





"""simple docstring"""




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


a						: Optional[int]     	=			logging.get_logger(__name__)

a						: int     	=			{
    '''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					Optional[int]      					="""gptj"""
							lowerCamelCase   :					str      					={
							    """max_position_embeddings""": """n_positions""",
							    """hidden_size""": """n_embd""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}






							def __init__(   self      ,					lowerCAmelCase__=5_0400      ,					lowerCAmelCase__=2048      ,					lowerCAmelCase__=4096      ,					lowerCAmelCase__=28      ,					lowerCAmelCase__=16      ,					lowerCAmelCase__=64      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__="gelu_new"      ,					lowerCAmelCase__=0.0      ,					lowerCAmelCase__=0.0      ,					lowerCAmelCase__=0.0      ,					lowerCAmelCase__=1E-5      ,					lowerCAmelCase__=0.02      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=5_0256      ,					lowerCAmelCase__=5_0256      ,					lowerCAmelCase__=False      ,					**lowerCAmelCase__      ,					)	-> Tuple:
								a      :		List[Any]	=				vocab_size
								a      :		Tuple	=				n_positions
								a      :		Optional[Any]	=				n_embd
								a      :		Any	=				n_layer
								a      :		Tuple	=				n_head
								a      :		Optional[Any]	=				n_inner
								a      :		Dict	=				rotary_dim
								a      :		Optional[int]	=				activation_function
								a      :		Any	=				resid_pdrop
								a      :		Optional[Any]	=				embd_pdrop
								a      :		Union[str, Any]	=				attn_pdrop
								a      :		Optional[int]	=				layer_norm_epsilon
								a      :		Union[str, Any]	=				initializer_range
								a      :		Optional[Any]	=				use_cache

								a      :		List[str]	=				bos_token_id
								a      :		List[Any]	=				eos_token_id

								super().__init__(
								    bos_token_id=lowerCAmelCase__      ,					eos_token_id=lowerCAmelCase__      ,					tie_word_embeddings=lowerCAmelCase__      ,					**lowerCAmelCase__      )


class        __UpperCamelCase				(						a__						):






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = "default"      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = False      ,					)	-> Union[str, Any]:
								super().__init__(lowerCAmelCase__      ,					task=lowerCAmelCase__      ,					patching_specs=lowerCAmelCase__      ,					use_past=lowerCAmelCase__      )
								if not getattr(self._config      ,					"pad_token_id"      ,					lowerCAmelCase__      ):
									# TODO: how to do that better?
									a      :		Optional[int]	=				0






							@property
							def     __a   (   self      )	-> Mapping[str, Mapping[int, str]]:
								a      :		Any	=				OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}      )
								if self.use_past:
									self.fill_with_past_key_values_(lowerCAmelCase__      ,					direction="inputs"      )
									a      :		int	=				{0: "batch", 1: "past_sequence + sequence"}
								else:
									a      :		Union[str, Any]	=				{0: "batch", 1: "sequence"}

								return common_inputs






							@property
							def     __a   (   self      )	-> int:
								return self._config.n_layer






							@property
							def     __a   (   self      )	-> int:
								return self._config.n_head






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = -1      ,					lowerCAmelCase__ = -1      ,					lowerCAmelCase__ = False      ,					lowerCAmelCase__ = None      ,					)	-> Mapping[str, Any]:
								a      :		Optional[int]	=				super(lowerCAmelCase__      ,					self      ).generate_dummy_inputs(
								    lowerCAmelCase__      ,					batch_size=lowerCAmelCase__      ,					seq_length=lowerCAmelCase__      ,					is_pair=lowerCAmelCase__      ,					framework=lowerCAmelCase__      )

								# We need to order the input in the way they appears in the forward()
								a      :		Optional[Any]	=				OrderedDict({"input_ids": common_inputs["input_ids"]}      )

								# Need to add the past_keys
								if self.use_past:
									if not is_torch_available():
										raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."      )
									else:
										import torch

										a,       a      :		List[Any]	=				common_inputs["input_ids"].shape
										# Not using the same length for past_key_values
										a      :		Optional[int]	=				seqlen + 2
										a      :		List[str]	=				(
										    batch,
										    self.num_attention_heads,
										    past_key_values_length,
										    self._config.hidden_size // self.num_attention_heads,
										)
										a      :		List[str]	=				[
										    (torch.zeros(lowerCAmelCase__      ), torch.zeros(lowerCAmelCase__      )) for _ in range(self.num_layers      )
										]

								a      :		List[Any]	=				common_inputs["attention_mask"]
								if self.use_past:
									a      :		int	=				ordered_inputs["attention_mask"].dtype
									a      :		List[Any]	=				torch.cat(
									    [ordered_inputs["attention_mask"], torch.ones(lowerCAmelCase__      ,					lowerCAmelCase__      ,					dtype=lowerCAmelCase__      )]      ,					dim=1      )

								return ordered_inputs






							@property
							def     __a   (   self      )	-> int:
								return 13









"""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()


