



"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
						A__  :  Dict       = None


A__  :  Any       = logging.get_logger(__name__)

A__  :  str       = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

A__  :  Union[str, Any]       = {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

A__  :  Optional[int]       = {
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}



class     __snake_case						(					_UpperCAmelCase						):
							_a      				=			VOCAB_FILES_NAMES
							_a      				=			PRETRAINED_VOCAB_FILES_MAP
							_a      				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_a      				=			['''input_ids''', '''attention_mask''']
							_a      				=			XLMRobertaTokenizer




							def __init__(	self					:	Tuple    ,    A_					:	Any=None    ,    A_					:	Union[str, Any]=None    ,    A_					:	List[str]="<s>"    ,    A_					:	List[Any]="</s>"    ,    A_					:	Any="</s>"    ,    A_					:	Tuple="<s>"    ,    A_					:	Optional[int]="<unk>"    ,    A_					:	Tuple="<pad>"    ,    A_					:	int="<mask>"    ,    **A_					:	int    ,    ):
									lowerCAmelCase_      :					Optional[int]     =			AddedToken(lowercase__    ,    lstrip=lowercase__    ,    rstrip=lowercase__) if isinstance(lowercase__    ,    lowercase__) else mask_token

									super().__init__(
									    lowercase__    ,    tokenizer_file=lowercase__    ,    bos_token=lowercase__    ,    eos_token=lowercase__    ,    sep_token=lowercase__    ,    cls_token=lowercase__    ,    unk_token=lowercase__    ,    pad_token=lowercase__    ,    mask_token=lowercase__    ,    **lowercase__    ,    )

									lowerCAmelCase_      :					Optional[Any]     =			vocab_file
									lowerCAmelCase_      :					Dict     =			False if not self.vocab_file else True




							def        UpperCAmelCase__      (	self					:	List[Any]    ,    A_					:	str    ,    A_					:	Dict = None):






									if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									lowerCAmelCase_      :					Any     =			[self.cls_token_id]
									lowerCAmelCase_      :					int     =			[self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep




							def        UpperCAmelCase__      (	self					:	List[Any]    ,    A_					:	List[str]    ,    A_					:	Union[str, Any] = None):
									lowerCAmelCase_      :					Dict     =			[self.sep_token_id]
									lowerCAmelCase_      :					Optional[Any]     =			[self.cls_token_id]

									if token_ids_a is None:
											return len(cls + token_ids_a + sep) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]







							def        UpperCAmelCase__      (	self					:	Optional[Any]    ,    A_					:	str    ,    A_					:	Any = None):






									if not self.can_save_slow_tokenizer:
											raise ValueError(
											    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
											    '''tokenizer.''')

									if not os.path.isdir(lowercase__):
											logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""")
											return
									lowerCAmelCase_      :					Any     =			os.path.join(
									    lowercase__    ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

									if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase__):
											copyfile(self.vocab_file    ,    lowercase__)

									return (out_vocab_file,)










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
						)





