





'''simple docstring'''





import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase			       =      logging.get_logger(__name__)

_lowerCamelCase			       =      '▁'

_lowerCamelCase			       =      {'vocab_file': 'vocab.txt', 'sentencepiece_model_ckpt': 'sentencepiece.bpe.model'}

_lowerCamelCase			       =      {
    'sentencepiece_model_file': 'sentencepiece.bpe.model',
    'vocab_file': 'vocab.txt',
}

_lowerCamelCase			       =      {
    'vocab_file': {
        'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
        'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
    },
    'sentencepiece_model_file': {
        'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
        'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
    },
}

_lowerCamelCase			       =      {
    'ernie-m-base': 514,
    'ernie-m-large': 514,
}

_lowerCamelCase			       =      {
    'ernie-m-base': {'do_lower_case': False},
    'ernie-m-large': {'do_lower_case': False},
}







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      ["""input_ids"""]

      UpperCAmelCase__						=      VOCAB_FILES_NAMES
      UpperCAmelCase__						=      PRETRAINED_INIT_CONFIGURATION
      UpperCAmelCase__						=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase__						=      PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase__						=      RESOURCE_FILES_NAMES


      def __init__(		self      ,							a__      ,							a__=None      ,							a__=False      ,							a__="utf8"      ,							a__="[UNK]"      ,							a__="[SEP]"      ,							a__="[PAD]"      ,							a__="[CLS]"      ,							a__="[MASK]"      ,							a__ = None      ,							**a__      ,							):



             """simple docstring"""





             _lowerCamelCase							:     int	=		{} if sp_model_kwargs is None else sp_model_kwargs
             super().__init__(
                 do_lower_case=__lowerCAmelCase      ,							unk_token=__lowerCAmelCase      ,							sep_token=__lowerCAmelCase      ,							pad_token=__lowerCAmelCase      ,							cls_token=__lowerCAmelCase      ,							mask_token=__lowerCAmelCase      ,							vocab_file=__lowerCAmelCase      ,							encoding=__lowerCAmelCase      ,							sp_model_kwargs=self.sp_model_kwargs      ,							**__lowerCAmelCase      ,							)
             _lowerCamelCase							:     Optional[Any]	=		do_lower_case
             _lowerCamelCase							:     str	=		sentencepiece_model_ckpt
             _lowerCamelCase							:     Union[str, Any]	=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
             self.sp_model.Load(__lowerCAmelCase)

             # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
             if vocab_file is not None:
                    _lowerCamelCase							:     List[Any]	=		self.load_vocab(filepath=__lowerCAmelCase)
             else:
                    _lowerCamelCase							:     Any	=		{self.sp_model.id_to_piece(__lowerCAmelCase): id for id in range(self.sp_model.get_piece_size())}
             _lowerCamelCase							:     Any	=		{v: k for k, v in self.vocab.items()}


      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             if text is None:
                    return None

             _lowerCamelCase							:     Optional[Any]	=		self.tokenize(__lowerCAmelCase)
             _lowerCamelCase,  _lowerCamelCase							:     Dict	=		'''''', []

             for i, ch in enumerate(__lowerCAmelCase):
                    if ch in self.SP_CHAR_MAPPING:
                           _lowerCamelCase							:     Tuple	=		self.SP_CHAR_MAPPING.get(__lowerCAmelCase)
                    else:
                           _lowerCamelCase							:     Dict	=		unicodedata.normalize('''NFKC'''      ,							__lowerCAmelCase)
                    if self.is_whitespace(__lowerCAmelCase):
                           continue
                    normalized_text += ch
                    char_mapping.extend([i] * len(__lowerCAmelCase))

             _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     List[Any]	=		normalized_text, [], 0

             if self.do_lower_case:
                    _lowerCamelCase							:     List[Any]	=		text.lower()

             for token in split_tokens:
                    if token[:1] == "▁":
                           _lowerCamelCase							:     Optional[Any]	=		token[1:]
                    _lowerCamelCase							:     Tuple	=		text[offset:].index(__lowerCAmelCase) + offset
                    _lowerCamelCase							:     List[Any]	=		start + len(__lowerCAmelCase)

                    token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1))
                    _lowerCamelCase							:     Dict	=		end
             return token_mapping


