



import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard



class     __snake_case						(					unittest.TestCase						):




							def        UpperCAmelCase__      (	self					:	str):
									lowerCAmelCase_      :					int     =			{
									    '''model_details''': {
									        '''Organization''': '''testing''',
									        '''Model date''': '''today''',
									        '''Model version''': '''v2.1, Developed by Test Corp in 2019.''',
									        '''Architecture''': '''Convolutional Neural Network.''',
									    },
									    '''metrics''': '''BLEU and ROUGE-1''',
									    '''evaluation_data''': {
									        '''Datasets''': {'''BLEU''': '''My-great-dataset-v1''', '''ROUGE-1''': '''My-short-dataset-v2.1'''},
									        '''Preprocessing''': '''See details on https://arxiv.org/pdf/1810.03993.pdf''',
									    },
									    '''training_data''': {
									        '''Dataset''': '''English Wikipedia dump dated 2018-12-01''',
									        '''Preprocessing''': (
									            '''Using SentencePiece vocabulary of size 52k tokens. See details on'''
									            ''' https://arxiv.org/pdf/1810.03993.pdf'''
									        ),
									    },
									    '''quantitative_analyses''': {'''BLEU''': 55.1, '''ROUGE-1''': 7_6},
									}




							def        UpperCAmelCase__      (	self					:	List[Any]):
									lowerCAmelCase_      :					Optional[int]     =			ModelCard.from_dict(self.inputs_dict)
									self.assertTrue(hasattr(A_    ,    '''model_details'''))
									self.assertTrue(hasattr(A_    ,    '''intended_use'''))
									self.assertTrue(hasattr(A_    ,    '''factors'''))
									self.assertTrue(hasattr(A_    ,    '''metrics'''))
									self.assertTrue(hasattr(A_    ,    '''evaluation_data'''))
									self.assertTrue(hasattr(A_    ,    '''training_data'''))
									self.assertTrue(hasattr(A_    ,    '''quantitative_analyses'''))
									self.assertTrue(hasattr(A_    ,    '''ethical_considerations'''))
									self.assertTrue(hasattr(A_    ,    '''caveats_and_recommendations'''))




							def        UpperCAmelCase__      (	self					:	Optional[Any]):
									lowerCAmelCase_      :					List[str]     =			ModelCard.from_dict(self.inputs_dict)
									lowerCAmelCase_      :					List[str]     =			json.loads(modelcard.to_json_string())
									for key, value in self.inputs_dict.items():
											self.assertEqual(obj[key]    ,    A_)




							def        UpperCAmelCase__      (	self					:	str):
									lowerCAmelCase_      :					Optional[Any]     =			ModelCard.from_dict(self.inputs_dict)

									with tempfile.TemporaryDirectory() as tmpdirname:
											lowerCAmelCase_      :					List[str]     =			os.path.join(A_    ,    '''modelcard.json''')
											model_card_first.to_json_file(A_)
											lowerCAmelCase_      :					Any     =			ModelCard.from_json_file(A_)

									self.assertEqual(model_card_second.to_dict()    ,    model_card_first.to_dict())







							def        UpperCAmelCase__      (	self					:	List[Any]):
									lowerCAmelCase_      :					Tuple     =			ModelCard.from_dict(self.inputs_dict)

									with tempfile.TemporaryDirectory() as tmpdirname:
											model_card_first.save_pretrained(A_)
											lowerCAmelCase_      :					Optional[Any]     =			ModelCard.from_pretrained(A_)

									self.assertEqual(model_card_second.to_dict()    ,    model_card_first.to_dict())










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
						)





