



'''simple docstring'''



class _UpperCamelCase		:  # Public class to implement a graph





						'''simple docstring'''





						def __init__(							self  :  List[Any]  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  list[list[bool]]):







													'''simple docstring'''

													__lowercase       	=row
													__lowercase       	=col
													__lowercase       	=graph

						def 				__lowerCamelCase     (							self  :  Optional[int]  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  list[list[bool]]):







													'''simple docstring'''

													return (
													    0 <= i < self.ROW
													    and 0 <= j < self.COL
													    and not visited[i][j]
													    and self.graph[i][j]
													)

						def 				__lowerCamelCase     (							self  :  List[Any]  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  list[list[bool]]):







													'''simple docstring'''

													__lowercase       	=[-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
													__lowercase       	=[-1, 0, 1, -1, 1, -1, 0, 1]
													__lowercase       	=True  # Make those cells visited
													for k in range(8):
																				if self.is_safe(i + row_nbr[k]  ,       j + col_nbr[k]  ,       _lowerCAmelCase):
																											self.diffs(i + row_nbr[k]  ,       j + col_nbr[k]  ,       _lowerCAmelCase)

						def 				__lowerCamelCase     (							self  :  Tuple):  # And finally, count all islands.







													'''simple docstring'''

													__lowercase       	=[[False for j in range(self.COL)] for i in range(self.ROW)]
													__lowercase       	=0
													for i in range(self.ROW):
																				for j in range(self.COL):
																											if visited[i][j] is False and self.graph[i][j] == 1:
																																		self.diffs(_lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase)
																																		count += 1
													return count








'''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
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    None



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase=0.9_99  ,   _lowerCAmelCase="cosine"  ,   ):



							"""simple docstring"""



							if alpha_transform_type == "cosine":

														def alpha_bar_fn(_lowerCAmelCase					):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

							elif alpha_transform_type == "exp":

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

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

							__lowercase       	=[]
							for i in range(_lowerCAmelCase					):
														__lowercase       	=i / num_diffusion_timesteps
														__lowercase       	=(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		(     A      ,       A		):





				'''simple docstring'''





				@register_to_config
				def __init__(							self  :  Any  ,       _lowerCAmelCase  :  int = 1_0_0_0  ,       _lowerCAmelCase  :  str = "fixed_small_log"  ,       _lowerCAmelCase  :  bool = True  ,       _lowerCAmelCase  :  Optional[float] = 1.0  ,       _lowerCAmelCase  :  str = "epsilon"  ,       _lowerCAmelCase  :  str = "squaredcos_cap_v2"  ,       ):







											'''simple docstring'''

											if beta_schedule != "squaredcos_cap_v2":
																		raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'')

											__lowercase       	=betas_for_alpha_bar(_lowerCAmelCase)

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

											# standard deviation of the initial noise distribution
											__lowercase       	=1.0

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

											__lowercase       	=variance_type

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  Optional[int] = None):







											'''simple docstring'''

											return sample

				def 				__lowerCamelCase     (							self  :  Tuple  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  Union[str, torch.device] = None):







											'''simple docstring'''

											__lowercase       	=num_inference_steps
											__lowercase       	=(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
											__lowercase       	=(np.arange(0  ,       _lowerCAmelCase) * step_ratio).round()[::-1].copy().astype(np.intaa)
											__lowercase       	=torch.from_numpy(_lowerCAmelCase).to(_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  str  ,       _lowerCAmelCase  :  List[str]  ,       _lowerCAmelCase  :  Dict=None  ,       _lowerCAmelCase  :  Dict=None  ,       _lowerCAmelCase  :  int=None):







											'''simple docstring'''

											if prev_timestep is None:
																		__lowercase       	=t - 1

											__lowercase       	=self.alphas_cumprod[t]
											__lowercase       	=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
											__lowercase       	=1 - alpha_prod_t
											__lowercase       	=1 - alpha_prod_t_prev

											if prev_timestep == t - 1:
																		__lowercase       	=self.betas[t]
											else:
																		__lowercase       	=1 - alpha_prod_t / 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
											__lowercase       	=beta_prod_t_prev / beta_prod_t * beta

											if variance_type is None:
																		__lowercase       	=self.config.variance_type

