

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


_lowerCamelCase      :					Optional[int]					    =      logging.get_logger(__name__)

_lowerCamelCase      :					List[str]					    =      {'''vocab_file''': '''spiece.model'''}

_lowerCamelCase      :					str					    =      {
    '''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
_lowerCamelCase      :					List[Any]					    =      {
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}

_lowerCamelCase      :					Dict					    =      '''▁'''





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    VOCAB_FILES_NAMES
       lowercase_						=    PRETRAINED_VOCAB_FILES_MAP
       lowercase_						=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_						=    ["""input_ids""", """attention_mask"""]

       def __init__(							self ,   lowercase__ ,   lowercase__="</s>" ,   lowercase__="<unk>" ,   lowercase__="<pad>" ,   lowercase__=1_0_0 ,   lowercase__=None ,   lowercase__ = None ,   lowercase__=True ,   **lowercase__ ,   ):







             '''simple docstring'''



             if extra_ids > 0 and additional_special_tokens is None:
                   __A    =[f'''<extra_id_{i}>''' for i in range(lowercase__     )]
             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 lowercase__     : bool('''extra_id''' in str(lowercase__     )     ) ,   lowercase__     )     )     )
                   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=lowercase__ ,   unk_token=lowercase__ ,   pad_token=lowercase__ ,   extra_ids=lowercase__ ,   additional_special_tokens=lowercase__ ,   sp_model_kwargs=self.sp_model_kwargs ,   legacy=lowercase__ ,   **lowercase__ ,   )

             __A    =vocab_file
             __A    =extra_ids

             __A    =spm.SentencePieceProcessor(**self.sp_model_kwargs     )
             self.sp_model.Load(lowercase__     )

       @staticmethod
       def     __UpperCamelCase     (							lowercase__ ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             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.''' ,   lowercase__ ,   )

             return max_model_length

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self.sp_model.get_piece_size() + self._extra_ids

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ={self.convert_ids_to_tokens(lowercase__     ): i for i in range(self.vocab_size     )}
             vocab.update(self.added_tokens_encoder     )
             return vocab

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = False     ):







             '''simple docstring'''



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

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return list(
                 set(filter(lambda lowercase__     : bool(re.search(R'''<extra_id_\d+>''' ,   lowercase__     )     ) is not None ,   self.additional_special_tokens     )     )     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return [self._convert_token_to_id(lowercase__     ) for token in self.get_sentinel_tokens()]

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             if len(lowercase__     ) > 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 ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __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 ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =self._add_eos_if_not_present(lowercase__     )
             if token_ids_a is None:
                   return token_ids_a
             else:
                   __A    =self._add_eos_if_not_present(lowercase__     )
                   return token_ids_a + token_ids_a

       def __getstate__(							self     ):







             '''simple docstring'''



             __A    =self.__dict__.copy()
             __A    =None
             return state

       def __setstate__(							self ,   lowercase__     ):







             '''simple docstring'''



             __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 ,   lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             if not self.legacy:
                   __A    =SPIECE_UNDERLINE + text.replace(lowercase__ ,   ''' '''     )
             return super().tokenize(lowercase__ ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             if not self.legacy:
                   __A    =text.startswith(lowercase__     )
                   if is_first:
                         __A    =text[1:]

             __A    =self.sp_model.encode(lowercase__ ,   out_type=lowercase__     )

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

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             if token.startswith('''<extra_id_'''     ):
                   __A    =re.match(R'''<extra_id_(\d+)>''' ,   lowercase__     )
                   __A    =int(match.group(1     )     )
                   return self.vocab_size - num - 1
             return self.sp_model.piece_to_id(lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             if index < self.sp_model.get_piece_size():
                   __A    =self.sp_model.IdToPiece(lowercase__     )
             else:
                   __A    =f'''<extra_id_{self.vocab_size - 1 - index}>'''
             return token

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __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(lowercase__     ) + token
                         __A    =True
                         __A    =[]
                   else:
                         current_sub_tokens.append(lowercase__     )
                         __A    =False
             out_string += self.sp_model.decode(lowercase__     )
             return out_string.strip()


       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             if not os.path.isdir(lowercase__     ):
                   logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
                   return
             __A    =os.path.join(
                 lowercase__ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

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

             return (out_vocab_file,)





import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


_lowerCamelCase      :					List[Any]					    =      logging.getLogger(__name__)





