



import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def     a(      A       :    Optional[Any] ,     A       :    str=0.999 ,     A       :    List[Any]="cosine" ,     )		->       str:
					"""simple docstring"""





					if alpha_transform_type == "cosine":

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

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(A       :    List[str]       ):
															return math.exp(t * -12.0       )

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

					a					     =   []
					for i in range(A       ):
										a					     =   i / num_diffusion_timesteps
										a					     =   (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							(   lowerCAmelCase,      lowerCAmelCase	):





	"""simple docstring"""
	__A							=							[e.name for e in KarrasDiffusionSchedulers]
	__A							=							2




	@register_to_config
	def __init__(self		,       lowerCamelCase_ = 1000		,       lowerCamelCase_ = 0.0_0085		,       lowerCamelCase_ = 0.012		,       lowerCamelCase_ = "linear"		,       lowerCamelCase_ = None		,       lowerCamelCase_ = "epsilon"		,       lowerCamelCase_ = "linspace"		,       lowerCamelCase_ = 0		,       ):
						"""simple docstring"""

						if trained_betas is not None:
											a					     =   torch.tensor(lowerCamelCase_		,       dtype=torch.floataa		)
						elif beta_schedule == "linear":
											a					     =   torch.linspace(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       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		,       lowerCamelCase_		,       dtype=torch.floataa		) ** 2
											)
						elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											a					     =   betas_for_alpha_bar(lowerCamelCase_		)
						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		)

						#  set all values
						self.set_timesteps(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=None		):
						"""simple docstring"""

						if schedule_timesteps is None:
											a					     =   self.timesteps

						a					     =   (schedule_timesteps == timestep).nonzero()

						# The sigma index that is taken for the **very** first `step`
						# is always the second index (or the last index if there is only 1)
						# This way we can ensure we don't accidentally skip a sigma in
						# case we start in the middle of the denoising schedule (e.g. for image-to-image)
						if len(self._index_counter		) == 0:
											a					     =   1 if len(lowerCamelCase_		) > 1 else 0
						else:
											a					     =   timestep.cpu().item() if torch.is_tensor(lowerCamelCase_		) else timestep
											a					     =   self._index_counter[timestep_int]

						return indices[pos].item()




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						if self.config.timestep_spacing in ["linspace", "trailing"]:
											return self.sigmas.max()

						return (self.sigmas.max() ** 2 + 1) ** 0.5




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   self.index_for_timestep(lowerCamelCase_		)

						if self.state_in_first_order:
											a					     =   self.sigmas[step_index]
						else:
											a					     =   self.sigmas_interpol[step_index]

						a					     =   sample / ((sigma**2 + 1) ** 0.5)
						return sample




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       ):
						"""simple docstring"""

						a					     =   num_inference_steps

						a					     =   num_train_timesteps or self.config.num_train_timesteps

						# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
						if self.config.timestep_spacing == "linspace":
											a					     =   np.linspace(0		,       num_train_timesteps - 1		,       lowerCamelCase_		,       dtype=lowerCamelCase_		)[::-1].copy()
						elif self.config.timestep_spacing == "leading":
											a					     =   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		,       lowerCamelCase_		) * step_ratio).round()[::-1].copy().astype(lowerCamelCase_		)
											timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
											a					     =   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(lowerCamelCase_		,       0		,       -step_ratio		)).round().copy().astype(lowerCamelCase_		)
											timesteps -= 1
						else:
											raise ValueError(
											    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''		)

						a					     =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
						a					     =   torch.from_numpy(np.log(lowerCamelCase_		)		).to(lowerCamelCase_		)

						a					     =   np.interp(lowerCamelCase_		,       np.arange(0		,       len(lowerCamelCase_		)		)		,       lowerCamelCase_		)
						a					     =   np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
						a					     =   torch.from_numpy(lowerCamelCase_		).to(device=lowerCamelCase_		)

						# interpolate sigmas
						a					     =   sigmas.log().lerp(sigmas.roll(1		).log()		,       0.5		).exp()

						a					     =   torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2		), sigmas[-1:]]		)
						a					     =   torch.cat(
						    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2		), sigmas_interpol[-1:]]		)

						if str(lowerCamelCase_		).startswith("mps"		):
											# mps does not support float64
											a					     =   torch.from_numpy(lowerCamelCase_		).to(lowerCamelCase_		,       dtype=torch.floataa		)
						else:
											a					     =   torch.from_numpy(lowerCamelCase_		).to(lowerCamelCase_		)

						# interpolate timesteps
						a					     =   self.sigma_to_t(lowerCamelCase_		).to(lowerCamelCase_		,       dtype=timesteps.dtype		)
						a					     =   torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])		,       dim=-1		).flatten()

						a					     =   torch.cat([timesteps[:1], interleaved_timesteps]		)

						a					     =   None

						# for exp beta schedules, such as the one for `pipeline_shap_e.py`
						# we need an index counter
						a					     =   defaultdict(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   sigma.log()

						# get distribution
						a					     =   log_sigma - self.log_sigmas[:, None]

						# get sigmas range
						a					     =   dists.ge(0		).cumsum(dim=0		).argmax(dim=0		).clamp(max=self.log_sigmas.shape[0] - 2		)
						a					     =   low_idx + 1

						a					     =   self.log_sigmas[low_idx]
						a					     =   self.log_sigmas[high_idx]

						# interpolate sigmas
						a					     =   (low - log_sigma) / (low - high)
						a					     =   w.clamp(0		,       1		)

						# transform interpolation to time range
						a					     =   (1 - w) * low_idx + w * high_idx
						a					     =   t.view(sigma.shape		)
						return t




