



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

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


a_			=					logging.getLogger(__name__)



class  __lowerCAmelCase     (	lowerCAmelCase__   ):




 def __init__(						self	,				__UpperCAmelCase=-1				):



    '''simple docstring'''
    # in NER datasets, the last column is usually reserved for NER label
    __lowerCamelCase													=    label_idx




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    if isinstance(__UpperCAmelCase	,				__UpperCAmelCase				):
       __lowerCamelCase													=    mode.value
    __lowerCamelCase													=    os.path.join(__UpperCAmelCase	,				F"""{mode}.txt"""				)
    __lowerCamelCase													=    1
    __lowerCamelCase													=    []
    with open(__UpperCAmelCase	,				encoding='''utf-8'''				) as f:
       __lowerCamelCase													=    []
       __lowerCamelCase													=    []
       for line in f:
          if line.startswith('''-DOCSTART-'''				) or line == "" or line == "\n":
             if words:
                examples.append(InputExample(guid=F"""{mode}-{guid_index}"""	,				words=__UpperCAmelCase	,				labels=__UpperCAmelCase				)				)
                guid_index += 1
                __lowerCamelCase													=    []
                __lowerCamelCase													=    []
          else:
             __lowerCamelCase													=    line.split(''' '''				)
             words.append(splits[0]				)
             if len(__UpperCAmelCase				) > 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=__UpperCAmelCase	,				labels=__UpperCAmelCase				)				)
    return examples




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



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




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    if path:
       with open(__UpperCAmelCase	,				'''r'''				) as f:
          __lowerCamelCase													=    f.read().splitlines()
       if "O" not in labels:
          __lowerCamelCase													=    ['''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     (	lowerCAmelCase__   ):




 def __init__(						self				):



    '''simple docstring'''
    # in CONLL2003 dataset chunk column is second-to-last
    super().__init__(label_idx=-2				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    if path:
       with open(__UpperCAmelCase	,				'''r'''				) as f:
          __lowerCamelCase													=    f.read().splitlines()
       if "O" not in labels:
          __lowerCamelCase													=    ['''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     (	lowerCAmelCase__   ):




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    if isinstance(__UpperCAmelCase	,				__UpperCAmelCase				):
       __lowerCamelCase													=    mode.value
    __lowerCamelCase													=    os.path.join(__UpperCAmelCase	,				F"""{mode}.txt"""				)
    __lowerCamelCase													=    1
    __lowerCamelCase													=    []

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




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    0
    for sentence in parse_incr(__UpperCAmelCase				):
       __lowerCamelCase													=    preds_list[example_id]
       __lowerCamelCase													=    ''''''
       for token in sentence:
          out += F"""{token["form"]} ({token["upos"]}|{s_p.pop(0				)}) """
       out += "\n"
       writer.write(__UpperCAmelCase				)
       example_id += 1




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    if path:
       with open(__UpperCAmelCase	,				'''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",
       ]











import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a_			=					logging.get_logger(__name__)
a_			=					{"""vocab_file""": """spiece.model"""}

a_			=					{
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    }
}

a_			=					{
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

a_			=					"""▁"""



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  VOCAB_FILES_NAMES
 lowerCAmelCase__       					=  PRETRAINED_VOCAB_FILES_MAP
 lowerCAmelCase__       					=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=False	,				__UpperCAmelCase="[CLS]"	,				__UpperCAmelCase="[SEP]"	,				__UpperCAmelCase="<unk>"	,				__UpperCAmelCase="[SEP]"	,				__UpperCAmelCase="<pad>"	,				__UpperCAmelCase="[CLS]"	,				__UpperCAmelCase="[MASK]"	,				__UpperCAmelCase = None	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    # Mask token behave like a normal word, i.e. include the space before it and
    # is included in the raw text, there should be a match in a non-normalized sentence.
    __lowerCamelCase													=    (
        AddedToken(__UpperCAmelCase	,				lstrip=__UpperCAmelCase	,				rstrip=__UpperCAmelCase	,				normalized=__UpperCAmelCase				)
        if isinstance(__UpperCAmelCase	,				__UpperCAmelCase				)
        else mask_token
    )

