


'''simple docstring'''






import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
 from ...tokenization_utils_base import TextInput
from ...utils import logging


a_   :      Tuple				    =      logging.get_logger(__name__)

a_   :      Dict				    =      {"vocab_file": "spiece.model"}

a_   :      Tuple				    =      {
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
a_   :      Optional[int]				    =      {
    "t5-small": 5_1_2,
    "t5-base": 5_1_2,
    "t5-large": 5_1_2,
    "t5-3b": 5_1_2,
    "t5-11b": 5_1_2,
}

a_   :      List[str]				    =      "▁"




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		VOCAB_FILES_NAMES
 _lowerCAmelCase	   =		PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase	   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase	   =		["""input_ids""", """attention_mask"""]
 def __init__(	self ,			__magic_name__ ,			__magic_name__="</s>" ,			__magic_name__="<unk>" ,			__magic_name__="<pad>" ,			__magic_name__=1_00 ,			__magic_name__=None ,			__magic_name__ = None ,			__magic_name__=True ,			**__magic_name__ ,			)   ->							None:
      # Add extra_ids to the special token list
      if extra_ids > 0 and additional_special_tokens is None:
           _a      =							[f'<extra_id_{i}>' for i in range(__magic_name__  )]
      elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra_id special tokens
           _a      =							len(set(filter(lambda __magic_name__  : bool('extra_id' in str(__magic_name__  )  ) ,			__magic_name__  )  )  )
           if extra_tokens != extra_ids:
                raise ValueError(
                    f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
                    ' tokens'  )
      if legacy:
           logger.warning_once(
               f'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
               ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'  )

      _a      =							legacy
      _a      =							{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          eos_token=__magic_name__ ,			unk_token=__magic_name__ ,			pad_token=__magic_name__ ,			extra_ids=__magic_name__ ,			additional_special_tokens=__magic_name__ ,			sp_model_kwargs=self.sp_model_kwargs ,			legacy=__magic_name__ ,			**__magic_name__ ,			)

      _a      =							vocab_file
      _a      =							extra_ids

      _a      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(__magic_name__  )
 @staticmethod
 def 							__UpperCAmelCase						(	__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Optional[int]:
      if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
           _a      =							TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
           if init_max_model_length is not None and init_max_model_length != max_model_length:
                return init_max_model_length
           elif init_max_model_length is None:
                warnings.warn(
                    'This tokenizer was incorrectly instantiated with a model max length of'
                    f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
                    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
                    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
                    f' {pretrained_model_name_or_path} automatically truncating your input to'
                    f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
                    f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
                    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
                    ' instantiate this tokenizer with `model_max_length` set to your preferred value.' ,			__magic_name__ ,			)

      return max_model_length
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      return self.sp_model.get_piece_size() + self._extra_ids
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							{self.convert_ids_to_tokens(__magic_name__  ): i for i in range(self.vocab_size  )}
      vocab.update(self.added_tokens_encoder  )
      return vocab
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None ,			__magic_name__ = False  )   ->							List[int]:
      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__magic_name__ ,			token_ids_a=__magic_name__ ,			already_has_special_tokens=__magic_name__  )

      # normal case: some special tokens
      if token_ids_a is None:
           return ([0] * len(__magic_name__  )) + [1]
      return ([0] * len(__magic_name__  )) + [1] + ([0] * len(__magic_name__  )) + [1]
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      return list(
          set(filter(lambda __magic_name__  : bool(re.search(r'<extra_id_\d+>' ,			__magic_name__  )  ) is not None ,			self.additional_special_tokens  )  )  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      return [self._convert_token_to_id(__magic_name__  ) for token in self.get_sentinel_tokens()]
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[int]:
      if len(__magic_name__  ) > 0 and token_ids[-1] == self.eos_token_id:
           warnings.warn(
               f'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
               ' eos tokens being added.'  )
           return token_ids
      else:
           return token_ids + [self.eos_token_id]
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							List[int]:
      _a      =							[self.eos_token_id]

