"""simple docstring"""


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






class        __UpperCAmelCase						(     enum.Enum     ):
				A__				:       Union[str, Any]  							=	'''timm'''
				A__				:       str  							=	'''transformers'''





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> int:




						'''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(__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       ,   )							-> str:




						'''simple docstring'''



						if out_indices is None and out_features is None:
												lowerCamelCase__ =[len(__lowerCAmelCase    ) - 1]
												lowerCamelCase__ =[stage_names[-1]]
						elif out_indices is None and out_features is not None:
												lowerCamelCase__ =[stage_names.index(__lowerCAmelCase    ) 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_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   )							-> Tuple[List[str], List[int]]:




						'''simple docstring'''



						verify_out_features_out_indices(out_features=__lowerCAmelCase       ,   out_indices=__lowerCAmelCase       ,   stage_names=__lowerCAmelCase    )
						lowerCamelCase__ ,				lowerCamelCase__ =_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        __UpperCAmelCase						:
				A__				:       Optional[BackboneType]  							=	None

				def 		_a     (				self     ,			_lowerCamelCase				):
										if getattr(self     ,			"_backbone"     ,			_lowerCamelCase				) 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=_lowerCamelCase     ,			out_indices=_lowerCamelCase     ,			stage_names=self.stage_names				)
										lowerCamelCase__ ,				lowerCamelCase__ =out_features, out_indices

				def 		_a     (				self     ,			_lowerCamelCase				):
										lowerCamelCase__ =getattr(_lowerCamelCase     ,			"stage_names"				)
										lowerCamelCase__ =getattr(_lowerCamelCase     ,			"out_features"     ,			_lowerCamelCase				)
										lowerCamelCase__ =getattr(_lowerCamelCase     ,			"out_indices"     ,			_lowerCamelCase				)

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

				def 		_a     (				self     ,			_lowerCamelCase				):
										lowerCamelCase__ =config

										lowerCamelCase__ =getattr(_lowerCamelCase     ,			"use_timm_backbone"     ,			_lowerCamelCase				)
										lowerCamelCase__ =BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

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

				@property
				def 		_a     (				self				):
										return self._out_features

				@out_features.setter
				def 		_a     (				self     ,			_lowerCamelCase				):
										lowerCamelCase__ ,				lowerCamelCase__ =get_aligned_output_features_output_indices(
										    out_features=_lowerCamelCase     ,			out_indices=_lowerCamelCase     ,			stage_names=self.stage_names				)

				@property
				def 		_a     (				self				):
										return self._out_indices

				@out_indices.setter
				def 		_a     (				self     ,			_lowerCamelCase				):
										lowerCamelCase__ ,				lowerCamelCase__ =get_aligned_output_features_output_indices(
										    out_features=_lowerCamelCase     ,			out_indices=_lowerCamelCase     ,			stage_names=self.stage_names				)

				@property
				def 		_a     (				self				):
										# 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 		_a     (				self				):
										return [self.out_feature_channels[name] for name in self.out_features]

				def 		_a     (				self     ,			*_lowerCamelCase     ,			**_lowerCamelCase				):
										lowerCamelCase__ =dict(inspect.signature(self.forward				).parameters				)
										lowerCamelCase__ ={k: v for k, v in kwargs.items() if k in signature}
										return self(*_lowerCamelCase     ,			**_lowerCamelCase				)

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			):
										raise NotImplementedError("This method should be implemented by the derived class."				)






				def 		_a     (				self				):
										lowerCamelCase__ =super().to_dict()
										lowerCamelCase__ =output.pop("_out_features"				)
										lowerCamelCase__ =output.pop("_out_indices"				)
										return output






class        __UpperCAmelCase						:

				@property
				def 		_a     (				self				):
										return self._out_features

				@out_features.setter
				def 		_a     (				self     ,			_lowerCamelCase				):
										lowerCamelCase__ ,				lowerCamelCase__ =get_aligned_output_features_output_indices(
										    out_features=_lowerCamelCase     ,			out_indices=_lowerCamelCase     ,			stage_names=self.stage_names				)

				@property
				def 		_a     (				self				):
										return self._out_indices

				@out_indices.setter
				def 		_a     (				self     ,			_lowerCamelCase				):
										lowerCamelCase__ ,				lowerCamelCase__ =get_aligned_output_features_output_indices(
										    out_features=_lowerCamelCase     ,			out_indices=_lowerCamelCase     ,			stage_names=self.stage_names				)






				def 		_a     (				self				):
										lowerCamelCase__ =super().to_dict()
										lowerCamelCase__ =output.pop("_out_features"				)
										lowerCamelCase__ =output.pop("_out_indices"				)
										return output



"""simple docstring"""


import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel






class        __UpperCAmelCase						(     utils.Parser     ):
				A__				:       str  							=	"halfcheetah-medium-expert-v2"
				A__				:       str  							=	"config.offline"





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						lowerCamelCase__ ,				lowerCamelCase__ =utils.load_model(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   epoch=__lowerCAmelCase       ,   device=__lowerCAmelCase    )
						lowerCamelCase__ =TrajectoryTransformerModel(gpt.config    )

						trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()    )
						lowerCamelCase__ =gpt.pos_emb
						trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()    )
						trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()    )
						trajectory_transformer.head.load_state_dict(gpt.head.state_dict()    )

						for i, block in enumerate(gpt.blocks    ):
												trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()    )
												trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()    )
												trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()    )

												trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()    )
												trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()    )
												trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()    )
												trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()    )

						torch.save(trajectory_transformer.state_dict()       ,   "pytorch_model.bin"    )


if __name__ == "__main__":
	a							=Parser().parse_args('plan')
	convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
	    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
	)


