






"""simple docstring"""





import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


A__					: List[str]=	logging.getLogger(__name__)
@dataclass
class    __lowerCamelCase    :
   a      :				str
   a      :				List[str]
   a      :				Optional[List[str]]







@dataclass
class    __lowerCamelCase    :
   a      :				List[int]
   a      :				List[int]
   a      :				Optional[List[int]] 						=None
   a      :				Optional[List[int]] 						=None







class    __lowerCamelCase    (  _a  ):
   a      :				Optional[Any] 						="""train"""
   a      :				List[Any] 						="""dev"""
   a      :				Optional[Any] 						="""test"""







class    __lowerCamelCase    :

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_ ,				snake_case_      )		->       List[InputExample]:
       raise NotImplementedError

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_      )		->       List[str]:
       raise NotImplementedError







   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_=False ,				snake_case_="[CLS]" ,				snake_case_=1 ,				snake_case_="[SEP]" ,				snake_case_=False ,				snake_case_=False ,				snake_case_=0 ,				snake_case_=0 ,				snake_case_=-100 ,				snake_case_=0 ,				snake_case_=True ,				)		->       List[InputFeatures]:
       UpperCamelCase__         =    {label: i for i, label in enumerate(snake_case_      )}

       UpperCamelCase__         =    []
       for ex_index, example in enumerate(snake_case_      ):
           if ex_index % 1_0000 == 0:
               logger.info('Writing example %d of %d' ,				snake_case_ ,				len(snake_case_      )      )

           UpperCamelCase__         =    []
           UpperCamelCase__         =    []
           for word, label in zip(example.words ,				example.labels      ):
               UpperCamelCase__         =    tokenizer.tokenize(snake_case_      )

               # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
               if len(snake_case_      ) > 0:
                   tokens.extend(snake_case_      )
                   # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                   label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(snake_case_      ) - 1)      )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
           UpperCamelCase__         =    tokenizer.num_special_tokens_to_add()
           if len(snake_case_      ) > max_seq_length - special_tokens_count:
               UpperCamelCase__         =    tokens[: (max_seq_length - special_tokens_count)]
               UpperCamelCase__         =    label_ids[: (max_seq_length - special_tokens_count)]

           # The convention in BERT is:
           # (a) For sequence pairs:
           #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
           #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
           # (b) For single sequences:
           #  tokens:   [CLS] the dog is hairy . [SEP]
           #  type_ids:   0   0   0   0  0     0   0
           #
           # Where "type_ids" are used to indicate whether this is the first
           # sequence or the second sequence. The embedding vectors for `type=0` and
           # `type=1` were learned during pre-training and are added to the wordpiece
           # embedding vector (and position vector). This is not *strictly* necessary
           # since the [SEP] token unambiguously separates the sequences, but it makes
           # it easier for the model to learn the concept of sequences.
           #
           # For classification tasks, the first vector (corresponding to [CLS]) is
           # used as the "sentence vector". Note that this only makes sense because
           # the entire model is fine-tuned.
           tokens += [sep_token]
           label_ids += [pad_token_label_id]
           if sep_token_extra:
               # roberta uses an extra separator b/w pairs of sentences
               tokens += [sep_token]
               label_ids += [pad_token_label_id]
           UpperCamelCase__         =    [sequence_a_segment_id] * len(snake_case_      )

           if cls_token_at_end:
               tokens += [cls_token]
               label_ids += [pad_token_label_id]
               segment_ids += [cls_token_segment_id]
           else:
               UpperCamelCase__         =    [cls_token] + tokens
               UpperCamelCase__         =    [pad_token_label_id] + label_ids
               UpperCamelCase__         =    [cls_token_segment_id] + segment_ids

           UpperCamelCase__         =    tokenizer.convert_tokens_to_ids(snake_case_      )

           # The mask has 1 for real tokens and 0 for padding tokens. Only real
           # tokens are attended to.
           UpperCamelCase__         =    [1 if mask_padding_with_zero else 0] * len(snake_case_      )

