
"""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							(						lowerCAmelCase__							):
      __SCREAMING_SNAKE_CASE				:							torch.FloatTensor
      __SCREAMING_SNAKE_CASE				:							torch.FloatTensor




def       lowercase__  (   lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Optional[int]=0.999	,			lowerCAmelCase			:   str="cosine"	,			)	-> List[str]:






    """simple docstring"""




    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(lowerCAmelCase			:   Union[str, Any]	):
            return math.exp(t * -12.0	)

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

    UpperCAmelCase       =   []
    for i in range(lowerCAmelCase	):
        UpperCAmelCase       =   i / num_diffusion_timesteps
        UpperCAmelCase       =   (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        _UpperCAmelCase							(						lowerCAmelCase__       ,     lowerCAmelCase__							):
      __SCREAMING_SNAKE_CASE				:							List[str]							 =		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:


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

          UpperCAmelCase       =   1.0 - self.betas
          UpperCAmelCase       =   torch.cumprod(self.alphas				,    dim=0							)
          UpperCAmelCase       =   torch.tensor(1.0							)

          UpperCAmelCase       =   torch.tensor(1.0							)

          # standard deviation of the initial noise distribution
          UpperCAmelCase       =   1.0

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

          UpperCAmelCase       =   eta




      def       a_ (							self				,    lowercase_				,    lowercase_ = None							)  ->   torch.FloatTensor:
          return sample




      def       a_ (							self				,    lowercase_				,    lowercase_ = 1_0				,    lowercase_ = 1_0				,    lowercase_ = None				,    )  ->   Optional[int]:
          UpperCAmelCase       =   min(self.config.num_train_timesteps				,    _UpperCAmelCase							)
          UpperCAmelCase       =   num_inference_steps

          UpperCAmelCase       =   []

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

          UpperCAmelCase       =   num_inference_steps
          while t >= 1:
              UpperCAmelCase       =   t - 1
              timesteps.append(_UpperCAmelCase							)

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

          UpperCAmelCase       =   np.array(_UpperCAmelCase							) * (self.config.num_train_timesteps // self.num_inference_steps)
          UpperCAmelCase       =   torch.from_numpy(_UpperCAmelCase							).to(_UpperCAmelCase							)




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

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

          return variance




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = None				,    lowercase_ = True				,    )  ->   Union[RePaintSchedulerOutput, Tuple]:
          UpperCAmelCase       =   timestep
          UpperCAmelCase       =   timestep - self.config.num_train_timesteps // self.num_inference_steps

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

          # 3. Clip "predicted x_0"
          if self.config.clip_sample:
              UpperCAmelCase       =   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
          UpperCAmelCase       =   model_output.device
          UpperCAmelCase       =   randn_tensor(model_output.shape				,    generator=_UpperCAmelCase				,    device=_UpperCAmelCase				,    dtype=model_output.dtype							)
          UpperCAmelCase       =   self.eta * self._get_variance(_UpperCAmelCase							) ** 0.5

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

          # 6. compute "direction pointing to x_t" of formula (12)
          # from https://arxiv.org/pdf/2010.02502.pdf
          UpperCAmelCase       =   (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       =   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       =   (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       =   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       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_=None							)  ->   List[Any]:
          UpperCAmelCase       =   self.config.num_train_timesteps // self.num_inference_steps

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

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

          return sample




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_				,    )  ->   torch.FloatTensor:
          raise NotImplementedError('Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'							)




      def __len__(							self							)  ->   Union[str, Any]:
          return self.config.num_train_timesteps





"""simple docstring"""

from __future__ import annotations

SCREAMING_SNAKE_CASE_         =  '''Alexander Joslin'''




def       lowercase__  (   lowerCAmelCase			:   int	)	-> int:






    """simple docstring"""




    if number <= 0:
        UpperCAmelCase       =   F"n must be greater than 0. Got n = {number}"
        raise ValueError(lowerCAmelCase	)

    UpperCAmelCase       =   [number + 1] * (number + 1)

    # starting position
    UpperCAmelCase       =   0
    for i in range(1	,			lowerCAmelCase	):
        UpperCAmelCase       =   min(table[i + 1]	,			table[i] + 1	)
        # check if out of bounds
        if i * 2 <= number:
            UpperCAmelCase       =   min(table[i * 2]	,			table[i] + 1	)
        # check if out of bounds
        if i * 3 <= number:
            UpperCAmelCase       =   min(table[i * 3]	,			table[i] + 1	)
    return table[number]


if __name__ == "__main__":
   import doctest

   doctest.testmod()



