





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 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							torch.FloatTensor
   a		:							torch.FloatTensor
   a		:							Optional[torch.FloatTensor]     	=	None






class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase       ):

   '''simple docstring'''


   a		:							str     	=	2




   @register_to_config
   def __init__(					self,	__magic_name__ = 0.02,	__magic_name__ = 100,	__magic_name__ = 1.007,	__magic_name__ = 80,	__magic_name__ = 0.05,	__magic_name__ = 50,	)				->   int:






       """simple docstring"""
       # standard deviation of the initial noise distribution
       UpperCamelCase__							:       str      =   sigma_max

       # setable values
       UpperCamelCase__							:       int      =   None
       UpperCamelCase__							:       np.IntTensor      =   None
       UpperCamelCase__							:       torch.FloatTensor      =   None  # sigma(t_i)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   torch.FloatTensor:






       """simple docstring"""
       return sample




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   num_inference_steps
       UpperCamelCase__							:       Optional[Any]      =   np.arange(0,	self.num_inference_steps							)[::-1].copy()
       UpperCamelCase__							:       Optional[Any]      =   torch.from_numpy(__magic_name__							).to(__magic_name__							)
       UpperCamelCase__							:       str      =   [
           (
               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__							:       Optional[int]      =   torch.tensor(__magic_name__,	dtype=torch.floataa,	device=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__ = None							)				->   Tuple[torch.FloatTensor, float]:






       """simple docstring"""
       if self.config.s_min <= sigma <= self.config.s_max:
           UpperCamelCase__							:       Optional[Any]      =   min(self.config.s_churn / self.num_inference_steps,	2**0.5 - 1							)
       else:
           UpperCamelCase__							:       Optional[int]      =   0

       # sample eps ~ N(0, S_noise^2 * I)
       UpperCamelCase__							:       Optional[Any]      =   self.config.s_noise * randn_tensor(sample.shape,	generator=__magic_name__							).to(sample.device							)
       UpperCamelCase__							:       List[Any]      =   sigma + gamma * sigma
       UpperCamelCase__							:       List[Any]      =   sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__ = True,	)				->   Union[KarrasVeOutput, Tuple]:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   sample_hat + sigma_hat * model_output
       UpperCamelCase__							:       Optional[Any]      =   (sample_hat - pred_original_sample) / sigma_hat
       UpperCamelCase__							:       List[str]      =   sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
           return (sample_prev, derivative)

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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__ = True,	)				->   Union[KarrasVeOutput, Tuple]:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   sample_prev + sigma_prev * model_output
       UpperCamelCase__							:       str      =   (sample_prev - pred_original_sample) / sigma_prev
       UpperCamelCase__							:       Tuple      =   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=__magic_name__,	derivative=__magic_name__,	pred_original_sample=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   Optional[Any]:






       """simple docstring"""
       raise NotImplementedError()








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


UpperCAmelCase_  							=				logging.get_logger(__name__)






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Any     	=	"upernet"




   def __init__(					self,	__magic_name__=None,	__magic_name__=512,	__magic_name__=0.02,	__magic_name__=[1, 2, 3, 6],	__magic_name__=True,	__magic_name__=0.4,	__magic_name__=384,	__magic_name__=256,	__magic_name__=1,	__magic_name__=False,	__magic_name__=255,	**__magic_name__,	)				->   Dict:






       """simple docstring"""
       super().__init__(**__magic_name__							)

       if backbone_config is None:
           logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''							)
           UpperCamelCase__							:       str      =   CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							)
       elif isinstance(__magic_name__,	__magic_name__							):
           UpperCamelCase__							:       Tuple      =   backbone_config.get('''model_type'''							)
           UpperCamelCase__							:       List[str]      =   CONFIG_MAPPING[backbone_model_type]
           UpperCamelCase__							:       Optional[int]      =   config_class.from_dict(__magic_name__							)

       UpperCamelCase__							:       Dict      =   backbone_config
       UpperCamelCase__							:       int      =   hidden_size
       UpperCamelCase__							:       int      =   initializer_range
       UpperCamelCase__							:       Dict      =   pool_scales
       UpperCamelCase__							:       Tuple      =   use_auxiliary_head
       UpperCamelCase__							:       Tuple      =   auxiliary_loss_weight
       UpperCamelCase__							:       Optional[int]      =   auxiliary_in_channels
       UpperCamelCase__							:       Optional[Any]      =   auxiliary_channels
       UpperCamelCase__							:       Any      =   auxiliary_num_convs
       UpperCamelCase__							:       Union[str, Any]      =   auxiliary_concat_input
       UpperCamelCase__							:       List[str]      =   loss_ignore_index




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   copy.deepcopy(self.__dict__							)
       UpperCamelCase__							:       int      =   self.backbone_config.to_dict()
       UpperCamelCase__							:       Any      =   self.__class__.model_type
       return output

