



'''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


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

a     :    int      						=					{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

a     :    Optional[int]      						=					{
    """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"""
        ),
    },
}

a     :    Union[str, Any]      						=					{
    """squeezebert/squeezebert-uncased""": 5_1_2,
    """squeezebert/squeezebert-mnli""": 5_1_2,
    """squeezebert/squeezebert-mnli-headless""": 5_1_2,
}


a     :    Tuple      						=					{
    """squeezebert/squeezebert-uncased""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli-headless""": {"""do_lower_case""": True},
}

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				VOCAB_FILES_NAMES
	lowercase    =				PRETRAINED_VOCAB_FILES_MAP
	lowercase    =				PRETRAINED_INIT_CONFIGURATION
	lowercase    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase    =				SqueezeBertTokenizer





	def __init__(						self	,	A=None	,	A=None	,	A=True	,	A="[UNK]"	,	A="[SEP]"	,	A="[PAD]"	,	A="[CLS]"	,	A="[MASK]"	,	A=True	,	A=None	,	**A	,	)    ->    Union[str, Any]:
								super().__init__(
								    A	,	tokenizer_file=A	,	do_lower_case=A	,	unk_token=A	,	sep_token=A	,	pad_token=A	,	cls_token=A	,	mask_token=A	,	tokenize_chinese_chars=A	,	strip_accents=A	,	**A	,	)

								UpperCAmelCase  :	Dict							  =	json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
								if (
								    normalizer_state.get("""lowercase"""	,	A					) != do_lower_case
								    or normalizer_state.get("""strip_accents"""	,	A					) != strip_accents
								    or normalizer_state.get("""handle_chinese_chars"""	,	A					) != tokenize_chinese_chars
								):
															UpperCAmelCase  :	Tuple							  =	getattr(A	,	normalizer_state.pop("""type"""					)					)
															UpperCAmelCase  :	Optional[int]							  =	do_lower_case
															UpperCAmelCase  :	Any							  =	strip_accents
															UpperCAmelCase  :	List[Any]							  =	tokenize_chinese_chars
															UpperCAmelCase  :	Dict							  =	normalizer_class(**A					)

								UpperCAmelCase  :	int							  =	do_lower_case





	def 				_lowercase(						self	,	A	,	A=None					)    ->    Union[str, Any]:
								UpperCAmelCase  :	Optional[Any]							  =	[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 				_lowercase(						self	,	A	,	A = None					)    ->    List[int]:
								UpperCAmelCase  :	Optional[Any]							  =	[self.sep_token_id]
								UpperCAmelCase  :	List[str]							  =	[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 				_lowercase(						self	,	A	,	A = None					)    ->    Tuple[str]:
								UpperCAmelCase  :	Optional[int]							  =	self._tokenizer.model.save(A	,	name=A					)
								return tuple(A					)






'''simple docstring'''




def    __lowerCamelCase						(      _lowercase = "The quick brown fox jumps over the lazy dog"			,       )   ->    bool:
							UpperCAmelCase  :	Union[str, Any]							  =	set()

							# Replace all the whitespace in our sentence
							UpperCAmelCase  :	List[str]							  =	input_str.replace(""" """			,       """"""	)
							for alpha in input_str:
														if "a" <= alpha.lower() <= "z":
																					frequency.add(alpha.lower()	)
							return len(_lowercase	) == 2_6



def    __lowerCamelCase						(      _lowercase = "The quick brown fox jumps over the lazy dog"			,       )   ->    bool:
							UpperCAmelCase  :	Tuple							  =	[False] * 2_6
							for char in input_str:
														if char.islower():
																					UpperCAmelCase  :	Any							  =	True
														elif char.isupper():
																					UpperCAmelCase  :	Union[str, Any]							  =	True
							return all(_lowercase	)



def    __lowerCamelCase						(      _lowercase = "The quick brown fox jumps over the lazy dog"			,       )   ->    bool:
							return len({char for char in input_str.lower() if char.isalpha()}	) == 2_6



def    __lowerCamelCase						(      )   ->    None:
							from timeit import timeit

							UpperCAmelCase  :	str							  =	"""from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
							print(timeit("""is_pangram()"""			,       setup=_lowercase	)	)
							print(timeit("""is_pangram_faster()"""			,       setup=_lowercase	)	)
							print(timeit("""is_pangram_fastest()"""			,       setup=_lowercase	)	)
							# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
							# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							benchmark()

