





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


_SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)

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

_SCREAMING_SNAKE_CASE							=						{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}
class SCREAMING_SNAKE_CASE_  (    __UpperCAmelCase    ):






						def __init__(					self					:					Optional[int]						,      _A					:					List[Any]						,      _A					:					int=False						,      _A					:					Optional[Any]=True						,      _A					:					Union[str, Any]=False						,      _A					:					Dict="<s>"						,      _A					:					int="</s>"						,      _A					:					Union[str, Any]="<unk>"						,      _A					:					Optional[Any]="<sep>"						,      _A					:					str="<pad>"						,      _A					:					Tuple="<cls>"						,      _A					:					int="<mask>"						,      _A					:					Dict=["<eop>", "<eod>"]						,      _A					:					Optional[Dict[str, Any]] = None						,      **_A					:					str						,      )							->			Optional[int]:







							"""simple docstring"""


							snake_case_						:	str      =		AddedToken(__SCREAMING_SNAKE_CASE						,      lstrip=__SCREAMING_SNAKE_CASE						,      rstrip=__SCREAMING_SNAKE_CASE		) if isinstance(__SCREAMING_SNAKE_CASE						,      __SCREAMING_SNAKE_CASE		) else mask_token

							snake_case_						:	int      =		{} if sp_model_kwargs is None else sp_model_kwargs

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

							snake_case_						:	Any      =		3

							snake_case_						:	int      =		do_lower_case
							snake_case_						:	str      =		remove_space
							snake_case_						:	Optional[int]      =		keep_accents
							snake_case_						:	str      =		vocab_file

							snake_case_						:	List[str]      =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
							self.sp_model.Load(__SCREAMING_SNAKE_CASE		)

							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.'		)
							snake_case_						:	str      =		jieba
							snake_case_						:	Tuple      =		str.maketrans(' \n'						,      '\u2582\u2583'		)






						@property
						# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
						def UpperCAmelCase_ (					self					:					Optional[int]		)							->			Union[str, Any]:







							"""simple docstring"""


							return len(self.sp_model		)






						def UpperCAmelCase_ (					self					:					str		)							->			Tuple:







							"""simple docstring"""


							snake_case_						:	Dict      =		{self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE		): i for i in range(self.vocab_size		)}
							vocab.update(self.added_tokens_encoder		)
							return vocab






						def __getstate__(					self					:					str		)							->			Optional[int]:







							"""simple docstring"""


							snake_case_						:	Union[str, Any]      =		self.__dict__.copy()
							snake_case_						:	Any      =		None
							return state






						def __setstate__(					self					:					Union[str, Any]						,      _A					:					Union[str, Any]		)							->			Dict:







							"""simple docstring"""


							snake_case_						:	List[str]      =		d

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

							snake_case_						:	int      =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
							self.sp_model.Load(self.vocab_file		)






						def UpperCAmelCase_ (					self					:					Optional[Any]						,      _A					:					List[Any]		)							->			str:







							"""simple docstring"""


							if self.remove_space:
								snake_case_						:	Dict      =		' '.join(inputs.strip().split()		)
							else:
								snake_case_						:	Any      =		inputs
							snake_case_						:	Tuple      =		outputs.replace('``'						,      '"'		).replace('\'\''						,      '"'		)

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

							return outputs






						def UpperCAmelCase_ (					self					:					Dict						,      _A					:					str		)							->			Union[str, Any]:







							"""simple docstring"""


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

							return new_pieces






						def UpperCAmelCase_ (					self					:					Optional[Any]						,      _A					:					Union[str, Any]		)							->			Optional[Any]:







							"""simple docstring"""


							return self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE		)






						def UpperCAmelCase_ (					self					:					Union[str, Any]						,      _A					:					Union[str, Any]		)							->			Dict:







							"""simple docstring"""


							return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE		)






						def UpperCAmelCase_ (					self					:					str						,      _A					:					List[str]		)							->			str:







							"""simple docstring"""


							snake_case_						:	Union[str, Any]      =		''.join(__SCREAMING_SNAKE_CASE		).replace(__SCREAMING_SNAKE_CASE						,      ' '		).strip()
							return out_string






						def UpperCAmelCase_ (					self					:					Dict						,      _A					:					List[int]						,      _A					:					Optional[List[int]] = None		)							->			int:







