



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


class    _lowercase (enum.Enum   ):
				'''simple docstring'''







				lowercase__     =    """timm"""
				lowercase__     =    """transformers"""




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

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



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						):
					verify_out_features_out_indices(out_features=_lowerCAmelCase ,						out_indices=_lowerCAmelCase ,						stage_names=_lowerCAmelCase)
					UpperCamelCase_	,     UpperCamelCase_			= _align_output_features_output_indices(
					    out_features=_lowerCAmelCase ,						out_indices=_lowerCAmelCase ,						stage_names=_lowerCAmelCase)
					# Verify that the aligned out_features and out_indices are valid
					verify_out_features_out_indices(out_features=_lowerCAmelCase ,						out_indices=_lowerCAmelCase ,						stage_names=_lowerCAmelCase)
					return output_features, output_indices


class    _lowercase :
				'''simple docstring'''







				lowercase__     =    None

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									if getattr(self  ,			"_backbone"  ,			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']
									UpperCamelCase_			= [stage["module"] for stage in self._backbone.feature_info.info]
									UpperCamelCase_			= [stage["num_chs"] for stage in self._backbone.feature_info.info]
									UpperCamelCase_			= self._backbone.feature_info.out_indices
									UpperCamelCase_			= self._backbone.feature_info.module_name()

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

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= getattr(snake_case__  ,			"stage_names"     )
									UpperCamelCase_			= getattr(snake_case__  ,			"out_features"  ,			snake_case__     )
									UpperCamelCase_			= getattr(snake_case__  ,			"out_indices"  ,			snake_case__     )

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

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= config

									UpperCamelCase_			= getattr(snake_case__  ,			"use_timm_backbone"  ,			snake_case__     )
									UpperCamelCase_			= BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

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

				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return self._out_features

				@out_features.setter
				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= get_aligned_output_features_output_indices(
									    out_features=snake_case__  ,			out_indices=snake_case__  ,			stage_names=self.stage_names     )

				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return self._out_indices

				@out_indices.setter
				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= get_aligned_output_features_output_indices(
									    out_features=snake_case__  ,			out_indices=snake_case__  ,			stage_names=self.stage_names     )

				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names     )}

				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return [self.out_feature_channels[name] for name in self.out_features]

				def    _lowerCamelCase       (			self  ,			*snake_case__  ,			**snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= dict(inspect.signature(self.forward     ).parameters     )
									UpperCamelCase_			= {k: v for k, v in kwargs.items() if k in signature}
									return self(*snake_case__  ,			**snake_case__     )

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__ = None  ,			snake_case__ = None  ,			snake_case__ = None  ,			):

									'''simple docstring'''


									raise NotImplementedError("This method should be implemented by the derived class."     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= super().to_dict()
									UpperCamelCase_			= output.pop("_out_features"     )
									UpperCamelCase_			= output.pop("_out_indices"     )
									return output




class    _lowercase :
				'''simple docstring'''







				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return self._out_features

				@out_features.setter
				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= get_aligned_output_features_output_indices(
									    out_features=snake_case__  ,			out_indices=snake_case__  ,			stage_names=self.stage_names     )

				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return self._out_indices

				@out_indices.setter
				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= get_aligned_output_features_output_indices(
									    out_features=snake_case__  ,			out_indices=snake_case__  ,			stage_names=self.stage_names     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= super().to_dict()
									UpperCamelCase_			= output.pop("_out_features"     )
									UpperCamelCase_			= output.pop("_out_indices"     )
									return output





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase :				List[Any]							={"""configuration_qdqbert""": ["""QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """QDQBertConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				Dict							=[
	    """QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """QDQBertForMaskedLM""",
	    """QDQBertForMultipleChoice""",
	    """QDQBertForNextSentencePrediction""",
	    """QDQBertForQuestionAnswering""",
	    """QDQBertForSequenceClassification""",
	    """QDQBertForTokenClassification""",
	    """QDQBertLayer""",
	    """QDQBertLMHeadModel""",
	    """QDQBertModel""",
	    """QDQBertPreTrainedModel""",
	    """load_tf_weights_in_qdqbert""",
	]


if TYPE_CHECKING:
	from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_qdqbert import (
		    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    QDQBertForMaskedLM,
		    QDQBertForMultipleChoice,
		    QDQBertForNextSentencePrediction,
		    QDQBertForQuestionAnswering,
		    QDQBertForSequenceClassification,
		    QDQBertForTokenClassification,
		    QDQBertLayer,
		    QDQBertLMHeadModel,
		    QDQBertModel,
		    QDQBertPreTrainedModel,
		    load_tf_weights_in_qdqbert,
		)


else:
	import sys

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