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    lowercase_  (  __A						):



	"""simple docstring"""





	UpperCAmelCase_  :					Union[str, Any]   =				42
	UpperCAmelCase_  :					Any   =				None

def    SCREAMING_SNAKE_CASE_     (						snake_case__   ,  snake_case__=0.9_99   ,  snake_case__="cosine"   ,  )     ->	Optional[Any]:
							if alpha_transform_type == "cosine":

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

							elif alpha_transform_type == "exp":

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

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

							lowerCAmelCase			=		[]
							for i in range(A__							):
														lowerCAmelCase			=		i / num_diffusion_timesteps
														lowerCAmelCase			=		(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    lowercase_  (  __A      ,     __A						):



	"""simple docstring"""





	UpperCAmelCase_  :					Any   =				1


	@register_to_config
	def __init__(							self		,			__SCREAMING_SNAKE_CASE = 1000		,			__SCREAMING_SNAKE_CASE = 0.0_0_0_1		,			__SCREAMING_SNAKE_CASE = 0.0_2		,			__SCREAMING_SNAKE_CASE = "linear"		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = True		,			__SCREAMING_SNAKE_CASE = True		,			__SCREAMING_SNAKE_CASE = 0		,			__SCREAMING_SNAKE_CASE = "epsilon"		,			__SCREAMING_SNAKE_CASE = 1.0		,			**__SCREAMING_SNAKE_CASE		,			)						->Any:
								if kwargs.get('''set_alpha_to_one'''		,			lowerCAmelCase_				) is not None:
															lowerCAmelCase			=		(
															    "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
															)
															deprecate('''set_alpha_to_one'''		,			'''1.0.0'''		,			lowerCAmelCase_		,			standard_warn=lowerCAmelCase_				)
															lowerCAmelCase			=		kwargs["set_alpha_to_one"]
								if trained_betas is not None:
															lowerCAmelCase			=		torch.tensor(lowerCAmelCase_		,			dtype=torch.floataa				)
								elif beta_schedule == "linear":
															lowerCAmelCase			=		torch.linspace(lowerCAmelCase_		,			lowerCAmelCase_		,			lowerCAmelCase_		,			dtype=torch.floataa				)
								elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															lowerCAmelCase			=		(
															    torch.linspace(beta_start**0.5		,			beta_end**0.5		,			lowerCAmelCase_		,			dtype=torch.floataa				) ** 2
															)
								elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															lowerCAmelCase			=		betas_for_alpha_bar(lowerCAmelCase_				)
								else:
															raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"				)

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

								# standard deviation of the initial noise distribution
								lowerCAmelCase			=		1.0

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


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE = None				)						->torch.FloatTensor:
								return sample


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE = None				)						->str:

								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."				)

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


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE = 0.0		,			__SCREAMING_SNAKE_CASE = False		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = True		,			)						->Union[DDIMSchedulerOutput, Tuple]:
								# 1. get previous step value (=t+1)
								lowerCAmelCase			=		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
								lowerCAmelCase			=		self.alphas_cumprod[timestep]
								lowerCAmelCase			=		(
								    self.alphas_cumprod[prev_timestep]
								    if prev_timestep < self.config.num_train_timesteps
								    else self.final_alpha_cumprod
								)

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




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











"""simple docstring"""
from torch import nn

def 				snake_case      (   A__						):
			if act_fn in ["swish", "silu"]:
						return nn.SiLU()
			elif act_fn == "mish":
						return nn.Mish()
			elif act_fn == "gelu":
						return nn.GELU()
			else:
						raise ValueError(F"""Unsupported activation function: {act_fn}"""						)



