





import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        NystromformerForMaskedLM,
        NystromformerForMultipleChoice,
        NystromformerForQuestionAnswering,
        NystromformerForSequenceClassification,
        NystromformerForTokenClassification,
        NystromformerModel,
    )
    from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST





class 			A__   :




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase=13 ,  lowercase=7 ,  lowercase=True ,  lowercase=True ,  lowercase=True ,  lowercase=True ,  lowercase=99 ,  lowercase=32 ,  lowercase=5 ,  lowercase=4 ,  lowercase=37 ,  lowercase="gelu" ,  lowercase=0.1 ,  lowercase=0.1 ,  lowercase=512 ,  lowercase=16 ,  lowercase=2 ,  lowercase=0.02 ,  lowercase=3 ,  lowercase=4 ,  lowercase=None ,  )      ->						List[Any]:







          '''simple docstring'''

          a__						:     Any			=    parent
          a__						:     int			=    batch_size
          a__						:     Dict			=    seq_length
          a__						:     Tuple			=    is_training
          a__						:     Any			=    use_input_mask
          a__						:     Optional[Any]			=    use_token_type_ids
          a__						:     Dict			=    use_labels
          a__						:     Optional[int]			=    vocab_size
          a__						:     List[Any]			=    hidden_size
          a__						:     int			=    num_hidden_layers
          a__						:     Optional[Any]			=    num_attention_heads
          a__						:     str			=    intermediate_size
          a__						:     Optional[int]			=    hidden_act
          a__						:     Dict			=    hidden_dropout_prob
          a__						:     Optional[int]			=    attention_probs_dropout_prob
          a__						:     Tuple			=    max_position_embeddings
          a__						:     Dict			=    type_vocab_size
          a__						:     Any			=    type_sequence_label_size
          a__						:     List[str]			=    initializer_range
          a__						:     List[str]			=    num_labels
          a__						:     Optional[Any]			=    num_choices
          a__						:     str			=    scope

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







          '''simple docstring'''

          a__						:     Union[str, Any]			=    ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size)

          a__						:     Tuple			=    None
          if self.use_input_mask:
                 a__						:     Optional[int]			=    random_attention_mask([self.batch_size, self.seq_length])

          a__						:     Any			=    None
          if self.use_token_type_ids:
                 a__						:     List[Any]			=    ids_tensor([self.batch_size, self.seq_length] ,  self.type_vocab_size)

          a__						:     str			=    None
          a__						:     List[Any]			=    None
          a__						:     List[str]			=    None
          if self.use_labels:
                 a__						:     Any			=    ids_tensor([self.batch_size] ,  self.type_sequence_label_size)
                 a__						:     str			=    ids_tensor([self.batch_size, self.seq_length] ,  self.num_labels)
                 a__						:     str			=    ids_tensor([self.batch_size] ,  self.num_choices)

          a__						:     Union[str, Any]			=    self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

          return NystromformerConfig(
              vocab_size=self.vocab_size ,  hidden_size=self.hidden_size ,  num_hidden_layers=self.num_hidden_layers ,  num_attention_heads=self.num_attention_heads ,  intermediate_size=self.intermediate_size ,  hidden_act=self.hidden_act ,  hidden_dropout_prob=self.hidden_dropout_prob ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob ,  max_position_embeddings=self.max_position_embeddings ,  type_vocab_size=self.type_vocab_size ,  is_decoder=lowercase ,  initializer_range=self.initializer_range ,  )

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    NystromformerModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:     List[Any]			=    model(lowercase ,  attention_mask=lowercase ,  token_type_ids=lowercase)
          a__						:     int			=    model(lowercase ,  token_type_ids=lowercase)
          a__						:     Optional[Any]			=    model(lowercase)
          self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, self.seq_length, self.hidden_size))

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						str:







          '''simple docstring'''

          a__						:     List[str]			=    NystromformerForMaskedLM(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:     int			=    model(lowercase ,  attention_mask=lowercase ,  token_type_ids=lowercase ,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.seq_length, self.vocab_size))

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







          '''simple docstring'''

          a__						:     Any			=    NystromformerForQuestionAnswering(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:     str			=    model(
              lowercase ,  attention_mask=lowercase ,  token_type_ids=lowercase ,  start_positions=lowercase ,  end_positions=lowercase ,  )
          self.parent.assertEqual(result.start_logits.shape ,  (self.batch_size, self.seq_length))
          self.parent.assertEqual(result.end_logits.shape ,  (self.batch_size, self.seq_length))

