



import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
class      snake_case__       (A__	):







      """simple docstring"""




      __lowerCAmelCase  :torch.FloatTensor
      __lowerCAmelCase  :torch.FloatTensor




def 			lowerCAmelCase_  (						_lowercase       :     Dict		,     _lowercase       :     Optional[Any]=0.999		,     _lowercase       :     List[str]="cosine"		,     )							-> Any:







   """simple docstring"""
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

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

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

   a__     :		Optional[Any]		 =						[]
   for i in range(_lowercase):
      a__     :		Dict		 =						i / num_diffusion_timesteps
      a__     :		Optional[Any]		 =						(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(_lowercase) / alpha_bar_fn(_lowercase)		,     _lowercase))
   return torch.tensor(_lowercase		,     dtype=torch.floataa)





class      snake_case__       (A__      ,			A__	):







      """simple docstring"""




      __lowerCAmelCase  :Union[str, Any]			      =  1

      @register_to_config
      def __init__(     self      ,     __lowercase = 1_0_0_0      ,     __lowercase = 0.0_0_0_1      ,     __lowercase = 0.0_2      ,     __lowercase = "linear"      ,     __lowercase = 0.0      ,     __lowercase = None      ,     __lowercase = True      ,     )      ->   Tuple:
         """simple docstring"""




         if trained_betas is not None:
            a__     :		Union[str, Any]		 =						torch.from_numpy(__lowercase						)
         elif beta_schedule == "linear":
            a__     :		Optional[int]		 =						torch.linspace(__lowercase      ,     __lowercase      ,     __lowercase      ,     dtype=torch.floataa						)
         elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            a__     :		List[Any]		 =						(
                torch.linspace(beta_start**0.5      ,     beta_end**0.5      ,     __lowercase      ,     dtype=torch.floataa						) ** 2
            )
         elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            a__     :		int		 =						betas_for_alpha_bar(__lowercase						)
         elif beta_schedule == "sigmoid":
            # GeoDiff sigmoid schedule
            a__     :		str		 =						torch.linspace(-6      ,     6      ,     __lowercase						)
            a__     :		Tuple		 =						torch.sigmoid(__lowercase						) * (beta_end - beta_start) + beta_start
         else:
            raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''						)

         a__     :		str		 =						1.0 - self.betas
         a__     :		str		 =						torch.cumprod(self.alphas      ,     dim=0						)
         a__     :		str		 =						torch.tensor(1.0						)

         a__     :		List[Any]		 =						torch.tensor(1.0						)

         # standard deviation of the initial noise distribution
         a__     :		List[Any]		 =						1.0

         # setable values
         a__     :		Any		 =						None
         a__     :		Optional[Any]		 =						torch.from_numpy(np.arange(0      ,     __lowercase						)[::-1].copy()						)

         a__     :		int		 =						eta

      def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase = None						)      ->   torch.FloatTensor:
         """simple docstring"""




         return sample

      def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase = 1_0      ,     __lowercase = 1_0      ,     __lowercase = None      ,     )      ->   Any:
         """simple docstring"""




         a__     :		int		 =						min(self.config.num_train_timesteps      ,     __lowercase						)
         a__     :		List[str]		 =						num_inference_steps

         a__     :		List[Any]		 =						[]

         a__     :		Optional[int]		 =						{}
         for j in range(0      ,     num_inference_steps - jump_length      ,     __lowercase						):
            a__     :		Optional[Any]		 =						jump_n_sample - 1

         a__     :		Optional[int]		 =						num_inference_steps
         while t >= 1:
            a__     :		Any		 =						t - 1
            timesteps.append(__lowercase						)

            if jumps.get(__lowercase      ,     0						) > 0:
               a__     :		int		 =						jumps[t] - 1
               for _ in range(__lowercase						):
                  a__     :		List[Any]		 =						t + 1
                  timesteps.append(__lowercase						)

         a__     :		Union[str, Any]		 =						np.array(__lowercase						) * (self.config.num_train_timesteps // self.num_inference_steps)
         a__     :		List[str]		 =						torch.from_numpy(__lowercase						).to(__lowercase						)

      def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   int:
         """simple docstring"""




         a__     :		int		 =						t - self.config.num_train_timesteps // self.num_inference_steps

         a__     :		str		 =						self.alphas_cumprod[t]
         a__     :		List[str]		 =						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
         a__     :		Optional[Any]		 =						1 - alpha_prod_t
         a__     :		str		 =						1 - alpha_prod_t_prev

