
"""simple docstring"""





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		int    	=						["""image_processor""", """tokenizer"""]
  SCREAMING_SNAKE_CASE__     :		Any    	=						"""EfficientNetImageProcessor"""
  SCREAMING_SNAKE_CASE__     :		Any    	=						("""BertTokenizer""", """BertTokenizerFast""")







  def __init__(						self							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     super().__init__(lowercase_							,  lowercase_    )







  def __call__(						self							,  lowercase_=None							,  lowercase_=None							,  lowercase_="max_length"							,  lowercase_=64							,  lowercase_=None							,  **lowercase_    ):

     """simple docstring"""
     if text is None and images is None:
        raise ValueError("You have to specify either text or images. Both cannot be none."    )

     if text is not None:
        UpperCAmelCase_   :   List[str]     	=       self.tokenizer(
            lowercase_							,  padding=lowercase_							,  max_length=lowercase_							,  return_tensors=lowercase_							,  **lowercase_    )

     if images is not None:
        UpperCAmelCase_   :   Optional[Any]     	=       self.image_processor(lowercase_							,  return_tensors=lowercase_							,  **lowercase_    )

     if text is not None and images is not None:
        UpperCAmelCase_   :   Tuple     	=       image_features.pixel_values
        return encoding
     elif text is not None:
        return encoding
     else:
        return BatchEncoding(data=dict(**lowercase_    )							,  tensor_type=lowercase_    )







  def 			UpperCamelCase__   (						self							,  *lowercase_							,  **lowercase_    ):

     """simple docstring"""
     return self.tokenizer.batch_decode(*lowercase_							,  **lowercase_    )







  def 			UpperCamelCase__   (						self							,  *lowercase_							,  **lowercase_    ):

     """simple docstring"""
     return self.tokenizer.decode(*lowercase_							,  **lowercase_    )







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[Any]     	=       self.tokenizer.model_input_names
     UpperCAmelCase_   :   int     	=       self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )








"""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
		)


