




"""simple docstring"""

import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()




def    snake_case__						(							lowerCAmelCase :		Any					,       lowerCAmelCase :		Any					,       lowerCAmelCase :		Any					,       lowerCAmelCase :		Dict					,       lowerCAmelCase :		Dict		):
					# Initialise PyTorch model
					__snake_case        =						BertConfig.from_pretrained(
					    """bert-large-cased"""					,       vocab_size=lowerCAmelCase					,       max_position_embeddings=512					,       is_decoder=lowerCAmelCase					,       add_cross_attention=lowerCAmelCase					,       )
					__snake_case        =						bert_config.to_dict()
					del bert_config_dict["type_vocab_size"]
					__snake_case        =						BertGenerationConfig(**lowerCAmelCase		)
					if is_encoder:
										__snake_case        =						BertGenerationEncoder(lowerCAmelCase		)
					else:
										__snake_case        =						BertGenerationDecoder(lowerCAmelCase		)
					print(F'''Building PyTorch model from configuration: {config}'''		)

					# Load weights from tf checkpoint
					load_tf_weights_in_bert_generation(
					    lowerCAmelCase					,       lowerCAmelCase					,       model_class="""bert"""					,       is_encoder_named_decoder=lowerCAmelCase					,       is_encoder=lowerCAmelCase					,       )

					# Save pytorch-model
					print(F'''Save PyTorch model and config to {pytorch_dump_path}'''		)
					model.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
		UpperCamelCase__			:     Dict			       =				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		parser.add_argument(
		    "--is_encoder_named_decoder",
		    action="store_true",
		    help="If decoder has to be renamed to encoder in PyTorch model.",
		)
		parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
		parser.add_argument("--vocab_size", default=5_03_58, type=int, help="Vocab size of model")
		UpperCamelCase__			:     Optional[Any]			       =				parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.tf_hub_path,
		    args.pytorch_dump_path,
		    args.is_encoder_named_decoder,
		    args.vocab_size,
		    is_encoder=args.is_encoder,
		)








"""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
		)