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             return len(self.vocab)


      def 				__snake_case			(		self):



             """simple docstring"""





             return dict(self.vocab      ,							**self.added_tokens_encoder)


      def __getstate__(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		self.__dict__.copy()
             _lowerCamelCase							:     Optional[int]	=		None
             return state


      def __setstate__(		self      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		d

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

             _lowerCamelCase							:     Optional[Any]	=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
             self.sp_model.Load(self.sentencepiece_model_ckpt)


      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             return "".join((self.SP_CHAR_MAPPING.get(__lowerCAmelCase      ,							__lowerCAmelCase) for c in text))


      def 				__snake_case			(		self      ,							a__      ,							a__=False      ,							a__=64      ,							a__=0.1):



             """simple docstring"""





             if self.sp_model_kwargs.get('''enable_sampling''') is True:
                    _lowerCamelCase							:     str	=		True
             if self.sp_model_kwargs.get('''alpha''') is not None:
                    _lowerCamelCase							:     int	=		self.sp_model_kwargs.get('''alpha''')
             if self.sp_model_kwargs.get('''nbest_size''') is not None:
                    _lowerCamelCase							:     Dict	=		self.sp_model_kwargs.get('''nbest_size''')

             if not enable_sampling:
                    _lowerCamelCase							:     Any	=		self.sp_model.EncodeAsPieces(__lowerCAmelCase)
             else:
                    _lowerCamelCase							:     Optional[Any]	=		self.sp_model.SampleEncodeAsPieces(__lowerCAmelCase      ,							__lowerCAmelCase      ,							__lowerCAmelCase)
             _lowerCamelCase							:     Any	=		[]
             for pi, piece in enumerate(__lowerCAmelCase):
                    if piece == SPIECE_UNDERLINE:
                           if not pieces[pi + 1].startswith(__lowerCAmelCase) and pi != 0:
                                  new_pieces.append(__lowerCAmelCase)
                                  continue
                           else:
                                  continue
                    _lowerCamelCase							:     Optional[Any]	=		0
                    for i, chunk in enumerate(__lowerCAmelCase):
                           if chunk == SPIECE_UNDERLINE:
                                  continue
                           if self.is_ch_char(__lowerCAmelCase) or self.is_punct(__lowerCAmelCase):
                                  if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                         new_pieces.append(piece[lst_i:i])
                                  new_pieces.append(__lowerCAmelCase)
                                  _lowerCamelCase							:     List[Any]	=		i + 1
                           elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                                  if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                         new_pieces.append(piece[lst_i:i])
                                  _lowerCamelCase							:     List[Any]	=		i
                           elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                                  if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                         new_pieces.append(piece[lst_i:i])
                                  _lowerCamelCase							:     List[Any]	=		i
                    if len(__lowerCAmelCase) > lst_i:
                           new_pieces.append(piece[lst_i:])
             return new_pieces


      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     List[str]	=		''''''.join(__lowerCAmelCase).replace(__lowerCAmelCase      ,							''' ''').strip()
             return out_string


      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     List[str]	=		self.convert_ids_to_tokens(__lowerCAmelCase)
             _lowerCamelCase							:     List[Any]	=		''''''.join(__lowerCAmelCase).replace(__lowerCAmelCase      ,							''' ''').strip()
             return out_string


      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             return self.vocab.get(__lowerCAmelCase      ,							self.vocab.get(self.unk_token))


      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             return self.reverse_vocab.get(__lowerCAmelCase      ,							self.unk_token)


      def 				__snake_case			(		self      ,							a__      ,							a__=None):



             """simple docstring"""





             if token_ids_a is None:
                    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             _lowerCamelCase							:     str	=		[self.cls_token_id]
             _lowerCamelCase							:     List[Any]	=		[self.sep_token_id]
             return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep


      def 				__snake_case			(		self      ,							a__      ,							a__=None):



