


'''simple docstring'''



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

import sentencepiece as spm

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


__A	='▁'

__A	={'vocab_file': 'spiece.model'}

__A	={
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

__A	={
    'google/pegasus-xsum': 5_12,
}


__A	=logging.get_logger(__name__)


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict   =					VOCAB_FILES_NAMES

					lowerCAmelCase      :int   =					VOCAB_FILES_NAMES
					lowerCAmelCase      :Optional[int]   =					PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase      :str   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase      :int   =					['''input_ids''', '''attention_mask''']




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase="<pad>"							,							_lowerCamelCase="</s>"							,							_lowerCamelCase="<unk>"							,							_lowerCamelCase="<mask_2>"							,							_lowerCamelCase="<mask_1>"							,							_lowerCamelCase=None							,							_lowerCamelCase=103							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__  :					Dict        =     offset
								if additional_special_tokens is not None:
											if not isinstance(_lowerCamelCase							,							_lowerCamelCase):
														raise TypeError(
														    f'''additional_special_tokens should be of type {type(_lowerCamelCase)}, but is'''
														    f''' {type(_lowerCamelCase)}''')

											UpperCAmelCase__  :					Any        =     (
											    ([mask_token_sent] + additional_special_tokens)
											    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
											    else additional_special_tokens
											)
											# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
											additional_special_tokens_extended += [
											    f'''<unk_{i}>''' for i in range(len(_lowerCamelCase)							,							self.offset - 1)
											]

											if len(set(_lowerCamelCase)) != len(_lowerCamelCase):
														raise ValueError(
														    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
														    f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''')
											UpperCAmelCase__  :					int        =     additional_special_tokens_extended
								else:
											UpperCAmelCase__  :					Tuple        =     [mask_token_sent] if mask_token_sent is not None else []
											additional_special_tokens += [f'''<unk_{i}>''' for i in range(2							,							self.offset)]

								UpperCAmelCase__  :					Union[str, Any]        =     {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    eos_token=_lowerCamelCase							,							unk_token=_lowerCamelCase							,							mask_token=_lowerCamelCase							,							pad_token=_lowerCamelCase							,							mask_token_sent=_lowerCamelCase							,							offset=_lowerCamelCase							,							additional_special_tokens=_lowerCamelCase							,							sp_model_kwargs=self.sp_model_kwargs							,							**_lowerCamelCase							,							)
								UpperCAmelCase__  :					Union[str, Any]        =     mask_token_sent
								UpperCAmelCase__  :					str        =     vocab_file
								UpperCAmelCase__  :					Optional[int]        =     spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(_lowerCamelCase)

								# add special tokens to encoder dict
								UpperCAmelCase__  :					Dict[int, str]        =     {
								    0: self.pad_token,
								    1: self.eos_token,
								}

								if self.mask_token_sent is not None:
											self.encoder.update(
											    {
											        2: self.mask_token_sent,
											        3: self.mask_token,
											    })

								if self.offset > 0:
											# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
											# mask_token_sent is already added to list -> so start at 1
											self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1							,							self.offset - 1)})

								UpperCAmelCase__  :					Dict[str, int]        =     {v: k for k, v in self.encoder.items()}




					@property
					def 				snake_case__						(      self):
								return len(self.sp_model) + self.offset




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[str]        =     {self.convert_ids_to_tokens(_lowerCamelCase): i for i in range(self.vocab_size)}
								vocab.update(self.added_tokens_encoder)
								return vocab




					def __getstate__(      self):
								UpperCAmelCase__  :					Dict        =     self.__dict__.copy()
								UpperCAmelCase__  :					Tuple        =     None
								return state




					def __setstate__(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Tuple        =     d

								# for backward compatibility
								if not hasattr(self							,							"""sp_model_kwargs"""):
											UpperCAmelCase__  :					List[str]        =     {}

								UpperCAmelCase__  :					Union[str, Any]        =     spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(self.vocab_file)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								return self.sp_model.encode(_lowerCamelCase							,							out_type=_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								if token in self.decoder:
											return self.decoder[token]
								elif token in self.added_tokens_decoder:
											return self.added_tokens_decoder[token]
								UpperCAmelCase__  :					Any        =     self.sp_model.piece_to_id(_lowerCamelCase)
								return sp_id + self.offset




					def 				snake_case__						(      self							,							_lowerCamelCase):
								if index in self.encoder:
											return self.encoder[index]
								elif index in self.added_tokens_encoder:
											return self.added_tokens_encoder[index]
								else:
											UpperCAmelCase__  :					Optional[int]        =     self.sp_model.IdToPiece(index - self.offset)
								return token




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					List[Any]        =     []
								UpperCAmelCase__  :					List[Any]        =     """"""
								for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
														out_string += self.sp_model.decode(_lowerCamelCase) + token
														UpperCAmelCase__  :					Tuple        =     []
											else:
														current_sub_tokens.append(_lowerCamelCase)
								out_string += self.sp_model.decode(_lowerCamelCase)
								return out_string.strip()




					def 				snake_case__						(      self							,							_lowerCamelCase=False):
								return 1




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[Any]        =     set(self.all_special_ids)  # call it once instead of inside list comp
								all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

								return [1 if x in all_special_ids else 0 for x in seq]




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = False):
								if already_has_special_tokens:
											return self._special_token_mask(_lowerCamelCase)
								elif token_ids_a is None:
											return self._special_token_mask(_lowerCamelCase) + [1]
								else:
											return self._special_token_mask(token_ids_a + token_ids_a) + [1]




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase=None):
								if token_ids_a is None:
											return token_ids_a + [self.eos_token_id]
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return token_ids_a + token_ids_a + [self.eos_token_id]




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								if not os.path.isdir(_lowerCamelCase):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
											return
								UpperCAmelCase__  :					List[Any]        =     os.path.join(
								    _lowerCamelCase							,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

								if os.path.abspath(self.vocab_file) != os.path.abspath(_lowerCamelCase) and os.path.isfile(self.vocab_file):
											copyfile(self.vocab_file							,							_lowerCamelCase)
								elif not os.path.isfile(self.vocab_file):
											with open(_lowerCamelCase							,							"""wb""") as fi:
														UpperCAmelCase__  :					Dict        =     self.sp_model.serialized_model_proto()
														fi.write(_lowerCamelCase)

								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__)