'''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


_a   :	List[Any] 							=   logging.get_logger(__name__)

_a   :	Optional[Any] 							=   {"vocab_file": "spiece.model"}

_a   :	Optional[Any] 							=   {
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    }
}






class 		__A       (__magic_name__   ):






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

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

              super().__init__(
                  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_				,     sp_model_kwargs=self.sp_model_kwargs				,     **UpperCamelCase_				,     )

              __UpperCAmelCase  :				Union[str, Any]       				=		3

              __UpperCAmelCase  :				Tuple       				=		do_lower_case
              __UpperCAmelCase  :				List[str]       				=		remove_space
              __UpperCAmelCase  :				Union[str, Any]       				=		keep_accents
              __UpperCAmelCase  :				Union[str, Any]       				=		vocab_file

              __UpperCAmelCase  :				Dict       				=		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
              self.sp_model.Load(UpperCamelCase_      )

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






       @property
       # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
       def 			_snake_case						(      self      ):
              return len(self.sp_model      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[str]       				=		{self.convert_ids_to_tokens(UpperCamelCase_      ): i for i in range(self.vocab_size      )}
              vocab.update(self.added_tokens_encoder      )
              return vocab






       def __getstate__(      self      ):
              __UpperCAmelCase  :				Optional[Any]       				=		self.__dict__.copy()
              __UpperCAmelCase  :				Dict       				=		None
              return state






       def __setstate__(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				int       				=		d

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

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






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              if self.remove_space:
                     __UpperCAmelCase  :				List[str]       				=		" ".join(inputs.strip().split()      )
              else:
                     __UpperCAmelCase  :				str       				=		inputs
              __UpperCAmelCase  :				Any       				=		outputs.replace("``"				,     "\""      ).replace("''"				,     "\""      )

              if not self.keep_accents:
                     __UpperCAmelCase  :				Optional[int]       				=		unicodedata.normalize("NFKD"				,     UpperCamelCase_      )
                     __UpperCAmelCase  :				Optional[int]       				=		"".join([c for c in outputs if not unicodedata.combining(UpperCamelCase_      )]      )
              if self.do_lower_case:
                     __UpperCAmelCase  :				str       				=		outputs.lower()

              return outputs






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

              return new_pieces






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              return self.sp_model.PieceToId(UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              return self.sp_model.IdToPiece(UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Optional[int]       				=		"".join(UpperCamelCase_      ).replace(UpperCamelCase_				,     " "      ).strip()
              return out_string






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              __UpperCAmelCase  :				Optional[int]       				=		[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				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     UpperCamelCase_ = False      ):

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

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






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              __UpperCAmelCase  :				int       				=		[self.sep_token_id]
              __UpperCAmelCase  :				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 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              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_      ) and os.path.isfile(self.vocab_file      ):
                     copyfile(self.vocab_file				,     UpperCamelCase_      )
              elif not os.path.isfile(self.vocab_file      ):
                     with open(UpperCamelCase_				,     "wb"      ) as fi:
                            __UpperCAmelCase  :				Optional[int]       				=		self.sp_model.serialized_model_proto()
                            fi.write(UpperCamelCase_      )

              return (out_vocab_file,)







       def 			_snake_case						(      self				,     *UpperCamelCase_				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				str       				=		super()._decode(*UpperCamelCase_				,     **UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[Any]       				=		text.replace(" "				,     ""      ).replace("\u2582"				,     " "      ).replace("\u2583"				,     "\n"      )
              return text







'''simple docstring'''




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
   import torch

   from transformers.generation import (
       MaxLengthCriteria,
       MaxNewTokensCriteria,
       MaxTimeCriteria,
       StoppingCriteriaList,
       validate_stopping_criteria,
   )






@require_torch
class 		__A       (unittest.TestCase   ):






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		3
              __UpperCAmelCase  :				Tuple       				=		2_50

              __UpperCAmelCase  :				str       				=		ids_tensor((batch_size, length)				,     UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		torch.ones((batch_size, length)				,     device=UpperCamelCase_				,     dtype=torch.float      ) / length
              return input_ids, scores






       def 			_snake_case						(      self      ):
              __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		self._get_tensors(5      )

              __UpperCAmelCase  :				Tuple       				=		StoppingCriteriaList(
                  [
                      MaxLengthCriteria(max_length=10      ),
                      MaxTimeCriteria(max_time=0.1      ),
                  ]      )

              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				int       				=		self._get_tensors(9      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		self._get_tensors(10      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				int       				=		MaxLengthCriteria(max_length=10      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		self._get_tensors(5      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Dict       				=		self._get_tensors(9      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		self._get_tensors(10      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[Any]       				=		MaxNewTokensCriteria(start_length=5				,     max_new_tokens=5      )

              __UpperCAmelCase				, __UpperCAmelCase  :				List[str]       				=		self._get_tensors(5      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Dict       				=		self._get_tensors(9      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[Any]       				=		self._get_tensors(10      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase  :				Union[str, Any]       				=		StoppingCriteriaList([criteria]      )
              self.assertEqual(criteria_list.max_length				,     10      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[Any]       				=		self._get_tensors(5      )

              __UpperCAmelCase  :				str       				=		MaxTimeCriteria(max_time=0.1      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase  :				str       				=		MaxTimeCriteria(max_time=0.1				,     initial_timestamp=time.time() - 0.2      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )







       def 			_snake_case						(      self      ):
              validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      )				,     10      )

              with self.assertWarns(UpperCamelCase_      ):
                     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      )				,     11      )

              __UpperCAmelCase  :				Optional[int]       				=		validate_stopping_criteria(StoppingCriteriaList()				,     11      )

              self.assertEqual(len(UpperCamelCase_      )				,     1      )