             """simple docstring"""





             if offset_mapping_a is None:
                    return [(0, 0)] + offset_mapping_a + [(0, 0)]

             return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]


      def 				__snake_case			(		self      ,							a__      ,							a__=None      ,							a__=False):



             """simple docstring"""





             if already_has_special_tokens:
                    if token_ids_a is not None:
                           raise ValueError(
                               '''You should not supply a second sequence if the provided sequence of '''
                               '''ids is already formatted with special tokens for the model.''')
                    return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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


      def 				__snake_case			(		self      ,							a__      ,							a__ = None):



             """simple docstring"""





             if token_ids_a is None:
                    # [CLS] X [SEP]
                    return (len(__lowerCAmelCase) + 2) * [0]

             # [CLS] A [SEP] [SEP] B [SEP]
             return [0] * (len(__lowerCAmelCase) + 1) + [1] * (len(__lowerCAmelCase) + 3)


      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             if "\u4e00" <= char <= "\u9fff":
                    return True
             return False


      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             if ("a" <= char <= "z") or ("A" <= char <= "Z"):
                    return True
             return False


      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             if char in ",;:.?!~，；：。？！《》【】":
                    return True
             return False


      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             if char == " " or char == "\t" or char == "\n" or char == "\r":
                    return True
             if len(__lowerCAmelCase) == 1:
                    _lowerCamelCase							:     Tuple	=		unicodedata.category(__lowerCAmelCase)
                    if cat == "Zs":
                           return True
             return False


      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		{}
             with io.open(__lowerCAmelCase      ,							'''r'''      ,							encoding='''utf-8''') as f:
                    for index, line in enumerate(__lowerCAmelCase):
                           _lowerCamelCase							:     Dict	=		line.rstrip('''\n''')
                           _lowerCamelCase							:     str	=		int(__lowerCAmelCase)

             return token_to_idx



      def 				__snake_case			(		self      ,							a__      ,							a__ = None):



             """simple docstring"""





             _lowerCamelCase							:     int	=		0
             if os.path.isdir(__lowerCAmelCase):
                    _lowerCamelCase							:     Tuple	=		os.path.join(
                        __lowerCAmelCase      ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
             else:
                    _lowerCamelCase							:     Dict	=		(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
             with open(__lowerCAmelCase      ,							'''w'''      ,							encoding='''utf-8''') as writer:
                    for token, token_index in sorted(self.vocab.items()      ,							key=lambda a__: kv[1]):
                           if index != token_index:
                                  logger.warning(
                                      F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
                                      ''' Please check that the vocabulary is not corrupted!''')
                                  _lowerCamelCase							:     Optional[int]	=		token_index
                           writer.write(token + '''\n''')
                           index += 1

             _lowerCamelCase							:     Union[str, Any]	=		os.path.join(__lowerCAmelCase      ,							'''sentencepiece.bpe.model''')
             with open(__lowerCAmelCase      ,							'''wb''') as fi:
                    _lowerCamelCase							:     Any	=		self.sp_model.serialized_model_proto()
                    fi.write(__lowerCAmelCase)

             return (vocab_file,)








import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_lowerCamelCase			       =      logging.getLogger(__name__)







@dataclass
class 			__A       :




      """simple docstring"""







      UpperCAmelCase__						=      field(
          metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}  )
      UpperCAmelCase__						=      field(
          default=lowerCamelCase__ ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
      UpperCAmelCase__						=      field(
          default="""NER""" ,metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}  )
      UpperCAmelCase__						=      field(
          default=lowerCamelCase__ ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
      UpperCAmelCase__						=      field(default=lowerCamelCase__ ,metadata={"""help""": """Set this flag to use fast tokenization."""}  )
      # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
      # or just modify its tokenizer_config.json.
      UpperCAmelCase__						=      field(
          default=lowerCamelCase__ ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} ,)







@dataclass
class 			__A       :




      """simple docstring"""







      UpperCAmelCase__						=      field(
          metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}  )
      UpperCAmelCase__						=      field(
          default=lowerCamelCase__ ,metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""} ,)
      UpperCAmelCase__						=      field(
          default=128 ,metadata={
              """help""": (
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )
          } ,)
      UpperCAmelCase__						=      field(
          default=lowerCamelCase__ ,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}  )




def    __UpperCAmelCase(      ):
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.

