
# 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 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							torch.FloatTensor
     UpperCAmelCase__    :							Optional[torch.FloatTensor]									=		None



def 						lowercase_      (       A__			,		A__=0.999			,		A__="cosine"			,		)							->							Any:
    """simple docstring"""

    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

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

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

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







class 					lowerCamelCase		(  A_							,			A_  ):
     UpperCAmelCase__    :							str									=		1





     @register_to_config
     def __init__(self				:   Dict    ,   _A				:   int = 1_0_0_0    ,   _A				:   float = 0.00_01    ,   _A				:   float = 0.02    ,   _A				:   str = "linear"    ,   _A				:   Optional[Union[np.ndarray, List[float]]] = None    ,   _A				:   bool = True    ,   _A				:   bool = True    ,   _A				:   int = 0    ,   _A				:   str = "epsilon"    ,   _A				:   float = 1.0    ,   **_A				:   Union[str, Any]    ,   )						->						Union[str, Any]:
         if kwargs.get("set_alpha_to_one"    ,   _A       ) is not None:
             snake_case								= (
                 "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       )
             snake_case								= kwargs["set_alpha_to_one"]
         if trained_betas is not None:
             snake_case								= torch.tensor(_A    ,   dtype=torch.floataa       )
         elif beta_schedule == "linear":
             snake_case								= torch.linspace(_A    ,   _A    ,   _A    ,   dtype=torch.floataa       )
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             snake_case								= (
                 torch.linspace(beta_start**0.5    ,   beta_end**0.5    ,   _A    ,   dtype=torch.floataa       ) ** 2
             )
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             snake_case								= betas_for_alpha_bar(_A       )
         else:
             raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'       )

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

         # standard deviation of the initial noise distribution
         snake_case								= 1.0

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





     def 		UpperCAmelCase(self				:   Any    ,   _A				:   torch.FloatTensor    ,   _A				:   Optional[int] = None       )						->						torch.FloatTensor:
         return sample





     def 		UpperCAmelCase(self				:   str    ,   _A				:   int    ,   _A				:   Union[str, torch.device] = None       )						->						Union[str, Any]:

         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.'       )

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





     def 		UpperCAmelCase(self				:   str    ,   _A				:   torch.FloatTensor    ,   _A				:   int    ,   _A				:   torch.FloatTensor    ,   _A				:   float = 0.0    ,   _A				:   bool = False    ,   _A				:   Optional[torch.FloatTensor] = None    ,   _A				:   bool = True    ,   )						->						Union[DDIMSchedulerOutput, Tuple]:
         # 1. get previous step value (=t+1)
         snake_case								= 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
         snake_case								= self.alphas_cumprod[timestep]
         snake_case								= (
             self.alphas_cumprod[prev_timestep]
             if prev_timestep < self.config.num_train_timesteps
             else self.final_alpha_cumprod
         )

         snake_case								= 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":
             snake_case								= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
             snake_case								= model_output
         elif self.config.prediction_type == "sample":
             snake_case								= model_output
             snake_case								= (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
         elif self.config.prediction_type == "v_prediction":
             snake_case								= (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
             snake_case								= (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:
             snake_case								= 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
         snake_case								= (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
         snake_case								= 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				:   int       )						->						Tuple:
         return self.config.num_train_timesteps



from __future__ import annotations



def 						lowercase_      (       A__			,		A__			,		A__		)							->							int | float:
    """simple docstring"""

    if len(A__		) == 0:
        raise ValueError("find_max() arg is an empty sequence"		)
    if (
        left >= len(A__		)
        or left < -len(A__		)
        or right >= len(A__		)
        or right < -len(A__		)
    ):
        raise IndexError("list index out of range"		)
    if left == right:
        return nums[left]
    snake_case								= (left + right) >> 1  # the middle
    snake_case								= find_max(A__			,		A__			,		A__		)  # find max in range[left, mid]
    snake_case								= find_max(A__			,		mid + 1			,		A__		)  # find max in range[mid + 1, right]

    return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)

