




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




class 		UpperCamelCase__     (enum.Enum					):

    '''simple docstring'''



    lowerCamelCase_				:     Optional[int]     					=				'timm'
    lowerCamelCase_				:     Dict     					=				'transformers'




def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			List[str]:
       if stage_names is None:
              raise ValueError("Stage_names must be set for transformers backbones"   )

       if out_features is not None:
              if not isinstance(_SCREAMING_SNAKE_CASE			,(list,)   ):
                     raise ValueError(f'''out_features must be a list {type(_SCREAMING_SNAKE_CASE   )}'''   )
              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(_SCREAMING_SNAKE_CASE			,(list, tuple)   ):
                     raise ValueError(f'''out_indices must be a list or tuple, got {type(_SCREAMING_SNAKE_CASE   )}'''   )
              if any(idx >= len(_SCREAMING_SNAKE_CASE   ) 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(_SCREAMING_SNAKE_CASE   ) != len(_SCREAMING_SNAKE_CASE   ):
                     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				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,)     ->			Any:
       if out_indices is None and out_features is None:
              lowerCamelCase       :				Any      			= [len(_SCREAMING_SNAKE_CASE   ) - 1]
              lowerCamelCase       :				Union[str, Any]      			= [stage_names[-1]]
       elif out_indices is None and out_features is not None:
              lowerCamelCase       :				str      			= [stage_names.index(_SCREAMING_SNAKE_CASE   ) for layer in out_features]
       elif out_features is None and out_indices is not None:
              lowerCamelCase       :				Tuple      			= [stage_names[idx] for idx in out_indices]
       return out_features, out_indices

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,)     ->			Optional[Any]:
       verify_out_features_out_indices(out_features=_SCREAMING_SNAKE_CASE			,out_indices=_SCREAMING_SNAKE_CASE			,stage_names=_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Optional[Any]      			= _align_output_features_output_indices(
           out_features=_SCREAMING_SNAKE_CASE			,out_indices=_SCREAMING_SNAKE_CASE			,stage_names=_SCREAMING_SNAKE_CASE   )
       # Verify that the aligned out_features and out_indices are valid
       verify_out_features_out_indices(out_features=_SCREAMING_SNAKE_CASE			,out_indices=_SCREAMING_SNAKE_CASE			,stage_names=_SCREAMING_SNAKE_CASE   )
       return output_features, output_indices




class 		UpperCamelCase__     :

    '''simple docstring'''



    lowerCamelCase_				:     Optional[BackboneType]     					=				None







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				None:




           if getattr(self					,  "_backbone"					,  UpperCamelCase__					) 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']
           lowerCamelCase       :				str      			= [stage["""module"""] for stage in self._backbone.feature_info.info]
           lowerCamelCase       :				Any      			= [stage["""num_chs"""] for stage in self._backbone.feature_info.info]
           lowerCamelCase       :				Optional[Any]      			= self._backbone.feature_info.out_indices
           lowerCamelCase       :				int      			= self._backbone.feature_info.module_name()

           # We verify the out indices and out features are valid
           verify_out_features_out_indices(
               out_features=UpperCamelCase__					,  out_indices=UpperCamelCase__					,  stage_names=self.stage_names					)
           lowerCamelCase       :				Dict      			= out_features, out_indices







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				None:
           lowerCamelCase       :				Union[str, Any]      			= getattr(UpperCamelCase__					,  "stage_names"					)
           lowerCamelCase       :				Optional[Any]      			= getattr(UpperCamelCase__					,  "out_features"					,  UpperCamelCase__					)
           lowerCamelCase       :				int      			= getattr(UpperCamelCase__					,  "out_indices"					,  UpperCamelCase__					)

           lowerCamelCase       :				str      			= stage_names
           lowerCamelCase       :				Optional[int]      			= get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__					,  out_indices=UpperCamelCase__					,  stage_names=UpperCamelCase__					)
           # Number of channels for each stage. This is set in the transformer backbone model init
           lowerCamelCase       :				List[str]      			= None







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				None:
           lowerCamelCase       :				Optional[int]      			= config

           lowerCamelCase       :				List[Any]      			= getattr(UpperCamelCase__					,  "use_timm_backbone"					,  UpperCamelCase__					)
           lowerCamelCase       :				int      			= BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

           if self.backbone_type == BackboneType.TIMM:
                  self._init_timm_backbone(UpperCamelCase__					)
           elif self.backbone_type == BackboneType.TRANSFORMERS:
                  self._init_transformers_backbone(UpperCamelCase__					)
           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					,  UpperCamelCase__					)					->				Dict:
           lowerCamelCase       :				Union[str, Any]      			= get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__					,  out_indices=UpperCamelCase__					,  stage_names=self.stage_names					)







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







    @out_indices.setter
    def    _lowercase					(   self					,  UpperCamelCase__					)					->				Union[str, Any]:
           lowerCamelCase       :				List[Any]      			= get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__					,  out_indices=UpperCamelCase__					,  stage_names=self.stage_names					)







    @property
    def    _lowercase					(   self					)					->				List[Any]:
           return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names					)}







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







    def    _lowercase					(   self					,  *UpperCamelCase__					,  **UpperCamelCase__					)					->				Union[str, Any]:
           lowerCamelCase       :				Dict      			= dict(inspect.signature(self.forward					).parameters					)
           lowerCamelCase       :				Dict      			= {k: v for k, v in kwargs.items() if k in signature}
           return self(*UpperCamelCase__					,  **UpperCamelCase__					)







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







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




class 		UpperCamelCase__     :

    '''simple docstring'''



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







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







    @property
    def    _lowercase					(   self					)					->				Optional[int]:
           return self._out_indices







    @out_indices.setter
    def    _lowercase					(   self					,  UpperCamelCase__					)					->				Optional[int]:
           lowerCamelCase       :				int      			= get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__					,  out_indices=UpperCamelCase__					,  stage_names=self.stage_names					)







    def    _lowercase					(   self					)					->				int:
           lowerCamelCase       :				Optional[int]      			= super().to_dict()
           lowerCamelCase       :				int      			= output.pop("_out_features"					)
           lowerCamelCase       :				Any      			= output.pop("_out_indices"					)
           return output












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__	:				Optional[int]  =       {
    'configuration_gptsan_japanese': ['GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTSanJapaneseConfig'],
    'tokenization_gptsan_japanese': ['GPTSanJapaneseTokenizer'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__	:				Optional[int]  =       [
           'GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
           'GPTSanJapaneseForConditionalGeneration',
           'GPTSanJapaneseModel',
           'GPTSanJapanesePreTrainedModel',
       ]
       SCREAMING_SNAKE_CASE__	:				List[str]  =       [
           'GPTSanJapaneseTokenizer',
       ]


if TYPE_CHECKING:
       from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
       from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gptsan_japanese import (
                  GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTSanJapaneseForConditionalGeneration,
                  GPTSanJapaneseModel,
                  GPTSanJapanesePreTrainedModel,
              )
              from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
       import sys

       SCREAMING_SNAKE_CASE__	:				Optional[Any]  =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