      if token_ids_a is None:
           return len(token_ids_a + eos  ) * [0]
      return len(token_ids_a + eos + token_ids_a + eos  ) * [0]
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							List[int]:
      _a      =							self._add_eos_if_not_present(__magic_name__  )
      if token_ids_a is None:
           return token_ids_a
      else:
           _a      =							self._add_eos_if_not_present(__magic_name__  )
           return token_ids_a + token_ids_a
 def __getstate__(	self  )   ->							str:
      _a      =							self.__dict__.copy()
      _a      =							None
      return state
 def __setstate__(	self ,			__magic_name__  )   ->							Tuple:
      _a      =							d

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

      _a      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(self.vocab_file  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			**__magic_name__  )   ->							List[str]:
      # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
      # the beginning of the text
      if not self.legacy:
           _a      =							SPIECE_UNDERLINE + text.replace(__magic_name__ ,			' '  )
      return super().tokenize(__magic_name__ ,			**__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			**__magic_name__  )   ->							str:
      if not self.legacy:
           _a      =							text.startswith(__magic_name__  )
           if is_first:
                _a      =							text[1:]

      _a      =							self.sp_model.encode(__magic_name__ ,			out_type=__magic_name__  )

      if not self.legacy and not is_first and not text.startswith(' '  ) and tokens[0].startswith(__magic_name__  ):
           _a      =							([tokens[0][1:]] if len(tokens[0]  ) > 1 else []) + tokens[1:]
      return tokens
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Tuple:
      if token.startswith('<extra_id_'  ):
           _a      =							re.match(r'<extra_id_(\d+)>' ,			__magic_name__  )
           _a      =							int(match.group(1  )  )
           return self.vocab_size - num - 1
      return self.sp_model.piece_to_id(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[Any]:
      if index < self.sp_model.get_piece_size():
           _a      =							self.sp_model.IdToPiece(__magic_name__  )
      else:
           _a      =							f'<extra_id_{self.vocab_size - 1 - index}>'
      return token
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							int:
      _a      =							[]
      _a      =							''
      _a      =							False
      for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
                if not prev_is_special:
                     out_string += " "
                out_string += self.sp_model.decode(__magic_name__  ) + token
                _a      =							True
                _a      =							[]
           else:
                current_sub_tokens.append(__magic_name__  )
                _a      =							False
      out_string += self.sp_model.decode(__magic_name__  )
      return out_string.strip()




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							Tuple[str]:
      if not os.path.isdir(__magic_name__  ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'  )
           return
      _a      =							os.path.join(
          __magic_name__ ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

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

      return (out_vocab_file,)





'''simple docstring'''






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 		_A			(lowerCAmelCase__					:int			,  lowerCAmelCase__					:Any			,  lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:Dict						)				->					Optional[int]:




     '''simple docstring'''



     if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
          _a      =							np.full((len(lowerCAmelCase__						), sequence_length, 2)			,  lowerCAmelCase__						)
     else:
          _a      =							np.full((len(lowerCAmelCase__						), sequence_length)			,  lowerCAmelCase__						)

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

     return out_tensor.tolist()






def 		_A			(lowerCAmelCase__					:Any						)				->					Union[str, Any]:




     '''simple docstring'''



     _a      =							ord(lowerCAmelCase__						)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
          return True
     _a      =							unicodedata.category(lowerCAmelCase__						)
     if cat.startswith('P'						):
          return True
     return False






@dataclass
class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		42
 _lowerCAmelCase	   =		True
 _lowerCAmelCase	   =		None
 _lowerCAmelCase	   =		None
 _lowerCAmelCase	   =		-1_0_0
 _lowerCAmelCase	   =		"pt"
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Any:
      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(
          __magic_name__ ,			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(__magic_name__  ) + [self.label_pad_token_id] * (sequence_length - len(__magic_name__  )) for label in labels
           ]
      else:
           _a      =							[
               [self.label_pad_token_id] * (sequence_length - len(__magic_name__  )) + list(__magic_name__  ) for label in labels
           ]

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

      return batch