    __lowerCamelCase													=    {} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        do_lower_case=__UpperCAmelCase	,				remove_space=__UpperCAmelCase	,				keep_accents=__UpperCAmelCase	,				bos_token=__UpperCAmelCase	,				eos_token=__UpperCAmelCase	,				unk_token=__UpperCAmelCase	,				sep_token=__UpperCAmelCase	,				pad_token=__UpperCAmelCase	,				cls_token=__UpperCAmelCase	,				mask_token=__UpperCAmelCase	,				sp_model_kwargs=self.sp_model_kwargs	,				**__UpperCAmelCase	,				)

    __lowerCamelCase													=    do_lower_case
    __lowerCamelCase													=    remove_space
    __lowerCamelCase													=    keep_accents
    __lowerCamelCase													=    vocab_file

    __lowerCamelCase													=    spm.SentencePieceProcessor(**self.sp_model_kwargs				)
    self.sp_model.Load(__UpperCAmelCase				)




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return len(self.sp_model				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    {self.convert_ids_to_tokens(__UpperCAmelCase				): i for i in range(self.vocab_size				)}
    vocab.update(self.added_tokens_encoder				)
    return vocab




 def __getstate__(						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.__dict__.copy()
    __lowerCamelCase													=    None
    return state




 def __setstate__(						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    d

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

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




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    if self.remove_space:
       __lowerCamelCase													=    ''' '''.join(inputs.strip().split()				)
    else:
       __lowerCamelCase													=    inputs
    __lowerCamelCase													=    outputs.replace('''``'''	,				'''"'''				).replace('''\'\''''	,				'''"'''				)

    if not self.keep_accents:
       __lowerCamelCase													=    unicodedata.normalize('''NFKD'''	,				__UpperCAmelCase				)
       __lowerCamelCase													=    ''''''.join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase				)]				)
    if self.do_lower_case:
       __lowerCamelCase													=    outputs.lower()

    return outputs




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    self.preprocess_text(__UpperCAmelCase				)
    __lowerCamelCase													=    self.sp_model.encode(__UpperCAmelCase	,				out_type=__UpperCAmelCase				)
    __lowerCamelCase													=    []
    for piece in pieces:
       if len(__UpperCAmelCase				) > 1 and piece[-1] == str(''','''				) and piece[-2].isdigit():
          __lowerCamelCase													=    self.sp_model.EncodeAsPieces(piece[:-1].replace(__UpperCAmelCase	,				''''''				)				)
          if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
             if len(cur_pieces[0]				) == 1:
                __lowerCamelCase													=    cur_pieces[1:]
             else:
                __lowerCamelCase													=    cur_pieces[0][1:]
          cur_pieces.append(piece[-1]				)
          new_pieces.extend(__UpperCAmelCase				)
       else:
          new_pieces.append(__UpperCAmelCase				)

    return new_pieces




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    return self.sp_model.PieceToId(__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    return self.sp_model.IdToPiece(__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    []
    __lowerCamelCase													=    ''''''
    __lowerCamelCase													=    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(__UpperCAmelCase				) + token
          __lowerCamelCase													=    True
          __lowerCamelCase													=    []
       else:
          current_sub_tokens.append(__UpperCAmelCase				)
          __lowerCamelCase													=    False
    out_string += self.sp_model.decode(__UpperCAmelCase				)
    return out_string.strip()




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase = None				):



    '''simple docstring'''
    __lowerCamelCase													=    [self.sep_token_id]
    __lowerCamelCase													=    [self.cls_token_id]
    if token_ids_a is None:
       return cls + token_ids_a + sep
    return cls + token_ids_a + sep + token_ids_a + sep




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase = None	,				__UpperCAmelCase = False				):



    '''simple docstring'''

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

    if token_ids_a is not None:
       return [1] + ([0] * len(__UpperCAmelCase				)) + [1] + ([0] * len(__UpperCAmelCase				)) + [1]
    return [1] + ([0] * len(__UpperCAmelCase				)) + [1]




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase = None				):



    '''simple docstring'''
    __lowerCamelCase													=    [self.sep_token_id]
    __lowerCamelCase													=    [self.cls_token_id]

    if token_ids_a is None:
       return len(cls + token_ids_a + sep				) * [0]
    return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase = None				):



    '''simple docstring'''
    if not os.path.isdir(__UpperCAmelCase				):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""				)
       return
    __lowerCamelCase													=    os.path.join(
        __UpperCAmelCase	,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

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

    return (out_vocab_file,)