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return self.sample is None




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = True		,       ):
						"""simple docstring"""

						a					     =   self.index_for_timestep(lowerCamelCase_		)

						# advance index counter by 1
						a					     =   timestep.cpu().item() if torch.is_tensor(lowerCamelCase_		) else timestep
						self._index_counter[timestep_int] += 1

						if self.state_in_first_order:
											a					     =   self.sigmas[step_index]
											a					     =   self.sigmas_interpol[step_index + 1]
											a					     =   self.sigmas[step_index + 1]
						else:
											# 2nd order / KDPM2's method
											a					     =   self.sigmas[step_index - 1]
											a					     =   self.sigmas_interpol[step_index]
											a					     =   self.sigmas[step_index]

						# currently only gamma=0 is supported. This usually works best anyways.
						# We can support gamma in the future but then need to scale the timestep before
						# passing it to the model which requires a change in API
						a					     =   0
						a					     =   sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
											a					     =   sigma_hat if self.state_in_first_order else sigma_interpol
											a					     =   sample - sigma_input * model_output
						elif self.config.prediction_type == "v_prediction":
											a					     =   sigma_hat if self.state_in_first_order else sigma_interpol
											a					     =   model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
											    sample / (sigma_input**2 + 1)
											)
						elif self.config.prediction_type == "sample":
											raise NotImplementedError("prediction_type not implemented yet: sample"		)
						else:
											raise ValueError(
											    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''		)

						if self.state_in_first_order:
											# 2. Convert to an ODE derivative for 1st order
											a					     =   (sample - pred_original_sample) / sigma_hat
											# 3. delta timestep
											a					     =   sigma_interpol - sigma_hat

											# store for 2nd order step
											a					     =   sample
						else:
											# DPM-Solver-2
											# 2. Convert to an ODE derivative for 2nd order
											a					     =   (sample - pred_original_sample) / sigma_interpol

											# 3. delta timestep
											a					     =   sigma_next - sigma_hat

											a					     =   self.sample
											a					     =   None

						a					     =   sample + derivative * dt

						if not return_dict:
											return (prev_sample,)

						return SchedulerOutput(prev_sample=lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   self.sigmas.to(device=original_samples.device		,       dtype=original_samples.dtype		)
						if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase_		):
											# mps does not support float64
											a					     =   self.timesteps.to(original_samples.device		,       dtype=torch.floataa		)
											a					     =   timesteps.to(original_samples.device		,       dtype=torch.floataa		)
						else:
											a					     =   self.timesteps.to(original_samples.device		)
											a					     =   timesteps.to(original_samples.device		)

						a					     =   [self.index_for_timestep(lowerCamelCase_		,       lowerCamelCase_		) for t in timesteps]

						a					     =   sigmas[step_indices].flatten()
						while len(sigma.shape		) < len(original_samples.shape		):
											a					     =   sigma.unsqueeze(-1		)

						a					     =   original_samples + noise * sigma
						return noisy_samples




	def __len__(self		):
						"""simple docstring"""

						return self.config.num_train_timesteps





# 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 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							42
	__A							=							None






def     a(      A       :    Optional[Any] ,     A       :    Any=0.999 ,     A       :    Dict="cosine" ,     )		->       Optional[int]:
					"""simple docstring"""





					if alpha_transform_type == "cosine":

										def alpha_bar_fn(A       :    Optional[int]       ):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2

					elif alpha_transform_type == "exp":

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

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

					a					     =   []
					for i in range(A       ):
										a					     =   i / num_diffusion_timesteps
										a					     =   (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							(   lowerCAmelCase,      lowerCAmelCase	):





	"""simple docstring"""
	__A							=							1




	@register_to_config
	def __init__(self		,       lowerCamelCase_ = 1000		,       lowerCamelCase_ = 0.0001		,       lowerCamelCase_ = 0.02		,       lowerCamelCase_ = "linear"		,       lowerCamelCase_ = None		,       lowerCamelCase_ = True		,       lowerCamelCase_ = True		,       lowerCamelCase_ = 0		,       lowerCamelCase_ = "epsilon"		,       lowerCamelCase_ = 1.0		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						if kwargs.get("set_alpha_to_one"		,       lowerCamelCase_		) 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"		,       lowerCamelCase_		,       standard_warn=lowerCamelCase_		)
											a					     =   kwargs["set_alpha_to_one"]
						if trained_betas is not None:
											a					     =   torch.tensor(lowerCamelCase_		,       dtype=torch.floataa		)
						elif beta_schedule == "linear":
											a					     =   torch.linspace(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       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		,       lowerCamelCase_		,       dtype=torch.floataa		) ** 2
											)
						elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											a					     =   betas_for_alpha_bar(lowerCamelCase_		)
						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		,       lowerCamelCase_		).copy().astype(np.intaa		)		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		):
						"""simple docstring"""

						return sample




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = 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.'''		)

						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		,       lowerCamelCase_		) * step_ratio).round().copy().astype(np.intaa		)
						a					     =   torch.from_numpy(lowerCamelCase_		).to(lowerCamelCase_		)
						self.timesteps += self.config.steps_offset




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = 0.0		,       lowerCamelCase_ = False		,       lowerCamelCase_ = None		,       lowerCamelCase_ = True		,       ):
						"""simple docstring"""

						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=lowerCamelCase_		,       pred_original_sample=lowerCamelCase_		)




	def __len__(self		):
						"""simple docstring"""

						return self.config.num_train_timesteps
