





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ConditionalDetrImageProcessor
class     SCREAMING_SNAKE_CASE       (unittest.TestCase				):


   def __init__(   self      :    List[str]      ,					a      :    Any      ,					a      :    List[str]=7      ,					a      :    List[Any]=3      ,					a      :    Optional[int]=30      ,					a      :    List[Any]=400      ,					a      :    List[str]=True      ,					a      :    Dict=None      ,					a      :    Optional[Any]=True      ,					a      :    str=[0.5, 0.5, 0.5]      ,					a      :    Tuple=[0.5, 0.5, 0.5]      ,					a      :    Tuple=True      ,					a      :    Union[str, Any]=1 / 255      ,					a      :    List[str]=True      ,					)->       Dict:





     """simple docstring"""







     lowercase__           =  size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
     lowercase__           =  parent
     lowercase__           =  batch_size
     lowercase__           =  num_channels
     lowercase__           =  min_resolution
     lowercase__           =  max_resolution
     lowercase__           =  do_resize
     lowercase__           =  size
     lowercase__           =  do_normalize
     lowercase__           =  image_mean
     lowercase__           =  image_std
     lowercase__           =  do_rescale
     lowercase__           =  rescale_factor
     lowercase__           =  do_pad


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       str:





     """simple docstring"""







     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_rescale": self.do_rescale,
         "rescale_factor": self.rescale_factor,
         "do_pad": self.do_pad,
     }


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    Dict      ,					a      :    List[Any]=False	)->       Optional[int]:





     """simple docstring"""







     if not batched:
       lowercase__           =  image_inputs[0]
       if isinstance(a      ,					Image.Image	):
         lowercase__      ,   lowercase__           =  image.size
       else:
         lowercase__      ,   lowercase__           =  image.shape[1], image.shape[2]
       if w < h:
         lowercase__           =  int(self.size['shortest_edge'] * h / w	)
         lowercase__           =  self.size['shortest_edge']
       elif w > h:
         lowercase__           =  self.size['shortest_edge']
         lowercase__           =  int(self.size['shortest_edge'] * w / h	)
       else:
         lowercase__           =  self.size['shortest_edge']
         lowercase__           =  self.size['shortest_edge']

     else:
       lowercase__           =  []
       for image in image_inputs:
         lowercase__      ,   lowercase__           =  self.get_expected_values([image]	)
         expected_values.append((expected_height, expected_width)	)
       lowercase__           =  max(a      ,					key=lambda a	: item[0]	)[0]
       lowercase__           =  max(a      ,					key=lambda a	: item[1]	)[1]

     return expected_height, expected_width






@require_torch
@require_vision
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase      ,     unittest.TestCase				):
   _UpperCamelCase   :		Union[str, Any]			 =	ConditionalDetrImageProcessor if is_vision_available() else None


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       str:





     """simple docstring"""







     lowercase__           =  ConditionalDetrImageProcessingTester(self	)


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





     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Dict:





     """simple docstring"""







     lowercase__           =  self.image_processing_class(**self.image_processor_dict	)
     self.assertTrue(hasattr(a      ,					'image_mean'	)	)
     self.assertTrue(hasattr(a      ,					'image_std'	)	)
     self.assertTrue(hasattr(a      ,					'do_normalize'	)	)
     self.assertTrue(hasattr(a      ,					'do_resize'	)	)
     self.assertTrue(hasattr(a      ,					'size'	)	)


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





     """simple docstring"""







     lowercase__           =  self.image_processing_class.from_dict(self.image_processor_dict	)
     self.assertEqual(image_processor.size      ,					{'shortest_edge': 18, 'longest_edge': 1_333}	)
     self.assertEqual(image_processor.do_pad      ,					a	)

     lowercase__           =  self.image_processing_class.from_dict(
         self.image_processor_dict      ,					size=42      ,					max_size=84      ,					pad_and_return_pixel_mask=a	)
     self.assertEqual(image_processor.size      ,					{'shortest_edge': 42, 'longest_edge': 84}	)
     self.assertEqual(image_processor.do_pad      ,					a	)


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





     """simple docstring"""







     pass


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





     """simple docstring"""







     lowercase__           =  self.image_processing_class(**self.image_processor_dict	)
     # create random PIL images
     lowercase__           =  prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=a	)
     for image in image_inputs:
       self.assertIsInstance(a      ,					Image.Image	)

     # Test not batched input
     lowercase__           =  image_processing(image_inputs[0]      ,					return_tensors='pt'	).pixel_values

     lowercase__      ,   lowercase__           =  self.image_processor_tester.get_expected_values(a	)

     self.assertEqual(
         encoded_images.shape      ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,					)

     # Test batched
     lowercase__      ,   lowercase__           =  self.image_processor_tester.get_expected_values(a      ,					batched=a	)

     lowercase__           =  image_processing(a      ,					return_tensors='pt'	).pixel_values
     self.assertEqual(
         encoded_images.shape      ,					(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )      ,					)


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





     """simple docstring"""







     lowercase__           =  self.image_processing_class(**self.image_processor_dict	)
     # create random numpy tensors
     lowercase__           =  prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=a      ,					numpify=a	)
     for image in image_inputs:
       self.assertIsInstance(a      ,					np.ndarray	)

