

"""simple docstring"""





import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel







class        SCREAMING_SNAKE_CASE      (	utils.Parser ):






					"""simple docstring"""



					lowercase__       				= "halfcheetah-medium-expert-v2"
					lowercase__       				= "config.offline"

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_		,  A_		,  A_				):
	lowerCAmelCase__       ,lowerCAmelCase__			:		Dict						       =			utils.load_model(A_		,  A_		,  A_		,  epoch=A_		,  device=A_				)
	lowerCAmelCase__			:		Tuple						       =			TrajectoryTransformerModel(gpt.config				)

	trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()				)
	lowerCAmelCase__			:		List[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__":
							__UpperCamelCase						:   List[str]							   =	Parser().parse_args('''plan''')
							convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
							    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
							)







"""simple docstring"""





from typing import TYPE_CHECKING

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


__UpperCamelCase						:   Union[str, Any]							   =	{
    '''configuration_layoutlm''': ['''LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMConfig''', '''LayoutLMOnnxConfig'''],
    '''tokenization_layoutlm''': ['''LayoutLMTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase						:   Union[str, Any]							   =	['''LayoutLMTokenizerFast''']

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

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase						:   str							   =	[
							    '''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

							__UpperCamelCase						:   int							   =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




