



'''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 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    42
 UpperCAmelCase		      =    42







def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_=0.9_9_9			,  lowerCAmelCase_="cosine"			,  )->						Any:





       '''simple docstring'''


       if alpha_transform_type == "cosine":

              def alpha_bar_fn(lowerCAmelCase_						):
                     return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2						) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(lowerCAmelCase_						):
                     return math.exp(t * -1_2.0						)

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

       _UpperCAmelCase    :		List[Any]     	=					[]
       for i in range(lowerCAmelCase_						):
              _UpperCAmelCase    :		Union[str, Any]     	=					i / num_diffusion_timesteps
              _UpperCAmelCase    :		Union[str, Any]     	=					(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 				lowercase			(    _lowerCamelCase   ,   _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    1







 @register_to_config
 def __init__(				self    ,a_ = 1_000    ,a_ = 0.0001    ,a_ = 0.02    ,a_ = "linear"    ,a_ = 0.0    ,a_ = None    ,a_ = True    ,)						->  str:
        if trained_betas is not None:
               _UpperCAmelCase    :		str     	=					torch.from_numpy(a__				)
        elif beta_schedule == "linear":
               _UpperCAmelCase    :		Union[str, Any]     	=					torch.linspace(a__    ,a__    ,a__    ,dtype=torch.floataa				)
        elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _UpperCAmelCase    :		List[Any]     	=					(
                   torch.linspace(beta_start**0.5    ,beta_end**0.5    ,a__    ,dtype=torch.floataa				) ** 2
               )
        elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _UpperCAmelCase    :		Tuple     	=					betas_for_alpha_bar(a__				)
        elif beta_schedule == "sigmoid":
               # GeoDiff sigmoid schedule
               _UpperCAmelCase    :		List[str]     	=					torch.linspace(-6    ,6    ,a__				)
               _UpperCAmelCase    :		Optional[int]     	=					torch.sigmoid(a__				) * (beta_end - beta_start) + beta_start
        else:
               raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''				)

        _UpperCAmelCase    :		Optional[Any]     	=					1.0 - self.betas
        _UpperCAmelCase    :		Tuple     	=					torch.cumprod(self.alphas    ,dim=0				)
        _UpperCAmelCase    :		int     	=					torch.tensor(1.0				)

        _UpperCAmelCase    :		Any     	=					torch.tensor(1.0				)

        # standard deviation of the initial noise distribution
        _UpperCAmelCase    :		Union[str, Any]     	=					1.0

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

        _UpperCAmelCase    :		Tuple     	=					eta







 def _snake_case					(				self    ,a_    ,a_ = None				)						->  Dict:
        return sample







 def _snake_case					(				self    ,a_    ,a_ = 10    ,a_ = 10    ,a_ = None    ,)						->  int:
        _UpperCAmelCase    :		Any     	=					min(self.config.num_train_timesteps    ,a__				)
        _UpperCAmelCase    :		List[Any]     	=					num_inference_steps

        _UpperCAmelCase    :		Union[str, Any]     	=					[]

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

        _UpperCAmelCase    :		List[Any]     	=					num_inference_steps
        while t >= 1:
               _UpperCAmelCase    :		Tuple     	=					t - 1
               timesteps.append(a__				)

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

        _UpperCAmelCase    :		List[Any]     	=					np.array(a__				) * (self.config.num_train_timesteps // self.num_inference_steps)
        _UpperCAmelCase    :		Optional[Any]     	=					torch.from_numpy(a__				).to(a__				)







 def _snake_case					(				self    ,a_				)						->  Union[str, Any]:
        _UpperCAmelCase    :		int     	=					t - self.config.num_train_timesteps // self.num_inference_steps

