




'''simple docstring'''







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_				):
       __a       :  torch.FloatTensor
       __a       :  torch.FloatTensor
       __a       :  Optional[torch.FloatTensor]            =      None






class     _a						(			A_							,							A_				):
       __a       :  Optional[int]            =      2
       @register_to_config
       def __init__(			self		:			List[Any]     ,       lowercase		:			int = 0.02     ,       lowercase		:			Optional[int] = 100     ,       lowercase		:			List[str] = 1.007     ,       lowercase		:			Union[str, Any] = 80     ,       lowercase		:			Union[str, Any] = 0.05     ,       lowercase		:			str = 50     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	sigma_max

           # setable values
           UpperCAmelCase     =	None
           UpperCAmelCase     =	None
           UpperCAmelCase     =	None  # sigma(t_i)
       def 					A   (			self		:			Optional[Any]     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			List[Any] = None       ):



           '''simple docstring'''

           return sample
       def 					A   (			self		:			List[str]     ,       lowercase		:			Tuple     ,       lowercase		:			str = None       ):



           '''simple docstring'''

           UpperCAmelCase     =	num_inference_steps
           UpperCAmelCase     =	np.arange(0     ,       self.num_inference_steps       )[::-1].copy()
           UpperCAmelCase     =	torch.from_numpy(lowercase       ).to(lowercase       )
           UpperCAmelCase     =	[
               (
                   self.config.sigma_max**2
                   * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
               )
               for i in self.timesteps
           ]
           UpperCAmelCase     =	torch.tensor(lowercase     ,       dtype=torch.floataa     ,       device=lowercase       )
       def 					A   (			self		:			int     ,       lowercase		:			Dict     ,       lowercase		:			Tuple     ,       lowercase		:			Union[str, Any] = None       ):



           '''simple docstring'''

           if self.config.s_min <= sigma <= self.config.s_max:
               UpperCAmelCase     =	min(self.config.s_churn / self.num_inference_steps     ,       2**0.5 - 1       )
           else:
               UpperCAmelCase     =	0

           # sample eps ~ N(0, S_noise^2 * I)
           UpperCAmelCase     =	self.config.s_noise * randn_tensor(sample.shape     ,       generator=lowercase       ).to(sample.device       )
           UpperCAmelCase     =	sigma + gamma * sigma
           UpperCAmelCase     =	sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

           return sample_hat, sigma_hat
       def 					A   (			self		:			Dict     ,       lowercase		:			List[Any]     ,       lowercase		:			Dict     ,       lowercase		:			int     ,       lowercase		:			Optional[Any]     ,       lowercase		:			Any = True     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	sample_hat + sigma_hat * model_output
           UpperCAmelCase     =	(sample_hat - pred_original_sample) / sigma_hat
           UpperCAmelCase     =	sample_hat + (sigma_prev - sigma_hat) * derivative

           if not return_dict:
               return (sample_prev, derivative)

           return KarrasVeOutput(
               prev_sample=lowercase     ,       derivative=lowercase     ,       pred_original_sample=lowercase       )
       def 					A   (			self		:			str     ,       lowercase		:			Optional[int]     ,       lowercase		:			List[Any]     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			str     ,       lowercase		:			Any     ,       lowercase		:			Any     ,       lowercase		:			Any = True     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	sample_prev + sigma_prev * model_output
           UpperCAmelCase     =	(sample_prev - pred_original_sample) / sigma_prev
           UpperCAmelCase     =	sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

           if not return_dict:
               return (sample_prev, derivative)

           return KarrasVeOutput(
               prev_sample=lowercase     ,       derivative=lowercase     ,       pred_original_sample=lowercase       )







       def 					A   (			self		:			List[Any]     ,       lowercase		:			Any     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			Dict       ):



           '''simple docstring'''

           raise NotImplementedError()










'''simple docstring'''







from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




