


import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel





class    lowerCAmelCase__				(			utils.Parser       ):






						"""simple docstring"""
						__UpperCAmelCase   :		str  					=	"halfcheetah-medium-expert-v2"
						__UpperCAmelCase   :		str  					=	"config.offline"







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_   ,lowerCamelCase_			:							Optional[int]      =       utils.load_model(lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					epoch=lowerCAmelCase_      ,					device=lowerCAmelCase_)
	lowerCamelCase_			:							Any      =       TrajectoryTransformerModel(gpt.config)

	trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict())
	lowerCamelCase_			:							Union[str, Any]      =       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__":
							__magic_name__       					=      Parser().parse_args('''plan''')
							convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
							    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
							)





from typing import TYPE_CHECKING

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


__magic_name__       					=      {'''configuration_detr''': ['''DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetrConfig''', '''DetrOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      ['''DetrFeatureExtractor''']
							__magic_name__       					=      ['''DetrImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      [
							    '''DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DetrForObjectDetection''',
							    '''DetrForSegmentation''',
							    '''DetrModel''',
							    '''DetrPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_detr import DetrFeatureExtractor
														from .image_processing_detr import DetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_detr import (
														    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DetrForObjectDetection,
														    DetrForSegmentation,
														    DetrModel,
														    DetrPreTrainedModel,
														)

else:
							import sys

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

