






'''simple docstring'''




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      _UpperCAmelCase    (  snake_case_		):




   """simple docstring"""

   snake_case					       =		42
   snake_case					       =		42





def       __lowercase		(    __lowercase     ,						__lowercase=0.999     ,						__lowercase="cosine"     ,						) ->		List[str]:

      '''simple docstring'''


      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

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

      _A      =			[]
      for i in range(__lowercase ):
            _A      =			i / num_diffusion_timesteps
            _A      =			(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      _UpperCAmelCase    (  snake_case_							,					snake_case_		):




   """simple docstring"""

   snake_case					       =		1

   @register_to_config
   def __init__(	self				:			List[str]   ,   __UpperCAmelCase				:			int = 1000   ,   __UpperCAmelCase				:			float = 0.0001   ,   __UpperCAmelCase				:			float = 0.02   ,   __UpperCAmelCase				:			str = "linear"   ,   __UpperCAmelCase				:			float = 0.0   ,   __UpperCAmelCase				:			Optional[np.ndarray] = None   ,   __UpperCAmelCase				:			bool = True   ,   ):





         '''simple docstring'''





         if trained_betas is not None:
               _A      =			torch.from_numpy(__UpperCAmelCase	)
         elif beta_schedule == "linear":
               _A      =			torch.linspace(__UpperCAmelCase   ,   __UpperCAmelCase   ,   __UpperCAmelCase   ,   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   ,   __UpperCAmelCase   ,   dtype=torch.floataa	) ** 2
               )
         elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _A      =			betas_for_alpha_bar(__UpperCAmelCase	)
         elif beta_schedule == "sigmoid":
               # GeoDiff sigmoid schedule
               _A      =			torch.linspace(-6   ,   6   ,   __UpperCAmelCase	)
               _A      =			torch.sigmoid(__UpperCAmelCase	) * (beta_end - beta_start) + beta_start
         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	)
         _A      =			torch.tensor(1.0	)

         _A      =			torch.tensor(1.0	)

         # standard deviation of the initial noise distribution
         _A      =			1.0

         # setable values
         _A      =			None
         _A      =			torch.from_numpy(np.arange(0   ,   __UpperCAmelCase	)[::-1].copy()	)

         _A      =			eta

   def 		lowerCAmelCase				(	self				:			List[str]   ,   __UpperCAmelCase				:			torch.FloatTensor   ,   __UpperCAmelCase				:			Optional[int] = None	):





         '''simple docstring'''





         return sample

   def 		lowerCAmelCase				(	self				:			List[Any]   ,   __UpperCAmelCase				:			int   ,   __UpperCAmelCase				:			int = 10   ,   __UpperCAmelCase				:			int = 10   ,   __UpperCAmelCase				:			Union[str, torch.device] = None   ,   ):





         '''simple docstring'''





         _A      =			min(self.config.num_train_timesteps   ,   __UpperCAmelCase	)
         _A      =			num_inference_steps

         _A      =			[]

         _A      =			{}
         for j in range(0   ,   num_inference_steps - jump_length   ,   __UpperCAmelCase	):
               _A      =			jump_n_sample - 1

         _A      =			num_inference_steps
         while t >= 1:
               _A      =			t - 1
               timesteps.append(__UpperCAmelCase	)

               if jumps.get(__UpperCAmelCase   ,   0	) > 0:
                     _A      =			jumps[t] - 1
                     for _ in range(__UpperCAmelCase	):
                           _A      =			t + 1
                           timesteps.append(__UpperCAmelCase	)

         _A      =			np.array(__UpperCAmelCase	) * (self.config.num_train_timesteps // self.num_inference_steps)
         _A      =			torch.from_numpy(__UpperCAmelCase	).to(__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			Tuple   ,   __UpperCAmelCase				:			Union[str, Any]	):





         '''simple docstring'''





