






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 				_A			(     UpperCamelCase				):
    """simple docstring"""

    lowerCamelCase	:							Tuple				= VOCAB_FILES_NAMES
    lowerCamelCase	:							Optional[Any]				= PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase	:							int				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase	:							Any				= 'left'
    lowerCamelCase	:							int				= XLNetTokenizer



    def __init__(				self      :     Any ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=None ,     __SCREAMING_SNAKE_CASE      :     int=None ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=False ,     __SCREAMING_SNAKE_CASE      :     List[str]=True ,     __SCREAMING_SNAKE_CASE      :     List[str]=False ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]="<s>" ,     __SCREAMING_SNAKE_CASE      :     Optional[int]="</s>" ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]="<unk>" ,     __SCREAMING_SNAKE_CASE      :     List[str]="<sep>" ,     __SCREAMING_SNAKE_CASE      :     Optional[int]="<pad>" ,     __SCREAMING_SNAKE_CASE      :     Tuple="<cls>" ,     __SCREAMING_SNAKE_CASE      :     Tuple="<mask>" ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=["<eop>", "<eod>"] ,     **__SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     )      ->							List[Any]:
           # Mask token behave like a normal word, i.e. include the space before it
           __UpperCAmelCase     				=AddedToken(__SCREAMING_SNAKE_CASE ,     lstrip=__SCREAMING_SNAKE_CASE ,     rstrip=__SCREAMING_SNAKE_CASE      ) if isinstance(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ) else mask_token

           super().__init__(
               vocab_file=__SCREAMING_SNAKE_CASE ,     tokenizer_file=__SCREAMING_SNAKE_CASE ,     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 ,     **__SCREAMING_SNAKE_CASE ,     )

           __UpperCAmelCase     				=3
           __UpperCAmelCase     				=do_lower_case
           __UpperCAmelCase     				=remove_space
           __UpperCAmelCase     				=keep_accents
           __UpperCAmelCase     				=vocab_file
           __UpperCAmelCase     				=False if not self.vocab_file else True



    def _a							(				self      :     Optional[Any] ,     __SCREAMING_SNAKE_CASE      :     List[int] ,     __SCREAMING_SNAKE_CASE      :     Optional[List[int]] = None      )      ->							List[int]:
           __UpperCAmelCase     				=[self.sep_token_id]
           __UpperCAmelCase     				=[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 _a							(				self      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     List[int] ,     __SCREAMING_SNAKE_CASE      :     Optional[List[int]] = None      )      ->							List[int]:
           __UpperCAmelCase     				=[self.sep_token_id]
           __UpperCAmelCase     				=[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 _a							(				self      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     str ,     __SCREAMING_SNAKE_CASE      :     Optional[str] = None      )      ->							Tuple[str]:
           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(__SCREAMING_SNAKE_CASE      ):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
                  return
           __UpperCAmelCase     				=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      ):
                  copyfile(self.vocab_file ,     __SCREAMING_SNAKE_CASE      )

           return (out_vocab_file,)











'''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   UpperCAmelCase      (			unittest.TestCase							):
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :str				)->				Any:
     A__							=     3
     A__							=     2_50

     A__							=     ids_tensor((batch_size, length)		,			lowercase_				)
     A__							=     torch.ones((batch_size, length)		,			device=lowercase_		,			dtype=torch.float				) / length
     return input_ids, scores
 def 	UpperCAmelCase_				(							self :str				)->				Dict:
     A__,      A__							=     self._get_tensors(5				)

     A__							=     StoppingCriteriaList(
         [
             MaxLengthCriteria(max_length=10				),
             MaxTimeCriteria(max_time=0.1				),
         ]				)

     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(9				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				List[Any]:
     A__							=     MaxLengthCriteria(max_length=10				)

     A__,      A__							=     self._get_tensors(5				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(9				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)
 def 	UpperCAmelCase_				(							self :Tuple				)->				int:
     A__							=     MaxNewTokensCriteria(start_length=5		,			max_new_tokens=5				)

     A__,      A__							=     self._get_tensors(5				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(9				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)

     A__							=     StoppingCriteriaList([criteria]				)
     self.assertEqual(criteria_list.max_length		,			10				)
 def 	UpperCAmelCase_				(							self :Dict				)->				int:
     A__,      A__							=     self._get_tensors(5				)

     A__							=     MaxTimeCriteria(max_time=0.1				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__							=     MaxTimeCriteria(max_time=0.1		,			initial_timestamp=time.time() - 0.2				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)



 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				str:
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)		,			10				)

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

     A__							=     validate_stopping_criteria(StoppingCriteriaList()		,			11				)

     self.assertEqual(len(lowercase_				)		,			1				)



