

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


_UpperCAmelCase		:   Optional[Any]										=       logging.get_logger(__name__)

_UpperCAmelCase		:   List[Any]										=       {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase		:   Optional[Any]										=       {
    """vocab_file""": {
        """yjernite/retribert-base-uncased""": (
            """https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """yjernite/retribert-base-uncased""": (
            """https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

_UpperCAmelCase		:   Union[str, Any]										=       {
    """yjernite/retribert-base-uncased""": 512,
}


_UpperCAmelCase		:   str										=       {
    """yjernite/retribert-base-uncased""": {"""do_lower_case""": True},
}







class  lowercase				(       SCREAMING_SNAKE_CASE_			):
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE					: int       			=			PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE					: Dict       			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE					: str       			=			PRETRAINED_INIT_CONFIGURATION
				__SCREAMING_SNAKE_CASE					: str       			=			RetriBertTokenizer
				__SCREAMING_SNAKE_CASE					: str       			=			['''input_ids''', '''attention_mask''']




				def __init__(					self      ,   snake_case=None      ,   snake_case=None      ,   snake_case=True      ,   snake_case="[UNK]"      ,   snake_case="[SEP]"      ,   snake_case="[PAD]"      ,   snake_case="[CLS]"      ,   snake_case="[MASK]"      ,   snake_case=True      ,   snake_case=None      ,   **snake_case      ,   ):
											super().__init__(
											    __UpperCAmelCase      ,   tokenizer_file=__UpperCAmelCase      ,   do_lower_case=__UpperCAmelCase      ,   unk_token=__UpperCAmelCase      ,   sep_token=__UpperCAmelCase      ,   pad_token=__UpperCAmelCase      ,   cls_token=__UpperCAmelCase      ,   mask_token=__UpperCAmelCase      ,   tokenize_chinese_chars=__UpperCAmelCase      ,   strip_accents=__UpperCAmelCase      ,   **__UpperCAmelCase      ,   )

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

											snake_case_							=    do_lower_case




				def     a							(					self      ,   snake_case      ,   snake_case=None       ):
											snake_case_							=    [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     a							(					self      ,   snake_case      ,   snake_case = None       ):
											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      ,   snake_case      ,   snake_case = None       ):
											snake_case_							=    self._tokenizer.model.save(__UpperCAmelCase      ,   name=__UpperCAmelCase       )
											return tuple(__UpperCAmelCase       )









'''simple docstring'''





def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase = "The quick brown fox jumps over the lazy dog"							,    ):



    """simple docstring"""





    lowerCAmelCase__ :  str							      =					set()

    # Replace all the whitespace in our sentence
    lowerCAmelCase__ :  Tuple							      =					input_str.replace(""" """							,    """"""			)
    for alpha in input_str:
        if "a" <= alpha.lower() <= "z":
            frequency.add(alpha.lower()			)
    return len(UpperCamelCase			) == 26
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase = "The quick brown fox jumps over the lazy dog"							,    ):



    """simple docstring"""





    lowerCAmelCase__ :  Any							      =					[False] * 26
    for char in input_str:
        if char.islower():
            lowerCAmelCase__ :  Optional[Any]							      =					True
        elif char.isupper():
            lowerCAmelCase__ :  Any							      =					True
    return all(UpperCamelCase			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase = "The quick brown fox jumps over the lazy dog"							,    ):



    """simple docstring"""





    return len({char for char in input_str.lower() if char.isalpha()}			) == 26
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    from timeit import timeit

    lowerCAmelCase__ :  Union[str, Any]							      =					"""from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
    print(timeit("""is_pangram()"""							,    setup=UpperCamelCase			)			)
    print(timeit("""is_pangram_faster()"""							,    setup=UpperCamelCase			)			)
    print(timeit("""is_pangram_fastest()"""							,    setup=UpperCamelCase			)			)
    # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
    # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()


