''    )

        __a					=  num_inference_steps
        __a					=  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
        __a					=  (np.arange(0  ,       __A    ) * step_ratio).round().copy().astype(np.intaa    )
        __a					=  torch.from_numpy(__A    ).to(__A    )
        self.timesteps += self.config.steps_offset


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A = 0.0  ,       __A = False  ,       __A = None  ,       __A = True  ,       ):
        # 1. get previous step value (=t+1)
        __a					=  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
        __a					=  self.alphas_cumprod[timestep]
        __a					=  (
            self.alphas_cumprod[prev_timestep]
            if prev_timestep < self.config.num_train_timesteps
            else self.final_alpha_cumprod
        )

        __a					=  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":
           __a					=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           __a					=  model_output
        elif self.config.prediction_type == "sample":
           __a					=  model_output
           __a					=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
        elif self.config.prediction_type == "v_prediction":
           __a					=  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
           __a					=  (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:
           __a					=  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
        __a					=  (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
        __a					=  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=__A  ,       pred_original_sample=__A    )


     def __len__(	self    ):
        return self.config.num_train_timesteps









from bisect import bisect
from itertools import accumulate





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				sorted(zip(__lowerCamelCase				,     __lowerCamelCase       )				,     key=lambda __lowerCamelCase       : x[0] / x[1]				,     reverse=__lowerCamelCase       )
	a							,					a									=				[i[0] for i in r], [i[1] for i in r]
	a									=				list(accumulate(__lowerCamelCase       )       )
	a									=				bisect(__lowerCamelCase				,     __lowerCamelCase       )
	return (
	    0
	    if k == 0
	    else sum(vl[:k]       ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
	    if k != n
	    else sum(vl[:k]       )
	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

