




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


_lowerCamelCase      			=						logging.get_logger(__name__)

_lowerCamelCase      			=						{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_lowerCamelCase      			=						{
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

_lowerCamelCase      			=						{
    'squeezebert/squeezebert-uncased': 5_12,
    'squeezebert/squeezebert-mnli': 5_12,
    'squeezebert/squeezebert-mnli-headless': 5_12,
}


_lowerCamelCase      			=						{
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}



class       a					(       _A      ):

					'''simple docstring'''





					lowerCAmelCase    :	int       		=  VOCAB_FILES_NAMES
					lowerCAmelCase    :	List[str]       		=  PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase    :	List[Any]       		=  PRETRAINED_INIT_CONFIGURATION
					lowerCAmelCase    :	Any       		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase    :	List[Any]       		=  SqueezeBertTokenizer
					def __init__(     self       :      List[Any] ,   __snake_case       :      Any=None ,   __snake_case       :      List[str]=None ,   __snake_case       :      List[Any]=True ,   __snake_case       :      Union[str, Any]="[UNK]" ,   __snake_case       :      List[str]="[SEP]" ,   __snake_case       :      Dict="[PAD]" ,   __snake_case       :      Any="[CLS]" ,   __snake_case       :      Dict="[MASK]" ,   __snake_case       :      str=True ,   __snake_case       :      List[str]=None ,   **__snake_case       :      Optional[int] ,   ):
									super().__init__(
									    __snake_case ,   tokenizer_file=__snake_case ,   do_lower_case=__snake_case ,   unk_token=__snake_case ,   sep_token=__snake_case ,   pad_token=__snake_case ,   cls_token=__snake_case ,   mask_token=__snake_case ,   tokenize_chinese_chars=__snake_case ,   strip_accents=__snake_case ,   **__snake_case ,   )

									UpperCAmelCase_										=					json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
									if (
									    normalizer_state.get('''lowercase''' ,   __snake_case    ) != do_lower_case
									    or normalizer_state.get('''strip_accents''' ,   __snake_case    ) != strip_accents
									    or normalizer_state.get('''handle_chinese_chars''' ,   __snake_case    ) != tokenize_chinese_chars
									):
													UpperCAmelCase_										=					getattr(__snake_case ,   normalizer_state.pop('''type'''    )    )
													UpperCAmelCase_										=					do_lower_case
													UpperCAmelCase_										=					strip_accents
													UpperCAmelCase_										=					tokenize_chinese_chars
													UpperCAmelCase_										=					normalizer_class(**__snake_case    )

									UpperCAmelCase_										=					do_lower_case
					def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      Dict ,   __snake_case       :      List[Any]=None    ):
									UpperCAmelCase_										=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

									if token_ids_a:
													output += token_ids_a + [self.sep_token_id]

									return output
					def   lowerCamelCase_       (     self       :      List[str] ,   __snake_case       :      List[int] ,   __snake_case       :      Optional[List[int]] = None    ):
									UpperCAmelCase_										=					[self.sep_token_id]
									UpperCAmelCase_										=					[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   lowerCamelCase_       (     self       :      Optional[int] ,   __snake_case       :      str ,   __snake_case       :      Optional[str] = None    ):
									UpperCAmelCase_										=					self._tokenizer.model.save(__snake_case ,   name=__snake_case    )
									return tuple(__snake_case    )











import argparse
import logging
import pickle
from collections import Counter


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

if __name__ == "__main__":
 _lowerCamelCase      			=						argparse.ArgumentParser(
     description='Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'
 )
 parser.add_argument(
     '--data_file', type=str, default='data/dump.bert-base-uncased.pickle', help='The binarized dataset.'
 )
 parser.add_argument(
     '--token_counts_dump', type=str, default='data/token_counts.bert-base-uncased.pickle', help='The dump file.'
 )
 parser.add_argument('--vocab_size', default=3_05_22, type=int)
 _lowerCamelCase      			=						parser.parse_args()

 logger.info(F"Loading data from {args.data_file}")
 with open(args.data_file, 'rb') as fp:
  _lowerCamelCase      			=						pickle.load(fp)

 logger.info('Counting occurrences for MLM.')
 _lowerCamelCase      			=						Counter()
 for tk_ids in data:
  counter.update(tk_ids)
 _lowerCamelCase      			=						[0] * args.vocab_size
 for k, v in counter.items():
  _lowerCamelCase      			=						v

 logger.info(F"Dump to {args.token_counts_dump}")
 with open(args.token_counts_dump, 'wb') as handle:
  pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)





