

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					(      lowerCAmelCase__    ):
  __lowerCAmelCase       :		torch.FloatTensor
  __lowerCAmelCase       :		torch.FloatTensor






def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Optional[int]	,				_SCREAMING_SNAKE_CASE   :						Optional[int]=0.999	,				_SCREAMING_SNAKE_CASE   :						str="cosine"	,				):





    """simple docstring"""


    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

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

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

    SCREAMING_SNAKE_CASE_													=  []
    for i in range(_SCREAMING_SNAKE_CASE    ):
        SCREAMING_SNAKE_CASE_													=  i / num_diffusion_timesteps
        SCREAMING_SNAKE_CASE_													=  (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(_SCREAMING_SNAKE_CASE    ) / alpha_bar_fn(_SCREAMING_SNAKE_CASE    )	,				_SCREAMING_SNAKE_CASE    )    )
    return torch.tensor(_SCREAMING_SNAKE_CASE	,				dtype=torch.floataa    )





class  __snake_case					(      lowerCAmelCase__       ,	lowerCAmelCase__    ):
  __lowerCAmelCase       :		Dict  	=					1

  @register_to_config
  def __init__(   self  ,						_A = 1000  ,						_A = 0.0_0_0_1  ,						_A = 0.0_2  ,						_A = "linear"  ,						_A = 0.0  ,						_A = None  ,						_A = True  ,						):
      if trained_betas is not None:
          SCREAMING_SNAKE_CASE_													=  torch.from_numpy(_A)
      elif beta_schedule == "linear":
          SCREAMING_SNAKE_CASE_													=  torch.linspace(_A  ,						_A  ,						_A  ,						dtype=torch.floataa)
      elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          SCREAMING_SNAKE_CASE_													=  (
              torch.linspace(beta_start**0.5  ,						beta_end**0.5  ,						_A  ,						dtype=torch.floataa) ** 2
          )
      elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          SCREAMING_SNAKE_CASE_													=  betas_for_alpha_bar(_A)
      elif beta_schedule == "sigmoid":
          # GeoDiff sigmoid schedule
          SCREAMING_SNAKE_CASE_													=  torch.linspace(-6  ,						6  ,						_A)
          SCREAMING_SNAKE_CASE_													=  torch.sigmoid(_A) * (beta_end - beta_start) + beta_start
      else:
          raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""")

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

      SCREAMING_SNAKE_CASE_													=  torch.tensor(1.0)

      # standard deviation of the initial noise distribution
      SCREAMING_SNAKE_CASE_													=  1.0

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

      SCREAMING_SNAKE_CASE_													=  eta

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = None):
      return sample

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = 10  ,						_A = 10  ,						_A = None  ,						):
      SCREAMING_SNAKE_CASE_													=  min(self.config.num_train_timesteps  ,						_A)
      SCREAMING_SNAKE_CASE_													=  num_inference_steps

      SCREAMING_SNAKE_CASE_													=  []

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

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

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

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

  def   lowerCAmelCase__       (   self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  t - self.config.num_train_timesteps // self.num_inference_steps

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

      return variance

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A = None  ,						_A = True  ,						):
      SCREAMING_SNAKE_CASE_													=  timestep
      SCREAMING_SNAKE_CASE_													=  timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

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

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

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

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

      return sample

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


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





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						List[str]    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  Queue()
    SCREAMING_SNAKE_CASE_													=  [False] * len(_SCREAMING_SNAKE_CASE    )
    SCREAMING_SNAKE_CASE_													=  [-1] * len(_SCREAMING_SNAKE_CASE    )

    def bfs():
        while not queue.empty():
            SCREAMING_SNAKE_CASE_													=  queue.get()
            SCREAMING_SNAKE_CASE_													=  True

            for neighbour in graph[u]:
                if neighbour == u:
                    return False

                if color[neighbour] == -1:
                    SCREAMING_SNAKE_CASE_													=  1 - color[u]
                    queue.put(_SCREAMING_SNAKE_CASE    )

                elif color[neighbour] == color[u]:
                    return False

        return True

    for i in range(len(_SCREAMING_SNAKE_CASE    )    ):
        if not visited[i]:
            queue.put(_SCREAMING_SNAKE_CASE    )
            SCREAMING_SNAKE_CASE_													=  0
            if bfs() is False:
                return False

    return True


if __name__ == "__main__":
       # Adjacency List of graph
       print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))


