

"""simple docstring"""





import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

class      __a (   utils.Parser     ):

			"""simple docstring"""

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



def 							_lowerCAmelCase   (    UpperCAmelCase  :				List[Any]   ,				UpperCAmelCase  :				Optional[Any]   ,				UpperCAmelCase  :				Dict   ,				UpperCAmelCase  :				str   ,				UpperCAmelCase  :				str					):


					'''simple docstring'''






					UpperCamelCase__	,      UpperCamelCase__		:     Any								=utils.load_model(UpperCAmelCase   ,				UpperCAmelCase   ,				UpperCAmelCase   ,				epoch=UpperCAmelCase   ,				device=UpperCAmelCase					)
					UpperCamelCase__		:     Union[str, Any]								=TrajectoryTransformerModel(gpt.config					)

					trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()					)
					UpperCamelCase__		:     int								=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__":
				_SCREAMING_SNAKE_CASE							:	Dict										=			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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE							:	Any										=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:	Optional[Any]										=			{
    # pcqm4mv1 now deprecated
    """graphormer-base""": """https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json""",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}

class      __a (   snake_case__     ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_      =    'graphormer'
			SCREAMING_SNAKE_CASE_      =    ['past_key_values']






			def __init__(				self  :       Optional[int]    ,	lowercase_  :       int = 1    ,	lowercase_  :       int = 512 * 9    ,	lowercase_  :       int = 512 * 3    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 128    ,	lowercase_  :       int = 5    ,	lowercase_  :       int = 1024    ,	lowercase_  :       str = "multi_hop"    ,	lowercase_  :       int = 512    ,	lowercase_  :       bool = False    ,	lowercase_  :       int = 12    ,	lowercase_  :       int = 768    ,	lowercase_  :       int = 768    ,	lowercase_  :       int = 32    ,	lowercase_  :       float = 0.1    ,	lowercase_  :       float = 0.1    ,	lowercase_  :       float = 0.0    ,	lowercase_  :       bool = False    ,	lowercase_  :       bool = False    ,	lowercase_  :       bool = False    ,	lowercase_  :       str = "gelu"    ,	lowercase_  :       float = None    ,	lowercase_  :       bool = False    ,	lowercase_  :       int = 0    ,	lowercase_  :       bool = False    ,	lowercase_  :       float = 0.0    ,	lowercase_  :       int = 8    ,	lowercase_  :       int = None    ,	lowercase_  :       int = None    ,	lowercase_  :       bool = True    ,	lowercase_  :       bool = True    ,	lowercase_  :       List[str]=0    ,	lowercase_  :       Union[str, Any]=1    ,	lowercase_  :       str=2    ,	**lowercase_  :       str    ,	):
								UpperCamelCase__		:     Dict								=num_classes
								UpperCamelCase__		:     str								=num_atoms
								UpperCamelCase__		:     Union[str, Any]								=num_in_degree
								UpperCamelCase__		:     Optional[int]								=num_out_degree
								UpperCamelCase__		:     List[str]								=num_edges
								UpperCamelCase__		:     List[str]								=num_spatial
								UpperCamelCase__		:     Dict								=num_edge_dis
								UpperCamelCase__		:     Tuple								=edge_type
								UpperCamelCase__		:     str								=multi_hop_max_dist
								UpperCamelCase__		:     Optional[Any]								=spatial_pos_max
								UpperCamelCase__		:     Tuple								=max_nodes
								UpperCamelCase__		:     Optional[Any]								=num_hidden_layers
								UpperCamelCase__		:     Optional[Any]								=embedding_dim
								UpperCamelCase__		:     Dict								=embedding_dim
								UpperCamelCase__		:     Dict								=ffn_embedding_dim
								UpperCamelCase__		:     str								=num_attention_heads
								UpperCamelCase__		:     Tuple								=dropout
								UpperCamelCase__		:     List[str]								=attention_dropout
								UpperCamelCase__		:     List[str]								=layerdrop
								UpperCamelCase__		:     Tuple								=encoder_normalize_before
								UpperCamelCase__		:     List[Any]								=pre_layernorm
								UpperCamelCase__		:     Optional[Any]								=apply_graphormer_init
								UpperCamelCase__		:     Tuple								=activation_fn
								UpperCamelCase__		:     Any								=embed_scale
								UpperCamelCase__		:     List[str]								=freeze_embeddings
								UpperCamelCase__		:     Optional[Any]								=num_trans_layers_to_freeze
								UpperCamelCase__		:     List[Any]								=share_input_output_embed
								UpperCamelCase__		:     Union[str, Any]								=traceable
								UpperCamelCase__		:     List[Any]								=q_noise
								UpperCamelCase__		:     Union[str, Any]								=qn_block_size

								# These parameters are here for future extensions
								# atm, the model only supports self attention
								UpperCamelCase__		:     Any								=kdim
								UpperCamelCase__		:     Tuple								=vdim
								UpperCamelCase__		:     Any								=self_attention
								UpperCamelCase__		:     List[str]								=bias

								super().__init__(
								    pad_token_id=lowercase_    ,	bos_token_id=lowercase_    ,	eos_token_id=lowercase_    ,	**lowercase_    ,	)


