




'''simple docstring'''





from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available

@dataclass
class      _a    (		__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:					Any					       =		{
         """sld_warmup_steps""": 15,
         """sld_guidance_scale""": 20,
         """sld_threshold""": 0.0,
         """sld_momentum_scale""": 0.0,
         """sld_mom_beta""": 0.0,
     }
     SCREAMING_SNAKE_CASE_							:					Any					       =		{
         """sld_warmup_steps""": 10,
         """sld_guidance_scale""": 10_00,
         """sld_threshold""": 0.01,
         """sld_momentum_scale""": 0.3,
         """sld_mom_beta""": 0.4,
     }
     SCREAMING_SNAKE_CASE_							:					Tuple					       =		{
         """sld_warmup_steps""": 7,
         """sld_guidance_scale""": 20_00,
         """sld_threshold""": 0.025,
         """sld_momentum_scale""": 0.5,
         """sld_mom_beta""": 0.7,
     }
     SCREAMING_SNAKE_CASE_							:					Union[str, Any]					       =		{
         """sld_warmup_steps""": 0,
         """sld_guidance_scale""": 50_00,
         """sld_threshold""": 1.0,
         """sld_momentum_scale""": 0.5,
         """sld_mom_beta""": 0.7,
     }







@dataclass
class      _a    (		__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:					Optional[Any]					       =		42
     SCREAMING_SNAKE_CASE_							:					List[str]					       =		42
     SCREAMING_SNAKE_CASE_							:					Tuple					       =		42
     SCREAMING_SNAKE_CASE_							:					List[Any]					       =		42


if is_transformers_available() and is_torch_available():
   from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
   from .safety_checker import SafeStableDiffusionSafetyChecker








'''simple docstring'''





import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union

class      _a       (		enum.Enum							):
     SCREAMING_SNAKE_CASE_							:					Optional[int]					       =		"""timm"""
     SCREAMING_SNAKE_CASE_							:					int					       =		"""transformers"""







def 			__a			(     _UpperCamelCase:					Optional[Iterable[str]]      ,		_UpperCamelCase:					Optional[Iterable[int]]      ,		_UpperCamelCase:					Optional[Iterable[str]]					)		->				Union[str, Any]:

     """simple docstring"""




     if stage_names is None:
          raise ValueError("Stage_names must be set for transformers backbones"					)

     if out_features is not None:
          if not isinstance(_UpperCamelCase      ,		(list,)					):
               raise ValueError(F"""out_features must be a list {type(_UpperCamelCase					)}"""					)
          if any(feat not in stage_names for feat in out_features					):
               raise ValueError(F"""out_features must be a subset of stage_names: {stage_names} got {out_features}"""					)

     if out_indices is not None:
          if not isinstance(_UpperCamelCase      ,		(list, tuple)					):
               raise ValueError(F"""out_indices must be a list or tuple, got {type(_UpperCamelCase					)}"""					)
          if any(idx >= len(_UpperCamelCase					) for idx in out_indices					):
               raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"					)

     if out_features is not None and out_indices is not None:
          if len(_UpperCamelCase					) != len(_UpperCamelCase					):
               raise ValueError("out_features and out_indices should have the same length if both are set"					)
          if out_features != [stage_names[idx] for idx in out_indices]:
               raise ValueError("out_features and out_indices should correspond to the same stages if both are set"					)





def 			__a			(     _UpperCamelCase:					Optional[List[str]]      ,		_UpperCamelCase:					Optional[Union[List[int], Tuple[int]]]      ,		_UpperCamelCase:					List[str]      ,		)		->				Any:

     """simple docstring"""




     if out_indices is None and out_features is None:
          _snake_case							=   [len(_UpperCamelCase					) - 1]
          _snake_case							=   [stage_names[-1]]
     elif out_indices is None and out_features is not None:
          _snake_case							=   [stage_names.index(_UpperCamelCase					) for layer in out_features]
     elif out_features is None and out_indices is not None:
          _snake_case							=   [stage_names[idx] for idx in out_indices]
     return out_features, out_indices





def 			__a			(     _UpperCamelCase:					Optional[List[str]]      ,		_UpperCamelCase:					Optional[Union[List[int], Tuple[int]]]      ,		_UpperCamelCase:					List[str]      ,		)		->				Tuple[List[str], List[int]]:

