



'''simple docstring'''


from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
   import timm


if is_torch_available():
   from torch import Tensor

class  SCREAMING_SNAKE_CASE (  _a   ,						_a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  """pixel_values"""
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  TimmBackboneConfig






    def __init__(  self      :					str  ,						UpperCamelCase__      :					List[Any]  ,						**UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       requires_backends(self  ,						'timm' )
       super().__init__(UpperCamelCase__ )
       UpperCamelCase					=  config

       if config.backbone is None:
          raise ValueError('backbone is not set in the config. Please set it to a timm model name.' )

       if config.backbone not in timm.list_models():
          raise ValueError(f"""backbone {config.backbone} is not supported by timm.""" )

       if hasattr(UpperCamelCase__  ,						'out_features' ) and config.out_features is not None:
          raise ValueError('out_features is not supported by TimmBackbone. Please use out_indices instead.' )

       UpperCamelCase					=  getattr(UpperCamelCase__  ,						'use_pretrained_backbone'  ,						UpperCamelCase__ )
       if pretrained is None:
          raise ValueError('use_pretrained_backbone is not set in the config. Please set it to True or False.' )

       # We just take the final layer by default. This matches the default for the transformers models.
       UpperCamelCase					=  config.out_indices if getattr(UpperCamelCase__  ,						'out_indices'  ,						UpperCamelCase__ ) is not None else (-1,)

       UpperCamelCase					=  timm.create_model(
           config.backbone  ,						pretrained=UpperCamelCase__  ,						features_only=config.features_only  ,						in_chans=config.num_channels  ,						out_indices=UpperCamelCase__  ,						**UpperCamelCase__  ,						)
       # These are used to control the output of the model when called. If output_hidden_states is True, then
       # return_layers is modified to include all layers.
       UpperCamelCase					=  self._backbone.return_layers
       UpperCamelCase					=  {layer['module']: str(UpperCamelCase__ ) for i, layer in enumerate(self._backbone.feature_info.info )}
       super()._init_backbone(UpperCamelCase__ )






    @classmethod
    def  A       (  cls      :					List[Any]  ,						UpperCamelCase__      :					Any  ,						*UpperCamelCase__      :					List[str]  ,						**UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       requires_backends(cls  ,						['vision', 'timm'] )
       from ...models.timm_backbone import TimmBackboneConfig

       UpperCamelCase					=  kwargs.pop('config'  ,						TimmBackboneConfig() )

       UpperCamelCase					=  kwargs.pop('use_timm_backbone'  ,						UpperCamelCase__ )
       if not use_timm:
          raise ValueError('use_timm_backbone must be True for timm backbones' )

       UpperCamelCase					=  kwargs.pop('num_channels'  ,						config.num_channels )
       UpperCamelCase					=  kwargs.pop('features_only'  ,						config.features_only )
       UpperCamelCase					=  kwargs.pop('use_pretrained_backbone'  ,						config.use_pretrained_backbone )
       UpperCamelCase					=  kwargs.pop('out_indices'  ,						config.out_indices )
       UpperCamelCase					=  TimmBackboneConfig(
           backbone=UpperCamelCase__  ,						num_channels=UpperCamelCase__  ,						features_only=UpperCamelCase__  ,						use_pretrained_backbone=UpperCamelCase__  ,						out_indices=UpperCamelCase__  ,						)
       return super()._from_config(UpperCamelCase__  ,						**UpperCamelCase__ )






    def  A       (  self      :					Optional[int]  ,						UpperCamelCase__      :					Dict ):


       """simple docstring"""







       pass






    def  A       (  self      :					str  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					Optional[int]=None  ,						UpperCamelCase__      :					Optional[Any]=None  ,						UpperCamelCase__      :					Optional[Any]=None  ,						**UpperCamelCase__      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  return_dict if return_dict is not None else self.config.use_return_dict
       UpperCamelCase					=  (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       UpperCamelCase					=  output_attentions if output_attentions is not None else self.config.output_attentions

       if output_attentions:
          raise ValueError('Cannot output attentions for timm backbones at the moment' )

       if output_hidden_states:
          # We modify the return layers to include all the stages of the backbone
          UpperCamelCase					=  self._all_layers
          UpperCamelCase					=  self._backbone(UpperCamelCase__  ,						**UpperCamelCase__ )
          UpperCamelCase					=  self._return_layers
          UpperCamelCase					=  tuple(hidden_states[i] for i in self.out_indices )
       else:
          UpperCamelCase					=  self._backbone(UpperCamelCase__  ,						**UpperCamelCase__ )
          UpperCamelCase					=  None

       UpperCamelCase					=  tuple(UpperCamelCase__ )
       UpperCamelCase					=  tuple(UpperCamelCase__ ) if hidden_states is not None else None

       if not return_dict:
          UpperCamelCase					=  (feature_maps,)
          if output_hidden_states:
             UpperCamelCase					=  output + (hidden_states,)
          return output

       return BackboneOutput(feature_maps=UpperCamelCase__  ,						hidden_states=UpperCamelCase__  ,						attentions=UpperCamelCase__ )






'''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  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  None

class  SCREAMING_SNAKE_CASE (  _a   ,						_a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  2






    @register_to_config
    def __init__(  self      :					Union[str, Any]  ,						UpperCamelCase__      :					float = 0.0_2  ,						UpperCamelCase__      :					float = 1_0_0  ,						UpperCamelCase__      :					float = 1.0_0_7  ,						UpperCamelCase__      :					float = 8_0  ,						UpperCamelCase__      :					float = 0.0_5  ,						UpperCamelCase__      :					float = 5_0  ,						):


       """simple docstring"""







       UpperCamelCase					=  sigma_max

       # setable values
       UpperCamelCase					=  None
       UpperCamelCase					=  None
       UpperCamelCase					=  None  # sigma(t_i)






    def  A       (  self      :					str  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None ):


       """simple docstring"""







       return sample






    def  A       (  self      :					Union[str, Any]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Union[str, torch.device] = None ):


       """simple docstring"""







       UpperCamelCase					=  num_inference_steps
       UpperCamelCase					=  np.arange(0  ,						self.num_inference_steps )[::-1].copy()
       UpperCamelCase					=  torch.from_numpy(UpperCamelCase__ ).to(UpperCamelCase__ )
       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(UpperCamelCase__  ,						dtype=torch.floataa  ,						device=UpperCamelCase__ )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					Optional[torch.Generator] = 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=UpperCamelCase__ ).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      :					str  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					bool = 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=UpperCamelCase__  ,						derivative=UpperCamelCase__  ,						pred_original_sample=UpperCamelCase__ )






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					bool = 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=UpperCamelCase__  ,						derivative=UpperCamelCase__  ,						pred_original_sample=UpperCamelCase__ )






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					str ):


       """simple docstring"""







       raise NotImplementedError()