											# hacks - were probably added for training stability
											if variance_type == "fixed_small_log":
																		__lowercase       	=torch.log(torch.clamp(_lowerCAmelCase  ,       min=1e-20))
																		__lowercase       	=torch.exp(0.5 * variance)
											elif variance_type == "learned_range":
																		# NOTE difference with DDPM scheduler
																		__lowercase       	=variance.log()
																		__lowercase       	=beta.log()

																		__lowercase       	=(predicted_variance + 1) / 2
																		__lowercase       	=frac * max_log + (1 - frac) * min_log

											return variance

				def 				__lowerCamelCase     (							self  :  Union[str, Any]  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  Optional[int] = None  ,       _lowerCAmelCase  :  Optional[int]=None  ,       _lowerCAmelCase  :  bool = True  ,       ):







											'''simple docstring'''

											__lowercase       	=timestep

											if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
																		__lowercase	,				__lowercase       	=torch.split(_lowerCAmelCase  ,       sample.shape[1]  ,       dim=1)
											else:
																		__lowercase       	=None

											# 1. compute alphas, betas
											if prev_timestep is None:
																		__lowercase       	=t - 1

											__lowercase       	=self.alphas_cumprod[t]
											__lowercase       	=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
											__lowercase       	=1 - alpha_prod_t
											__lowercase       	=1 - alpha_prod_t_prev

											if prev_timestep == t - 1:
																		__lowercase       	=self.betas[t]
																		__lowercase       	=self.alphas[t]
											else:
																		__lowercase       	=1 - alpha_prod_t / alpha_prod_t_prev
																		__lowercase       	=1 - beta

											# 2. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
											if self.config.prediction_type == "epsilon":
																		__lowercase       	=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											elif self.config.prediction_type == "sample":
																		__lowercase       	=model_output
											else:
																		raise ValueError(
																		    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
																		    ' for the UnCLIPScheduler.')

											# 3. Clip "predicted x_0"
											if self.config.clip_sample:
																		__lowercase       	=torch.clamp(
																		    _lowerCAmelCase  ,       -self.config.clip_sample_range  ,       self.config.clip_sample_range)

											# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											__lowercase       	=(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
											__lowercase       	=alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

											# 5. Compute predicted previous sample µ_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											__lowercase       	=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

											# 6. Add noise
											__lowercase       	=0
											if t > 0:
																		__lowercase       	=randn_tensor(
																		    model_output.shape  ,       dtype=model_output.dtype  ,       generator=_lowerCAmelCase  ,       device=model_output.device)

																		__lowercase       	=self._get_variance(
																		    _lowerCAmelCase  ,       predicted_variance=_lowerCAmelCase  ,       prev_timestep=_lowerCAmelCase  ,       )

																		if self.variance_type == "fixed_small_log":
																									__lowercase       	=variance
																		elif self.variance_type == "learned_range":
																									__lowercase       	=(0.5 * variance).exp()
																		else:
																									raise ValueError(
																									    f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
																									    ' for the UnCLIPScheduler.')

																		__lowercase       	=variance * variance_noise

											__lowercase       	=pred_prev_sample + variance

											if not return_dict:
																		return (pred_prev_sample,)

											return UnCLIPSchedulerOutput(prev_sample=_lowerCAmelCase  ,       pred_original_sample=_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  torch.IntTensor  ,       ):







											'''simple docstring'''

											__lowercase       	=self.alphas_cumprod.to(device=original_samples.device  ,       dtype=original_samples.dtype)
											__lowercase       	=timesteps.to(original_samples.device)

											__lowercase       	=alphas_cumprod[timesteps] ** 0.5
											__lowercase       	=sqrt_alpha_prod.flatten()
											while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
																		__lowercase       	=sqrt_alpha_prod.unsqueeze(-1)

											__lowercase       	=(1 - alphas_cumprod[timesteps]) ** 0.5
											__lowercase       	=sqrt_one_minus_alpha_prod.flatten()
											while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
																		__lowercase       	=sqrt_one_minus_alpha_prod.unsqueeze(-1)

											__lowercase       	=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
											return noisy_samples