     """simple docstring"""




     verify_out_features_out_indices(out_features=_UpperCamelCase      ,		out_indices=_UpperCamelCase      ,		stage_names=_UpperCamelCase					)
     _snake_case							,      _snake_case							=   _align_output_features_output_indices(
         out_features=_UpperCamelCase      ,		out_indices=_UpperCamelCase      ,		stage_names=_UpperCamelCase					)
     # Verify that the aligned out_features and out_indices are valid
     verify_out_features_out_indices(out_features=_UpperCamelCase      ,		out_indices=_UpperCamelCase      ,		stage_names=_UpperCamelCase					)
     return output_features, output_indices

class      _a       :
     SCREAMING_SNAKE_CASE_							:					Optional[BackboneType]					       =		None






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    None:
          if getattr(self      ,"_backbone"      ,_SCREAMING_SNAKE_CASE					) is None:
               raise ValueError("self._backbone must be set before calling _init_timm_backbone"					)

          # These will diagree with the defaults for the transformers models e.g. for resnet50
          # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
          # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
          _snake_case							=   [stage["module"] for stage in self._backbone.feature_info.info]
          _snake_case							=   [stage["num_chs"] for stage in self._backbone.feature_info.info]
          _snake_case							=   self._backbone.feature_info.out_indices
          _snake_case							=   self._backbone.feature_info.module_name()

          # We verify the out indices and out features are valid
          verify_out_features_out_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)
          _snake_case							,      _snake_case							=   out_features, out_indices






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    None:
          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"stage_names"					)
          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"out_features"      ,_SCREAMING_SNAKE_CASE					)
          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"out_indices"      ,_SCREAMING_SNAKE_CASE					)

          _snake_case							=   stage_names
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=_SCREAMING_SNAKE_CASE					)
          # Number of channels for each stage. This is set in the transformer backbone model init
          _snake_case							=   None






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    None:
          _snake_case							=   config

          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"use_timm_backbone"      ,_SCREAMING_SNAKE_CASE					)
          _snake_case							=   BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

          if self.backbone_type == BackboneType.TIMM:
               self._init_timm_backbone(_SCREAMING_SNAKE_CASE					)
          elif self.backbone_type == BackboneType.TRANSFORMERS:
               self._init_transformers_backbone(_SCREAMING_SNAKE_CASE					)
          else:
               raise ValueError(f"""backbone_type {self.backbone_type} not supported."""					)






     @property
     def        _lowercase			(				self					)							->    int:
          return self._out_features






     @out_features.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






     @property
     def        _lowercase			(				self					)							->    Any:
          return self._out_indices






     @out_indices.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    int:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






     @property
     def        _lowercase			(				self					)							->    List[str]:
          # the current backbones will output the number of channels for each stage
          # even if that stage is not in the out_features list.
          return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names					)}






     @property
     def        _lowercase			(				self					)							->    Any:
          return [self.out_feature_channels[name] for name in self.out_features]






     def        _lowercase			(				self      ,*_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							=   dict(inspect.signature(self.forward					).parameters					)
          _snake_case							=   {k: v for k, v in kwargs.items() if k in signature}
          return self(*_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,)							->    Any:
          raise NotImplementedError("This method should be implemented by the derived class."					)






     def        _lowercase			(				self					)							->    Tuple:
          _snake_case							=   super().to_dict()
          _snake_case							=   output.pop("_out_features"					)
          _snake_case							=   output.pop("_out_indices"					)
          return output







class      _a       :






     @property
     def        _lowercase			(				self					)							->    List[str]:
          return self._out_features






     @out_features.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






     @property
     def        _lowercase			(				self					)							->    Any:
          return self._out_indices






     @out_indices.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    Optional[Any]:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   super().to_dict()
          _snake_case							=   output.pop("_out_features"					)
          _snake_case							=   output.pop("_out_indices"					)
          return output


