
'''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_layoutlm import LayoutLMTokenizer


UpperCAmelCase            = logging.get_logger(__name__)

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

UpperCAmelCase            = {
    "vocab_file": {
        "microsoft/layoutlm-base-uncased": (
            "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt"
        ),
        "microsoft/layoutlm-large-uncased": (
            "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "microsoft/layoutlm-base-uncased": (
            "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json"
        ),
        "microsoft/layoutlm-large-uncased": (
            "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase            = {
    "microsoft/layoutlm-base-uncased": 512,
    "microsoft/layoutlm-large-uncased": 512,
}

UpperCAmelCase            = {
    "microsoft/layoutlm-base-uncased": {"do_lower_case": True},
    "microsoft/layoutlm-large-uncased": {"do_lower_case": True},
}

class     a (					__magic_name__     ):
				_snake_case		     =			VOCAB_FILES_NAMES
				_snake_case		     =			PRETRAINED_VOCAB_FILES_MAP
				_snake_case		     =			PRETRAINED_INIT_CONFIGURATION
				_snake_case		     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		     =			LayoutLMTokenizer




				def __init__(				self						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					List[Any]=None,     SCREAMING_SNAKE_CASE_						:					Dict=None,     SCREAMING_SNAKE_CASE_						:					Tuple=True,     SCREAMING_SNAKE_CASE_						:					List[Any]="[UNK]",     SCREAMING_SNAKE_CASE_						:					Dict="[SEP]",     SCREAMING_SNAKE_CASE_						:					Dict="[PAD]",     SCREAMING_SNAKE_CASE_						:					int="[CLS]",     SCREAMING_SNAKE_CASE_						:					Union[str, Any]="[MASK]",     SCREAMING_SNAKE_CASE_						:					List[Any]=True,     SCREAMING_SNAKE_CASE_						:					Optional[int]=None,     **SCREAMING_SNAKE_CASE_						:					str,     ):
										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_,     )

										snake_case							:  Optional[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
										):
																snake_case							:  List[str]			=     getattr(SCREAMING_SNAKE_CASE_,     normalizer_state.pop('''type'''   )   )
																snake_case							:  Dict			=     do_lower_case
																snake_case							:  List[str]			=     strip_accents
																snake_case							:  List[Any]			=     tokenize_chinese_chars
																snake_case							:  Optional[int]			=     normalizer_class(**SCREAMING_SNAKE_CASE_   )

										snake_case							:  List[str]			=     do_lower_case




				def 						__snake_case				(				self						:					str,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Optional[int]=None   ):
										snake_case							:  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 						__snake_case				(				self						:					str,     SCREAMING_SNAKE_CASE_						:					List[int],     SCREAMING_SNAKE_CASE_						:					Optional[List[int]] = None   ):
										snake_case							:  str			=     [self.sep_token_id]
										snake_case							:  Tuple			=     [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 						__snake_case				(				self						:					str,     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					Optional[str] = None   ):
										snake_case							:  str			=     self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,     name=SCREAMING_SNAKE_CASE_   )
										return tuple(SCREAMING_SNAKE_CASE_   )



'''simple docstring'''





import math
from timeit import timeit

def   A			(							A_				:       int							):
						snake_case							:  List[str]			=     0
						snake_case							:  Any			=     abs(A_							)
						while True:
												snake_case							:  List[Any]			=     n // 10
												digits += 1
												if n == 0:
																		break
						return digits

def   A			(							A_				:       int							):
						return 1 if n == 0 else math.floor(math.log(abs(A_							)     ,				10							) + 1							)

def   A			(							A_				:       int							):
						return len(str(abs(A_							)							)							)

def   A			(							):
						from collections.abc import Callable

						def benchmark_a_function(A_				:       Callable     ,				A_				:       int							) -> None:
												snake_case							:  Optional[int]			=     F"""{func.__name__}({value})"""
												snake_case							:  Optional[int]			=     timeit(F"""__main__.{call}"""     ,				setup='''import __main__'''							)
												print(F"""{call}: {func(A_							)} -- {timing} seconds"""							)

						for value in (262144, 1125899906842624, 1267650600228229401496703205376):
												for func in (num_digits, num_digits_fast, num_digits_faster):
																		benchmark_a_function(A_     ,				A_							)
												print()


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()

