
'''simple docstring'''

import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel






class       A   (		utils.Parser			):
	snake_case__					:Optional[int]         =   'halfcheetah-medium-expert-v2'
	snake_case__					:Any         =   'config.offline'







def      A   (					UpperCamelCase_    :   Optional[int]  ,		UpperCamelCase_    :   Any  ,		UpperCamelCase_    :   List[str]  ,		UpperCamelCase_    :   Any  ,		UpperCamelCase_    :   Tuple					)    ->				int:







			'''simple docstring'''



			lowerCAmelCase__		,lowerCAmelCase__									=							utils.load_model(lowerCAmelCase__  ,		lowerCAmelCase__  ,		lowerCAmelCase__  ,		epoch=lowerCAmelCase__  ,		device=lowerCAmelCase__					)
			lowerCAmelCase__									=							TrajectoryTransformerModel(gpt.config					)

			trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()					)
			lowerCAmelCase__									=							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__      :       List[str] 		=						Parser().parse_args("plan")
		convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
		    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
		)






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


lowerCAmelCase__							= logging.get_logger(__name__)

lowerCAmelCase__							= {
    'dandelin/vilt-b32-mlm': 'https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'
}
class 				a_ (						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_	 =					'vilt'




 def __init__(			self					:  Optional[Any]			,    lowercase__					:  Dict=30_522			,    lowercase__					:  Tuple=2			,    lowercase__					:  int=2			,    lowercase__					:  Optional[int]=40			,    lowercase__					:  Optional[int]=768			,    lowercase__					:  Tuple=12			,    lowercase__					:  Tuple=12			,    lowercase__					:  Union[str, Any]=3_072			,    lowercase__					:  int="gelu"			,    lowercase__					:  Tuple=0.0			,    lowercase__					:  Dict=0.0			,    lowercase__					:  Optional[int]=0.02			,    lowercase__					:  Tuple=1e-12			,    lowercase__					:  Dict=384			,    lowercase__					:  int=32			,    lowercase__					:  List[str]=3			,    lowercase__					:  int=True			,    lowercase__					:  List[str]=-1			,    lowercase__					:  Union[str, Any]=False			,    lowercase__					:  List[str]=-1			,    **lowercase__					:  int			,    ):







        '''simple docstring'''



        super().__init__(tie_word_embeddings=lowercase__			,    **lowercase__)

        lowerCAmelCase__     	=					vocab_size
        lowerCAmelCase__     	=					type_vocab_size
        lowerCAmelCase__     	=					modality_type_vocab_size
        lowerCAmelCase__     	=					max_position_embeddings

        lowerCAmelCase__     	=					hidden_size
        lowerCAmelCase__     	=					num_hidden_layers
        lowerCAmelCase__     	=					num_attention_heads
        lowerCAmelCase__     	=					intermediate_size
        lowerCAmelCase__     	=					hidden_act
        lowerCAmelCase__     	=					hidden_dropout_prob
        lowerCAmelCase__     	=					attention_probs_dropout_prob
        lowerCAmelCase__     	=					initializer_range
        lowerCAmelCase__     	=					layer_norm_eps

        lowerCAmelCase__     	=					image_size
        lowerCAmelCase__     	=					patch_size
        lowerCAmelCase__     	=					num_channels
        lowerCAmelCase__     	=					qkv_bias
        lowerCAmelCase__     	=					max_image_length
        lowerCAmelCase__     	=					num_images
