



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase								=   logging.get_logger(__name__)

UpperCAmelCase								=   {
    """google/pegasus-x-base""": """https://huggingface.co/google/pegasus-x-base/resolve/main/config.json""",
    """google/pegasus-x-large""": """https://huggingface.co/google/pegasus-x-large/resolve/main/config.json""",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}

class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: Optional[Any]             =   """pegasus_x"""
							UpperCamelCase__			: Any             =   ["""past_key_values"""]
							UpperCamelCase__			: Optional[int]             =   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


							def __init__(							self					,					a_=96_103					,					a_=16_384					,					a_=16					,					a_=4_096					,					a_=16					,					a_=16					,					a_=4_096					,					a_=16					,					a_=0.0					,					a_=0.0					,					a_=True					,					a_=True					,					a_="gelu"					,					a_=1_024					,					a_=0.1					,					a_=0.0					,					a_=0.0					,					a_=0.02					,					a_=0					,					a_=True					,					a_=0					,					a_=1					,					a_=1					,					a_=32					,					a_=512					,					a_=True					,					**a_					,					):
											a__			 =						vocab_size
											a__			 =						max_position_embeddings
											a__			 =						d_model
											a__			 =						encoder_ffn_dim
											a__			 =						encoder_layers
											a__			 =						encoder_attention_heads
											a__			 =						decoder_ffn_dim
											a__			 =						decoder_layers
											a__			 =						decoder_attention_heads
											a__			 =						dropout
											a__			 =						attention_dropout
											a__			 =						activation_dropout
											a__			 =						activation_function
											a__			 =						init_std
											a__			 =						encoder_layerdrop
											a__			 =						decoder_layerdrop
											a__			 =						use_cache
											a__			 =						encoder_layers
											a__			 =						scale_embedding  # scale factor will be sqrt(d_model) if True

											a__			 =						num_global_tokens
											a__			 =						block_size
											a__			 =						stagger_local_blocks

											super().__init__(
											    pad_token_id=a_					,					eos_token_id=a_					,					is_encoder_decoder=a_					,					decoder_start_token_id=a_					,					forced_eos_token_id=a_					,					**a_					,					)


							@property
							def _a (							self						):
											return self.encoder_attention_heads







							@property
							def _a (							self						):
											return self.d_model






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
	)