         _A      =			t - self.config.num_train_timesteps // self.num_inference_steps

         _A      =			self.alphas_cumprod[t]
         _A      =			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
         _A      =			1 - alpha_prod_t
         _A      =			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      =			(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

         return variance

   def 		lowerCAmelCase				(	self				:			Optional[int]   ,   __UpperCAmelCase				:			torch.FloatTensor   ,   __UpperCAmelCase				:			int   ,   __UpperCAmelCase				:			torch.FloatTensor   ,   __UpperCAmelCase				:			torch.FloatTensor   ,   __UpperCAmelCase				:			torch.FloatTensor   ,   __UpperCAmelCase				:			Optional[torch.Generator] = None   ,   __UpperCAmelCase				:			bool = True   ,   ):





         '''simple docstring'''





         _A      =			timestep
         _A      =			timestep - self.config.num_train_timesteps // self.num_inference_steps

         # 1. compute alphas, betas
         _A      =			self.alphas_cumprod[t]
         _A      =			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
         _A      =			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      =			(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

         # 3. Clip "predicted x_0"
         if self.config.clip_sample:
               _A      =			torch.clamp(__UpperCAmelCase   ,   -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      =			model_output.device
         _A      =			randn_tensor(model_output.shape   ,   generator=__UpperCAmelCase   ,   device=__UpperCAmelCase   ,   dtype=model_output.dtype	)
         _A      =			self.eta * self._get_variance(__UpperCAmelCase	) ** 0.5

         _A      =			0
         if t > 0 and self.eta > 0:
               _A      =			std_dev_t * noise

         # 6. compute "direction pointing to x_t" of formula (12)
         # from https://arxiv.org/pdf/2010.02502.pdf
         _A      =			(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      =			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      =			(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      =			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=__UpperCAmelCase   ,   pred_original_sample=__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			List[Any]   ,   __UpperCAmelCase				:			Any   ,   __UpperCAmelCase				:			Union[str, Any]   ,   __UpperCAmelCase				:			List[str]=None	):





         '''simple docstring'''





         _A      =			self.config.num_train_timesteps // self.num_inference_steps

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

               # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
               _A      =			(1 - beta) ** 0.5 * sample + beta**0.5 * noise

         return sample

   def 		lowerCAmelCase				(	self				:			List[Any]   ,   __UpperCAmelCase				:			torch.FloatTensor   ,   __UpperCAmelCase				:			torch.FloatTensor   ,   __UpperCAmelCase				:			torch.IntTensor   ,   ):





         '''simple docstring'''





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

   def __len__(	self				:			Optional[Any]	):





         '''simple docstring'''





         return self.config.num_train_timesteps














'''simple docstring'''




from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class      _UpperCAmelCase    :




   """simple docstring"""

   def __init__(	self				:			Any   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         _A      =			data
         _A      =			None

   def __str__(	self				:			str	):





         '''simple docstring'''





         return f'''{self.data}'''





class      _UpperCAmelCase    :




   """simple docstring"""

   def __init__(	self				:			Optional[int]	):





         '''simple docstring'''





         _A      =			None
         _A      =			None

   def __iter__(	self				:			List[Any]	):





         '''simple docstring'''





         _A      =			self.front
         while node:
               yield node.data
               _A      =			node.next

   def __len__(	self				:			Union[str, Any]	):





         '''simple docstring'''





         return len(tuple(iter(self	)	)	)

   def __str__(	self				:			int	):





         '''simple docstring'''





         return " <- ".join(str(__UpperCAmelCase	) for item in self	)

   def 		lowerCAmelCase				(	self				:			List[Any]	):





         '''simple docstring'''





         return len(self	) == 0

   def 		lowerCAmelCase				(	self				:			int   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         _A      =			Node(__UpperCAmelCase	)
         if self.is_empty():
               _A      =			_A      =			node
         else:
               assert isinstance(self.rear   ,   __UpperCAmelCase	)
               _A      =			node
               _A      =			node

   def 		lowerCAmelCase				(	self				:			str	):





         '''simple docstring'''





         if self.is_empty():
               raise IndexError("dequeue from empty queue"	)
         assert isinstance(self.front   ,   __UpperCAmelCase	)
         _A      =			self.front
         _A      =			self.front.next
         if self.front is None:
               _A      =			None
         return node.data

   def 		lowerCAmelCase				(	self				:			int	):





         '''simple docstring'''





         _A      =			_A      =			None


if __name__ == "__main__":
    from doctest import testmod

    testmod()





