
"""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 					A_			(  A__     ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_			   =     42
							SCREAMING_SNAKE_CASE_			   =     42






def      lowerCAmelCase_     (	snake_case_	:    str							,					snake_case_	:    Union[str, Any]=0.999							,					snake_case_	:    Union[str, Any]="cosine"							,					)     ->Optional[int]:
			if alpha_transform_type == "cosine":

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

			elif alpha_transform_type == "exp":

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

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

			lowerCamelCase__			:  Union[str, Any]  =[]
			for i in range(snake_case_    ):
						lowerCamelCase__			:  Tuple  =i / num_diffusion_timesteps
						lowerCamelCase__			:  Dict  =(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 					A_			(  A__   ,      A__     ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_			   =     1





							@register_to_config
							def __init__(						self						:Optional[Any]  ,							lowerCamelCase_						:int = 1_000  ,							lowerCamelCase_						:float = 0.00_01  ,							lowerCamelCase_						:float = 0.02  ,							lowerCamelCase_						:str = "linear"  ,							lowerCamelCase_						:float = 0.0  ,							lowerCamelCase_						:Optional[np.ndarray] = None  ,							lowerCamelCase_						:bool = True  ,							):





										"""simple docstring"""

										if trained_betas is not None:
													lowerCamelCase__			:  int  =torch.from_numpy(lowerCamelCase_ )
										elif beta_schedule == "linear":
													lowerCamelCase__			:  Optional[Any]  =torch.linspace(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							dtype=torch.floataa )
										elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													lowerCamelCase__			:  int  =(
													    torch.linspace(beta_start**0.5  ,							beta_end**0.5  ,							lowerCamelCase_  ,							dtype=torch.floataa ) ** 2
													)
										elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													lowerCamelCase__			:  List[Any]  =betas_for_alpha_bar(lowerCamelCase_ )
										elif beta_schedule == "sigmoid":
													# GeoDiff sigmoid schedule
													lowerCamelCase__			:  List[Any]  =torch.linspace(-6  ,							6  ,							lowerCamelCase_ )
													lowerCamelCase__			:  int  =torch.sigmoid(lowerCamelCase_ ) * (beta_end - beta_start) + beta_start
										else:
													raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""" )

										lowerCamelCase__			:  Dict  =1.0 - self.betas
										lowerCamelCase__			:  Optional[int]  =torch.cumprod(self.alphas  ,							dim=0 )
										lowerCamelCase__			:  Optional[int]  =torch.tensor(1.0 )

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

										# standard deviation of the initial noise distribution
										lowerCamelCase__			:  Optional[Any]  =1.0

										# setable values
										lowerCamelCase__			:  List[Any]  =None
										lowerCamelCase__			:  Optional[int]  =torch.from_numpy(np.arange(0  ,							lowerCamelCase_ )[::-1].copy() )

										lowerCamelCase__			:  str  =eta





							def    UpperCAmelCase__							(						self						:Optional[int]  ,							lowerCamelCase_						:torch.FloatTensor  ,							lowerCamelCase_						:Optional[int] = None ):





										"""simple docstring"""

										return sample





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





										"""simple docstring"""

										lowerCamelCase__			:  Dict  =min(self.config.num_train_timesteps  ,							lowerCamelCase_ )
										lowerCamelCase__			:  List[str]  =num_inference_steps

										lowerCamelCase__			:  Optional[Any]  =[]

										lowerCamelCase__			:  List[str]  ={}
										for j in range(0  ,							num_inference_steps - jump_length  ,							lowerCamelCase_ ):
													lowerCamelCase__			:  str  =jump_n_sample - 1

										lowerCamelCase__			:  Union[str, Any]  =num_inference_steps
										while t >= 1:
													lowerCamelCase__			:  Dict  =t - 1
													timesteps.append(lowerCamelCase_ )

													if jumps.get(lowerCamelCase_  ,							0 ) > 0:
																lowerCamelCase__			:  Dict  =jumps[t] - 1
																for _ in range(lowerCamelCase_ ):
																			lowerCamelCase__			:  int  =t + 1
																			timesteps.append(lowerCamelCase_ )

										lowerCamelCase__			:  str  =np.array(lowerCamelCase_ ) * (self.config.num_train_timesteps // self.num_inference_steps)
										lowerCamelCase__			:  Dict  =torch.from_numpy(lowerCamelCase_ ).to(lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:List[str]  ,							lowerCamelCase_						:Optional[int] ):





										"""simple docstring"""

										lowerCamelCase__			:  str  =t - self.config.num_train_timesteps // self.num_inference_steps

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

										return variance





							def    UpperCAmelCase__							(						self						:Any  ,							lowerCamelCase_						:torch.FloatTensor  ,							lowerCamelCase_						:int  ,							lowerCamelCase_						:torch.FloatTensor  ,							lowerCamelCase_						:torch.FloatTensor  ,							lowerCamelCase_						:torch.FloatTensor  ,							lowerCamelCase_						:Optional[torch.Generator] = None  ,							lowerCamelCase_						:bool = True  ,							):





										"""simple docstring"""

										lowerCamelCase__			:  List[str]  =timestep
										lowerCamelCase__			:  int  =timestep - self.config.num_train_timesteps // self.num_inference_steps

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

										# 3. Clip "predicted x_0"
										if self.config.clip_sample:
													lowerCamelCase__			:  Tuple  =torch.clamp(lowerCamelCase_  ,							-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__			:  int  =model_output.device
										lowerCamelCase__			:  str  =randn_tensor(model_output.shape  ,							generator=lowerCamelCase_  ,							device=lowerCamelCase_  ,							dtype=model_output.dtype )
										lowerCamelCase__			:  Dict  =self.eta * self._get_variance(lowerCamelCase_ ) ** 0.5

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

										# 6. compute "direction pointing to x_t" of formula (12)
										# from https://arxiv.org/pdf/2010.02502.pdf
										lowerCamelCase__			:  List[str]  =(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__			:  int  =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__			:  List[Any]  =(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__			:  Union[str, Any]  =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=lowerCamelCase_  ,							pred_original_sample=lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:List[Any]  ,							lowerCamelCase_						:List[Any]  ,							lowerCamelCase_						:List[Any]  ,							lowerCamelCase_						:Union[str, Any]=None ):





										"""simple docstring"""

										lowerCamelCase__			:  Union[str, Any]  =self.config.num_train_timesteps // self.num_inference_steps

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

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

										return sample





							def    UpperCAmelCase__							(						self						:List[Any]  ,							lowerCamelCase_						:torch.FloatTensor  ,							lowerCamelCase_						:torch.FloatTensor  ,							lowerCamelCase_						:torch.IntTensor  ,							):





										"""simple docstring"""

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





							def __len__(						self						:str ):





										"""simple docstring"""

										return self.config.num_train_timesteps

"""simple docstring"""


from sklearn.neural_network import MLPClassifier

lowerCAmelCase			=		[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
lowerCAmelCase			=		[0, 1, 0, 0]


lowerCAmelCase			=		MLPClassifier(
    solver="""lbfgs""", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


lowerCAmelCase			=		[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
lowerCAmelCase			=		clf.predict(test)






def      lowerCAmelCase_     (	snake_case_	:    Tuple    )     ->List[Any]:
			return list(snake_case_    )


if __name__ == "__main__":
							import doctest

							doctest.testmod()