           # Zero-pad up to the sequence length.
           UpperCamelCase__         =    max_seq_length - len(snake_case_      )
           if pad_on_left:
               UpperCamelCase__         =    ([pad_token] * padding_length) + input_ids
               UpperCamelCase__         =    ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
               UpperCamelCase__         =    ([pad_token_segment_id] * padding_length) + segment_ids
               UpperCamelCase__         =    ([pad_token_label_id] * padding_length) + label_ids
           else:
               input_ids += [pad_token] * padding_length
               input_mask += [0 if mask_padding_with_zero else 1] * padding_length
               segment_ids += [pad_token_segment_id] * padding_length
               label_ids += [pad_token_label_id] * padding_length

           assert len(snake_case_      ) == max_seq_length
           assert len(snake_case_      ) == max_seq_length
           assert len(snake_case_      ) == max_seq_length
           assert len(snake_case_      ) == max_seq_length

           if ex_index < 5:
               logger.info('*** Example ***'      )
               logger.info('guid: %s' ,				example.guid      )
               logger.info('tokens: %s' ,				' '.join([str(snake_case_      ) for x in tokens]      )      )
               logger.info('input_ids: %s' ,				' '.join([str(snake_case_      ) for x in input_ids]      )      )
               logger.info('input_mask: %s' ,				' '.join([str(snake_case_      ) for x in input_mask]      )      )
               logger.info('segment_ids: %s' ,				' '.join([str(snake_case_      ) for x in segment_ids]      )      )
               logger.info('label_ids: %s' ,				' '.join([str(snake_case_      ) for x in label_ids]      )      )

           if "token_type_ids" not in tokenizer.model_input_names:
               UpperCamelCase__         =    None

           features.append(
               InputFeatures(
                   input_ids=snake_case_ ,				attention_mask=snake_case_ ,				token_type_ids=snake_case_ ,				label_ids=snake_case_      )      )
       return features


if is_torch_available():
  import torch
  from torch import nn
  from torch.utils.data import Dataset
  class    __lowerCamelCase    (  _a  ):
     a      :				List[InputFeatures]
     a      :				int 						=nn.CrossEntropyLoss().ignore_index

     def __init__(					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ = None ,				snake_case_=False ,				snake_case_ = Split.train ,				)		->       Tuple:
         # Load data features from cache or dataset file
         UpperCamelCase__         =    os.path.join(
             snake_case_ ,				'cached_{}_{}_{}'.format(mode.value ,				tokenizer.__class__.__name__ ,				str(snake_case_      )      ) ,				)

         # Make sure only the first process in distributed training processes the dataset,
         # and the others will use the cache.
         UpperCamelCase__         =    cached_features_file + '.lock'
         with FileLock(snake_case_      ):
             if os.path.exists(snake_case_      ) and not overwrite_cache:
                 logger.info(F'Loading features from cached file {cached_features_file}'      )
                 UpperCamelCase__         =    torch.load(snake_case_      )
             else:
                 logger.info(F'Creating features from dataset file at {data_dir}'      )
                 UpperCamelCase__         =    token_classification_task.read_examples_from_file(snake_case_ ,				snake_case_      )
                 # TODO clean up all this to leverage built-in features of tokenizers
                 UpperCamelCase__         =    token_classification_task.convert_examples_to_features(
                     snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				cls_token_at_end=bool(model_type in ['xlnet']      ) ,				cls_token=tokenizer.cls_token ,				cls_token_segment_id=2 if model_type in ['xlnet'] else 0 ,				sep_token=tokenizer.sep_token ,				sep_token_extra=snake_case_ ,				pad_on_left=bool(tokenizer.padding_side == 'left'      ) ,				pad_token=tokenizer.pad_token_id ,				pad_token_segment_id=tokenizer.pad_token_type_id ,				pad_token_label_id=self.pad_token_label_id ,				)
                 logger.info(F'Saving features into cached file {cached_features_file}'      )
                 torch.save(self.features ,				snake_case_      )

