



"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


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

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

lowercase__		:				Optional[Any]          =	{
    '''vocab_file''': {
        '''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt''',
        '''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt''',
        '''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/vocab.txt''',
        '''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/vocab.txt''',
        '''bert-base-multilingual-uncased''': (
            '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'''
        ),
        '''bert-base-multilingual-cased''': '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt''',
        '''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt''',
        '''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt''',
        '''bert-large-uncased-whole-word-masking''': (
            '''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'''
        ),
        '''bert-large-cased-whole-word-masking''': (
            '''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'''
        ),
        '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
            '''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'''
        ),
        '''bert-large-cased-whole-word-masking-finetuned-squad''': (
            '''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'''
        ),
        '''bert-base-cased-finetuned-mrpc''': (
            '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'''
        ),
        '''bert-base-german-dbmdz-cased''': '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt''',
        '''bert-base-german-dbmdz-uncased''': (
            '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'''
        ),
        '''TurkuNLP/bert-base-finnish-cased-v1''': (
            '''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'''
        ),
        '''TurkuNLP/bert-base-finnish-uncased-v1''': (
            '''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'''
        ),
        '''wietsedv/bert-base-dutch-cased''': (
            '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json''',
        '''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json''',
        '''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json''',
        '''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json''',
        '''bert-base-multilingual-uncased''': (
            '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'''
        ),
        '''bert-base-multilingual-cased''': (
            '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'''
        ),
        '''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json''',
        '''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json''',
        '''bert-large-uncased-whole-word-masking''': (
            '''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'''
        ),
        '''bert-large-cased-whole-word-masking''': (
            '''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'''
        ),
        '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
            '''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'''
        ),
        '''bert-large-cased-whole-word-masking-finetuned-squad''': (
            '''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'''
        ),
        '''bert-base-cased-finetuned-mrpc''': (
            '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'''
        ),
        '''bert-base-german-dbmdz-cased''': (
            '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'''
        ),
        '''bert-base-german-dbmdz-uncased''': (
            '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'''
        ),
        '''TurkuNLP/bert-base-finnish-cased-v1''': (
            '''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'''
        ),
        '''TurkuNLP/bert-base-finnish-uncased-v1''': (
            '''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'''
        ),
        '''wietsedv/bert-base-dutch-cased''': (
            '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'''
        ),
    },
}

lowercase__		:				Dict          =	{
    '''bert-base-uncased''': 5_1_2,
    '''bert-large-uncased''': 5_1_2,
    '''bert-base-cased''': 5_1_2,
    '''bert-large-cased''': 5_1_2,
    '''bert-base-multilingual-uncased''': 5_1_2,
    '''bert-base-multilingual-cased''': 5_1_2,
    '''bert-base-chinese''': 5_1_2,
    '''bert-base-german-cased''': 5_1_2,
    '''bert-large-uncased-whole-word-masking''': 5_1_2,
    '''bert-large-cased-whole-word-masking''': 5_1_2,
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': 5_1_2,
    '''bert-large-cased-whole-word-masking-finetuned-squad''': 5_1_2,
    '''bert-base-cased-finetuned-mrpc''': 5_1_2,
    '''bert-base-german-dbmdz-cased''': 5_1_2,
    '''bert-base-german-dbmdz-uncased''': 5_1_2,
    '''TurkuNLP/bert-base-finnish-cased-v1''': 5_1_2,
    '''TurkuNLP/bert-base-finnish-uncased-v1''': 5_1_2,
    '''wietsedv/bert-base-dutch-cased''': 5_1_2,
}

lowercase__		:				Tuple          =	{
    '''bert-base-uncased''': {'''do_lower_case''': True},
    '''bert-large-uncased''': {'''do_lower_case''': True},
    '''bert-base-cased''': {'''do_lower_case''': False},
    '''bert-large-cased''': {'''do_lower_case''': False},
    '''bert-base-multilingual-uncased''': {'''do_lower_case''': True},
    '''bert-base-multilingual-cased''': {'''do_lower_case''': False},
    '''bert-base-chinese''': {'''do_lower_case''': False},
    '''bert-base-german-cased''': {'''do_lower_case''': False},
    '''bert-large-uncased-whole-word-masking''': {'''do_lower_case''': True},
    '''bert-large-cased-whole-word-masking''': {'''do_lower_case''': False},
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': {'''do_lower_case''': True},
    '''bert-large-cased-whole-word-masking-finetuned-squad''': {'''do_lower_case''': False},
    '''bert-base-cased-finetuned-mrpc''': {'''do_lower_case''': False},
    '''bert-base-german-dbmdz-cased''': {'''do_lower_case''': False},
    '''bert-base-german-dbmdz-uncased''': {'''do_lower_case''': True},
    '''TurkuNLP/bert-base-finnish-cased-v1''': {'''do_lower_case''': False},
    '''TurkuNLP/bert-base-finnish-uncased-v1''': {'''do_lower_case''': True},
    '''wietsedv/bert-base-dutch-cased''': {'''do_lower_case''': False},
}







