
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 ):





	'''simple docstring'''



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


				'''simple docstring'''


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



	def 			A							(  self     :   List[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 			A							(  self     :   Dict      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   Optional[int]=False     ):


				'''simple docstring'''


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

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

				return expected_height, expected_width




@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		str          = ConditionalDetrImageProcessor if is_vision_available() else None



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      ConditionalDetrImageProcessingTester(self     )



	@property
	def 			A							(  self     :   int     ):


				'''simple docstring'''


				return self.image_processor_tester.prepare_image_processor_dict()



	def 			A							(  self     :   int     ):


				'''simple docstring'''


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



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      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      ,  lowercase     )

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



	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				pass



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


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

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

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase     )

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

				# Test batched
				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase      ,  batched=lowercase     )

				_snake_case          =      image_processing(lowercase      ,  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 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


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

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

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase     )

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

				# Test batched
				_snake_case          =      image_processing(lowercase      ,  return_tensors='pt'     ).pixel_values

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase      ,  batched=lowercase     )

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



	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


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

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

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase     )

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

				# Test batched
				_snake_case          =      image_processing(lowercase      ,  return_tensors='pt'     ).pixel_values

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase      ,  batched=lowercase     )

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



	@slow
	def 			A							(  self     :   int     ):


				'''simple docstring'''


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

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

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

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

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

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





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


				'''simple docstring'''


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

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

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

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

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

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

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

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

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

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


_lowerCamelCase	:    int          =      logging.getLogger(__name__)




@dataclass
class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	_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__					:





	'''simple docstring'''



	_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_2_8							,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 	a_		(	)			->  Optional[int]:
			# 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.

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

			_snake_case          =      import_module('tasks'			)
			try:
						_snake_case          =      getattr(__lowercase	, model_args.task_type			)
						_snake_case          =      token_classification_task_clazz()
			except AttributeError:
						raise ValueError(
						    f'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
						    f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''			)

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

			# Set seed
			set_seed(training_args.seed			)

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

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

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

			# Get datasets
			_snake_case          =      (
			    TokenClassificationDataset(
			        token_classification_task=__lowercase	, data_dir=data_args.data_dir	, tokenizer=__lowercase	, labels=__lowercase	, model_type=config.model_type	, max_seq_length=data_args.max_seq_length	, overwrite_cache=data_args.overwrite_cache	, mode=Split.train	, )
			    if training_args.do_train
			    else None
			)
			_snake_case          =      (
			    TokenClassificationDataset(
			        token_classification_task=__lowercase	, data_dir=data_args.data_dir	, tokenizer=__lowercase	, labels=__lowercase	, model_type=config.model_type	, max_seq_length=data_args.max_seq_length	, overwrite_cache=data_args.overwrite_cache	, mode=Split.dev	, )
			    if training_args.do_eval
			    else None
			)

			def align_predictions(__lowercase       :     np.ndarray	, __lowercase       :     np.ndarray			) -> Tuple[List[int], List[int]]:
						_snake_case          =      np.argmax(__lowercase	, axis=2			)

						_snake_case     , _snake_case          =      preds.shape

						_snake_case          =      [[] for _ in range(__lowercase			)]
						_snake_case          =      [[] for _ in range(__lowercase			)]

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

						return preds_list, out_label_list

			def compute_metrics(__lowercase       :     EvalPrediction			) -> Dict:
						_snake_case     , _snake_case          =      align_predictions(p.predictions	, p.label_ids			)
						return {
						    "accuracy_score": accuracy_score(__lowercase	, __lowercase			),
						    "precision": precision_score(__lowercase	, __lowercase			),
						    "recall": recall_score(__lowercase	, __lowercase			),
						    "f1": fa_score(__lowercase	, __lowercase			),
						}

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

			# Initialize our Trainer
			_snake_case          =      Trainer(
			    model=__lowercase	, args=__lowercase	, train_dataset=__lowercase	, eval_dataset=__lowercase	, compute_metrics=__lowercase	, data_collator=__lowercase	, )

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

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

						_snake_case          =      trainer.evaluate()

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

									results.update(__lowercase			)

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

						_snake_case     , _snake_case     , _snake_case          =      trainer.predict(__lowercase			)
						_snake_case     , _snake_case          =      align_predictions(__lowercase	, __lowercase			)

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

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

			return results






def 	a_		(	__lowercase       :     Optional[Any]			)			->  Optional[int]:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
			main()