



'''simple docstring'''
import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel



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


def 	lowercase_						( lowerCAmelCase__						:	Union[str, Any]      ,				lowerCAmelCase__						:	int      ,				lowerCAmelCase__						:	Dict      ,				lowerCAmelCase__						:	str      ,				lowerCAmelCase__						:	Optional[int]			):

			"""simple docstring"""





			__UpperCAmelCase					,	__UpperCAmelCase :   Union[str, Any]    					=  utils.load_model(lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				epoch=lowerCAmelCase__      ,				device=lowerCAmelCase__			)
			__UpperCAmelCase :   Dict    					=  TrajectoryTransformerModel(gpt.config			)

			trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
			__UpperCAmelCase :   Union[str, Any]    					=  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									=      Parser().parse_args('''plan''')
					convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
					    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
					)









'''simple docstring'''
from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest



@require_tokenizers
class _A	(     __SCREAMING_SNAKE_CASE     ):
		_SCREAMING_SNAKE_CASE       :     List[str]       =      DPRContextEncoderTokenizer
		_SCREAMING_SNAKE_CASE       :     Optional[int]       =      DPRContextEncoderTokenizerFast
		_SCREAMING_SNAKE_CASE       :     Optional[Any]       =      True



@require_tokenizers
class _A	(     __SCREAMING_SNAKE_CASE     ):
		_SCREAMING_SNAKE_CASE       :     str       =      DPRQuestionEncoderTokenizer
		_SCREAMING_SNAKE_CASE       :     List[str]       =      DPRQuestionEncoderTokenizerFast
		_SCREAMING_SNAKE_CASE       :     Union[str, Any]       =      True



@require_tokenizers
class _A	(     __SCREAMING_SNAKE_CASE     ):
		_SCREAMING_SNAKE_CASE       :     List[str]       =      DPRReaderTokenizer
		_SCREAMING_SNAKE_CASE       :     Optional[Any]       =      DPRReaderTokenizerFast
		_SCREAMING_SNAKE_CASE       :     Dict       =      True





		@slow
		def 				__A							(  self					)							->  List[Any]:

					'''simple docstring'''
					__UpperCAmelCase :   Dict    					=  self.tokenizer_class.from_pretrained("""bert-base-uncased"""					)

					__UpperCAmelCase :   int    					=  tokenizer.encode("""question sequence"""			,							add_special_tokens=__UpperCAmelCase					)
					__UpperCAmelCase :   Optional[Any]    					=  tokenizer.encode("""title sequence"""			,							add_special_tokens=__UpperCAmelCase					)
					__UpperCAmelCase :   Any    					=  tokenizer.encode("""text sequence """ * 4			,							add_special_tokens=__UpperCAmelCase					)
					__UpperCAmelCase :   Tuple    					=  [[101] + text_a + [102] + text_a + [102] + text_a]
					__UpperCAmelCase :   Optional[int]    					=  BatchEncoding({"""input_ids""": input_ids}					)

					__UpperCAmelCase :   str    					=  [[0] * len(input_ids[0]					)]
					__UpperCAmelCase :   str    					=  [[0] * len(input_ids[0]					)]
					__UpperCAmelCase :   Optional[int]    					=  [0]
					__UpperCAmelCase :   List[str]    					=  DPRReaderOutput(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)

					__UpperCAmelCase					,	__UpperCAmelCase :   Any    					=  8, 9
					__UpperCAmelCase :   Optional[int]    					=  10
					__UpperCAmelCase :   int    					=  10
					__UpperCAmelCase :   str    					=  tokenizer.decode_best_spans(__UpperCAmelCase			,							__UpperCAmelCase					)
					self.assertEqual(predicted_spans[0].start_index			,							__UpperCAmelCase					)
					self.assertEqual(predicted_spans[0].end_index			,							__UpperCAmelCase					)
					self.assertEqual(predicted_spans[0].doc_id			,							0					)





		@slow
		def 				__A							(  self					)							->  int:

					'''simple docstring'''
					__UpperCAmelCase :   Any    					=  self.tokenizer_class.from_pretrained("""bert-base-uncased"""					)

					__UpperCAmelCase :   List[Any]    					=  tokenizer.encode("""question sequence"""			,							add_special_tokens=__UpperCAmelCase					)
					__UpperCAmelCase :   List[str]    					=  tokenizer.encode("""title sequence"""			,							add_special_tokens=__UpperCAmelCase					)
					__UpperCAmelCase :   Union[str, Any]    					=  tokenizer.encode("""text sequence"""			,							add_special_tokens=__UpperCAmelCase					)
					__UpperCAmelCase :   Optional[int]    					=  [101] + text_a + [102] + text_a + [102] + text_a
					__UpperCAmelCase :   int    					=  tokenizer(questions=["""question sequence"""]			,							titles=["""title sequence"""]			,							texts=["""text sequence"""]					)
					self.assertIn("""input_ids"""			,							__UpperCAmelCase					)
					self.assertIn("""attention_mask"""			,							__UpperCAmelCase					)
					self.assertListEqual(encoded_input["""input_ids"""][0]			,							__UpperCAmelCase					)




