




'''simple docstring'''



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_xlnet import XLNetTokenizer
else:
		__A	=None


__A	=logging.get_logger(__name__)

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

__A	={
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
    },
}

__A	={
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

__A	='▁'

# Segments (not really needed)
__A	=0
__A	=1
__A	=2
__A	=3
__A	=4


class 		_snake_case					(     lowercase__	):
					lowerCAmelCase      :List[Any]   =					VOCAB_FILES_NAMES
					lowerCAmelCase      :Optional[Any]   =					PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase      :Dict   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase      :Dict   =					'''left'''
					lowerCAmelCase      :Union[str, Any]   =					XLNetTokenizer




					def __init__(      self							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							_lowerCamelCase=False							,							_lowerCamelCase=True							,							_lowerCamelCase=False							,							_lowerCamelCase="<s>"							,							_lowerCamelCase="</s>"							,							_lowerCamelCase="<unk>"							,							_lowerCamelCase="<sep>"							,							_lowerCamelCase="<pad>"							,							_lowerCamelCase="<cls>"							,							_lowerCamelCase="<mask>"							,							_lowerCamelCase=["<eop>", "<eod>"]							,							**_lowerCamelCase							,							):
								UpperCAmelCase__  :					List[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__							,							)

								UpperCAmelCase__  :					List[Any]        =     3
								UpperCAmelCase__  :					Tuple        =     do_lower_case
								UpperCAmelCase__  :					Tuple        =     remove_space
								UpperCAmelCase__  :					Optional[Any]        =     keep_accents
								UpperCAmelCase__  :					Any        =     vocab_file
								UpperCAmelCase__  :					Optional[Any]        =     False if not self.vocab_file else True




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__  :					Optional[Any]        =     [self.sep_token_id]
								UpperCAmelCase__  :					Union[str, Any]        =     [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 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__  :					str        =     [self.sep_token_id]
								UpperCAmelCase__  :					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 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = 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
								UpperCAmelCase__  :					List[Any]        =     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,)



'''simple docstring'''



from typing import TYPE_CHECKING

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


__A	={
    'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'NezhaForNextSentencePrediction',
		    'NezhaForMaskedLM',
		    'NezhaForPreTraining',
		    'NezhaForMultipleChoice',
		    'NezhaForQuestionAnswering',
		    'NezhaForSequenceClassification',
		    'NezhaForTokenClassification',
		    'NezhaModel',
		    'NezhaPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_nezha import (
				    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    NezhaForMaskedLM,
				    NezhaForMultipleChoice,
				    NezhaForNextSentencePrediction,
				    NezhaForPreTraining,
				    NezhaForQuestionAnswering,
				    NezhaForSequenceClassification,
				    NezhaForTokenClassification,
				    NezhaModel,
				    NezhaPreTrainedModel,
				)


else:
		import sys

		__A	=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)