



from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


UpperCAmelCase								=   logging.get_logger(__name__)

UpperCAmelCase								=   {
    """microsoft/layoutlm-base-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"""
    ),
    """microsoft/layoutlm-large-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"""
    ),
}

class 	__snake_case (				_UpperCAmelCase):







							'''simple docstring'''






							UpperCamelCase__			: List[Any]             =   '''layoutlm'''


							def __init__(							self					,					a_=30_522					,					a_=768					,					a_=12					,					a_=12					,					a_=3_072					,					a_="gelu"					,					a_=0.1					,					a_=0.1					,					a_=512					,					a_=2					,					a_=0.02					,					a_=1E-1_2					,					a_=0					,					a_="absolute"					,					a_=True					,					a_=1_024					,					**a_					,					):
											super().__init__(pad_token_id=lowerCAmelCase__					,					**lowerCAmelCase__						)
											a__			 =						vocab_size
											a__			 =						hidden_size
											a__			 =						num_hidden_layers
											a__			 =						num_attention_heads
											a__			 =						hidden_act
											a__			 =						intermediate_size
											a__			 =						hidden_dropout_prob
											a__			 =						attention_probs_dropout_prob
											a__			 =						max_position_embeddings
											a__			 =						type_vocab_size
											a__			 =						initializer_range
											a__			 =						layer_norm_eps
											a__			 =						position_embedding_type
											a__			 =						use_cache
											a__			 =						max_ad_position_embeddings




class 	__snake_case (				_UpperCAmelCase):







							'''simple docstring'''






							def __init__(							self					,					a_					,					a_ = "default"					,					a_ = None					,					):
											super().__init__(lowerCAmelCase__					,					task=lowerCAmelCase__					,					patching_specs=lowerCAmelCase__						)
											a__			 =						config.max_ad_position_embeddings - 1


							@property
							def _a (							self						):
											return OrderedDict(
											    [
											        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
											        ("""bbox""", {0: """batch""", 1: """sequence"""}),
											        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
											        ("""token_type_ids""", {0: """batch""", 1: """sequence"""}),
											    ]						)







							def _a (							self					,					a_					,					a_ = -1					,					a_ = -1					,					a_ = False					,					a_ = None					,					):
											a__			 =						super().generate_dummy_inputs(
											    lowerCAmelCase__					,					batch_size=lowerCAmelCase__					,					seq_length=lowerCAmelCase__					,					is_pair=lowerCAmelCase__					,					framework=lowerCAmelCase__						)

											# Generate a dummy bbox
											a__			 =						[48, 84, 73, 128]

											if not framework == TensorType.PYTORCH:
															raise NotImplementedError("""Exporting LayoutLM to ONNX is currently only supported for PyTorch."""						)

											if not is_torch_available():
															raise ValueError("""Cannot generate dummy inputs without PyTorch installed."""						)
											import torch

											a__ ,     a__			 =						input_dict["""input_ids"""].shape
											a__			 =						torch.tensor([*[box] * seq_length]						).tile(lowerCAmelCase__					,					1					,					1						)
											return input_dict






import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

class 	__snake_case (				utils.Parser):







							'''simple docstring'''






							UpperCamelCase__			: str             =   "halfcheetah-medium-expert-v2"
							UpperCamelCase__			: str             =   "config.offline"




def      A_ (		__a       :			List[Any]	,						__a       :			Union[str, Any]	,						__a       :			Optional[Any]	,						__a       :			Dict	,						__a       :			Union[str, Any]						):

				"""simple docstring"""
				a__ ,     a__			 =						utils.load_model(__a	,						__a	,						__a	,						epoch=__a	,						device=__a						)
				a__			 =						TrajectoryTransformerModel(gpt.config						)

				trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()						)
				a__			 =						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								=   Parser().parse_args("""plan""")
	convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
	    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
	)

