



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase								=   logging.get_logger(__name__)

UpperCAmelCase								=   {
    '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/config.json''',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}

class 	__snake_case (				snake_case_):







							'''simple docstring'''






							UpperCamelCase__			: Optional[Any]             =   """rembert"""


							def __init__(							self					,					a_=250_300					,					a_=1_152					,					a_=32					,					a_=18					,					a_=256					,					a_=1_664					,					a_=4_608					,					a_="gelu"					,					a_=0.0					,					a_=0.0					,					a_=0.1					,					a_=512					,					a_=2					,					a_=0.02					,					a_=1E-1_2					,					a_=True					,					a_=0					,					a_=312					,					a_=313					,					**a_					,					):
											super().__init__(pad_token_id=a_					,					bos_token_id=a_					,					eos_token_id=a_					,					**a_						)

											a__			 =						vocab_size
											a__			 =						input_embedding_size
											a__			 =						output_embedding_size
											a__			 =						max_position_embeddings
											a__			 =						hidden_size
											a__			 =						num_hidden_layers
											a__			 =						num_attention_heads
											a__			 =						intermediate_size
											a__			 =						hidden_act
											a__			 =						hidden_dropout_prob
											a__			 =						attention_probs_dropout_prob
											a__			 =						classifier_dropout_prob
											a__			 =						initializer_range
											a__			 =						type_vocab_size
											a__			 =						layer_norm_eps
											a__			 =						use_cache
											a__			 =						False




class 	__snake_case (				snake_case_):







							'''simple docstring'''






							@property
							def _a (							self						):


											if self.task == "multiple-choice":
															a__			 =						{0: "batch", 1: "choice", 2: "sequence"}
											else:
															a__			 =						{0: "batch", 1: "sequence"}
											return OrderedDict(
											    [
											        ("""input_ids""", dynamic_axis),
											        ("""attention_mask""", dynamic_axis),
											        ("""token_type_ids""", dynamic_axis),
											    ]						)







							@property
							def _a (							self						):
											return 1E-4






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
	)