         # For t > 0, compute predicted variance βt (see formula (6) and (7) from
         # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
         # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
         # variance to pred_sample
         # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
         # without eta.
         # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
         a__     :		int		 =						(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

         return variance

      def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase = None      ,     __lowercase = True      ,     )      ->   Union[RePaintSchedulerOutput, Tuple]:
         """simple docstring"""




         a__     :		Tuple		 =						timestep
         a__     :		Optional[Any]		 =						timestep - self.config.num_train_timesteps // self.num_inference_steps

         # 1. compute alphas, betas
         a__     :		Optional[int]		 =						self.alphas_cumprod[t]
         a__     :		Any		 =						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
         a__     :		Any		 =						1 - alpha_prod_t

         # 2. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
         a__     :		str		 =						(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

         # 3. Clip "predicted x_0"
         if self.config.clip_sample:
            a__     :		Tuple		 =						torch.clamp(__lowercase      ,     -1      ,     1						)

         # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
         # substitute formula (7) in the algorithm coming from DDPM paper
         # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
         # DDIM schedule gives the same results as DDPM with eta = 1.0
         # Noise is being reused in 7. and 8., but no impact on quality has
         # been observed.

         # 5. Add noise
         a__     :		List[str]		 =						model_output.device
         a__     :		Optional[int]		 =						randn_tensor(model_output.shape      ,     generator=__lowercase      ,     device=__lowercase      ,     dtype=model_output.dtype						)
         a__     :		Any		 =						self.eta * self._get_variance(__lowercase						) ** 0.5

         a__     :		List[str]		 =						0
         if t > 0 and self.eta > 0:
            a__     :		List[str]		 =						std_dev_t * noise

         # 6. compute "direction pointing to x_t" of formula (12)
         # from https://arxiv.org/pdf/2010.02502.pdf
         a__     :		Any		 =						(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

         # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         a__     :		Any		 =						alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

         # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
         a__     :		Any		 =						(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

         # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
         a__     :		List[str]		 =						mask * prev_known_part + (1.0 - mask) * prev_unknown_part

         if not return_dict:
            return (
                pred_prev_sample,
                pred_original_sample,
            )

         return RePaintSchedulerOutput(prev_sample=__lowercase      ,     pred_original_sample=__lowercase						)

      def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase=None						)      ->   Optional[Any]:
         """simple docstring"""




         a__     :		str		 =						self.config.num_train_timesteps // self.num_inference_steps

         for i in range(__lowercase						):
            a__     :		str		 =						self.betas[timestep + i]
            if sample.device.type == "mps":
               # randn does not work reproducibly on mps
               a__     :		Tuple		 =						randn_tensor(sample.shape      ,     dtype=sample.dtype      ,     generator=__lowercase						)
               a__     :		Optional[Any]		 =						noise.to(sample.device						)
            else:
               a__     :		List[Any]		 =						randn_tensor(sample.shape      ,     generator=__lowercase      ,     device=sample.device      ,     dtype=sample.dtype						)

            # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
            a__     :		List[Any]		 =						(1 - beta) ** 0.5 * sample + beta**0.5 * noise

         return sample

      def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     )      ->   torch.FloatTensor:
         """simple docstring"""




         raise NotImplementedError("""Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."""						)

      def __len__(     self						)      ->   Optional[int]:
         """simple docstring"""




         return self.config.num_train_timesteps









from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


_lowercase	:   Tuple    	=logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
class      snake_case__       (A__	):







						"""simple docstring"""




						__lowerCAmelCase  :torch.FloatTensor




class      snake_case__       (A__      ,			A__	):







						"""simple docstring"""




						__lowerCAmelCase  :Optional[int]			      =  1

						@register_to_config
						def __init__(     self      ,     __lowercase = 4_0      ,     __lowercase = 0.0_0_2      ,     __lowercase = 8_0.0      ,     __lowercase = 0.5      ,     __lowercase = 1.0      ,     __lowercase = 7.0      ,     __lowercase = True      ,     )      ->   str:
									"""simple docstring"""




									a__     :		Dict		 =						sigma_max

									a__     :		int		 =						np.linspace(0      ,     1      ,     __lowercase						)
									a__     :		int		 =						self._convert_to_karras(__lowercase						)
									a__     :		Any		 =						self.sigma_to_t(__lowercase						)

