
'''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_squeezebert import SqueezeBertTokenizer


lowercase			:				Tuple    		=			logging.get_logger(__name__)

lowercase			:				Optional[Any]    		=			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase			:				Union[str, Any]    		=			{
    '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'
        ),
    },
}

lowercase			:				List[str]    		=			{
    'squeezebert/squeezebert-uncased': 5_12,
    'squeezebert/squeezebert-mnli': 5_12,
    'squeezebert/squeezebert-mnli-headless': 5_12,
}


lowercase			:				List[str]    		=			{
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}





class  A				(							__snake_case						):
     __magic_name__						=		VOCAB_FILES_NAMES
     __magic_name__						=		PRETRAINED_VOCAB_FILES_MAP
     __magic_name__						=		PRETRAINED_INIT_CONFIGURATION
     __magic_name__						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__						=		SqueezeBertTokenizer





     def __init__(							self						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE="[UNK]"						,			SCREAMING_SNAKE_CASE="[SEP]"						,			SCREAMING_SNAKE_CASE="[PAD]"						,			SCREAMING_SNAKE_CASE="[CLS]"						,			SCREAMING_SNAKE_CASE="[MASK]"						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=None						,			**SCREAMING_SNAKE_CASE						,			) ->				int:






         """simple docstring"""






         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						,			)

         A       :   int					=						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
         ):
             A       :   Union[str, Any]					=						getattr(SCREAMING_SNAKE_CASE						,			normalizer_state.pop('''type'''	)	)
             A       :   Dict					=						do_lower_case
             A       :   Dict					=						strip_accents
             A       :   Any					=						tokenize_chinese_chars
             A       :   Optional[int]					=						normalizer_class(**SCREAMING_SNAKE_CASE	)

         A       :   str					=						do_lower_case





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=None	) ->				Optional[Any]:






         """simple docstring"""






         A       :   List[str]					=						[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						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				List[int]:






         """simple docstring"""






         A       :   Union[str, Any]					=						[self.sep_token_id]
         A       :   Dict					=						[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						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				Tuple[str]:






         """simple docstring"""






         A       :   str					=						self._tokenizer.model.save(SCREAMING_SNAKE_CASE						,			name=SCREAMING_SNAKE_CASE	)
         return tuple(SCREAMING_SNAKE_CASE	)






'''simple docstring'''




import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset







@pytest.mark.parametrize('''dataset_size'''		,				[None, 400 * 2**20, 600 * 2**20]						)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,				['''default''', 0, 100 * 2**20, 900 * 2**20]						)
def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    if input_in_memory_max_size != "default":
        monkeypatch.setattr(datasets.config		,				'''IN_MEMORY_MAX_SIZE'''		,				snake_case__						)
    A       :   Dict					=						datasets.config.IN_MEMORY_MAX_SIZE
    if input_in_memory_max_size == "default":
        assert in_memory_max_size == 0
    else:
        assert in_memory_max_size == input_in_memory_max_size
    if dataset_size and in_memory_max_size:
        A       :   Dict					=						dataset_size < in_memory_max_size
    else:
        A       :   Tuple					=						False
    A       :   int					=						is_small_dataset(snake_case__						)
    assert result == expected