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     int			=    self.num_labels
          a__						:     Optional[Any]			=    NystromformerForSequenceClassification(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:     Tuple			=    model(lowercase ,  attention_mask=lowercase ,  token_type_ids=lowercase ,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels))

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						List[str]:







          '''simple docstring'''

          a__						:     Tuple			=    self.num_labels
          a__						:     int			=    NystromformerForTokenClassification(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:     str			=    model(lowercase ,  attention_mask=lowercase ,  token_type_ids=lowercase ,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.seq_length, self.num_labels))

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Any:







          '''simple docstring'''

          a__						:     Optional[int]			=    self.num_choices
          a__						:     Tuple			=    NystromformerForMultipleChoice(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:     Optional[Any]			=    input_ids.unsqueeze(1).expand(-1 ,  self.num_choices ,  -1).contiguous()
          a__						:     Tuple			=    token_type_ids.unsqueeze(1).expand(-1 ,  self.num_choices ,  -1).contiguous()
          a__						:     str			=    input_mask.unsqueeze(1).expand(-1 ,  self.num_choices ,  -1).contiguous()
          a__						:     Optional[int]			=    model(
              lowercase ,  attention_mask=lowercase ,  token_type_ids=lowercase ,  labels=lowercase ,  )
          self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_choices))

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







          '''simple docstring'''

          a__						:     List[Any]			=    self.prepare_config_and_inputs()
          (
              (
              a__
          )			,							(
              a__
          )			,							(
              a__
          )			,							(
              a__
          )			,							(
              a__
          )			,							(
              a__
          )			,							(
              a__
          )			,							
          )						:     str			=    config_and_inputs
          a__						:     Any			=    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
          return config, inputs_dict





@require_torch
class 			A__   (							__UpperCAmelCase			,  __UpperCAmelCase			,  unittest.TestCase     ):




   """simple docstring"""



   __A       :    Any	  =  (
       (
           NystromformerModel,
           NystromformerForMaskedLM,
           NystromformerForMultipleChoice,
           NystromformerForQuestionAnswering,
           NystromformerForSequenceClassification,
           NystromformerForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   __A       :    str	  =  (
       {
           '''feature-extraction''': NystromformerModel,
           '''fill-mask''': NystromformerForMaskedLM,
           '''question-answering''': NystromformerForQuestionAnswering,
           '''text-classification''': NystromformerForSequenceClassification,
           '''token-classification''': NystromformerForTokenClassification,
           '''zero-shot''': NystromformerForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __A       :    Optional[Any]	  =  False
   __A       :    Tuple	  =  False

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







          '''simple docstring'''

          a__						:     int			=    NystromformerModelTester(self)
          a__						:     Any			=    ConfigTester(self ,  config_class=lowercase ,  hidden_size=37)

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          self.config_tester.run_common_tests()

   def 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          a__						:     str			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     Tuple			=    self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
                 a__						:     Optional[Any]			=    type
                 self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     List[Any]			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*lowercase)

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__						:     List[Any]			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*lowercase)

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*lowercase)

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







          '''simple docstring'''

          a__						:     Union[str, Any]			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*lowercase)

   def 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          a__						:     List[Any]			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*lowercase)

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







          '''simple docstring'''

          for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:     int			=    NystromformerModel.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)





@require_torch
class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   @slow
   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     List[str]			=    NystromformerModel.from_pretrained('uw-madison/nystromformer-512')
          a__						:     Tuple			=    torch.tensor([[0, 1, 2, 3, 4, 5]])

          with torch.no_grad():
                 a__						:     List[Any]			=    model(lowercase)[0]

          a__						:     str			=    torch.Size((1, 6, 768))
          self.assertEqual(output.shape ,  lowercase)

          a__						:     str			=    torch.tensor(
              [[[-0.45_32, -0.09_36, 0.51_37], [-0.26_76, 0.06_28, 0.61_86], [-0.36_29, -0.17_26, 0.47_16]]])

          self.assertTrue(torch.allclose(output[:, :3, :3] ,  lowercase ,  atol=1e-4))

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







          '''simple docstring'''

          a__						:     Any			=    'the [MASK] of Belgium is Brussels'

          a__						:     List[str]			=    AutoTokenizer.from_pretrained('uw-madison/nystromformer-512')
          a__						:     Optional[int]			=    NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512')

          a__						:     List[Any]			=    tokenizer(lowercase ,  return_tensors='pt')

          with torch.no_grad():
                 a__						:     Union[str, Any]			=    model(encoding.input_ids).logits

          a__						:     str			=    token_logits[:, 2, :].argmax(-1)[0]

          self.assertEqual(tokenizer.decode(lowercase) ,  'capital')