       _lowerCamelCase							:     Dict	=		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
       if len(sys.argv      ) == 2 and sys.argv[1].endswith('''.json'''      ):
              # If we pass only one argument to the script and it's the path to a json file,
              # let's parse it to get our arguments.
              _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     Tuple	=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
       else:
              _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     List[Any]	=		parser.parse_args_into_dataclasses()

       if (
           os.path.exists(training_args.output_dir      )
           and os.listdir(training_args.output_dir      )
           and training_args.do_train
           and not training_args.overwrite_output_dir
       ):
              raise ValueError(
                  F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
                  ''' --overwrite_output_dir to overcome.'''      )

       _lowerCamelCase							:     Optional[Any]	=		import_module('''tasks'''      )
       try:
              _lowerCamelCase							:     Dict	=		getattr(lowercase_ ,    model_args.task_type      )
              _lowerCamelCase							:     TokenClassificationTask	=		token_classification_task_clazz()
       except AttributeError:
              raise ValueError(
                  F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
                  F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""      )

       # Setup logging
       logging.basicConfig(
           format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,    datefmt='''%m/%d/%Y %H:%M:%S''' ,    level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,    )
       logger.warning(
           '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' ,    training_args.local_rank ,    training_args.device ,    training_args.n_gpu ,    bool(training_args.local_rank != -1      ) ,    training_args.fpaa ,    )
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank      ):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()
       logger.info('''Training/evaluation parameters %s''' ,    lowercase_      )

       # Set seed
       set_seed(training_args.seed      )

       # Prepare CONLL-2003 task
       _lowerCamelCase							:     List[str]	=		token_classification_task.get_labels(data_args.labels      )
       _lowerCamelCase							:     Dict[int, str]	=		dict(enumerate(lowercase_      )      )
       _lowerCamelCase							:     Any	=		len(lowercase_      )

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       _lowerCamelCase							:     List[Any]	=		AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path ,    num_labels=lowercase_ ,    idalabel=lowercase_ ,    labelaid={label: i for i, label in enumerate(lowercase_      )} ,    cache_dir=model_args.cache_dir ,    )
       _lowerCamelCase							:     List[str]	=		AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,    cache_dir=model_args.cache_dir ,    use_fast=model_args.use_fast ,    )
       _lowerCamelCase							:     Tuple	=		AutoModelForTokenClassification.from_pretrained(
           model_args.model_name_or_path ,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path      ) ,    config=lowercase_ ,    cache_dir=model_args.cache_dir ,    )

       # Get datasets
       _lowerCamelCase							:     Optional[int]	=		(
           TokenClassificationDataset(
               token_classification_task=lowercase_ ,    data_dir=data_args.data_dir ,    tokenizer=lowercase_ ,    labels=lowercase_ ,    model_type=config.model_type ,    max_seq_length=data_args.max_seq_length ,    overwrite_cache=data_args.overwrite_cache ,    mode=Split.train ,    )
           if training_args.do_train
           else None
       )
       _lowerCamelCase							:     Dict	=		(
           TokenClassificationDataset(
               token_classification_task=lowercase_ ,    data_dir=data_args.data_dir ,    tokenizer=lowercase_ ,    labels=lowercase_ ,    model_type=config.model_type ,    max_seq_length=data_args.max_seq_length ,    overwrite_cache=data_args.overwrite_cache ,    mode=Split.dev ,    )
           if training_args.do_eval
           else None
       )

       def align_predictions(lowercase_ ,    lowercase_      ) -> Tuple[List[int], List[int]]:
              _lowerCamelCase							:     List[Any]	=		np.argmax(lowercase_ ,    axis=2      )

