



import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
     import torch

     from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
     from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor



class 		_lowercase		:

   def __init__(      self			:       List[str]							,   __lowerCAmelCase			:       Optional[int]							,   __lowerCAmelCase			:       Optional[int]=13							,   __lowerCAmelCase			:       str=32							,   __lowerCAmelCase			:       str=3							,   __lowerCAmelCase			:       int=4							,   __lowerCAmelCase			:       List[str]=[10, 20, 30, 40]							,   __lowerCAmelCase			:       Any=[2, 2, 3, 2]							,   __lowerCAmelCase			:       Any=True							,   __lowerCAmelCase			:       int=True							,   __lowerCAmelCase			:       str=37							,   __lowerCAmelCase			:       List[Any]="gelu"							,   __lowerCAmelCase			:       int=10							,   __lowerCAmelCase			:       str=0.0_2							,   __lowerCAmelCase			:       int=["stage2", "stage3", "stage4"]							,   __lowerCAmelCase			:       List[str]=[2, 3, 4]							,   __lowerCAmelCase			:       str=None							,   )       ->				Optional[Any]:


    """simple docstring"""






    a						=		parent
    a						=		batch_size
    a						=		image_size
    a						=		num_channels
    a						=		num_stages
    a						=		hidden_sizes
    a						=		depths
    a						=		is_training
    a						=		use_labels
    a						=		intermediate_size
    a						=		hidden_act
    a						=		num_labels
    a						=		initializer_range
    a						=		out_features
    a						=		out_indices
    a						=		scope

   def 		A							(      self			:       Optional[Any]							)       ->				int:


    """simple docstring"""






    a						=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

    a						=		None
    if self.use_labels:
     a						=		ids_tensor([self.batch_size]							,   self.num_labels							)

    a						=		self.get_config()

    return config, pixel_values, labels

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


    """simple docstring"""






    return ConvNextVaConfig(
        num_channels=self.num_channels							,   hidden_sizes=self.hidden_sizes							,   depths=self.depths							,   num_stages=self.num_stages							,   hidden_act=self.hidden_act							,   is_decoder=__lowerCAmelCase							,   initializer_range=self.initializer_range							,   out_features=self.out_features							,   out_indices=self.out_indices							,   num_labels=self.num_labels							,   )

   def 		A							(      self			:       Any							,   __lowerCAmelCase			:       List[str]							,   __lowerCAmelCase			:       Optional[Any]							,   __lowerCAmelCase			:       Dict							)       ->				Optional[int]:


    """simple docstring"""






    a						=		ConvNextVaModel(config=__lowerCAmelCase							)
    model.to(__lowerCAmelCase							)
    model.eval()
    a						=		model(__lowerCAmelCase							)
    # expected last hidden states: B, C, H // 32, W // 32
    self.parent.assertEqual(
        result.last_hidden_state.shape							,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,   )

   def 		A							(      self			:       int							,   __lowerCAmelCase			:       Dict							,   __lowerCAmelCase			:       Dict							,   __lowerCAmelCase			:       Union[str, Any]							)       ->				Dict:


    """simple docstring"""






    a						=		ConvNextVaForImageClassification(__lowerCAmelCase							)
    model.to(__lowerCAmelCase							)
    model.eval()
    a						=		model(__lowerCAmelCase							,   labels=__lowerCAmelCase							)
    self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.num_labels)							)

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


    """simple docstring"""






    a						=		ConvNextVaBackbone(config=__lowerCAmelCase							)
    model.to(__lowerCAmelCase							)
    model.eval()
    a						=		model(__lowerCAmelCase							)

    # verify hidden states
    self.parent.assertEqual(len(result.feature_maps							)							,   len(config.out_features							)							)
    self.parent.assertListEqual(list(result.feature_maps[0].shape							)							,   [self.batch_size, self.hidden_sizes[1], 4, 4]							)

    # verify channels
    self.parent.assertEqual(len(model.channels							)							,   len(config.out_features							)							)
    self.parent.assertListEqual(model.channels							,   config.hidden_sizes[1:]							)

    # verify backbone works with out_features=None
    a						=		None
    a						=		ConvNextVaBackbone(config=__lowerCAmelCase							)
    model.to(__lowerCAmelCase							)
    model.eval()
    a						=		model(__lowerCAmelCase							)

    # verify feature maps
    self.parent.assertEqual(len(result.feature_maps							)							,   1							)
    self.parent.assertListEqual(list(result.feature_maps[0].shape							)							,   [self.batch_size, self.hidden_sizes[-1], 1, 1]							)

