
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:
      lowercase__          =None


lowercase__          =logging.get_logger(__name__)

lowercase__          ={'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowercase__          ={
    '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',
    },
}

lowercase__          ={
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

lowercase__          ='▁'

# Segments (not really needed)
lowercase__          =0
lowercase__          =1
lowercase__          =2
lowercase__          =3
lowercase__          =4
class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE				:							List[Any]     	=				"left"
   _SCREAMING_SNAKE_CASE				:							Any     	=				XLNetTokenizer
   def __init__(self			: int ,    snake_case_			: Optional[int]=None ,    snake_case_			: Optional[int]=None ,    snake_case_			: Tuple=False ,    snake_case_			: Any=True ,    snake_case_			: Any=False ,    snake_case_			: Dict="<s>" ,    snake_case_			: int="</s>" ,    snake_case_			: Optional[Any]="<unk>" ,    snake_case_			: Union[str, Any]="<sep>" ,    snake_case_			: Tuple="<pad>" ,    snake_case_			: List[str]="<cls>" ,    snake_case_			: Union[str, Any]="<mask>" ,    snake_case_			: Union[str, Any]=["<eop>", "<eod>"] ,    **snake_case_			: Union[str, Any] ,    ):
    # Mask token behave like a normal word, i.e. include the space before it
    __a			:  Tuple		    =  AddedToken(_a ,    lstrip=_a ,    rstrip=_a       ) if isinstance(_a ,    _a       ) else mask_token

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

    __a			:  int		    =  3
    __a			:  str		    =  do_lower_case
    __a			:  Optional[int]		    =  remove_space
    __a			:  Union[str, Any]		    =  keep_accents
    __a			:  Optional[int]		    =  vocab_file
    __a			:  Optional[Any]		    =  False if not self.vocab_file else True
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: List[str] ,    snake_case_			: int = None       ):
    __a			:  Optional[int]		    =  [self.sep_token_id]
    __a			:  Optional[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 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: int ,    snake_case_			: Tuple = None       ):
    __a			:  int		    =  [self.sep_token_id]
    __a			:  int		    =  [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 				lowerCAmelCase      (self			: Any ,    snake_case_			: Union[str, Any] ,    snake_case_			: List[Any] = None       ):
    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(_a       ):
     logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
     return
    __a			:  Optional[Any]		    =  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       ):
     copyfile(self.vocab_file ,    _a       )

    return (out_vocab_file,)


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        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Any  ):
 if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
  __a			:  Union[str, Any]		    =  np.full((len(lowerCAmelCase__  ), sequence_length, 2)  ,       lowerCAmelCase__  )
 else:
  __a			:  str		    =  np.full((len(lowerCAmelCase__  ), sequence_length)  ,       lowerCAmelCase__  )

 for i, tensor in enumerate(lowerCAmelCase__  ):
  if padding_side == "right":
   if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
    __a			:  Any		    =  tensor[:sequence_length]
   else:
    __a			:  List[Any]		    =  tensor[:sequence_length]
  else:
   if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
    __a			:  Dict		    =  tensor[:sequence_length]
   else:
    __a			:  int		    =  tensor[:sequence_length]

 return out_tensor.tolist()




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[Any]  ):
 __a			:  str		    =  ord(lowerCAmelCase__  )
 if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
  return True
 __a			:  List[str]		    =  unicodedata.category(lowerCAmelCase__  )
 if cat.startswith('''P'''  ):
  return True
 return False



@dataclass
class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							PreTrainedTokenizerBase
   _SCREAMING_SNAKE_CASE				:							Union[bool, str, PaddingStrategy]     	=				True
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   _SCREAMING_SNAKE_CASE				:							int     	=				-100
   _SCREAMING_SNAKE_CASE				:							str     	=				"pt"
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: Tuple       ):
    import torch

    __a			:  Union[str, Any]		    =  '''label''' if '''label''' in features[0].keys() else '''labels'''
    __a			:  Tuple		    =  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    __a			:  Union[str, Any]		    =  self.tokenizer.pad(
        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			:  List[str]		    =  torch.tensor(batch['''entity_ids''']       ).shape[1]
    __a			:  Tuple		    =  self.tokenizer.padding_side
    if padding_side == "right":
     __a			:  Union[str, Any]		    =  [
         list(snake_case_       ) + [self.label_pad_token_id] * (sequence_length - len(snake_case_       )) for label in labels
     ]
    else:
     __a			:  Dict		    =  [
         [self.label_pad_token_id] * (sequence_length - len(snake_case_       )) + list(snake_case_       ) for label in labels
     ]

    __a			:  Dict		    =  [feature['''ner_tags'''] for feature in features]
    __a			:  Optional[Any]		    =  padding_tensor(snake_case_ ,    -1 ,    snake_case_ ,    snake_case_       )
    __a			:  Union[str, Any]		    =  [feature['''original_entity_spans'''] for feature in features]
    __a			:  Optional[int]		    =  padding_tensor(snake_case_ ,    (-1, -1) ,    snake_case_ ,    snake_case_       )
    __a			:  List[str]		    =  {k: torch.tensor(snake_case_ ,    dtype=torch.intaa       ) for k, v in batch.items()}

    return batch
