





import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase      ,     unittest.TestCase				):
   _UpperCamelCase   :		str			 =	XLMTokenizer
   _UpperCamelCase   :		Any			 =	False


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       Union[str, Any]:





     """simple docstring"""







     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     lowercase__           =  [
         'l',
         'o',
         'w',
         'e',
         'r',
         's',
         't',
         'i',
         'd',
         'n',
         'w</w>',
         'r</w>',
         't</w>',
         'lo',
         'low',
         'er</w>',
         'low</w>',
         'lowest</w>',
         'newer</w>',
         'wider</w>',
         '<unk>',
     ]
     lowercase__           =  dict(zip(a      ,					range(len(a	)	)	)	)
     lowercase__           =  ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

     lowercase__           =  os.path.join(self.tmpdirname      ,					VOCAB_FILES_NAMES['vocab_file']	)
     lowercase__           =  os.path.join(self.tmpdirname      ,					VOCAB_FILES_NAMES['merges_file']	)
     with open(self.vocab_file      ,					'w'	) as fp:
       fp.write(json.dumps(a	)	)
     with open(self.merges_file      ,					'w'	) as fp:
       fp.write('\n'.join(a	)	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					a      :    Any	)->       Any:





     """simple docstring"""







     lowercase__           =  'lower newer'
     lowercase__           =  'lower newer'
     return input_text, output_text


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       Optional[Any]:





     """simple docstring"""







     lowercase__           =  XLMTokenizer(self.vocab_file      ,					self.merges_file	)

     lowercase__           =  'lower'
     lowercase__           =  ['low', 'er</w>']
     lowercase__           =  tokenizer.tokenize(a	)
     self.assertListEqual(a      ,					a	)

     lowercase__           =  tokens + ['<unk>']
     lowercase__           =  [14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(a	)      ,					a	)


   @slow
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       Any:





     """simple docstring"""







     lowercase__           =  XLMTokenizer.from_pretrained('xlm-mlm-en-2048'	)

     lowercase__           =  tokenizer.encode('sequence builders'      ,					add_special_tokens=a	)
     lowercase__           =  tokenizer.encode('multi-sequence build'      ,					add_special_tokens=a	)

     lowercase__           =  tokenizer.build_inputs_with_special_tokens(a	)
     lowercase__           =  tokenizer.build_inputs_with_special_tokens(a      ,					a	)

     assert encoded_sentence == [0] + text + [1]
     assert encoded_pair == [0] + text + [1] + text_a + [1]









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


lowercase_          =		logging.getLogger(__name__)
@dataclass
class     SCREAMING_SNAKE_CASE       :
   _UpperCamelCase   :		str			 =	field(
       metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}				)
   _UpperCamelCase   :		Optional[str]			 =	field(
       default=UpperCAmelCase      ,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
   _UpperCamelCase   :		Optional[str]			 =	field(
       default='NER'      ,     metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}				)
   _UpperCamelCase   :		Optional[str]			 =	field(
       default=UpperCAmelCase      ,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}				)
   _UpperCamelCase   :		bool			 =	field(default=UpperCAmelCase      ,     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   :		Optional[str]			 =	field(
       default=UpperCAmelCase      ,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}      ,     )






@dataclass
class     SCREAMING_SNAKE_CASE       :
   _UpperCamelCase   :		str			 =	field(
       metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}				)
   _UpperCamelCase   :		Optional[str]			 =	field(
       default=UpperCAmelCase      ,     metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}      ,     )
   _UpperCamelCase   :		int			 =	field(
       default=1_28      ,     metadata={
           'help': (
               'The maximum total input sequence length after tokenization. Sequences longer '
               'than this will be truncated, sequences shorter will be padded.'
           )
       }      ,     )
   _UpperCamelCase   :		bool			 =	field(
       default=UpperCAmelCase      ,     metadata={'help': 'Overwrite the cached training and evaluation sets'}				)






def       __UpperCamelCase  ()    ->       str:
  # 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.