class       UpperCamelCase__   (	__UpperCAmelCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE  				=     PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE  				=     PRETRAINED_INIT_CONFIGURATION
      _SCREAMING_SNAKE_CASE  				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE  				=     BertTokenizer



      def __init__(      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Tuple=None    ,			SCREAMING_SNAKE_CASE_				:      Dict=None    ,			SCREAMING_SNAKE_CASE_				:      Tuple=True    ,			SCREAMING_SNAKE_CASE_				:      Any="[UNK]"    ,			SCREAMING_SNAKE_CASE_				:      List[Any]="[SEP]"    ,			SCREAMING_SNAKE_CASE_				:      Any="[PAD]"    ,			SCREAMING_SNAKE_CASE_				:      int="[CLS]"    ,			SCREAMING_SNAKE_CASE_				:      Dict="[MASK]"    ,			SCREAMING_SNAKE_CASE_				:      List[str]=True    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=None    ,			**SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			):
        super().__init__(
            SCREAMING_SNAKE_CASE_    ,			tokenizer_file=SCREAMING_SNAKE_CASE_    ,			do_lower_case=SCREAMING_SNAKE_CASE_    ,			unk_token=SCREAMING_SNAKE_CASE_    ,			sep_token=SCREAMING_SNAKE_CASE_    ,			pad_token=SCREAMING_SNAKE_CASE_    ,			cls_token=SCREAMING_SNAKE_CASE_    ,			mask_token=SCREAMING_SNAKE_CASE_    ,			tokenize_chinese_chars=SCREAMING_SNAKE_CASE_    ,			strip_accents=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_    ,			)

        lowerCAmelCase_   :				Union[str, Any]								= json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
        if (
            normalizer_state.get('lowercase'    ,			SCREAMING_SNAKE_CASE_       ) != do_lower_case
            or normalizer_state.get('strip_accents'    ,			SCREAMING_SNAKE_CASE_       ) != strip_accents
            or normalizer_state.get('handle_chinese_chars'    ,			SCREAMING_SNAKE_CASE_       ) != tokenize_chinese_chars
        ):
          lowerCAmelCase_   :				int								= getattr(SCREAMING_SNAKE_CASE_    ,			normalizer_state.pop('type'       )       )
          lowerCAmelCase_   :				Optional[Any]								= do_lower_case
          lowerCAmelCase_   :				List[Any]								= strip_accents
          lowerCAmelCase_   :				int								= tokenize_chinese_chars
          lowerCAmelCase_   :				List[str]								= normalizer_class(**SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Union[str, Any]								= do_lower_case



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=None       ):
        lowerCAmelCase_   :				int								= [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      SCREAMING_SNAKE_CASE__ (      self				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      str = None       ):
        lowerCAmelCase_   :				Optional[int]								= [self.sep_token_id]
        lowerCAmelCase_   :				Any								= [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      SCREAMING_SNAKE_CASE__ (      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any] = None       ):
        lowerCAmelCase_   :				Optional[Any]								= self._tokenizer.model.save(SCREAMING_SNAKE_CASE_    ,			name=SCREAMING_SNAKE_CASE_       )
        return tuple(SCREAMING_SNAKE_CASE_       )










'''simple docstring'''




import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=10_24						,      lowerCamelCase__=10_24						,      lowerCamelCase__=False						,      **lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Any	=     AutoTokenizer.from_pretrained(lowerCamelCase__    )
			A_	:						Union[str, Any]	=     SeqaSeqDataset(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      type_path="""train"""						,      **lowerCamelCase__    )
			A_	:						Optional[Any]	=     tok.pad_token_id

			def get_lens(lowerCamelCase__    ):
						A_	:						int	=     tqdm(
						    DataLoader(lowerCamelCase__						,      batch_size=5_12						,      num_workers=8						,      shuffle=lowerCamelCase__						,      collate_fn=ds.collate_fn    )						,      desc=str(ds.len_file    )						,      )
						A_	:						int	=     []
						for batch in dl:
									A_	:						str	=     batch["""input_ids"""].ne(lowerCamelCase__    ).sum(1    ).tolist()
									A_	:						Tuple	=     batch["""labels"""].ne(lowerCamelCase__    ).sum(1    ).tolist()
									if consider_target:
												for src, tgt in zip(lowerCamelCase__						,      lowerCamelCase__    ):
															max_lens.append(max(lowerCamelCase__						,      lowerCamelCase__    )    )
									else:
												max_lens.extend(lowerCamelCase__    )
						return max_lens

			A_	:						str	=     get_lens(lowerCamelCase__    )
			A_	:						Dict	=     SeqaSeqDataset(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      type_path="""val"""						,      **lowerCamelCase__    )
			A_	:						List[Any]	=     get_lens(lowerCamelCase__    )
			pickle_save(lowerCamelCase__						,      train_ds.len_file    )
			pickle_save(lowerCamelCase__						,      val_ds.len_file    )


if __name__ == "__main__":
						fire.Fire(save_len_file)