


"""simple docstring"""







# 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_			(				UpperCamelCase):

	"""simple docstring"""





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




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_=0.999						,		lowerCAmelCase_="cosine"						,		):




					'''simple docstring'''


					if alpha_transform_type == "cosine":

										def alpha_bar_fn(lowerCAmelCase_     ):
															return math.cos((t + 0.008) / 1.008 * 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_			(				UpperCamelCase    ,						UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						Optional[int]					      =    1




	@register_to_config
	def __init__(				self    :       Dict    ,    UpperCAmelCase__    :       int = 1_0_0_0    ,    UpperCAmelCase__    :       float = 0.0_001    ,    UpperCAmelCase__    :       float = 0.02    ,    UpperCAmelCase__    :       str = "linear"    ,    UpperCAmelCase__    :       Optional[Union[np.ndarray, List[float]]] = None    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       int = 0    ,    UpperCAmelCase__    :       str = "epsilon"    ,    UpperCAmelCase__    :       float = 1.0    ,    **UpperCAmelCase__    :       Dict    ,    ) ->			List[Any]:
						if kwargs.get("set_alpha_to_one"    ,    UpperCAmelCase__     ) is not None:
											__SCREAMING_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"    ,    UpperCAmelCase__    ,    standard_warn=UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       kwargs["set_alpha_to_one"]
						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     )

						# 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.
						__SCREAMING_SNAKE_CASE		     =       torch.tensor(0.0     ) if set_alpha_to_zero else self.alphas_cumprod[-1]

						# standard deviation of the initial noise distribution
						__SCREAMING_SNAKE_CASE		     =       1.0

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




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




	def 						UpperCAmelCase_   (				self    :       int    ,    UpperCAmelCase__    :       int    ,    UpperCAmelCase__    :       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."""     )

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




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

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




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




"""simple docstring"""







from abc import ABC, abstractmethod
from argparse import ArgumentParser




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	@staticmethod
	@abstractmethod
	def 						UpperCAmelCase_   (				UpperCAmelCase__    :       ArgumentParser     ) ->			Optional[Any]:
						raise NotImplementedError()




	@abstractmethod
	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			List[str]:
						raise NotImplementedError()
