




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    __lowerCAmelCase			(	UpperCAmelCase__			):
			snake_case_				:			torch.FloatTensor
			snake_case_				:			torch.FloatTensor




def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_=0.999     ,   snake_case_="cosine"     ,   ):

						'''simple docstring'''


						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

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






class    __lowerCAmelCase			(	UpperCAmelCase__ ,		UpperCAmelCase__			):
			snake_case_				:			Dict								=					1







			@register_to_config
			def __init__(	self   :      int   ,   snake_case__   :      int = 1_000   ,   snake_case__   :      float = 0.0_001   ,   snake_case__   :      float = 0.02   ,   snake_case__   :      str = "linear"   ,   snake_case__   :      float = 0.0   ,   snake_case__   :      Optional[np.ndarray] = None   ,   snake_case__   :      bool = True   ,   ):




									"""simple docstring"""


									if trained_betas is not None:
															_UpperCAmelCase													=					torch.from_numpy(snake_case__				)
									elif beta_schedule == "linear":
															_UpperCAmelCase													=					torch.linspace(snake_case__   ,   snake_case__   ,   snake_case__   ,   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   ,   snake_case__   ,   dtype=torch.floataa				) ** 2
															)
									elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															_UpperCAmelCase													=					betas_for_alpha_bar(snake_case__				)
									elif beta_schedule == "sigmoid":
															# GeoDiff sigmoid schedule
															_UpperCAmelCase													=					torch.linspace(-6   ,   6   ,   snake_case__				)
															_UpperCAmelCase													=					torch.sigmoid(snake_case__				) * (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   ,   snake_case__				)[::-1].copy()				)

									_UpperCAmelCase													=					eta







			def UpperCamelCase      (	self   :      Optional[int]   ,   snake_case__   :      torch.FloatTensor   ,   snake_case__   :      Optional[int] = None				):




									"""simple docstring"""


									return sample







			def UpperCamelCase      (	self   :      int   ,   snake_case__   :      int   ,   snake_case__   :      int = 10   ,   snake_case__   :      int = 10   ,   snake_case__   :      Union[str, torch.device] = None   ,   ):




									"""simple docstring"""


									_UpperCAmelCase													=					min(self.config.num_train_timesteps   ,   snake_case__				)
									_UpperCAmelCase													=					num_inference_steps

									_UpperCAmelCase													=					[]

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

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

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

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







			def UpperCamelCase      (	self   :      Tuple   ,   snake_case__   :      Any				):




									"""simple docstring"""


									_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 UpperCamelCase      (	self   :      Optional[int]   ,   snake_case__   :      torch.FloatTensor   ,   snake_case__   :      int   ,   snake_case__   :      torch.FloatTensor   ,   snake_case__   :      torch.FloatTensor   ,   snake_case__   :      torch.FloatTensor   ,   snake_case__   :      Optional[torch.Generator] = None   ,   snake_case__   :      bool = True   ,   ):




									"""simple docstring"""


									_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(snake_case__   ,   -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=snake_case__   ,   device=snake_case__   ,   dtype=model_output.dtype				)
									_UpperCAmelCase													=					self.eta * self._get_variance(snake_case__				) ** 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=snake_case__   ,   pred_original_sample=snake_case__				)







			def UpperCamelCase      (	self   :      Union[str, Any]   ,   snake_case__   :      Any   ,   snake_case__   :      Union[str, Any]   ,   snake_case__   :      Dict=None				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.config.num_train_timesteps // self.num_inference_steps

									for i in range(snake_case__				):
															_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=snake_case__				)
																					_UpperCAmelCase													=					noise.to(sample.device				)
															else:
																					_UpperCAmelCase													=					randn_tensor(sample.shape   ,   generator=snake_case__   ,   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 UpperCamelCase      (	self   :      Optional[int]   ,   snake_case__   :      torch.FloatTensor   ,   snake_case__   :      torch.FloatTensor   ,   snake_case__   :      torch.IntTensor   ,   ):




									"""simple docstring"""


									raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."				)







			def __len__(	self   :      List[str]				):




									"""simple docstring"""


									return self.config.num_train_timesteps










class    __lowerCAmelCase			:







			def __init__(	self   :      Dict   ,   snake_case__   :      List[str]=None				):




									"""simple docstring"""


									_UpperCAmelCase													=					data
									_UpperCAmelCase													=					None







			def __repr__(	self   :      Union[str, Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					""
									_UpperCAmelCase													=					self
									while temp:
															string_rep += F"""<{temp.data}> ---> """
															_UpperCAmelCase													=					temp.next
									string_rep += "<END>"
									return string_rep




def 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''simple docstring'''


						if not elements_list:
												raise Exception("The Elements List is empty"						)

						# Set first element as Head
						_UpperCAmelCase													=					Node(elements_list[0]						)
						_UpperCAmelCase													=					head
						# Loop through elements from position 1
						for data in elements_list[1:]:
												_UpperCAmelCase													=					Node(snake_case_						)
												_UpperCAmelCase													=					current.next
						return head


lowercase_     :    str      =			[1, 3, 5, 32, 44, 12, 43]
print(f"""List: {list_data}""")
print('Creating Linked List from List.')
lowercase_     :    Dict      =			make_linked_list(list_data)
print('Linked List:')





print(linked_list)




