



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


lowerCAmelCase__		:     Union[str, Any]       						=logging.get_logger(__name__)


lowerCAmelCase__		:     int       						={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all BART models at https://huggingface.co/models?filter=bart
lowerCAmelCase__		:     Any       						={
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
    },
}

lowerCAmelCase__		:     Tuple       						={
    '''facebook/bart-base''': 1024,
    '''facebook/bart-large''': 1024,
    '''facebook/bart-large-mnli''': 1024,
    '''facebook/bart-large-cnn''': 1024,
    '''facebook/bart-large-xsum''': 1024,
    '''yjernite/bart_eli5''': 1024,
}

class    UpperCAmelCase_  (   UpperCAmelCase_       ):

						'''simple docstring'''




						UpperCamelCase__			: int				  =  VOCAB_FILES_NAMES
						UpperCamelCase__			: List[Any]				  =  PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase__			: str				  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase__			: Union[str, Any]				  =  ['input_ids', 'attention_mask']
						UpperCamelCase__			: Dict				  =  BartTokenizer






						def __init__(    self      ,							_A=None      ,							_A=None      ,							_A=None      ,							_A="replace"      ,							_A="<s>"      ,							_A="</s>"      ,							_A="</s>"      ,							_A="<s>"      ,							_A="<unk>"      ,							_A="<pad>"      ,							_A="<mask>"      ,							_A=False      ,							_A=True      ,							**_A      ,							):







													'''simple docstring'''

													super().__init__(
													    __UpperCAmelCase      ,							__UpperCAmelCase      ,							tokenizer_file=__UpperCAmelCase      ,							errors=__UpperCAmelCase      ,							bos_token=__UpperCAmelCase      ,							eos_token=__UpperCAmelCase      ,							sep_token=__UpperCAmelCase      ,							cls_token=__UpperCAmelCase      ,							unk_token=__UpperCAmelCase      ,							pad_token=__UpperCAmelCase      ,							mask_token=__UpperCAmelCase      ,							add_prefix_space=__UpperCAmelCase      ,							trim_offsets=__UpperCAmelCase      ,							**__UpperCAmelCase      ,							)

													__SCREAMING_SNAKE_CASE									=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
													if pre_tok_state.get('add_prefix_space'      ,							__UpperCAmelCase		) != add_prefix_space:
																				__SCREAMING_SNAKE_CASE									=					getattr(__UpperCAmelCase      ,							pre_tok_state.pop('type'		)		)
																				__SCREAMING_SNAKE_CASE									=					add_prefix_space
																				__SCREAMING_SNAKE_CASE									=					pre_tok_class(**__UpperCAmelCase		)

													__SCREAMING_SNAKE_CASE									=					add_prefix_space

													# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
													__SCREAMING_SNAKE_CASE									=					'post_processor'
													__SCREAMING_SNAKE_CASE									=					getattr(self.backend_tokenizer      ,							__UpperCAmelCase      ,							__UpperCAmelCase		)
													if tokenizer_component_instance:
																				__SCREAMING_SNAKE_CASE									=					json.loads(tokenizer_component_instance.__getstate__()		)

																				# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																				if "sep" in state:
																											__SCREAMING_SNAKE_CASE									=					tuple(state['sep']		)
																				if "cls" in state:
																											__SCREAMING_SNAKE_CASE									=					tuple(state['cls']		)

																				__SCREAMING_SNAKE_CASE									=					False

																				if state.get('add_prefix_space'      ,							__UpperCAmelCase		) != add_prefix_space:
																											__SCREAMING_SNAKE_CASE									=					add_prefix_space
																											__SCREAMING_SNAKE_CASE									=					True

																				if state.get('trim_offsets'      ,							__UpperCAmelCase		) != trim_offsets:
																											__SCREAMING_SNAKE_CASE									=					trim_offsets
																											__SCREAMING_SNAKE_CASE									=					True

																				if changes_to_apply:
																											__SCREAMING_SNAKE_CASE									=					getattr(__UpperCAmelCase      ,							state.pop('type'		)		)
																											__SCREAMING_SNAKE_CASE									=					component_class(**__UpperCAmelCase		)
																											setattr(self.backend_tokenizer      ,							__UpperCAmelCase      ,							__UpperCAmelCase		)






						@property
						def        _A	(    self		):







													'''simple docstring'''

													if self._mask_token is None:
																				if self.verbose:
																											logger.error('Using mask_token, but it is not set yet.'		)
																				return None
													return str(self._mask_token		)






						@mask_token.setter
						def        _A	(    self      ,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					AddedToken(__UpperCAmelCase      ,							lstrip=__UpperCAmelCase      ,							rstrip=__UpperCAmelCase		) if isinstance(__UpperCAmelCase      ,							__UpperCAmelCase		) else value
													__SCREAMING_SNAKE_CASE									=					value






						def        _A	(    self      ,							*_A      ,							**_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					kwargs.get('is_split_into_words'      ,							__UpperCAmelCase		)

													if is_split_into_words and not self.add_prefix_space:
																				raise ValueError(
																				    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
																				    'to use it with pretokenized inputs.'		)

													return super()._batch_encode_plus(*__UpperCAmelCase      ,							**__UpperCAmelCase		)






						def        _A	(    self      ,							*_A      ,							**_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					kwargs.get('is_split_into_words'      ,							__UpperCAmelCase		)

													if is_split_into_words and not self.add_prefix_space:
																				raise ValueError(
																				    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
																				    'to use it with pretokenized inputs.'		)

													return super()._encode_plus(*__UpperCAmelCase      ,							**__UpperCAmelCase		)






						def        _A	(    self      ,							_A      ,							_A = None		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					self._tokenizer.model.save(__UpperCAmelCase      ,							name=__UpperCAmelCase		)
													return tuple(__UpperCAmelCase		)






						def        _A	(    self      ,							_A      ,							_A=None		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
													if token_ids_a is None:
																				return output

													return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






						def        _A	(    self      ,							_A      ,							_A = None		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					[self.sep_token_id]
													__SCREAMING_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 + sep + token_ids_a + sep		) * [0]







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
)
UpperCamelCase__											=					logging.getLogger(__name__)




def    lowerCAmelCase_ (     )     ->				int:







      '''simple docstring'''


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

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

      logger.info("Start encoding"							)
      logger.info(f"""{len(__A							)} examples to process."""							)

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

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

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


if __name__ == "__main__":
      main()