     def __len__(					self      )		->       int:
         return len(self.features      )







     def __getitem__(					self ,				snake_case_      )		->       InputFeatures:
         return self.features[i]


if is_tf_available():
  import tensorflow as tf
  class    __lowerCamelCase    :
     a      :				List[InputFeatures]
     a      :				int 						=-1_0_0

     def __init__(					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ = None ,				snake_case_=False ,				snake_case_ = Split.train ,				)		->       List[Any]:
         UpperCamelCase__         =    token_classification_task.read_examples_from_file(snake_case_ ,				snake_case_      )
         # TODO clean up all this to leverage built-in features of tokenizers
         UpperCamelCase__         =    token_classification_task.convert_examples_to_features(
             snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				cls_token_at_end=bool(model_type in ['xlnet']      ) ,				cls_token=tokenizer.cls_token ,				cls_token_segment_id=2 if model_type in ['xlnet'] else 0 ,				sep_token=tokenizer.sep_token ,				sep_token_extra=snake_case_ ,				pad_on_left=bool(tokenizer.padding_side == 'left'      ) ,				pad_token=tokenizer.pad_token_id ,				pad_token_segment_id=tokenizer.pad_token_type_id ,				pad_token_label_id=self.pad_token_label_id ,				)

         def gen():
             for ex in self.features:
                 if ex.token_type_ids is None:
                     yield (
                         {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                         ex.label_ids,
                     )
                 else:
                     yield (
                         {
                             "input_ids": ex.input_ids,
                             "attention_mask": ex.attention_mask,
                             "token_type_ids": ex.token_type_ids,
                         },
                         ex.label_ids,
                     )

         if "token_type_ids" not in tokenizer.model_input_names:
             UpperCamelCase__         =    tf.data.Dataset.from_generator(
                 snake_case_ ,				({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa) ,				(
                     {'input_ids': tf.TensorShape([None]      ), 'attention_mask': tf.TensorShape([None]      )},
                     tf.TensorShape([None]      ),
                 ) ,				)
         else:
             UpperCamelCase__         =    tf.data.Dataset.from_generator(
                 snake_case_ ,				({'input_ids': tf.intaa, 'attention_mask': tf.intaa, 'token_type_ids': tf.intaa}, tf.intaa) ,				(
                     {
                         'input_ids': tf.TensorShape([None]      ),
                         'attention_mask': tf.TensorShape([None]      ),
                         'token_type_ids': tf.TensorShape([None]      ),
                     },
                     tf.TensorShape([None]      ),
                 ) ,				)

     def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
         UpperCamelCase__         =    self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features      )      )      )

         return self.dataset

     def __len__(					self      )		->       List[Any]:
         return len(self.features      )







     def __getitem__(					self ,				snake_case_      )		->       InputFeatures:
         return self.features[i]












"""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__					: Dict=	logging.get_logger(__name__)

A__					: str=	{"""vocab_file""": """spiece.model"""}

A__					: Union[str, Any]=	{
    """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__					: Union[str, Any]=	{
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}

A__					: Optional[Any]=	"""▁"""
class    __lowerCamelCase    (  _a  ):
   a      :				Dict 						=VOCAB_FILES_NAMES
   a      :				str 						=PRETRAINED_VOCAB_FILES_MAP
   a      :				Union[str, Any] 						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a      :				List[str] 						=["""input_ids""", """attention_mask"""]

   def __init__(					self ,				snake_case_ ,				snake_case_="</s>" ,				snake_case_="<unk>" ,				snake_case_="<pad>" ,				snake_case_=100 ,				snake_case_=None ,				snake_case_ = None ,				snake_case_=True ,				**snake_case_ ,				)		->       None:
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
           UpperCamelCase__         =    [F'<extra_id_{i}>' for i in range(snake_case_      )]
       elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra_id special tokens
           UpperCamelCase__         =    len(set(filter(lambda snake_case_      : bool('extra_id' in str(snake_case_      )      ) ,				snake_case_      )      )      )
           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'      )