  lowercase__           =  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.
    lowercase__      ,   lowercase__      ,   lowercase__           =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
  else:
    lowercase__      ,   lowercase__      ,   lowercase__           =  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.'		)

  lowercase__           =  import_module('tasks'		)
  try:
    lowercase__           =  getattr(_SCREAMING_SNAKE_CASE		,						model_args.task_type		)
    lowercase__           =  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'		,						_SCREAMING_SNAKE_CASE		)

  # Set seed
  set_seed(training_args.seed		)

  # Prepare CONLL-2003 task
  lowercase__           =  token_classification_task.get_labels(data_args.labels		)
  lowercase__           =  dict(enumerate(_SCREAMING_SNAKE_CASE		)		)
  lowercase__           =  len(_SCREAMING_SNAKE_CASE		)

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

  lowercase__           =  AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path		,						num_labels=_SCREAMING_SNAKE_CASE		,						idalabel=_SCREAMING_SNAKE_CASE		,						labelaid={label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE		)}		,						cache_dir=model_args.cache_dir		,						)
  lowercase__           =  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		,						)
  lowercase__           =  AutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path		,						from_tf=bool('.ckpt' in model_args.model_name_or_path		)		,						config=_SCREAMING_SNAKE_CASE		,						cache_dir=model_args.cache_dir		,						)

  # Get datasets
  lowercase__           =  (
      TokenClassificationDataset(
          token_classification_task=_SCREAMING_SNAKE_CASE		,						data_dir=data_args.data_dir		,						tokenizer=_SCREAMING_SNAKE_CASE		,						labels=_SCREAMING_SNAKE_CASE		,						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
  )
  lowercase__           =  (
      TokenClassificationDataset(
          token_classification_task=_SCREAMING_SNAKE_CASE		,						data_dir=data_args.data_dir		,						tokenizer=_SCREAMING_SNAKE_CASE		,						labels=_SCREAMING_SNAKE_CASE		,						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(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		) -> Tuple[List[int], List[int]]:
    lowercase__           =  np.argmax(_SCREAMING_SNAKE_CASE		,						axis=2		)

    lowercase__      ,   lowercase__           =  preds.shape

    lowercase__           =  [[] for _ in range(_SCREAMING_SNAKE_CASE		)]
    lowercase__           =  [[] for _ in range(_SCREAMING_SNAKE_CASE		)]

    for i in range(_SCREAMING_SNAKE_CASE		):
      for j in range(_SCREAMING_SNAKE_CASE		):
        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(_SCREAMING_SNAKE_CASE		) -> Dict:
    lowercase__      ,   lowercase__           =  align_predictions(p.predictions		,						p.label_ids		)
    return {
        "accuracy_score": accuracy_score(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		),
        "precision": precision_score(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		),
        "recall": recall_score(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		),
        "f1": fa_score(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		),
    }

  # Data collator
  lowercase__           =  DataCollatorWithPadding(_SCREAMING_SNAKE_CASE		,						pad_to_multiple_of=8		) if training_args.fpaa else None

  # Initialize our Trainer
  lowercase__           =  Trainer(
      model=_SCREAMING_SNAKE_CASE		,						args=_SCREAMING_SNAKE_CASE		,						train_dataset=_SCREAMING_SNAKE_CASE		,						eval_dataset=_SCREAMING_SNAKE_CASE		,						compute_metrics=_SCREAMING_SNAKE_CASE		,						data_collator=_SCREAMING_SNAKE_CASE		,						)

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

    lowercase__           =  trainer.evaluate()

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

      results.update(_SCREAMING_SNAKE_CASE		)

    # Predict
  if training_args.do_predict:
    lowercase__           =  TokenClassificationDataset(
        token_classification_task=_SCREAMING_SNAKE_CASE		,						data_dir=data_args.data_dir		,						tokenizer=_SCREAMING_SNAKE_CASE		,						labels=_SCREAMING_SNAKE_CASE		,						model_type=config.model_type		,						max_seq_length=data_args.max_seq_length		,						overwrite_cache=data_args.overwrite_cache		,						mode=Split.test		,						)

    lowercase__      ,   lowercase__      ,   lowercase__           =  trainer.predict(_SCREAMING_SNAKE_CASE		)
    lowercase__      ,   lowercase__           =  align_predictions(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)

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

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

  return results




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Any:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
      main()