              _lowerCamelCase,  _lowerCamelCase							:     Optional[Any]	=		preds.shape

              _lowerCamelCase							:     Optional[Any]	=		[[] for _ in range(lowercase_      )]
              _lowerCamelCase							:     str	=		[[] for _ in range(lowercase_      )]

              for i in range(lowercase_      ):
                     for j in range(lowercase_      ):
                            if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                                   out_label_list[i].append(label_map[label_ids[i][j]]      )
                                   preds_list[i].append(label_map[preds[i][j]]      )

              return preds_list, out_label_list

       def compute_metrics(lowercase_      ) -> Dict:
              _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		align_predictions(p.predictions ,    p.label_ids      )
              return {
                  "accuracy_score": accuracy_score(lowercase_ ,    lowercase_      ),
                  "precision": precision_score(lowercase_ ,    lowercase_      ),
                  "recall": recall_score(lowercase_ ,    lowercase_      ),
                  "f1": fa_score(lowercase_ ,    lowercase_      ),
              }

       # Data collator
       _lowerCamelCase							:     Optional[int]	=		DataCollatorWithPadding(lowercase_ ,    pad_to_multiple_of=8      ) if training_args.fpaa else None

       # Initialize our Trainer
       _lowerCamelCase							:     List[Any]	=		Trainer(
           model=lowercase_ ,    args=lowercase_ ,    train_dataset=lowercase_ ,    eval_dataset=lowercase_ ,    compute_metrics=lowercase_ ,    data_collator=lowercase_ ,    )

       # Training
       if training_args.do_train:
              trainer.train(
                  model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
              trainer.save_model()
              # For convenience, we also re-save the tokenizer to the same directory,
              # so that you can share your model easily on huggingface.co/models =)
              if trainer.is_world_process_zero():
                     tokenizer.save_pretrained(training_args.output_dir      )

    # Evaluation
       _lowerCamelCase							:     Tuple	=		{}
       if training_args.do_eval:
              logger.info('''*** Evaluate ***'''      )

              _lowerCamelCase							:     str	=		trainer.evaluate()

              _lowerCamelCase							:     Any	=		os.path.join(training_args.output_dir ,    '''eval_results.txt'''      )
              if trainer.is_world_process_zero():
                     with open(lowercase_ ,    '''w'''      ) as writer:
                            logger.info('''***** Eval results *****'''      )
                            for key, value in result.items():
                                   logger.info('''  %s = %s''' ,    lowercase_ ,    lowercase_      )
                                   writer.write('''%s = %s\n''' % (key, value)      )

                     results.update(lowercase_      )

    # Predict
       if training_args.do_predict:
              _lowerCamelCase							:     Union[str, Any]	=		TokenClassificationDataset(
                  token_classification_task=lowercase_ ,    data_dir=data_args.data_dir ,    tokenizer=lowercase_ ,    labels=lowercase_ ,    model_type=config.model_type ,    max_seq_length=data_args.max_seq_length ,    overwrite_cache=data_args.overwrite_cache ,    mode=Split.test ,    )

              _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     Dict	=		trainer.predict(lowercase_      )
              _lowerCamelCase,  _lowerCamelCase							:     Tuple	=		align_predictions(lowercase_ ,    lowercase_      )

              _lowerCamelCase							:     Tuple	=		os.path.join(training_args.output_dir ,    '''test_results.txt'''      )
              if trainer.is_world_process_zero():
                     with open(lowercase_ ,    '''w'''      ) as writer:
                            for key, value in metrics.items():
                                   logger.info('''  %s = %s''' ,    lowercase_ ,    lowercase_      )
                                   writer.write('''%s = %s\n''' % (key, value)      )

        # Save predictions
              _lowerCamelCase							:     List[str]	=		os.path.join(training_args.output_dir ,    '''test_predictions.txt'''      )
              if trainer.is_world_process_zero():
                     with open(lowercase_ ,    '''w'''      ) as writer:
                            with open(os.path.join(data_args.data_dir ,    '''test.txt'''      ) ,    '''r'''      ) as f:
                                   token_classification_task.write_predictions_to_file(lowercase_ ,    lowercase_ ,    lowercase_      )

       return results




def    __UpperCAmelCase(      lowercase_      ):
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
       main()

