






'''simple docstring'''







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

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


_A      :     Dict       	=logging.get_logger(__name__)

_A      :     str       	={'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_A      :     Optional[Any]       	={
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json''',
    },
}

class _lowercase      (							_lowercase						):




							def __init__(				self:							List[Any]     ,				UpperCamelCase__:							Optional[int]=None     ,				UpperCamelCase__:							Optional[int]=None     ,				UpperCamelCase__:							List[str]=False     ,				UpperCamelCase__:							Optional[int]=True     ,				UpperCamelCase__:							str=False     ,				UpperCamelCase__:							Optional[int]="<s>"     ,				UpperCamelCase__:							str="</s>"     ,				UpperCamelCase__:							Any="<unk>"     ,				UpperCamelCase__:							Any="<sep>"     ,				UpperCamelCase__:							Optional[Any]="<pad>"     ,				UpperCamelCase__:							int="<cls>"     ,				UpperCamelCase__:							Union[str, Any]="<mask>"     ,				UpperCamelCase__:							List[Any]=["<eop>", "<eod>"]     ,				**UpperCamelCase__:							Tuple     ,				):
														lowerCamelCase__   :      Union[str, Any]			    =			AddedToken(UpperCamelCase__     ,				lstrip=UpperCamelCase__     ,				rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__     ,				UpperCamelCase__	) else mask_token

														super().__init__(
														    vocab_file=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__     ,				additional_special_tokens=UpperCamelCase__     ,				**UpperCamelCase__     ,				)

														lowerCamelCase__   :      int			    =			3
														lowerCamelCase__   :      Tuple			    =			do_lower_case
														lowerCamelCase__   :      List[str]			    =			remove_space
														lowerCamelCase__   :      Optional[int]			    =			keep_accents
														lowerCamelCase__   :      Union[str, Any]			    =			vocab_file
														lowerCamelCase__   :      Union[str, Any]			    =			False if not self.vocab_file else True

														try:
																					import jieba
														except ModuleNotFoundError as error:
																					raise error.__class__(
																					    """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
																					    """See https://pypi.org/project/jieba/ for installation."""	)
														lowerCamelCase__   :      Union[str, Any]			    =			jieba
														lowerCamelCase__   :      Union[str, Any]			    =			str.maketrans(""" \n"""     ,				"""\u2582\u2583"""	)




							def 						lowerCamelCase_							(				self:							Any     ,				UpperCamelCase__:							List[int]     ,				UpperCamelCase__:							Optional[List[int]] = None	):
														lowerCamelCase__   :      Optional[int]			    =			[self.sep_token_id]
														lowerCamelCase__   :      Tuple			    =			[self.cls_token_id]
														if token_ids_a is None:
																					return token_ids_a + sep + cls
														return token_ids_a + sep + token_ids_a + sep + cls




							def 						lowerCamelCase_							(				self:							Optional[int]     ,				UpperCamelCase__:							List[int]     ,				UpperCamelCase__:							Optional[List[int]] = None	):
														lowerCamelCase__   :      Dict			    =			[self.sep_token_id]
														lowerCamelCase__   :      Any			    =			[2]

														if token_ids_a is None:
																					return len(token_ids_a + sep	) * [0] + cls_segment_id
														return len(token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1] + cls_segment_id




							def 						lowerCamelCase_							(				self:							Union[str, Any]     ,				UpperCamelCase__:							str     ,				UpperCamelCase__:							Optional[str] = None	):
														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
														lowerCamelCase__   :      Tuple			    =			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,)




							def 						lowerCamelCase_							(				self:							str     ,				UpperCamelCase__:							Optional[Any]     ,				*UpperCamelCase__:							Dict     ,				**UpperCamelCase__:							List[Any]	):
														lowerCamelCase__   :      Union[str, Any]			    =			[
														    """ """.join([x.translate(self.translator	) for x in self.jieba.cut(UpperCamelCase__     ,				cut_all=UpperCamelCase__	)]	)
														    for text in batch_text_or_text_pairs
														]
														return super()._batch_encode_plus(UpperCamelCase__     ,				*UpperCamelCase__     ,				**UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							Any     ,				*UpperCamelCase__:							Dict     ,				**UpperCamelCase__:							Tuple	):
														lowerCamelCase__   :      List[Any]			    =			super()._decode(*UpperCamelCase__     ,				**UpperCamelCase__	)
														lowerCamelCase__   :      Dict			    =			text.replace(""" """     ,				""""""	).replace("""\u2582"""     ,				""" """	).replace("""\u2583"""     ,				"""\n"""	)
														return text









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_A      :     Optional[Any]       	={
    '''configuration_realm''': ['''REALM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RealmConfig'''],
    '''tokenization_realm''': ['''RealmTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     str       	=['''RealmTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     List[Any]       	=[
			    '''REALM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''RealmEmbedder''',
			    '''RealmForOpenQA''',
			    '''RealmKnowledgeAugEncoder''',
			    '''RealmPreTrainedModel''',
			    '''RealmReader''',
			    '''RealmScorer''',
			    '''load_tf_weights_in_realm''',
			]
			_A      :     Union[str, Any]       	=['''RealmRetriever''']


if TYPE_CHECKING:
			from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
			from .tokenization_realm import RealmTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_realm import RealmTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_realm import (
						    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RealmEmbedder,
						    RealmForOpenQA,
						    RealmKnowledgeAugEncoder,
						    RealmPreTrainedModel,
						    RealmReader,
						    RealmScorer,
						    load_tf_weights_in_realm,
						)
						from .retrieval_realm import RealmRetriever


else:
			import sys

			_A      :     int       	=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

