




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_albert import AlbertTokenizer
else:
			lowerCAmelCase__		:					Any							=None

lowerCAmelCase__		:					List[Any]							=logging.get_logger(__name__)
lowerCAmelCase__		:					Any							={'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__		:					List[Any]							={
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase__		:					Optional[int]							={
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

lowerCAmelCase__		:					List[str]							='▁'

class        UpperCAmelCase_  (       lowercase__	):

						'''simple docstring'''




						UpperCamelCase__      :			Tuple      =  VOCAB_FILES_NAMES
						UpperCamelCase__      :			List[Any]      =  PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase__      :			Optional[int]      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase__      :			Optional[Any]      =  AlbertTokenizer






						def __init__(   self				,							_A=None				,							_A=None				,							_A=True				,							_A=True				,							_A=False				,							_A="[CLS]"				,							_A="[SEP]"				,							_A="<unk>"				,							_A="[SEP]"				,							_A="<pad>"				,							_A="[CLS]"				,							_A="[MASK]"				,							**_A				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= (
													    AddedToken(_UpperCamelCase				,							lstrip=_UpperCamelCase				,							rstrip=_UpperCamelCase				,							normalized=_UpperCamelCase		)
													    if isinstance(_UpperCamelCase				,							_UpperCamelCase		)
													    else mask_token
													)

													super().__init__(
													    _UpperCamelCase				,							tokenizer_file=_UpperCamelCase				,							do_lower_case=_UpperCamelCase				,							remove_space=_UpperCamelCase				,							keep_accents=_UpperCamelCase				,							bos_token=_UpperCamelCase				,							eos_token=_UpperCamelCase				,							unk_token=_UpperCamelCase				,							sep_token=_UpperCamelCase				,							pad_token=_UpperCamelCase				,							cls_token=_UpperCamelCase				,							mask_token=_UpperCamelCase				,							**_UpperCamelCase				,							)

													__SCREAMING_SNAKE_CASE									= do_lower_case
													__SCREAMING_SNAKE_CASE									= remove_space
													__SCREAMING_SNAKE_CASE									= keep_accents
													__SCREAMING_SNAKE_CASE									= vocab_file
													__SCREAMING_SNAKE_CASE									= False if not self.vocab_file else True






						def 	_A     (   self				,							_A				,							_A = None		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= [self.sep_token_id]
													__SCREAMING_SNAKE_CASE									= [self.cls_token_id]
													if token_ids_a is None:
																				return cls + token_ids_a + sep
													return cls + token_ids_a + sep + token_ids_a + sep






						def 	_A     (   self				,							_A				,							_A = None		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= [self.sep_token_id]
													__SCREAMING_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				,							_A				,							_A = None		):







													'''simple docstring'''

													if not self.can_save_slow_tokenizer:
																				raise ValueError(
																				    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
																				    'tokenizer.'		)

													if not os.path.isdir(_UpperCamelCase		):
																				logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
																				return
													__SCREAMING_SNAKE_CASE									= os.path.join(
													    _UpperCamelCase				,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

													if os.path.abspath(self.vocab_file		) != os.path.abspath(_UpperCamelCase		):
																				copyfile(self.vocab_file				,							_UpperCamelCase		)

													return (out_vocab_file,)











import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class        UpperCAmelCase_  (       UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[int]      =  LayoutLMTokenizer
						UpperCamelCase__      :			Any      =  LayoutLMTokenizerFast
						UpperCamelCase__      :			Optional[int]      =  True
						UpperCamelCase__      :			int      =  True






						def 	_A     (   self		):







													'''simple docstring'''

													super().setUp()

													__SCREAMING_SNAKE_CASE									= [
													    '[UNK]',
													    '[CLS]',
													    '[SEP]',
													    'want',
													    '##want',
													    '##ed',
													    'wa',
													    'un',
													    'runn',
													    '##ing',
													    ',',
													    'low',
													    'lowest',
													]
													__SCREAMING_SNAKE_CASE									= os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES['vocab_file']		)
													with open(self.vocab_file				,							'w'				,							encoding='utf-8'		) as vocab_writer:
																				vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)






						def 	_A     (   self				,							**_A		):







													'''simple docstring'''

													return LayoutLMTokenizer.from_pretrained(self.tmpdirname				,							**_A		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 'UNwant\u00E9d,running'
													__SCREAMING_SNAKE_CASE									= 'unwanted, running'
													return input_text, output_text






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.tokenizer_class(self.vocab_file		)

													__SCREAMING_SNAKE_CASE									= tokenizer.tokenize('UNwant\u00E9d,running'		)
													self.assertListEqual(_A				,							['un', '##want', '##ed', ',', 'runn', '##ing']		)
													self.assertListEqual(tokenizer.convert_tokens_to_ids(_A		)				,							[7, 4, 5, 10, 8, 9]		)






						def 	_A     (   self		):







													'''simple docstring'''

													pass