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       def __init__(							self ,   lowercase__=-1     ):







             '''simple docstring'''



             __A    =label_idx

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             if isinstance(lowercase__ ,   lowercase__     ):
                   __A    =mode.value
             __A    =os.path.join(lowercase__ ,   f'''{mode}.txt'''     )
             __A    =1
             __A    =[]
             with open(lowercase__ ,   encoding='''utf-8'''     ) as f:
                   __A    =[]
                   __A    =[]
                   for line in f:
                         if line.startswith('''-DOCSTART-'''     ) or line == "" or line == "\n":
                               if words:
                                     examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,   words=lowercase__ ,   labels=lowercase__     )     )
                                     guid_index += 1
                                     __A    =[]
                                     __A    =[]
                         else:
                               __A    =line.split(''' '''     )
                               words.append(splits[0]     )
                               if len(lowercase__     ) > 1:
                                     labels.append(splits[self.label_idx].replace('''\n''' ,   ''''''     )     )
                               else:
                                     # Examples could have no label for mode = "test"
                                     labels.append('''O'''     )
                   if words:
                         examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,   words=lowercase__ ,   labels=lowercase__     )     )
             return examples

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =0
             for line in test_input_reader:
                   if line.startswith('''-DOCSTART-'''     ) or line == "" or line == "\n":
                         writer.write(lowercase__     )
                         if not preds_list[example_id]:
                               example_id += 1
                   elif preds_list[example_id]:
                         __A    =line.split()[0] + ''' ''' + preds_list[example_id].pop(0     ) + '''\n'''
                         writer.write(lowercase__     )
                   else:
                         logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.''' ,   line.split()[0]     )


       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             if path:
                   with open(lowercase__ ,   '''r'''     ) as f:
                         __A    =f.read().splitlines()
                   if "O" not in labels:
                         __A    =['''O'''] + labels
                   return labels
             else:
                   return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       def __init__(							self     ):







             '''simple docstring'''



             super().__init__(label_idx=-2     )


       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             if path:
                   with open(lowercase__ ,   '''r'''     ) as f:
                         __A    =f.read().splitlines()
                   if "O" not in labels:
                         __A    =['''O'''] + labels
                   return labels
             else:
                   return [
                       "O",
                       "B-ADVP",
                       "B-INTJ",
                       "B-LST",
                       "B-PRT",
                       "B-NP",
                       "B-SBAR",
                       "B-VP",
                       "B-ADJP",
                       "B-CONJP",
                       "B-PP",
                       "I-ADVP",
                       "I-INTJ",
                       "I-LST",
                       "I-PRT",
                       "I-NP",
                       "I-SBAR",
                       "I-VP",
                       "I-ADJP",
                       "I-CONJP",
                       "I-PP",
                   ]





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             if isinstance(lowercase__ ,   lowercase__     ):
                   __A    =mode.value
             __A    =os.path.join(lowercase__ ,   f'''{mode}.txt'''     )
             __A    =1
             __A    =[]

             with open(lowercase__ ,   encoding='''utf-8'''     ) as f:
                   for sentence in parse_incr(lowercase__     ):
                         __A    =[]
                         __A    =[]
                         for token in sentence:
                               words.append(token['''form''']     )
                               labels.append(token['''upos''']     )
                         assert len(lowercase__     ) == len(lowercase__     )
                         if words:
                               examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,   words=lowercase__ ,   labels=lowercase__     )     )
                               guid_index += 1
             return examples

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =0
             for sentence in parse_incr(lowercase__     ):
                   __A    =preds_list[example_id]
                   __A    =''''''
                   for token in sentence:
                         out += f'''{token['form']} ({token['upos']}|{s_p.pop(0     )}) '''
                   out += "\n"
                   writer.write(lowercase__     )
                   example_id += 1


       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             if path:
                   with open(lowercase__ ,   '''r'''     ) as f:
                         return f.read().splitlines()
             else:
                   return [
                       "ADJ",
                       "ADP",
                       "ADV",
                       "AUX",
                       "CCONJ",
                       "DET",
                       "INTJ",
                       "NOUN",
                       "NUM",
                       "PART",
                       "PRON",
                       "PROPN",
                       "PUNCT",
                       "SCONJ",
                       "SYM",
                       "VERB",
                       "X",
                   ]


