"""simple docstring"""


from typing import TYPE_CHECKING

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


a							={
    'configuration_led': ['LED_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LEDConfig'],
    'tokenization_led': ['LEDTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=['LEDTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=[
	    'LED_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'LEDForConditionalGeneration',
	    'LEDForQuestionAnswering',
	    'LEDForSequenceClassification',
	    'LEDModel',
	    'LEDPreTrainedModel',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=['TFLEDForConditionalGeneration', 'TFLEDModel', 'TFLEDPreTrainedModel']


if TYPE_CHECKING:
	from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
	from .tokenization_led import LEDTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_led_fast import LEDTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_led import (
		    LED_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LEDForConditionalGeneration,
		    LEDForQuestionAnswering,
		    LEDForSequenceClassification,
		    LEDModel,
		    LEDPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
	import sys

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



"""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
	)


