




"""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_  (	snake_case						):
		UpperCamelCase									=42
		UpperCamelCase									=42







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase=0.999					,							__UpperCamelCase="cosine"					,							):
			if alpha_transform_type == "cosine":

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

			elif alpha_transform_type == "exp":

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

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

			__lowercase	:       int				=   []
			for i in range(__UpperCamelCase					):
						__lowercase	:       List[str]				=   i / num_diffusion_timesteps
						__lowercase	:       Union[str, Any]				=   (i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar_fn(__UpperCamelCase					) / alpha_bar_fn(__UpperCamelCase					)					,							__UpperCamelCase					)					)
			return torch.tensor(__UpperCamelCase					,							dtype=torch.floataa					)


class        UpperCAmelCase_  (	snake_case ,			snake_case						):
		UpperCamelCase									=1
		@register_to_config
		def __init__(		self							,		UpperCamelCase_ = 10_00							,		UpperCamelCase_ = 0.0_0_0_1							,		UpperCamelCase_ = 0.0_2							,		UpperCamelCase_ = "linear"							,		UpperCamelCase_ = 0.0							,		UpperCamelCase_ = None							,		UpperCamelCase_ = True							,		)		->   List[str]:
					if trained_betas is not None:
								__lowercase	:       List[Any]				=   torch.from_numpy(UpperCamelCase_   )
					elif beta_schedule == "linear":
								__lowercase	:       Tuple				=   torch.linspace(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		dtype=torch.floataa   )
					elif beta_schedule == "scaled_linear":
								# this schedule is very specific to the latent diffusion model.
								__lowercase	:       Tuple				=   (
								    torch.linspace(beta_start**0.5							,		beta_end**0.5							,		UpperCamelCase_							,		dtype=torch.floataa   ) ** 2
								)
					elif beta_schedule == "squaredcos_cap_v2":
								# Glide cosine schedule
								__lowercase	:       Union[str, Any]				=   betas_for_alpha_bar(UpperCamelCase_   )
					elif beta_schedule == "sigmoid":
								# GeoDiff sigmoid schedule
								__lowercase	:       Tuple				=   torch.linspace(-6							,		6							,		UpperCamelCase_   )
								__lowercase	:       int				=   torch.sigmoid(UpperCamelCase_   ) * (beta_end - beta_start) + beta_start
					else:
								raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""   )

					__lowercase	:       Union[str, Any]				=   1.0 - self.betas
					__lowercase	:       Tuple				=   torch.cumprod(self.alphas							,		dim=0   )
					__lowercase	:       Union[str, Any]				=   torch.tensor(1.0   )

					__lowercase	:       List[Any]				=   torch.tensor(1.0   )

					# standard deviation of the initial noise distribution
					__lowercase	:       List[Any]				=   1.0

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

					__lowercase	:       Dict				=   eta
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_ = None   )		->   torch.FloatTensor:
					return sample
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_ = 10							,		UpperCamelCase_ = 10							,		UpperCamelCase_ = None							,		)		->   Union[str, Any]:
					__lowercase	:       Any				=   min(self.config.num_train_timesteps							,		UpperCamelCase_   )
					__lowercase	:       Optional[int]				=   num_inference_steps

					__lowercase	:       Optional[Any]				=   []

					__lowercase	:       str				=   {}
					for j in range(0							,		num_inference_steps - jump_length							,		UpperCamelCase_   ):
								__lowercase	:       Any				=   jump_n_sample - 1

					__lowercase	:       Union[str, Any]				=   num_inference_steps
					while t >= 1:
								__lowercase	:       Any				=   t - 1
								timesteps.append(UpperCamelCase_   )

								if jumps.get(UpperCamelCase_							,		0   ) > 0:
											__lowercase	:       Optional[Any]				=   jumps[t] - 1
											for _ in range(UpperCamelCase_   ):
														__lowercase	:       List[str]				=   t + 1
														timesteps.append(UpperCamelCase_   )

					__lowercase	:       Union[str, Any]				=   np.array(UpperCamelCase_   ) * (self.config.num_train_timesteps // self.num_inference_steps)
					__lowercase	:       Union[str, Any]				=   torch.from_numpy(UpperCamelCase_   ).to(UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   Dict:
					__lowercase	:       str				=   t - self.config.num_train_timesteps // self.num_inference_steps

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

					return variance
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_ = None							,		UpperCamelCase_ = True							,		)		->   Union[RePaintSchedulerOutput, Tuple]:
					__lowercase	:       Optional[int]				=   timestep
					__lowercase	:       str				=   timestep - self.config.num_train_timesteps // self.num_inference_steps

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

					# 3. Clip "predicted x_0"
					if self.config.clip_sample:
								__lowercase	:       Tuple				=   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
					__lowercase	:       str				=   model_output.device
					__lowercase	:       Union[str, Any]				=   randn_tensor(model_output.shape							,		generator=UpperCamelCase_							,		device=UpperCamelCase_							,		dtype=model_output.dtype   )
					__lowercase	:       Any				=   self.eta * self._get_variance(UpperCamelCase_   ) ** 0.5

					__lowercase	:       Optional[Any]				=   0
					if t > 0 and self.eta > 0:
								__lowercase	:       str				=   std_dev_t * noise

					# 6. compute "direction pointing to x_t" of formula (12)
					# from https://arxiv.org/pdf/2010.02502.pdf
					__lowercase	:       Optional[int]				=   (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
					__lowercase	:       Optional[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
					__lowercase	:       Dict				=   (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
					__lowercase	:       Optional[int]				=   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 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_=None   )		->   Optional[Any]:
					__lowercase	:       Any				=   self.config.num_train_timesteps // self.num_inference_steps

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

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

					return sample
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		)		->   torch.FloatTensor:
					raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''   )
		def __len__(		self   )		->   int:
					return self.config.num_train_timesteps









"""simple docstring"""




# 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      (				__UpperCamelCase					):
			__lowercase	:       int				=   Queue()
			__lowercase	:       str				=   [False] * len(__UpperCamelCase					)
			__lowercase	:       str				=   [-1] * len(__UpperCamelCase					)

			def bfs():
						while not queue.empty():
									__lowercase	:       int				=   queue.get()
									__lowercase	:       List[str]				=   True

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

												if color[neighbour] == -1:
															__lowercase	:       str				=   1 - color[u]
															queue.put(__UpperCamelCase					)

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

						return True

			for i in range(len(__UpperCamelCase					)					):
						if not visited[i]:
									queue.put(__UpperCamelCase					)
									__lowercase	:       int				=   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]}))



