
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_albert import AlbertTokenizer
else:
			_lowerCamelCase	:    Any          =      None

_lowerCamelCase	:    List[str]          =      logging.get_logger(__name__)
_lowerCamelCase	:    List[str]          =      {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase	:    str          =      {
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

_lowerCamelCase	:    int          =      {
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

_lowerCamelCase	:    List[Any]          =      '''▁'''




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Dict          = VOCAB_FILES_NAMES
	_UpperCAmelCase	:		Any          = PRETRAINED_VOCAB_FILES_MAP
	_UpperCAmelCase	:		Optional[int]          = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_UpperCAmelCase	:		Tuple          = AlbertTokenizer



	def __init__(  self     :   int      ,  lowercase     :   int=None      ,  lowercase     :   Any=None      ,  lowercase     :   str=True      ,  lowercase     :   List[Any]=True      ,  lowercase     :   Optional[Any]=False      ,  lowercase     :   Optional[Any]="[CLS]"      ,  lowercase     :   int="[SEP]"      ,  lowercase     :   List[Any]="<unk>"      ,  lowercase     :   Tuple="[SEP]"      ,  lowercase     :   str="<pad>"      ,  lowercase     :   Optional[int]="[CLS]"      ,  lowercase     :   Tuple="[MASK]"      ,  **lowercase     :   List[str]      ,  ):


				'''simple docstring'''


				_snake_case          =      (
				    AddedToken(lowercase      ,  lstrip=lowercase      ,  rstrip=lowercase      ,  normalized=lowercase     )
				    if isinstance(lowercase      ,  lowercase     )
				    else mask_token
				)

				super().__init__(
				    lowercase      ,  tokenizer_file=lowercase      ,  do_lower_case=lowercase      ,  remove_space=lowercase      ,  keep_accents=lowercase      ,  bos_token=lowercase      ,  eos_token=lowercase      ,  unk_token=lowercase      ,  sep_token=lowercase      ,  pad_token=lowercase      ,  cls_token=lowercase      ,  mask_token=lowercase      ,  **lowercase      ,  )

				_snake_case          =      do_lower_case
				_snake_case          =      remove_space
				_snake_case          =      keep_accents
				_snake_case          =      vocab_file
				_snake_case          =      False if not self.vocab_file else True



	def 			A							(  self     :   Union[str, Any]      ,  lowercase     :   List[int]      ,  lowercase     :   Optional[List[int]] = None     ):


				'''simple docstring'''


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



	def 			A							(  self     :   Tuple      ,  lowercase     :   List[int]      ,  lowercase     :   Optional[List[int]] = None     ):


				'''simple docstring'''


				_snake_case          =      [self.sep_token_id]
				_snake_case          =      [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     ) * [0] + len(token_ids_a + sep     ) * [1]





	def 			A							(  self     :   Tuple      ,  lowercase     :   str      ,  lowercase     :   Optional[str] = None     ):


				'''simple docstring'''


				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
				_snake_case          =      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 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
)
_lowerCamelCase	:    Tuple          =      logging.getLogger(__name__)






def 	a_		(	)			->  Any:
			_snake_case          =      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.'			)
			_snake_case          =      parser.parse_args()

			logger.info(f'''Loading Tokenizer ({args.tokenizer_name})'''			)
			if args.tokenizer_type == "bert":
						_snake_case          =      BertTokenizer.from_pretrained(args.tokenizer_name			)
						_snake_case          =      tokenizer.special_tokens_map['cls_token']  # `[CLS]`
						_snake_case          =      tokenizer.special_tokens_map['sep_token']  # `[SEP]`
			elif args.tokenizer_type == "roberta":
						_snake_case          =      RobertaTokenizer.from_pretrained(args.tokenizer_name			)
						_snake_case          =      tokenizer.special_tokens_map['cls_token']  # `<s>`
						_snake_case          =      tokenizer.special_tokens_map['sep_token']  # `</s>`
			elif args.tokenizer_type == "gpt2":
						_snake_case          =      GPTaTokenizer.from_pretrained(args.tokenizer_name			)
						_snake_case          =      tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
						_snake_case          =      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:
						_snake_case          =      fp.readlines()

			logger.info('Start encoding'			)
			logger.info(f'''{len(__lowercase			)} examples to process.'''			)

			_snake_case          =      []
			_snake_case          =      0
			_snake_case          =      10_000
			_snake_case          =      time.time()
			for text in data:
						_snake_case          =      f'''{bos} {text.strip()} {sep}'''
						_snake_case          =      tokenizer.encode(__lowercase	, add_special_tokens=__lowercase			)
						rslt.append(__lowercase			)

						iter += 1
						if iter % interval == 0:
									_snake_case          =      time.time()
									logger.info(f'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''			)
									_snake_case          =      time.time()
			logger.info('Finished binarization'			)
			logger.info(f'''{len(__lowercase			)} examples processed.'''			)

			_snake_case          =      f'''{args.dump_file}.{args.tokenizer_name}.pickle'''
			_snake_case          =      tokenizer.vocab_size
			if vocab_size < (1 << 16):
						_snake_case          =      [np.uintaa(__lowercase			) for d in rslt]
			else:
						_snake_case          =      [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()