    # verify channels
    self.parent.assertEqual(len(model.channels							)							,   1							)
    self.parent.assertListEqual(model.channels							,   [config.hidden_sizes[-1]]							)

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


    """simple docstring"""






    a						=		self.prepare_config_and_inputs()
    a		,  a		,  a						=		config_and_inputs
    a						=		{"pixel_values": pixel_values}
    return config, inputs_dict




   def 		A							(      self			:       Dict							)       ->				Optional[int]:


    """simple docstring"""






    a						=		self.prepare_config_and_inputs()
    a		,  a		,  a						=		config_and_inputs
    a						=		{"pixel_values": pixel_values, "labels": labels}
    return config, inputs_dict



@require_torch
class 		_lowercase		(       UpperCAmelCase__,   UpperCAmelCase__,   unittest.TestCase	):
   _UpperCAmelCase     	=			(
       (
           ConvNextVaModel,
           ConvNextVaForImageClassification,
           ConvNextVaBackbone,
       )
       if is_torch_available()
       else ()
   )
   _UpperCAmelCase     	=			(
       {'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
       if is_torch_available()
       else {}
   )

   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False

   def 		A							(      self			:       List[str]							)       ->				List[Any]:


    """simple docstring"""






    a						=		ConvNextVaModelTester(self							)
    a						=		ConfigTester(self							,   config_class=__lowerCAmelCase							,   has_text_modality=__lowerCAmelCase							,   hidden_size=37							)

   def 		A							(      self			:       Tuple							)       ->				Dict:


    """simple docstring"""






    self.create_and_test_config_common_properties()
    self.config_tester.create_and_test_config_to_json_string()
    self.config_tester.create_and_test_config_to_json_file()
    self.config_tester.create_and_test_config_from_and_save_pretrained()
    self.config_tester.create_and_test_config_with_num_labels()
    self.config_tester.check_config_can_be_init_without_params()
    self.config_tester.check_config_arguments_init()

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


    """simple docstring"""






    return

   @unittest.skip(reason="ConvNextV2 does not use inputs_embeds"							)
   def 		A							(      self			:       List[Any]							)       ->				List[Any]:


    """simple docstring"""






    pass

   @unittest.skip(reason="ConvNextV2 does not support input and output embeddings"							)
   def 		A							(      self			:       int							)       ->				List[Any]:


    """simple docstring"""






    pass

   @unittest.skip(reason="ConvNextV2 does not use feedforward chunking"							)
   def 		A							(      self			:       Optional[int]							)       ->				Dict:


    """simple docstring"""






    pass

   def 		A							(      self			:       List[str]							)       ->				List[str]:


    """simple docstring"""






    if not self.model_tester.is_training:
     return

    for model_class in self.all_model_classes:
     a		,  a						=		self.model_tester.prepare_config_and_inputs_with_labels()
     a						=		True

     if model_class.__name__ in [
         *get_values(__lowerCAmelCase							),
         *get_values(__lowerCAmelCase							),
     ]:
      continue

     a						=		model_class(__lowerCAmelCase							)
     model.to(__lowerCAmelCase							)
     model.train()
     a						=		self._prepare_for_class(__lowerCAmelCase							,   __lowerCAmelCase							,   return_labels=__lowerCAmelCase							)
     a						=		model(**__lowerCAmelCase							).loss
     loss.backward()

   def 		A							(      self			:       Optional[int]							)       ->				List[str]:


    """simple docstring"""






    if not self.model_tester.is_training:
     return

    for model_class in self.all_model_classes:
     a		,  a						=		self.model_tester.prepare_config_and_inputs_with_labels()
     a						=		False
     a						=		True

     if (
         model_class.__name__
         in [*get_values(__lowerCAmelCase							), *get_values(__lowerCAmelCase							)]
         or not model_class.supports_gradient_checkpointing
     ):
      continue

     a						=		model_class(__lowerCAmelCase							)
     model.to(__lowerCAmelCase							)
     model.gradient_checkpointing_enable()
     model.train()
     a						=		self._prepare_for_class(__lowerCAmelCase							,   __lowerCAmelCase							,   return_labels=__lowerCAmelCase							)
     a						=		model(**__lowerCAmelCase							).loss
     loss.backward()

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


    """simple docstring"""






    a		,  a						=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     a						=		model_class(__lowerCAmelCase							)
     a						=		inspect.signature(model.forward							)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     a						=		[*signature.parameters.keys()]

     a						=		["pixel_values"]
     self.assertListEqual(arg_names[:1]							,   __lowerCAmelCase							)

   def 		A							(      self			:       Dict							)       ->				Dict:


    """simple docstring"""






    a						=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__lowerCAmelCase							)

   def 		A							(      self			:       Tuple							)       ->				List[Any]:


    """simple docstring"""






    def check_hidden_states_output(__lowerCAmelCase			:       Union[str, Any]							,   __lowerCAmelCase			:       Dict							,   __lowerCAmelCase			:       Tuple							):
     a						=		model_class(__lowerCAmelCase							)
     model.to(__lowerCAmelCase							)
     model.eval()

     with torch.no_grad():
      a						=		model(**self._prepare_for_class(__lowerCAmelCase							,   __lowerCAmelCase							)							)

     a						=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

     a						=		self.model_tester.num_stages
     self.assertEqual(len(__lowerCAmelCase							)							,   expected_num_stages + 1							)

     # ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
     self.assertListEqual(
         list(hidden_states[0].shape[-2:]							)							,   [self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,   )

    a		,  a						=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     a						=		True
     check_hidden_states_output(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     a						=		True

     check_hidden_states_output(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							)

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


    """simple docstring"""






    a						=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase							)




   @slow
   def 		A							(      self			:       Tuple							)       ->				List[str]:


    """simple docstring"""






    for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     a						=		ConvNextVaModel.from_pretrained(__lowerCAmelCase							)
     self.assertIsNotNone(__lowerCAmelCase							)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 a						=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
 return image




@require_torch
@require_vision
class 		_lowercase		(       unittest.TestCase	):

   @cached_property
   def 		A							(      self			:       Optional[int]							)       ->				str:


    """simple docstring"""






    return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224"							) if is_vision_available() else None




   @slow
   def 		A							(      self			:       List[str]							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224"							).to(__lowerCAmelCase							)

    a						=		self.default_image_processor
    a						=		prepare_img()
    a						=		preprocessor(images=__lowerCAmelCase							,   return_tensors="pt"							).to(__lowerCAmelCase							)

    # forward pass
    with torch.no_grad():
     a						=		model(**__lowerCAmelCase							)

    # verify the logits
    a						=		torch.Size((1, 1000)							)
    self.assertEqual(outputs.logits.shape							,   __lowerCAmelCase							)

    a						=		torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6]							).to(__lowerCAmelCase							)
    self.assertTrue(torch.allclose(outputs.logits[0, :3]							,   __lowerCAmelCase							,   atol=1E-4							)							)






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


A_		:     int        =     logging.getLogger(__name__)



@dataclass
class 		_lowercase		:
   _UpperCAmelCase     	=			field(
       metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}	)
   _UpperCAmelCase     	=			field(
       default=UpperCAmelCase__,   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=UpperCAmelCase__,   metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}	)
   _UpperCAmelCase     	=			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     	=			field(
       default=UpperCAmelCase__,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''},   )



@dataclass
class 		_lowercase		:
   _UpperCAmelCase     	=			field(
       metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}	)
   _UpperCAmelCase     	=			field(
       default=UpperCAmelCase__,   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=UpperCAmelCase__,   metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}	)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 a						=		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						=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
 else:
  a		,  a		,  a						=		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						=		import_module("tasks"					)
 try:
  a						=		getattr(UpperCAmelCase__ ,					model_args.task_type					)
  a						=		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" ,					UpperCAmelCase__					)

 # Set seed
 set_seed(training_args.seed					)

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

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

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

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

  a		,  a						=		preds.shape

  a						=		[[] for _ in range(UpperCAmelCase__					)]
  a						=		[[] for _ in range(UpperCAmelCase__					)]

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

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

 # Initialize our Trainer
 a						=		Trainer(
     model=UpperCAmelCase__ ,					args=UpperCAmelCase__ ,					train_dataset=UpperCAmelCase__ ,					eval_dataset=UpperCAmelCase__ ,					compute_metrics=UpperCAmelCase__ ,					data_collator=UpperCAmelCase__ ,					)

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

  a						=		trainer.evaluate()

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

   results.update(UpperCAmelCase__					)

    # Predict
 if training_args.do_predict:
  a						=		TokenClassificationDataset(
      token_classification_task=UpperCAmelCase__ ,					data_dir=data_args.data_dir ,					tokenizer=UpperCAmelCase__ ,					labels=UpperCAmelCase__ ,					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						=		trainer.predict(UpperCAmelCase__					)
  a		,  a						=		align_predictions(UpperCAmelCase__ ,					UpperCAmelCase__					)

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

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

 return results


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Tuple					):






 '''simple docstring'''

 main()


if __name__ == "__main__":
     main()

