



"""simple docstring"""




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     __snake_case						(					unittest.TestCase						,UpperCamelCase_						):




							def        UpperCAmelCase__      (	self					:	Any):
									lowerCAmelCase_      :					Dict     =			load_tool('''image-captioning''')
									self.tool.setup()
									lowerCAmelCase_      :					Optional[int]     =			load_tool('''image-captioning'''    ,    remote=A_)




							def        UpperCAmelCase__      (	self					:	int):
									lowerCAmelCase_      :					List[str]     =			Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
									lowerCAmelCase_      :					str     =			self.tool(A_)
									self.assertEqual(A_    ,    '''two cats sleeping on a couch''')




							def        UpperCAmelCase__      (	self					:	Optional[Any]):
									lowerCAmelCase_      :					Dict     =			Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
									lowerCAmelCase_      :					Optional[Any]     =			self.remote_tool(A_)
									self.assertEqual(A_    ,    '''two cats sleeping on a couch''')




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







							def        UpperCAmelCase__      (	self					:	int):
									lowerCAmelCase_      :					Tuple     =			Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
									lowerCAmelCase_      :					Dict     =			self.remote_tool(image=A_)
									self.assertEqual(A_    ,    '''two cats sleeping on a couch''')










import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel



class     __snake_case						(					utils.Parser						):
							_a      				=			"halfcheetah-medium-expert-v2"
							_a      				=			"config.offline"





def       UpperCamelCase(  __UpperCamelCase							:		List[Any]					,__UpperCamelCase							:		Any					,__UpperCamelCase							:		Tuple					,__UpperCamelCase							:		Optional[Any]					,__UpperCamelCase							:		List[Any]			):
		lowerCAmelCase_ ,  lowerCAmelCase_      :					Optional[Any]     =			utils.load_model(__UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase					,epoch=__UpperCamelCase					,device=__UpperCamelCase			)
		lowerCAmelCase_      :					List[str]     =			TrajectoryTransformerModel(gpt.config			)

		trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
		lowerCAmelCase_      :					str     =			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__":
						A__  :  Any       = Parser().parse_args('''plan''')
						convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
						    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
						)





