




import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
			import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__		:					List[str]							=logging.get_logger(__name__)

lowerCAmelCase__		:					str							={'''vocab_file''': '''vocab.txt'''}

lowerCAmelCase__		:					List[Any]							={
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

lowerCAmelCase__		:					Optional[Any]							={
    '''openbmb/cpm-ant-10b''': 1024,
}






def      __lowercase       (			a__       )    ->			Optional[Any]:
							__SCREAMING_SNAKE_CASE									= collections.OrderedDict()
							with open(a__							,       'r'							,       encoding='utf-8'       ) as reader:
														__SCREAMING_SNAKE_CASE									= reader.readlines()
							for index, token in enumerate(a__       ):
														__SCREAMING_SNAKE_CASE									= token.rstrip('\n'       )
														__SCREAMING_SNAKE_CASE									= index
							return vocab

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A="<unk>"				,							_A=200		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= vocab
													__SCREAMING_SNAKE_CASE									= unk_token
													__SCREAMING_SNAKE_CASE									= max_input_chars_per_word






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= list(_A		)
													if len(_A		) > self.max_input_chars_per_word:
																				return [self.unk_token]

													__SCREAMING_SNAKE_CASE									= 0
													__SCREAMING_SNAKE_CASE									= []
													while start < len(_A		):
																				__SCREAMING_SNAKE_CASE									= len(_A		)
																				__SCREAMING_SNAKE_CASE									= None
																				while start < end:
																											__SCREAMING_SNAKE_CASE									= ''.join(chars[start:end]		)
																											if substr in self.vocab:
																																		__SCREAMING_SNAKE_CASE									= substr
																																		break
																											end -= 1
																				if cur_substr is None:
																											sub_tokens.append(self.unk_token		)
																											start += 1
																				else:
																											sub_tokens.append(_A		)
																											__SCREAMING_SNAKE_CASE									= end

													return sub_tokens







class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Tuple      =  VOCAB_FILES_NAMES
						UpperCamelCase__      :			Any      =  PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase__      :			List[str]      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase__      :			Any      =  ['''input_ids''', '''attention_mask''']
						UpperCamelCase__      :			Dict      =  False






						def __init__(   self				,							_A				,							_A="<d>"				,							_A="</d>"				,							_A="<s>"				,							_A="</s>"				,							_A="<pad>"				,							_A="<unk>"				,							_A="</n>"				,							_A="</_>"				,							_A="left"				,							**_A				,							):







													'''simple docstring'''

													requires_backends(self				,							['jieba']		)
													super().__init__(
													    bod_token=_A				,							eod_token=_A				,							bos_token=_A				,							eos_token=_A				,							pad_token=_A				,							unk_token=_A				,							line_token=_A				,							space_token=_A				,							padding_side=_A				,							**_A				,							)
													__SCREAMING_SNAKE_CASE									= bod_token
													__SCREAMING_SNAKE_CASE									= eod_token
													__SCREAMING_SNAKE_CASE									= load_vocab(_A		)
													__SCREAMING_SNAKE_CASE									= self.encoder[space_token]
													__SCREAMING_SNAKE_CASE									= self.encoder[line_token]

													del self.encoder[space_token]
													del self.encoder[line_token]

													__SCREAMING_SNAKE_CASE									= collections.OrderedDict(sorted(self.encoder.items()				,							key=lambda _A		: x[1]		)		)
													__SCREAMING_SNAKE_CASE									= {v: k for k, v in self.encoder.items()}

													__SCREAMING_SNAKE_CASE									= WordpieceTokenizer(vocab=self.encoder				,							unk_token=self.unk_token		)






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return self.encoder[self.bod_token]






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return self.encoder[self.eod_token]






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return self.encoder["\n"]






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return len(self.encoder		)






						def 	_A     (   self		):







													'''simple docstring'''

													return dict(self.encoder				,							**self.added_tokens_encoder		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= []
													for x in jieba.cut(_A				,							cut_all=_A		):
																				output_tokens.extend(self.wordpiece_tokenizer.tokenize(_A		)		)
													return output_tokens






						def 	_A     (   self				,							_A				,							**_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= [i for i in token_ids if i >= 0]
													__SCREAMING_SNAKE_CASE									= [
													    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
													]
													return super()._decode(_A				,							**_A		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													return token in self.encoder






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													return "".join(_A		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													return self.encoder.get(_A				,							self.encoder.get(self.unk_token		)		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													return self.decoder.get(_A				,							self.unk_token		)






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







													'''simple docstring'''

													if os.path.isdir(_A		):
																				__SCREAMING_SNAKE_CASE									= os.path.join(
																				    _A				,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)
													else:
																				__SCREAMING_SNAKE_CASE									= (filename_prefix + '-' if filename_prefix else '') + save_directory
													__SCREAMING_SNAKE_CASE									= 0
													if " " in self.encoder:
																				__SCREAMING_SNAKE_CASE									= self.encoder[' ']
																				del self.encoder[" "]
													if "\n" in self.encoder:
																				__SCREAMING_SNAKE_CASE									= self.encoder['\n']
																				del self.encoder["\n"]
													__SCREAMING_SNAKE_CASE									= collections.OrderedDict(sorted(self.encoder.items()				,							key=lambda _A		: x[1]		)		)
													with open(_A				,							'w'				,							encoding='utf-8'		) as writer:
																				for token, token_index in self.encoder.items():
																											if index != token_index:
																																		logger.warning(
																																		    f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
																																		    ' Please check that the vocabulary is not corrupted!'		)
																																		__SCREAMING_SNAKE_CASE									= token_index
																											writer.write(token + '\n'		)
																											index += 1
													return (vocab_file,)






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







													'''simple docstring'''

													if token_ids_a is None:
																				return [self.bos_token_id] + token_ids_a
													return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a






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







													'''simple docstring'''

													if already_has_special_tokens:
																				return super().get_special_tokens_mask(
																				    token_ids_a=_A				,							token_ids_a=_A				,							already_has_special_tokens=_A		)

													if token_ids_a is not None:
																				return [1] + ([0] * len(_A		)) + [1] + ([0] * len(_A		))
													return [1] + ([0] * len(_A		))











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






