


# 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__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					torch.FloatTensor
			_UpperCamelCase		:					Optional[torch.FloatTensor]    =		None



def        UpperCamelCase_(				_A   :Optional[int]				,     _A   :str=0.9_9_9				,     _A   :Dict="cosine"				,     )->		Dict:
			if alpha_transform_type == "cosine":

						def alpha_bar_fn(_A   :Dict  ):
									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(_A   :Any  ):
									return math.exp(t * -1_2.0  )

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

			UpperCamelCase__					=       []
			for i in range(_A  ):
						UpperCamelCase__					=       i / num_diffusion_timesteps
						UpperCamelCase__					=       (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__	(							UpperCAmelCase					,	UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Optional[int]    =		1




			@register_to_config
			def __init__(							self      ,						snake_case = 1000      ,						snake_case = 0.0001      ,						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      ,						):

						'''simple docstring'''


						if kwargs.get("set_alpha_to_one"      ,						snake_case     ) is not None:
									UpperCamelCase__					=       (
									    "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__					=       kwargs["set_alpha_to_one"]
						if trained_betas is not None:
									UpperCamelCase__					=       torch.tensor(snake_case      ,						dtype=torch.floataa     )
						elif beta_schedule == "linear":
									UpperCamelCase__					=       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__					=       (
									    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__					=       betas_for_alpha_bar(snake_case     )
						else:
									raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''     )

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

						# standard deviation of the initial noise distribution
						UpperCamelCase__					=       1.0

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




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = None     ):

						'''simple docstring'''


						return sample




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = None     ):

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




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case = 0.0      ,						snake_case = False      ,						snake_case = None      ,						snake_case = True      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       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__					=       self.alphas_cumprod[timestep]
						UpperCamelCase__					=       (
						    self.alphas_cumprod[prev_timestep]
						    if prev_timestep < self.config.num_train_timesteps
						    else self.final_alpha_cumprod
						)

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

						'''simple docstring'''


						return self.config.num_train_timesteps






from abc import ABC, abstractmethod
from argparse import ArgumentParser







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			@staticmethod
			@abstractmethod
			def 				snake_case__				(							snake_case     ):

						'''simple docstring'''


						raise NotImplementedError()







			@abstractmethod
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						raise NotImplementedError()