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

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

       UpperCamelCase__         =    vocab_file
       UpperCamelCase__         =    extra_ids

       UpperCamelCase__         =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(snake_case_      )

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_ ,				snake_case_ ,				snake_case_      )		->       Optional[Any]:
       if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
           UpperCamelCase__         =    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.' ,				snake_case_ ,				)

       return max_model_length

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       return self.sp_model.get_piece_size() + self._extra_ids

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    {self.convert_ids_to_tokens(snake_case_      ): i for i in range(self.vocab_size      )}
       vocab.update(self.added_tokens_encoder      )
       return vocab

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None ,				snake_case_ = False      )		->       List[int]:
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=snake_case_ ,				token_ids_a=snake_case_ ,				already_has_special_tokens=snake_case_      )

       # normal case: some special tokens
       if token_ids_a is None:
           return ([0] * len(snake_case_      )) + [1]
       return ([0] * len(snake_case_      )) + [1] + ([0] * len(snake_case_      )) + [1]

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       return list(
           set(filter(lambda snake_case_      : bool(re.search(r'<extra_id_\d+>' ,				snake_case_      )      ) is not None ,				self.additional_special_tokens      )      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       return [self._convert_token_to_id(snake_case_      ) for token in self.get_sentinel_tokens()]

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[int]:
       if len(snake_case_      ) > 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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None      )		->       List[int]:
       UpperCamelCase__         =    [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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None      )		->       List[int]:
       UpperCamelCase__         =    self._add_eos_if_not_present(snake_case_      )
       if token_ids_a is None:
           return token_ids_a
       else:
           UpperCamelCase__         =    self._add_eos_if_not_present(snake_case_      )
           return token_ids_a + token_ids_a

   def __getstate__(					self      )		->       str:
       UpperCamelCase__         =    self.__dict__.copy()
       UpperCamelCase__         =    None
       return state

   def __setstate__(					self ,				snake_case_      )		->       Any:
       UpperCamelCase__         =    d

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

       UpperCamelCase__         =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(self.vocab_file      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				**snake_case_      )		->       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:
           UpperCamelCase__         =    SPIECE_UNDERLINE + text.replace(snake_case_ ,				' '      )
       return super().tokenize(snake_case_ ,				**snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				**snake_case_      )		->       List[Any]:
       if not self.legacy:
           UpperCamelCase__         =    text.startswith(snake_case_      )
           if is_first:
               UpperCamelCase__         =    text[1:]

       UpperCamelCase__         =    self.sp_model.encode(snake_case_ ,				out_type=snake_case_      )

       if not self.legacy and not is_first and not text.startswith(' '      ) and tokens[0].startswith(snake_case_      ):
           UpperCamelCase__         =    ([tokens[0][1:]] if len(tokens[0]      ) > 1 else []) + tokens[1:]
       return tokens

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[str]:
       if token.startswith('<extra_id_'      ):
           UpperCamelCase__         =    re.match(r'<extra_id_(\d+)>' ,				snake_case_      )
           UpperCamelCase__         =    int(match.group(1      )      )
           return self.vocab_size - num - 1
       return self.sp_model.piece_to_id(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Optional[int]:
       if index < self.sp_model.get_piece_size():
           UpperCamelCase__         =    self.sp_model.IdToPiece(snake_case_      )
       else:
           UpperCamelCase__         =    F'<extra_id_{self.vocab_size - 1 - index}>'
       return token

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[str]:
       UpperCamelCase__         =    []
       UpperCamelCase__         =    ''
       UpperCamelCase__         =    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(snake_case_      ) + token
               UpperCamelCase__         =    True
               UpperCamelCase__         =    []
           else:
               current_sub_tokens.append(snake_case_      )
               UpperCamelCase__         =    False
       out_string += self.sp_model.decode(snake_case_      )
       return out_string.strip()







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

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

       return (out_vocab_file,)




