




"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase__			:     Tuple			       =				logging.get_logger(__name__)

UpperCamelCase__			:     List[Any]			       =				{
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}
class      A__   (   lowercase_  ):
	A_				:					List[Any]      =   """plbart"""
	A_				:					Union[str, Any]      =   ["""past_key_values"""]
	A_				:					Dict      =   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




	def __init__( self	:						Dict   ,     lowercase	:						Any=50_005   ,     lowercase	:						int=1_024   ,     lowercase	:						Tuple=6   ,     lowercase	:						List[Any]=3_072   ,     lowercase	:						Any=12   ,     lowercase	:						Optional[int]=6   ,     lowercase	:						Dict=3_072   ,     lowercase	:						Any=12   ,     lowercase	:						List[Any]=0.0   ,     lowercase	:						Any=0.0   ,     lowercase	:						Dict=True   ,     lowercase	:						Any=True   ,     lowercase	:						Optional[Any]="gelu"   ,     lowercase	:						Optional[Any]=768   ,     lowercase	:						Optional[Any]=0.1   ,     lowercase	:						Dict=0.1   ,     lowercase	:						Dict=0.0   ,     lowercase	:						List[str]=0.02   ,     lowercase	:						List[Any]=0.0   ,     lowercase	:						Any=True   ,     lowercase	:						str=1   ,     lowercase	:						List[str]=0   ,     lowercase	:						List[Any]=2   ,     lowercase	:						List[Any]=2   ,     **lowercase	:						str   ,     )       ->			str:
						__snake_case        =						vocab_size
						__snake_case        =						max_position_embeddings
						__snake_case        =						d_model
						__snake_case        =						encoder_ffn_dim
						__snake_case        =						encoder_layers
						__snake_case        =						encoder_attention_heads
						__snake_case        =						decoder_ffn_dim
						__snake_case        =						decoder_layers
						__snake_case        =						decoder_attention_heads
						__snake_case        =						dropout
						__snake_case        =						attention_dropout
						__snake_case        =						activation_dropout
						__snake_case        =						activation_function
						__snake_case        =						init_std
						__snake_case        =						encoder_layerdrop
						__snake_case        =						decoder_layerdrop
						__snake_case        =						classifier_dropout
						__snake_case        =						use_cache
						__snake_case        =						encoder_layers
						__snake_case        =						scale_embedding  # scale factor will be sqrt(d_model) if True
						super().__init__(
						    pad_token_id=lowercase   ,     bos_token_id=lowercase   ,     eos_token_id=lowercase   ,     is_encoder_decoder=lowercase   ,     forced_eos_token_id=lowercase   ,     **lowercase   ,     )

class      A__   (   lowercase_  ):




	@property
	def 				a_		( self	:						List[str]						)       ->			Mapping[str, Mapping[int, str]]:
						return OrderedDict(
						    [
						        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
						        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
						    ]						)




	@property
	def 				a_		( self	:						Union[str, Any]						)       ->			Mapping[str, Mapping[int, str]]:
						if self.use_past:
											return OrderedDict(
											    [
											        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
											        ("""past_keys""", {0: """batch""", 2: """sequence"""}),
											        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
											    ]						)
						else:
											return OrderedDict(
											    [
											        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
											        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
											    ]						)








"""simple docstring"""

import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel
class      A__   (   utils.Parser  ):
	A_				:					str      =   "halfcheetah-medium-expert-v2"
	A_				:					str      =   "config.offline"




def    snake_case__						(							lowerCAmelCase :		Optional[int]					,       lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		List[str]					,       lowerCAmelCase :		Optional[int]					,       lowerCAmelCase :		List[Any]		):
					__snake_case   ,   __snake_case        =						utils.load_model(lowerCAmelCase					,       lowerCAmelCase					,       lowerCAmelCase					,       epoch=lowerCAmelCase					,       device=lowerCAmelCase		)
					__snake_case        =						TrajectoryTransformerModel(gpt.config		)

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


