

import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel






class     lowerCamelCase_			(  utils.Parser  ):
	_lowercase		:      str    				=							"halfcheetah-medium-expert-v2"
	_lowercase		:      str    				=							"config.offline"






def        __SCREAMING_SNAKE_CASE    (      a__						:      str ,a__						:      Optional[int] ,a__						:      str ,a__						:      Union[str, Any] ,a__						:      Optional[int]					)				->							Any:
	__A   : Optional[int]								= utils.load_model(a__ ,a__ ,a__ ,epoch=a__ ,device=a__					)
	__A   : List[Any]								= TrajectoryTransformerModel(gpt.config					)

	trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()					)
	__A   : Optional[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__":
			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
			)








import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
			from PIL import Image






class     lowerCamelCase_			(  unittest.TestCase	,			_lowercase  ):




	def 					lowerCAmelCase_					(    self	:    Dict       ):
		__A   : str								= load_tool("""image-captioning"""       )
		self.tool.setup()
		__A   : Dict								= load_tool("""image-captioning"""				,			remote=__A       )




	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
		__A   : List[Any]								= Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       )
		__A   : Optional[Any]								= self.tool(__A       )
		self.assertEqual(__A				,			"""two cats sleeping on a couch"""       )




	def 					lowerCAmelCase_					(    self	:    Dict       ):
		__A   : Any								= Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       )
		__A   : List[str]								= self.remote_tool(__A       )
		self.assertEqual(__A				,			"""two cats sleeping on a couch"""       )




	def 					lowerCAmelCase_					(    self	:    Union[str, Any]       ):
		__A   : Optional[Any]								= Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       )
		__A   : str								= self.tool(image=__A       )
		self.assertEqual(__A				,			"""two cats sleeping on a couch"""       )




	def 					lowerCAmelCase_					(    self	:    str       ):
		__A   : Dict								= Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       )
		__A   : List[Any]								= self.remote_tool(image=__A       )
		self.assertEqual(__A				,			"""two cats sleeping on a couch"""       )





