


'''simple docstring'''



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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


__lowercase     :						Any	=     logging.get_logger(__name__)

__lowercase     :						List[str]	=     {'vocab_file': 'spiece.model'}

__lowercase     :						str	=     {
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




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

						'''simple docstring'''





						__a      :	List[Any]    =  AddedToken(__a						,    lstrip=__a						,    rstrip=__a							) if isinstance(__a						,    __a							) else mask_token

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

						super().__init__(
						    do_lower_case=__a						,    remove_space=__a						,    keep_accents=__a						,    bos_token=__a						,    eos_token=__a						,    unk_token=__a						,    sep_token=__a						,    pad_token=__a						,    cls_token=__a						,    mask_token=__a						,    additional_special_tokens=__a						,    sp_model_kwargs=self.sp_model_kwargs						,    **__a						,    )

						__a      :	Union[str, Any]    =  3

						__a      :	Dict    =  do_lower_case
						__a      :	List[str]    =  remove_space
						__a      :	Any    =  keep_accents
						__a      :	Tuple    =  vocab_file

						__a      :	Any    =  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
						self.sp_model.Load(__a							)

						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.'							)
						__a      :	List[Any]    =  jieba
						__a      :	List[str]    =  str.maketrans(' \n'						,    '\u2582\u2583'							)




	@property
	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return len(self.sp_model							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  {self.convert_ids_to_tokens(__a							): i for i in range(self.vocab_size							)}
						vocab.update(self.added_tokens_encoder							)
						return vocab




	def __getstate__(       self							):

						'''simple docstring'''





						__a      :	str    =  self.__dict__.copy()
						__a      :	int    =  None
						return state




	def __setstate__(       self						,    __a							):

						'''simple docstring'''





						__a      :	Optional[int]    =  d

						# for backward compatibility
						if not hasattr(self						,    'sp_model_kwargs'							):
											__a      :	Tuple    =  {}

						__a      :	Any    =  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
						self.sp_model.Load(self.vocab_file							)




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						if self.remove_space:
											__a      :	Union[str, Any]    =  ' '.join(inputs.strip().split()							)
						else:
											__a      :	List[Any]    =  inputs
						__a      :	Optional[Any]    =  outputs.replace('``'						,    '"'							).replace('\'\''						,    '"'							)

						if not self.keep_accents:
											__a      :	Any    =  unicodedata.normalize('NFKD'						,    __a							)
											__a      :	List[Any]    =  ''.join([c for c in outputs if not unicodedata.combining(__a							)]							)
						if self.do_lower_case:
											__a      :	List[Any]    =  outputs.lower()

						return outputs




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	Optional[int]    =  self.preprocess_text(__a							)
						__a      :	List[Any]    =  self.sp_model.encode(__a						,    out_type=__a							)
						__a      :	int    =  []
						for piece in pieces:
											if len(__a							) > 1 and piece[-1] == str(','							) and piece[-2].isdigit():
																__a      :	Any    =  self.sp_model.EncodeAsPieces(piece[:-1].replace(__a						,    ''							)							)
																if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																					if len(cur_pieces[0]							) == 1:
																										__a      :	int    =  cur_pieces[1:]
																					else:
																										__a      :	Union[str, Any]    =  cur_pieces[0][1:]
																cur_pieces.append(piece[-1]							)
																new_pieces.extend(__a							)
											else:
																new_pieces.append(__a							)

						return new_pieces




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						return self.sp_model.PieceToId(__a							)




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						return self.sp_model.IdToPiece(__a							)




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	List[str]    =  ''.join(__a							).replace(__a						,    ' '							).strip()
						return out_string




	def 				__UpperCAmelCase							(       self						,    __a						,    __a = None							):

						'''simple docstring'''





						__a      :	List[str]    =  [self.sep_token_id]
						__a      :	List[str]    =  [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 				__UpperCAmelCase							(       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 ([0] * len(__a							)) + [1] + ([0] * len(__a							)) + [1, 1]
						return ([0] * len(__a							)) + [1, 1]




	def 				__UpperCAmelCase							(       self						,    __a						,    __a = None							):

						'''simple docstring'''





						__a      :	Dict    =  [self.sep_token_id]
						__a      :	Tuple    =  [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 				__UpperCAmelCase							(       self						,    __a						,    __a = None							):

						'''simple docstring'''





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

						if os.path.abspath(self.vocab_file							) != os.path.abspath(__a							) and os.path.isfile(self.vocab_file							):
											copyfile(self.vocab_file						,    __a							)
						elif not os.path.isfile(self.vocab_file							):
											with open(__a						,    'wb'							) as fi:
																__a      :	int    =  self.sp_model.serialized_model_proto()
																fi.write(__a							)

						return (out_vocab_file,)





	def 				__UpperCAmelCase							(       self						,    *__a						,    **__a							):

						'''simple docstring'''





						__a      :	int    =  super()._decode(*__a						,    **__a							)
						__a      :	List[Any]    =  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


__lowercase     :						Union[str, Any]	=     {
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	pass

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase     :						List[str]	=     [
	    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'RoCBertForCausalLM',
	    'RoCBertForMaskedLM',
	    'RoCBertForMultipleChoice',
	    'RoCBertForPreTraining',
	    'RoCBertForQuestionAnswering',
	    'RoCBertForSequenceClassification',
	    'RoCBertForTokenClassification',
	    'RoCBertLayer',
	    'RoCBertModel',
	    'RoCBertPreTrainedModel',
	    'load_tf_weights_in_roc_bert',
	]

if TYPE_CHECKING:
	from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
	from .tokenization_roc_bert import RoCBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		raise OptionalDependencyNotAvailable()

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roc_bert import (
		    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RoCBertForCausalLM,
		    RoCBertForMaskedLM,
		    RoCBertForMultipleChoice,
		    RoCBertForPreTraining,
		    RoCBertForQuestionAnswering,
		    RoCBertForSequenceClassification,
		    RoCBertForTokenClassification,
		    RoCBertLayer,
		    RoCBertModel,
		    RoCBertPreTrainedModel,
		    load_tf_weights_in_roc_bert,
		)


else:
	import sys

	__lowercase     :						Any	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



