
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
)
snake_case :		Any							      =  logging.getLogger(__name__)






def   lowerCAmelCase_						(					)				->		List[Any]:







	'''simple docstring'''


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

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

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

	__magic_name__		:		Optional[Any]	   =     []
	__magic_name__		:		Dict	   =     0
	__magic_name__		:		str	   =     10000
	__magic_name__		:		Union[str, Any]	   =     time.time()
	for text in data:
		__magic_name__		:		Union[str, Any]	   =     F'''{bos} {text.strip()} {sep}'''
		__magic_name__		:		int	   =     tokenizer.encode(_snake_case      ,   add_special_tokens=_snake_case   )
		rslt.append(_snake_case   )

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

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


if __name__ == "__main__":
				main()


import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def   lowerCAmelCase_						(					_snake_case      :  List[str]      ,   _snake_case      :  Tuple      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]   )				->		Optional[Any]:







	'''simple docstring'''


	if isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		Union[str, Any]	   =     np.full((len(_snake_case   ), sequence_length, 2)      ,   _snake_case   )
	else:
		__magic_name__		:		List[Any]	   =     np.full((len(_snake_case   ), sequence_length)      ,   _snake_case   )

	for i, tensor in enumerate(_snake_case   ):
		if padding_side == "right":
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Union[str, Any]	   =     tensor[:sequence_length]
		else:
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		List[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]

	return out_tensor.tolist()






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]   )				->		Tuple:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     ord(_snake_case   )
	if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
		return True
	__magic_name__		:		Any	   =     unicodedata.category(_snake_case   )
	if cat.startswith("P"   ):
		return True
	return False





@dataclass
class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    42
			UpperCamelCase__          =    True
			UpperCamelCase__          =    None
			UpperCamelCase__          =    None
			UpperCamelCase__          =    -100
			UpperCamelCase__          =    "pt"







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				import torch

				__magic_name__		:		List[str]	   =     "label" if "label" in features[0].keys() else "labels"
				__magic_name__		:		Union[str, Any]	   =     [feature[label_name] for feature in features] if label_name in features[0].keys() else None
				__magic_name__		:		Optional[int]	   =     self.tokenizer.pad(
				    _a						,   padding=self.padding						,   max_length=self.max_length						,   pad_to_multiple_of=self.pad_to_multiple_of						,   return_tensors="pt" if labels is None else None						,   )

				if labels is None:
					return batch

				__magic_name__		:		Dict	   =     torch.tensor(batch["entity_ids"]		).shape[1]
				__magic_name__		:		List[Any]	   =     self.tokenizer.padding_side
				if padding_side == "right":
					__magic_name__		:		str	   =     [
					    list(_a		) + [self.label_pad_token_id] * (sequence_length - len(_a		)) for label in labels
					]
				else:
					__magic_name__		:		int	   =     [
					    [self.label_pad_token_id] * (sequence_length - len(_a		)) + list(_a		) for label in labels
					]

				__magic_name__		:		Dict	   =     [feature["ner_tags"] for feature in features]
				__magic_name__		:		List[Any]	   =     padding_tensor(_a						,   -1						,   _a						,   _a		)
				__magic_name__		:		Any	   =     [feature["original_entity_spans"] for feature in features]
				__magic_name__		:		Any	   =     padding_tensor(_a						,   (-1, -1)						,   _a						,   _a		)
				__magic_name__		:		List[Any]	   =     {k: torch.tensor(_a						,   dtype=torch.intaa		) for k, v in batch.items()}

				return batch