									# setable values
									a__     :		Optional[int]		 =						None
									a__     :		Union[str, Any]		 =						torch.from_numpy(__lowercase						)
									a__     :		Tuple		 =						torch.from_numpy(__lowercase						)
									a__     :		Optional[Any]		 =						False
									a__     :		List[str]		 =						False

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase=None						)      ->   Union[str, Any]:
									"""simple docstring"""




									if schedule_timesteps is None:
												a__     :		int		 =						self.timesteps

									a__     :		Optional[int]		 =						(schedule_timesteps == timestep).nonzero()
									return indices.item()

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase						)      ->   torch.FloatTensor:
									"""simple docstring"""




									if isinstance(__lowercase      ,     torch.Tensor						):
												a__     :		str		 =						timestep.to(self.timesteps.device						)
									a__     :		Optional[Any]		 =						self.index_for_timestep(__lowercase						)
									a__     :		Tuple		 =						self.sigmas[step_idx]

									a__     :		Optional[Any]		 =						sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

									a__     :		List[str]		 =						True
									return sample

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   Dict:
									"""simple docstring"""




									if not isinstance(__lowercase      ,     np.ndarray						):
												a__     :		Optional[Any]		 =						np.array(__lowercase      ,     dtype=np.floataa						)

									a__     :		str		 =						1_0_0_0 * 0.2_5 * np.log(sigmas + 1E-44						)

									return timesteps

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase = None      ,     __lowercase = None      ,     __lowercase = None      ,     )      ->   List[Any]:
									"""simple docstring"""




									if num_inference_steps is None and timesteps is None:
												raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""						)

									if num_inference_steps is not None and timesteps is not None:
												raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""						)

									# Follow DDPMScheduler custom timesteps logic
									if timesteps is not None:
												for i in range(1      ,     len(__lowercase						)						):
															if timesteps[i] >= timesteps[i - 1]:
																		raise ValueError("""`timesteps` must be in descending order."""						)

												if timesteps[0] >= self.config.num_train_timesteps:
															raise ValueError(
															    F'''`timesteps` must start before `self.config.train_timesteps`:'''
															    F''' {self.config.num_train_timesteps}.'''						)

												a__     :		List[str]		 =						np.array(__lowercase      ,     dtype=np.intaa						)
												a__     :		str		 =						True
									else:
												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__     :		List[str]		 =						num_inference_steps

												a__     :		str		 =						self.config.num_train_timesteps // self.num_inference_steps
												a__     :		int		 =						(np.arange(0      ,     __lowercase						) * step_ratio).round()[::-1].copy().astype(np.intaa						)
												a__     :		Union[str, Any]		 =						False

									# Map timesteps to Karras sigmas directly for multistep sampling
									# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
									a__     :		int		 =						self.config.num_train_timesteps
									a__     :		Optional[Any]		 =						timesteps[::-1].copy()
									a__     :		Optional[Any]		 =						ramp / (num_train_timesteps - 1)
									a__     :		Union[str, Any]		 =						self._convert_to_karras(__lowercase						)
									a__     :		Any		 =						self.sigma_to_t(__lowercase						)

									a__     :		Dict		 =						np.concatenate([sigmas, [self.sigma_min]]						).astype(np.floataa						)
									a__     :		List[str]		 =						torch.from_numpy(__lowercase						).to(device=__lowercase						)

									if str(__lowercase						).startswith("""mps"""						):
												# mps does not support float64
												a__     :		int		 =						torch.from_numpy(__lowercase						).to(__lowercase      ,     dtype=torch.floataa						)
									else:
												a__     :		int		 =						torch.from_numpy(__lowercase						).to(device=__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   Optional[Any]:
									"""simple docstring"""




									a__     :		float		 =						self.config.sigma_min
									a__     :		float		 =						self.config.sigma_max

									a__     :		Optional[int]		 =						self.config.rho
									a__     :		List[str]		 =						sigma_min ** (1 / rho)
									a__     :		Any		 =						sigma_max ** (1 / rho)
									a__     :		Any		 =						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   Optional[int]:
									"""simple docstring"""




