





"""simple docstring"""




from ....configuration_utils import PretrainedConfig
from ....utils import logging


a						: Dict     	=			logging.get_logger(__name__)

a						: Optional[Any]     	=			{
    '''CarlCochet/trajectory-transformer-halfcheetah-medium-v2''': (
        '''https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'''
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					int      					="""trajectory_transformer"""
							lowerCamelCase   :					int      					=["""past_key_values"""]
							lowerCamelCase   :					Optional[int]      					={
							    """hidden_size""": """n_embd""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}






							def __init__(   self      ,					lowerCAmelCase__=100      ,					lowerCAmelCase__=5      ,					lowerCAmelCase__=1      ,					lowerCAmelCase__=1      ,					lowerCAmelCase__=249      ,					lowerCAmelCase__=6      ,					lowerCAmelCase__=17      ,					lowerCAmelCase__=25      ,					lowerCAmelCase__=4      ,					lowerCAmelCase__=4      ,					lowerCAmelCase__=128      ,					lowerCAmelCase__=0.1      ,					lowerCAmelCase__=0.1      ,					lowerCAmelCase__=0.1      ,					lowerCAmelCase__=0.0_006      ,					lowerCAmelCase__=512      ,					lowerCAmelCase__=0.02      ,					lowerCAmelCase__=1E-12      ,					lowerCAmelCase__=1      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=1      ,					lowerCAmelCase__=5_0256      ,					lowerCAmelCase__=5_0256      ,					**lowerCAmelCase__      ,					)	-> List[Any]:
								a      :		str	=				vocab_size
								a      :		List[str]	=				action_weight
								a      :		int	=				reward_weight
								a      :		Dict	=				value_weight
								a      :		List[Any]	=				max_position_embeddings
								a      :		Any	=				block_size
								a      :		int	=				action_dim
								a      :		Union[str, Any]	=				observation_dim
								a      :		List[str]	=				transition_dim
								a      :		Optional[Any]	=				learning_rate
								a      :		Tuple	=				n_layer
								a      :		Dict	=				n_head
								a      :		Optional[Any]	=				n_embd
								a      :		Optional[int]	=				embd_pdrop
								a      :		Optional[Any]	=				attn_pdrop
								a      :		Union[str, Any]	=				resid_pdrop
								a      :		List[str]	=				initializer_range
								a      :		Tuple	=				layer_norm_eps
								a      :		Union[str, Any]	=				kaiming_initializer_range
								a      :		int	=				use_cache
								super().__init__(pad_token_id=lowerCAmelCase__      ,					bos_token_id=lowerCAmelCase__      ,					eos_token_id=lowerCAmelCase__      ,					**lowerCAmelCase__      )









"""simple docstring"""




import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
a						: Union[str, Any]     	=			logging.getLogger(__name__)






def       _SCREAMING_SNAKE_CASE		(	) ->Tuple:






	'''simple docstring'''






	a      :		Dict	=				argparse.ArgumentParser(
	    description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."					)
	parser.add_argument("--file_path"    ,       type=_lowercase    ,       default="data/dump.txt"    ,       help="The path to the data."					)
	parser.add_argument("--tokenizer_type"    ,       type=_lowercase    ,       default="bert"    ,       choices=["bert", "roberta", "gpt2"]					)
	parser.add_argument("--tokenizer_name"    ,       type=_lowercase    ,       default="bert-base-uncased"    ,       help="The tokenizer to use."					)
	parser.add_argument("--dump_file"    ,       type=_lowercase    ,       default="data/dump"    ,       help="The dump file prefix."					)
	a      :		Dict	=				parser.parse_args()

	logger.info(F"""Loading Tokenizer ({args.tokenizer_name})"""					)
	if args.tokenizer_type == "bert":
		a      :		Optional[Any]	=				BertTokenizer.from_pretrained(args.tokenizer_name					)
		a      :		str	=				tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
		a      :		List[str]	=				tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
	elif args.tokenizer_type == "roberta":
		a      :		Tuple	=				RobertaTokenizer.from_pretrained(args.tokenizer_name					)
		a      :		Union[str, Any]	=				tokenizer.special_tokens_map["cls_token"]  # `<s>`
		a      :		str	=				tokenizer.special_tokens_map["sep_token"]  # `</s>`
	elif args.tokenizer_type == "gpt2":
		a      :		List[Any]	=				GPTaTokenizer.from_pretrained(args.tokenizer_name					)
		a      :		Optional[int]	=				tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
		a      :		List[Any]	=				tokenizer.special_tokens_map["eos_token"]  # `<|endoftext|>`

	logger.info(F"""Loading text from {args.file_path}"""					)
	with open(args.file_path    ,       "r"    ,       encoding="utf8"					) as fp:
		a      :		List[Any]	=				fp.readlines()

	logger.info("Start encoding"					)
	logger.info(F"""{len(_lowercase					)} examples to process."""					)

	a      :		Optional[Any]	=				[]
	a      :		Optional[Any]	=				0
	a      :		int	=				1_0000
	a      :		Dict	=				time.time()
	for text in data:
		a      :		List[Any]	=				F"""{bos} {text.strip()} {sep}"""
		a      :		Optional[int]	=				tokenizer.encode(_lowercase    ,       add_special_tokens=_lowercase					)
		rslt.append(_lowercase					)

		iter += 1
		if iter % interval == 0:
			a      :		Optional[Any]	=				time.time()
			logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""					)
			a      :		Optional[Any]	=				time.time()
	logger.info("Finished binarization"					)
	logger.info(F"""{len(_lowercase					)} examples processed."""					)

	a      :		Optional[int]	=				F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
	a      :		Tuple	=				tokenizer.vocab_size
	if vocab_size < (1 << 16):
		a      :		Optional[int]	=				[np.uintaa(_lowercase					) for d in rslt]
	else:
		a      :		Optional[Any]	=				[np.intaa(_lowercase					) for d in rslt]
	random.shuffle(rslt_					)
	logger.info(F"""Dump to {dp_file}"""					)
	with open(_lowercase    ,       "wb"					) as handle:
		pickle.dump(rslt_    ,       _lowercase    ,       protocol=pickle.HIGHEST_PROTOCOL					)


if __name__ == "__main__":
		main()


