



"""simple docstring"""




from typing import TYPE_CHECKING

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


A__  :  Tuple       = {
    '''configuration_layoutlm''': ['''LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMConfig''', '''LayoutLMOnnxConfig'''],
    '''tokenization_layoutlm''': ['''LayoutLMTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__  :  str       = ['''LayoutLMTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__  :  List[str]       = [
						    '''LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''LayoutLMForMaskedLM''',
						    '''LayoutLMForSequenceClassification''',
						    '''LayoutLMForTokenClassification''',
						    '''LayoutLMForQuestionAnswering''',
						    '''LayoutLMModel''',
						    '''LayoutLMPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__  :  Union[str, Any]       = [
						    '''TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFLayoutLMForMaskedLM''',
						    '''TFLayoutLMForSequenceClassification''',
						    '''TFLayoutLMForTokenClassification''',
						    '''TFLayoutLMForQuestionAnswering''',
						    '''TFLayoutLMMainLayer''',
						    '''TFLayoutLMModel''',
						    '''TFLayoutLMPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
						from .tokenization_layoutlm import LayoutLMTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_layoutlm import (
												    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LayoutLMForMaskedLM,
												    LayoutLMForQuestionAnswering,
												    LayoutLMForSequenceClassification,
												    LayoutLMForTokenClassification,
												    LayoutLMModel,
												    LayoutLMPreTrainedModel,
												)
						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_layoutlm import (
												    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFLayoutLMForMaskedLM,
												    TFLayoutLMForQuestionAnswering,
												    TFLayoutLMForSequenceClassification,
												    TFLayoutLMForTokenClassification,
												    TFLayoutLMMainLayer,
												    TFLayoutLMModel,
												    TFLayoutLMPreTrainedModel,
												)

else:
						import sys

						A__  :  Dict       = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel



class     __snake_case						(					utils.Parser						):
							_a      				=			"halfcheetah-medium-expert-v2"
							_a      				=			"config.offline"





def       UpperCamelCase(  __UpperCamelCase							:		List[Any]					,__UpperCamelCase							:		Any					,__UpperCamelCase							:		Tuple					,__UpperCamelCase							:		Optional[Any]					,__UpperCamelCase							:		List[Any]			):
		lowerCAmelCase_ ,  lowerCAmelCase_      :					Optional[Any]     =			utils.load_model(__UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase					,epoch=__UpperCamelCase					,device=__UpperCamelCase			)
		lowerCAmelCase_      :					List[str]     =			TrajectoryTransformerModel(gpt.config			)

		trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
		lowerCAmelCase_      :					str     =			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__  :  Any       = Parser().parse_args('''plan''')
						convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
						    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
						)