									a__     :		int		 =						self.config.sigma_data

									a__     :		List[Any]		 =						sigma_data**2 / (sigma**2 + sigma_data**2)
									a__     :		Optional[Any]		 =						sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   int:
									"""simple docstring"""




									a__     :		str		 =						self.config.sigma_min
									a__     :		Dict		 =						self.config.sigma_data

									a__     :		Dict		 =						sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
									a__     :		List[Any]		 =						(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase = None      ,     __lowercase = True      ,     )      ->   Union[CMStochasticIterativeSchedulerOutput, Tuple]:
									"""simple docstring"""




									if (
									    isinstance(__lowercase      ,     __lowercase						)
									    or isinstance(__lowercase      ,     torch.IntTensor						)
									    or isinstance(__lowercase      ,     torch.LongTensor						)
									):
												raise ValueError(
												    (
												        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
												        F''' `{self.__class__}.step()` is not supported. Make sure to pass'''
												        """ one of the `scheduler.timesteps` as a timestep."""
												    )      ,     )

									if not self.is_scale_input_called:
												logger.warning(
												    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
												    """See `StableDiffusionPipeline` for a usage example."""						)

									if isinstance(__lowercase      ,     torch.Tensor						):
												a__     :		Optional[int]		 =						timestep.to(self.timesteps.device						)

									a__     :		Tuple		 =						self.config.sigma_min
									a__     :		Tuple		 =						self.config.sigma_max

									a__     :		Union[str, Any]		 =						self.index_for_timestep(__lowercase						)

									# sigma_next corresponds to next_t in original implementation
									a__     :		List[Any]		 =						self.sigmas[step_index]
									if step_index + 1 < self.config.num_train_timesteps:
												a__     :		int		 =						self.sigmas[step_index + 1]
									else:
												# Set sigma_next to sigma_min
												a__     :		Dict		 =						self.sigmas[-1]

									# Get scalings for boundary conditions
									a__ ,		a__     :		Any		 =						self.get_scalings_for_boundary_condition(__lowercase						)

									# 1. Denoise model output using boundary conditions
									a__     :		Optional[int]		 =						c_out * model_output + c_skip * sample
									if self.config.clip_denoised:
												a__     :		Dict		 =						denoised.clamp(-1      ,     1						)

									# 2. Sample z ~ N(0, s_noise^2 * I)
									# Noise is not used for onestep sampling.
									if len(self.timesteps						) > 1:
												a__     :		Optional[Any]		 =						randn_tensor(
												    model_output.shape      ,     dtype=model_output.dtype      ,     device=model_output.device      ,     generator=__lowercase						)
									else:
												a__     :		Any		 =						torch.zeros_like(__lowercase						)
									a__     :		Union[str, Any]		 =						noise * self.config.s_noise

									a__     :		Union[str, Any]		 =						sigma_next.clamp(min=__lowercase      ,     max=__lowercase						)

									# 3. Return noisy sample
									# tau = sigma_hat, eps = sigma_min
									a__     :		Union[str, Any]		 =						denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

									if not return_dict:
												return (prev_sample,)

									return CMStochasticIterativeSchedulerOutput(prev_sample=__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     )      ->   torch.FloatTensor:
									"""simple docstring"""




									a__     :		Union[str, Any]		 =						self.sigmas.to(device=original_samples.device      ,     dtype=original_samples.dtype						)
									if original_samples.device.type == "mps" and torch.is_floating_point(__lowercase						):
												# mps does not support float64
												a__     :		List[str]		 =						self.timesteps.to(original_samples.device      ,     dtype=torch.floataa						)
												a__     :		Any		 =						timesteps.to(original_samples.device      ,     dtype=torch.floataa						)
									else:
												a__     :		str		 =						self.timesteps.to(original_samples.device						)
												a__     :		List[str]		 =						timesteps.to(original_samples.device						)

									a__     :		Optional[Any]		 =						[(schedule_timesteps == t).nonzero().item() for t in timesteps]

									a__     :		Tuple		 =						sigmas[step_indices].flatten()
									while len(sigma.shape						) < len(original_samples.shape						):
												a__     :		List[Any]		 =						sigma.unsqueeze(-1						)

									a__     :		Optional[int]		 =						original_samples + noise * sigma
									return noisy_samples

						def __len__(     self						)      ->   int:
									"""simple docstring"""




									return self.config.num_train_timesteps




