
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_				(  A     ):




      '''simple docstring'''







      a		:torch.FloatTensor
      a		:torch.FloatTensor
def lowerCamelCase_       (			A			:      Union[str, Any]		,					A			:      List[Any]=0.9_9_9		,					A			:      int="cosine"		,					):




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

            def alpha_bar_fn(A			:      List[str]						):
                  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(A			:      Dict						):
                  return math.exp(t * -1_2.0						)

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

      lowerCAmelCase_						=      []
      for i in range(A						):
            lowerCAmelCase_						=      i / num_diffusion_timesteps
            lowerCAmelCase_						=      (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 			UpperCamelCase_				(  A					,       A     ):




      '''simple docstring'''







      a		:Optional[int]						 =      1







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

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

            lowerCAmelCase_						=      torch.tensor(1.0)

            # standard deviation of the initial noise distribution
            lowerCAmelCase_						=      1.0

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

            lowerCAmelCase_						=      eta







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = None):
            return sample







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = 10					,					_UpperCAmelCase = 10					,					_UpperCAmelCase = None					,					):
            lowerCAmelCase_						=      min(self.config.num_train_timesteps					,					_UpperCAmelCase)
            lowerCAmelCase_						=      num_inference_steps

            lowerCAmelCase_						=      []

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

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

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

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







      def lowercase__		( self					,					_UpperCAmelCase):
            lowerCAmelCase_						=      t - self.config.num_train_timesteps // self.num_inference_steps

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

            return variance







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase = None					,					_UpperCAmelCase = True					,					):
            lowerCAmelCase_						=      timestep
            lowerCAmelCase_						=      timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

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

            # 6. compute "direction pointing to x_t" of formula (12)
            # from https://arxiv.org/pdf/2010.02502.pdf
            lowerCAmelCase_						=      (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
            lowerCAmelCase_						=      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
            lowerCAmelCase_						=      (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
            lowerCAmelCase_						=      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 lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase=None):
            lowerCAmelCase_						=      self.config.num_train_timesteps // self.num_inference_steps

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

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

            return sample







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					):
            raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.''')







      def __len__( self):
            return self.config.num_train_timesteps





from __future__ import annotations
def lowerCamelCase_       (			A			:      list[int]						):




      """simple docstring"""
      if not list_of_ints:
            return []

      if min(A						) < 0:
            raise ValueError('''All numbers must be positive'''						)

      lowerCAmelCase_						=      max(len(bin(A						)[2:]						) for x in list_of_ints						)
      return _msd_radix_sort(A		,					A						)
def lowerCamelCase_       (			A			:      list[int]		,					A			:      int						):




      """simple docstring"""
      if bit_position == 0 or len(A						) in [0, 1]:
            return list_of_ints

      lowerCAmelCase_						=      []
      lowerCAmelCase_						=      []
      # Split numbers based on bit at bit_position from the right
      for number in list_of_ints:
            if (number >> (bit_position - 1)) & 1:
                  # number has a one at bit bit_position
                  ones.append(A						)
            else:
                  # number has a zero at bit bit_position
                  zeros.append(A						)

    # recursively split both lists further
      lowerCAmelCase_						=      _msd_radix_sort(A		,					bit_position - 1						)
      lowerCAmelCase_						=      _msd_radix_sort(A		,					bit_position - 1						)

      # recombine lists
      lowerCAmelCase_						=      zeros
      res.extend(A						)

      return res
def lowerCamelCase_       (			A			:      list[int]						):




      """simple docstring"""
      lowerCAmelCase_						=      len(A						)
      if not list_of_ints or length == 1:
            return

      if min(A						) < 0:
            raise ValueError('''All numbers must be positive'''						)

      lowerCAmelCase_						=      max(len(bin(A						)[2:]						) for x in list_of_ints						)
      _msd_radix_sort_inplace(A		,					A		,					0		,					A						)
def lowerCamelCase_       (			A			:      list[int]		,					A			:      int		,					A			:      int		,					A			:      int						):




      """simple docstring"""
      if bit_position == 0 or end_index - begin_index <= 1:
            return

      bit_position -= 1

      lowerCAmelCase_						=      begin_index
      lowerCAmelCase_						=      end_index - 1
      while i <= j:
            lowerCAmelCase_						=      False
            if not (list_of_ints[i] >> bit_position) & 1:
                  # found zero at the beginning
                  i += 1
                  lowerCAmelCase_						=      True
            if (list_of_ints[j] >> bit_position) & 1:
                  # found one at the end
                  j -= 1
                  lowerCAmelCase_						=      True

            if changed:
                  continue

            lowerCAmelCase_			,		lowerCAmelCase_						=      list_of_ints[j], list_of_ints[i]
            j -= 1
            if j != i:
                  i += 1

      _msd_radix_sort_inplace(A		,					A		,					A		,					A						)
      _msd_radix_sort_inplace(A		,					A		,					A		,					A						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()