							"""simple docstring"""


							snake_case_						:	Any      =		[self.sep_token_id]
							snake_case_						:	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					:					str						,      _A					:					List[int]						,      _A					:					Optional[List[int]] = None						,      _A					:					bool = False		)							->			str:







							"""simple docstring"""


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

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






						def UpperCAmelCase_ (					self					:					Optional[int]						,      _A					:					List[int]						,      _A					:					Optional[List[int]] = None		)							->			Tuple:







							"""simple docstring"""


							snake_case_						:	Tuple      =		[self.sep_token_id]
							snake_case_						:	Union[str, 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 UpperCAmelCase_ (					self					:					Optional[Any]						,      _A					:					str						,      _A					:					Optional[str] = None		)							->			int:







							"""simple docstring"""


							if not os.path.isdir(__SCREAMING_SNAKE_CASE		):
								logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
								return
							snake_case_						:	Union[str, Any]      =		os.path.join(
							    __SCREAMING_SNAKE_CASE						,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

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

							return (out_vocab_file,)






						def UpperCAmelCase_ (					self					:					Any						,      *_A					:					int						,      **_A					:					List[Any]		)							->			Any:







							"""simple docstring"""


							snake_case_						:	Any      =		super()._decode(*__SCREAMING_SNAKE_CASE						,      **__SCREAMING_SNAKE_CASE		)
							snake_case_						:	Optional[int]      =		text.replace(' '						,      ''		).replace('\u2582'						,      ' '		).replace('\u2583'						,      '\n'		)
							return text






import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
        __a						       =				np.full((len(_UpperCAmelCase				), sequence_length, 2)   , _UpperCAmelCase				)
    else:
        __a						       =				np.full((len(_UpperCAmelCase				), sequence_length)   , _UpperCAmelCase				)

    for i, tensor in enumerate(_UpperCAmelCase				):
        if padding_side == "right":
            if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
                __a						       =				tensor[:sequence_length]
            else:
                __a						       =				tensor[:sequence_length]
        else:
            if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
                __a						       =				tensor[:sequence_length]
            else:
                __a						       =				tensor[:sequence_length]

    return out_tensor.tolist()





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				ord(_UpperCAmelCase				)
    if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
        return True
    __a						       =				unicodedata.category(_UpperCAmelCase				)
    if cat.startswith('''P'''				):
        return True
    return False

@dataclass
class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    PreTrainedTokenizerBase
       UpperCamelCase__  :    Union[bool, str, PaddingStrategy]											=						True
       UpperCamelCase__  :    Optional[int]											=						None
       UpperCamelCase__  :    Optional[int]											=						None
       UpperCamelCase__  :    int											=						-100
       UpperCamelCase__  :    str											=						"pt"



       def 		_lowerCamelCase			(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]):


           '''simple docstring'''

           import torch

           __a						       =				'''label''' if '''label''' in features[0].keys() else '''labels'''
           __a						       =				[feature[label_name] for feature in features] if label_name in features[0].keys() else None
           __a						       =				self.tokenizer.pad(
               __SCREAMING_SNAKE_CASE     ,		padding=self.padding     ,		max_length=self.max_length     ,		pad_to_multiple_of=self.pad_to_multiple_of     ,		return_tensors='''pt''' if labels is None else None     ,		)

           if labels is None:
               return batch

           __a						       =				torch.tensor(batch['''entity_ids''']).shape[1]
           __a						       =				self.tokenizer.padding_side
           if padding_side == "right":
               __a						       =				[
                   list(__SCREAMING_SNAKE_CASE) + [self.label_pad_token_id] * (sequence_length - len(__SCREAMING_SNAKE_CASE)) for label in labels
               ]
           else:
               __a						       =				[
                   [self.label_pad_token_id] * (sequence_length - len(__SCREAMING_SNAKE_CASE)) + list(__SCREAMING_SNAKE_CASE) for label in labels
               ]

           __a						       =				[feature['''ner_tags'''] for feature in features]
           __a						       =				padding_tensor(__SCREAMING_SNAKE_CASE     ,		-1     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				[feature['''original_entity_spans'''] for feature in features]
           __a						       =				padding_tensor(__SCREAMING_SNAKE_CASE     ,		(-1, -1)     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				{k: torch.tensor(__SCREAMING_SNAKE_CASE     ,		dtype=torch.intaa) for k, v in batch.items()}

           return batch