        _UpperCAmelCase    :		Any     	=					self.alphas_cumprod[t]
        _UpperCAmelCase    :		int     	=					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        _UpperCAmelCase    :		List[str]     	=					1 - alpha_prod_t
        _UpperCAmelCase    :		Optional[Any]     	=					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]
        _UpperCAmelCase    :		Union[str, Any]     	=					(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

        return variance







 def _snake_case					(				self    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_ = None    ,a_ = True    ,)						->  int:
        _UpperCAmelCase    :		List[str]     	=					timestep
        _UpperCAmelCase    :		str     	=					timestep - self.config.num_train_timesteps // self.num_inference_steps

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

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
               _UpperCAmelCase    :		Tuple     	=					torch.clamp(a__    ,-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
        _UpperCAmelCase    :		List[Any]     	=					model_output.device
        _UpperCAmelCase    :		Optional[int]     	=					randn_tensor(model_output.shape    ,generator=a__    ,device=a__    ,dtype=model_output.dtype				)
        _UpperCAmelCase    :		str     	=					self.eta * self._get_variance(a__				) ** 0.5

        _UpperCAmelCase    :		Dict     	=					0
        if t > 0 and self.eta > 0:
               _UpperCAmelCase    :		Any     	=					std_dev_t * noise

        # 6. compute "direction pointing to x_t" of formula (12)
        # from https://arxiv.org/pdf/2010.02502.pdf
        _UpperCAmelCase    :		Tuple     	=					(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
        _UpperCAmelCase    :		Union[str, 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
        _UpperCAmelCase    :		List[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
        _UpperCAmelCase    :		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=a__    ,pred_original_sample=a__				)







 def _snake_case					(				self    ,a_    ,a_    ,a_=None				)						->  Any:
        _UpperCAmelCase    :		Dict     	=					self.config.num_train_timesteps // self.num_inference_steps

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

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

        return sample







 def _snake_case					(				self    ,a_    ,a_    ,a_    ,)						->  Any:
        raise NotImplementedError("""Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."""				)







 def __len__(				self				)						->  Optional[int]:
        return self.config.num_train_timesteps










"""simple docstring"""






from __future__ import annotations

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							list:






       """simple docstring"""




       _UpperCAmelCase			     =  [None] * (len(SCREAMING_SNAKE_CASE				) + len(SCREAMING_SNAKE_CASE				))

       _UpperCAmelCase			     =  0  # pointer to current index for left Half
       _UpperCAmelCase			     =  0  # pointer to current index for the right Half
       _UpperCAmelCase			     =  0  # pointer to current index for the sorted array Half

       while pointera < len(SCREAMING_SNAKE_CASE				) and pointera < len(SCREAMING_SNAKE_CASE				):
              if left_half[pointera] < right_half[pointera]:
                     _UpperCAmelCase			     =  left_half[pointera]
                     pointera += 1
                     index += 1
              else:
                     _UpperCAmelCase			     =  right_half[pointera]
                     pointera += 1
                     index += 1
       while pointera < len(SCREAMING_SNAKE_CASE				):
              _UpperCAmelCase			     =  left_half[pointera]
              pointera += 1
              index += 1

       while pointera < len(SCREAMING_SNAKE_CASE				):
              _UpperCAmelCase			     =  right_half[pointera]
              pointera += 1
              index += 1

       return sorted_array

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							list:






       """simple docstring"""




       if len(SCREAMING_SNAKE_CASE				) <= 1:
              return array
       # the actual formula to calculate the middle element = left + (right - left) // 2
       # this avoids integer overflow in case of large N
       _UpperCAmelCase			     =  0 + (len(SCREAMING_SNAKE_CASE				) - 0) // 2

       # Split the array into halves till the array length becomes equal to One
       # merge the arrays of single length returned by mergeSort function and
       # pass them into the merge arrays function which merges the array
       _UpperCAmelCase			     =  array[:middle]
       _UpperCAmelCase			     =  array[middle:]

       return merge(merge_sort(SCREAMING_SNAKE_CASE				),merge_sort(SCREAMING_SNAKE_CASE				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






