




"""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  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = 42
 lowercase__			 = 42
 lowercase__			 = None







class  lowercase(					__a	,      __a			):







 '''simple docstring'''





 lowercase__			 = 2


 @register_to_config
 def __init__(    self:		Tuple,				a_:		float = 0.02,				a_:		float = 100,				a_:		float = 1.007,				a_:		float = 80,				a_:		float = 0.05,				a_:		float = 50,				):




   '''simple docstring'''



   _snake_case							:		Tuple								=  sigma_max

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


 def 			UpperCamelCase_       (    self:		Tuple,				a_:		torch.FloatTensor,				a_:		Optional[int] = None		):




   '''simple docstring'''



   return sample


 def 			UpperCamelCase_       (    self:		int,				a_:		int,				a_:		Union[str, torch.device] = None		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  num_inference_steps
   _snake_case							:		int								=  np.arange(0,				self.num_inference_steps		)[::-1].copy()
   _snake_case							:		Tuple								=  torch.from_numpy(a_		).to(a_		)
   _snake_case							:		int								=  [
       (
           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
   ]
   _snake_case							:		Dict								=  torch.tensor(a_,				dtype=torch.floataa,				device=a_		)


 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		torch.FloatTensor,				a_:		float,				a_:		Optional[torch.Generator] = None		):




   '''simple docstring'''



   if self.config.s_min <= sigma <= self.config.s_max:
     _snake_case							:		int								=  min(self.config.s_churn / self.num_inference_steps,				2**0.5 - 1		)
   else:
     _snake_case							:		List[str]								=  0

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

   return sample_hat, sigma_hat


 def 			UpperCamelCase_       (    self:		List[Any],				a_:		torch.FloatTensor,				a_:		float,				a_:		float,				a_:		torch.FloatTensor,				a_:		bool = True,				):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  sample_hat + sigma_hat * model_output
   _snake_case							:		Union[str, Any]								=  (sample_hat - pred_original_sample) / sigma_hat
   _snake_case							:		Tuple								=  sample_hat + (sigma_prev - sigma_hat) * derivative

   if not return_dict:
     return (sample_prev, derivative)

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


 def 			UpperCamelCase_       (    self:		List[str],				a_:		torch.FloatTensor,				a_:		float,				a_:		float,				a_:		torch.FloatTensor,				a_:		torch.FloatTensor,				a_:		torch.FloatTensor,				a_:		bool = True,				):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  sample_prev + sigma_prev * model_output
   _snake_case							:		Any								=  (sample_prev - pred_original_sample) / sigma_prev
   _snake_case							:		str								=  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=a_,				derivative=a_,				pred_original_sample=a_		)


 def 			UpperCamelCase_       (    self:		Any,				a_:		Optional[int],				a_:		int,				a_:		List[Any]		):




   '''simple docstring'''



   raise NotImplementedError()







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_      =    {
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    ['''MaskFormerFeatureExtractor''']
     A_      =    ['''MaskFormerImageProcessor''']


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    [
         '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MaskFormerForInstanceSegmentation''',
         '''MaskFormerModel''',
         '''MaskFormerPreTrainedModel''',
     ]
     A_      =    [
         '''MaskFormerSwinBackbone''',
         '''MaskFormerSwinModel''',
         '''MaskFormerSwinPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
     from .configuration_maskformer_swin import MaskFormerSwinConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_maskformer import MaskFormerFeatureExtractor
          from .image_processing_maskformer import MaskFormerImageProcessor
     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_maskformer import (
              MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              MaskFormerForInstanceSegmentation,
              MaskFormerModel,
              MaskFormerPreTrainedModel,
          )
          from .modeling_maskformer_swin import (
              MaskFormerSwinBackbone,
              MaskFormerSwinModel,
              MaskFormerSwinPreTrainedModel,
          )


else:
     import sys

     A_      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)