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				:	Union[str, Any]      						=  logging.getLogger(__name__)





@dataclass
class 			A__   :




   """simple docstring"""



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





@dataclass
class 			A__   :




   """simple docstring"""



   __A       :    str	  =  field(
       metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}     )
   __A       :    Optional[str]	  =  field(
       default=__UpperCAmelCase			,  metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}			,  )
   __A       :    int	  =  field(
       default=1_2_8			,  metadata={
           '''help''': (
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )
       }			,  )
   __A       :    bool	  =  field(
       default=__UpperCAmelCase			,  metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}     )



def A_     (		)  ->		Dict:
       # 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.

       a__						:     List[str]			=    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.
              a__			,							a__			,							a__						:     int			=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
       else:
              a__			,							a__			,							a__						:     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.'    )

       a__						:     Optional[Any]			=    import_module('tasks'    )
       try:
              a__						:     List[Any]			=    getattr(A__		,				model_args.task_type    )
              a__						:     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'		,				A__    )

       # Set seed
       set_seed(training_args.seed    )

       # Prepare CONLL-2003 task
       a__						:     Tuple			=    token_classification_task.get_labels(data_args.labels    )
       a__						:     Dict[int, str]			=    dict(enumerate(A__    )    )
       a__						:     Union[str, Any]			=    len(A__    )

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

       a__						:     Optional[Any]			=    AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path		,				num_labels=A__		,				idalabel=A__		,				labelaid={label: i for i, label in enumerate(A__    )}		,				cache_dir=model_args.cache_dir		,				)
       a__						:     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		,				)
       a__						:     List[Any]			=    AutoModelForTokenClassification.from_pretrained(
           model_args.model_name_or_path		,				from_tf=bool('.ckpt' in model_args.model_name_or_path    )		,				config=A__		,				cache_dir=model_args.cache_dir		,				)

       # Get datasets
       a__						:     int			=    (
           TokenClassificationDataset(
               token_classification_task=A__		,				data_dir=data_args.data_dir		,				tokenizer=A__		,				labels=A__		,				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
       )
       a__						:     Optional[int]			=    (
           TokenClassificationDataset(
               token_classification_task=A__		,				data_dir=data_args.data_dir		,				tokenizer=A__		,				labels=A__		,				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(A__		,				A__    ) -> Tuple[List[int], List[int]]:
              a__						:     Union[str, Any]			=    np.argmax(A__		,				axis=2    )

              a__			,							a__						:     Dict			=    preds.shape

              a__						:     Union[str, Any]			=    [[] for _ in range(A__    )]
              a__						:     Optional[int]			=    [[] for _ in range(A__    )]

              for i in range(A__    ):
                     for j in range(A__    ):
                            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(A__    ) -> Dict:
              a__			,							a__						:     Union[str, Any]			=    align_predictions(p.predictions		,				p.label_ids    )
              return {
                  "accuracy_score": accuracy_score(A__		,				A__    ),
                  "precision": precision_score(A__		,				A__    ),
                  "recall": recall_score(A__		,				A__    ),
                  "f1": fa_score(A__		,				A__    ),
              }

       # Data collator
       a__						:     Union[str, Any]			=    DataCollatorWithPadding(A__		,				pad_to_multiple_of=8    ) if training_args.fpaa else None

       # Initialize our Trainer
       a__						:     List[str]			=    Trainer(
           model=A__		,				args=A__		,				train_dataset=A__		,				eval_dataset=A__		,				compute_metrics=A__		,				data_collator=A__		,				)

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

              a__						:     Optional[Any]			=    trainer.evaluate()

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

                     results.update(A__    )

    # Predict
       if training_args.do_predict:
              a__						:     Optional[Any]			=    TokenClassificationDataset(
                  token_classification_task=A__		,				data_dir=data_args.data_dir		,				tokenizer=A__		,				labels=A__		,				model_type=config.model_type		,				max_seq_length=data_args.max_seq_length		,				overwrite_cache=data_args.overwrite_cache		,				mode=Split.test		,				)

              a__			,							a__			,							a__						:     Any			=    trainer.predict(A__    )
              a__			,							a__						:     Union[str, Any]			=    align_predictions(A__		,				A__    )

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

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

       return results



def A_     (		A__    )  ->		Tuple:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
    main()


