
"""simple docstring"""


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







class 		lowerCamelCase__					(					enum.Enum				):





			'''simple docstring'''
			lowerCamelCase  					=		'''timm'''
			lowerCamelCase  					=		'''transformers'''







def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> Any:
	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     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , )				-> Union[str, Any]:
	if out_indices is None and out_features is None:
		_lowerCAmelCase								=[len(__UpperCamelCase       ) - 1]
		_lowerCAmelCase								=[stage_names[-1]]
	elif out_indices is None and out_features is not None:
		_lowerCAmelCase								=[stage_names.index(__UpperCamelCase       ) for layer in out_features]
	elif out_features is None and out_indices is not None:
		_lowerCAmelCase								=[stage_names[idx] for idx in out_indices]
	return out_features, out_indices



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , )				-> Tuple[List[str], List[int]]:
	verify_out_features_out_indices(out_features=__UpperCamelCase    , out_indices=__UpperCamelCase    , stage_names=__UpperCamelCase       )
	_lowerCAmelCase						,							_lowerCAmelCase								=_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 		lowerCamelCase__					:





			'''simple docstring'''
			lowerCamelCase  					=		None



			def 					_lowerCAmelCase		(							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								=[stage["""module"""] for stage in self._backbone.feature_info.info]
				_lowerCAmelCase								=[stage["""num_chs"""] for stage in self._backbone.feature_info.info]
				_lowerCAmelCase								=self._backbone.feature_info.out_indices
				_lowerCAmelCase								=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						,							_lowerCAmelCase								=out_features, out_indices



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     None:
				_lowerCAmelCase								=getattr(__UpperCAmelCase     ,     """stage_names"""  )
				_lowerCAmelCase								=getattr(__UpperCAmelCase     ,     """out_features"""     ,     __UpperCAmelCase  )
				_lowerCAmelCase								=getattr(__UpperCAmelCase     ,     """out_indices"""     ,     __UpperCAmelCase  )

				_lowerCAmelCase								=stage_names
				_lowerCAmelCase						,							_lowerCAmelCase								=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								=None



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     None:
				_lowerCAmelCase								=config

				_lowerCAmelCase								=getattr(__UpperCAmelCase     ,     """use_timm_backbone"""     ,     __UpperCAmelCase  )
				_lowerCAmelCase								=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 					_lowerCAmelCase		(							self  ) ->     Optional[Any]:
				return self._out_features



			@out_features.setter
			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     List[str]:
				_lowerCAmelCase						,							_lowerCAmelCase								=get_aligned_output_features_output_indices(
				    out_features=__UpperCAmelCase     ,     out_indices=__UpperCAmelCase     ,     stage_names=self.stage_names  )



			@property
			def 					_lowerCAmelCase		(							self  ) ->     Tuple:
				return self._out_indices



			@out_indices.setter
			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     List[Any]:
				_lowerCAmelCase						,							_lowerCAmelCase								=get_aligned_output_features_output_indices(
				    out_features=__UpperCAmelCase     ,     out_indices=__UpperCAmelCase     ,     stage_names=self.stage_names  )



			@property
			def 					_lowerCAmelCase		(							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 					_lowerCAmelCase		(							self  ) ->     Union[str, Any]:
				return [self.out_feature_channels[name] for name in self.out_features]



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



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None     ,     __UpperCAmelCase = None     ,     __UpperCAmelCase = None     ,     ) ->     str:
				raise NotImplementedError("""This method should be implemented by the derived class."""  )



			def 					_lowerCAmelCase		(							self  ) ->     str:
				_lowerCAmelCase								=super().to_dict()
				_lowerCAmelCase								=output.pop("""_out_features"""  )
				_lowerCAmelCase								=output.pop("""_out_indices"""  )
				return output







class 		lowerCamelCase__					:





			'''simple docstring'''



			@property
			def 					_lowerCAmelCase		(							self  ) ->     Union[str, Any]:
				return self._out_features



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



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



			@out_indices.setter
			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     Union[str, Any]:
				_lowerCAmelCase						,							_lowerCAmelCase								=get_aligned_output_features_output_indices(
				    out_features=__UpperCAmelCase     ,     out_indices=__UpperCAmelCase     ,     stage_names=self.stage_names  )



			def 					_lowerCAmelCase		(							self  ) ->     Dict:
				_lowerCAmelCase								=super().to_dict()
				_lowerCAmelCase								=output.pop("""_out_features"""  )
				_lowerCAmelCase								=output.pop("""_out_indices"""  )
				return output





"""simple docstring"""


from typing import TYPE_CHECKING

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


__A       						=       {'configuration_bert_generation': ['BertGenerationConfig']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A       						=       ['BertGenerationTokenizer']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A       						=       [
			    'BertGenerationDecoder',
			    'BertGenerationEncoder',
			    'BertGenerationPreTrainedModel',
			    'load_tf_weights_in_bert_generation',
			]


if TYPE_CHECKING:
			from .configuration_bert_generation import BertGenerationConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bert_generation import BertGenerationTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bert_generation import (
						    BertGenerationDecoder,
						    BertGenerationEncoder,
						    BertGenerationPreTrainedModel,
						    load_tf_weights_in_bert_generation,
						)

else:
			import sys

			__A       						=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