     # Test not batched input
     lowercase__           =  image_processing(image_inputs[0]      ,					return_tensors='pt'	).pixel_values

     lowercase__      ,   lowercase__           =  self.image_processor_tester.get_expected_values(a	)

     self.assertEqual(
         encoded_images.shape      ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,					)

     # Test batched
     lowercase__           =  image_processing(a      ,					return_tensors='pt'	).pixel_values

     lowercase__      ,   lowercase__           =  self.image_processor_tester.get_expected_values(a      ,					batched=a	)

     self.assertEqual(
         encoded_images.shape      ,					(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )      ,					)


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





     """simple docstring"""







     lowercase__           =  self.image_processing_class(**self.image_processor_dict	)
     # create random PyTorch tensors
     lowercase__           =  prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=a      ,					torchify=a	)
     for image in image_inputs:
       self.assertIsInstance(a      ,					torch.Tensor	)

     # Test not batched input
     lowercase__           =  image_processing(image_inputs[0]      ,					return_tensors='pt'	).pixel_values

     lowercase__      ,   lowercase__           =  self.image_processor_tester.get_expected_values(a	)

     self.assertEqual(
         encoded_images.shape      ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,					)

     # Test batched
     lowercase__           =  image_processing(a      ,					return_tensors='pt'	).pixel_values

     lowercase__      ,   lowercase__           =  self.image_processor_tester.get_expected_values(a      ,					batched=a	)

     self.assertEqual(
         encoded_images.shape      ,					(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )      ,					)


   @slow
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       Tuple:





     """simple docstring"""







     lowercase__           =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
     with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'      ,					'r'	) as f:
       lowercase__           =  json.loads(f.read()	)

     lowercase__           =  {'image_id': 39_769, 'annotations': target}

     # encode them
     lowercase__           =  ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'	)
     lowercase__           =  image_processing(images=a      ,					annotations=a      ,					return_tensors='pt'	)

     # verify pixel values
     lowercase__           =  torch.Size([1, 3, 800, 1_066]	)
     self.assertEqual(encoding['pixel_values'].shape      ,					a	)

     lowercase__           =  torch.tensor([0.2796, 0.3138, 0.3481]	)
     self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,					a      ,					atol=1E-4	)	)

     # verify area
     lowercase__           =  torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]	)
     self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,					a	)	)
     # verify boxes
     lowercase__           =  torch.Size([6, 4]	)
     self.assertEqual(encoding['labels'][0]['boxes'].shape      ,					a	)
     lowercase__           =  torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]	)
     self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,					a      ,					atol=1E-3	)	)
     # verify image_id
     lowercase__           =  torch.tensor([39_769]	)
     self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,					a	)	)
     # verify is_crowd
     lowercase__           =  torch.tensor([0, 0, 0, 0, 0, 0]	)
     self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,					a	)	)
     # verify class_labels
     lowercase__           =  torch.tensor([75, 75, 63, 65, 17, 17]	)
     self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,					a	)	)
     # verify orig_size
     lowercase__           =  torch.tensor([480, 640]	)
     self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,					a	)	)
     # verify size
     lowercase__           =  torch.tensor([800, 1_066]	)
     self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,					a	)	)


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





     """simple docstring"""







     lowercase__           =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
     with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'      ,					'r'	) as f:
       lowercase__           =  json.loads(f.read()	)

     lowercase__           =  {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

     lowercase__           =  pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'	)

     # encode them
     lowercase__           =  ConditionalDetrImageProcessor(format='coco_panoptic'	)
     lowercase__           =  image_processing(images=a      ,					annotations=a      ,					masks_path=a      ,					return_tensors='pt'	)

     # verify pixel values
     lowercase__           =  torch.Size([1, 3, 800, 1_066]	)
     self.assertEqual(encoding['pixel_values'].shape      ,					a	)

     lowercase__           =  torch.tensor([0.2796, 0.3138, 0.3481]	)
     self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,					a      ,					atol=1E-4	)	)

     # verify area
     lowercase__           =  torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]	)
     self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,					a	)	)
     # verify boxes
     lowercase__           =  torch.Size([6, 4]	)
     self.assertEqual(encoding['labels'][0]['boxes'].shape      ,					a	)
     lowercase__           =  torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]	)
     self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,					a      ,					atol=1E-3	)	)
     # verify image_id
     lowercase__           =  torch.tensor([39_769]	)
     self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,					a	)	)
     # verify is_crowd
     lowercase__           =  torch.tensor([0, 0, 0, 0, 0, 0]	)
     self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,					a	)	)
     # verify class_labels
     lowercase__           =  torch.tensor([17, 17, 63, 75, 75, 93]	)
     self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,					a	)	)
     # verify masks
     lowercase__           =  822_873
     self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,					a	)
     # verify orig_size
     lowercase__           =  torch.tensor([480, 640]	)
     self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,					a	)	)
     # verify size
     lowercase__           =  torch.tensor([800, 1_066]	)
     self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,					a	)	)









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()


