


import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


__a       =					logging.getLogger(__name__)







class  __SCREAMING_SNAKE_CASE      (	a_      ):
      A :							Optional[Any]										=   'sequence-classification'


      def __init__(							self							,  SCREAMING_SNAKE_CASE__						):
            if type(lowercase_						) == dict:
                  lowercase	:      int          =						Namespace(**lowercase_						)
            lowercase	:      int          =						glue_output_modes[hparams.task]
            lowercase	:      List[Any]          =						glue_tasks_num_labels[hparams.task]

            super().__init__(lowercase_							,  lowercase_							,  self.mode						)


      def 				__lowerCamelCase	(							self							,  **SCREAMING_SNAKE_CASE__						):
            return self.model(**lowercase_						)


      def 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__						):
            lowercase	:      List[Any]          =						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

            if self.config.model_type not in ["distilbert", "bart"]:
                  lowercase	:      Optional[Any]          =						batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

            lowercase	:      Optional[int]          =						self(**lowercase_						)
            lowercase	:      Union[str, Any]          =						outputs[0]

            lowercase	:      Union[str, Any]          =						self.trainer.lr_schedulers[0]['''scheduler''']
            lowercase	:      Union[str, Any]          =						{'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
            return {"loss": loss, "log": tensorboard_logs}


      def 				__lowerCamelCase	(							self						):
            lowercase	:      Tuple          =						self.hparams
            lowercase	:      int          =						processors[args.task]()
            lowercase	:      int          =						processor.get_labels()

            for mode in ["train", "dev"]:
                  lowercase	:      int          =						self._feature_file(lowercase_						)
                  if os.path.exists(lowercase_						) and not args.overwrite_cache:
                        logger.info('''Loading features from cached file %s'''							,  lowercase_						)
                  else:
                        logger.info('''Creating features from dataset file at %s'''							,  args.data_dir						)
                        lowercase	:      int          =						(
                            processor.get_dev_examples(args.data_dir						)
                            if mode == '''dev'''
                            else processor.get_train_examples(args.data_dir						)
                        )
                        lowercase	:      Optional[int]          =						convert_examples_to_features(
                            lowercase_							,  self.tokenizer							,  max_length=args.max_seq_length							,  label_list=self.labels							,  output_mode=args.glue_output_mode							,  )
                        logger.info('''Saving features into cached file %s'''							,  lowercase_						)
                        torch.save(lowercase_							,  lowercase_						)


      def 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__ = False						):
            lowercase	:      Tuple          =						'''dev''' if mode == '''test''' else mode

            lowercase	:      Tuple          =						self._feature_file(lowercase_						)
            logger.info('''Loading features from cached file %s'''							,  lowercase_						)
            lowercase	:      Union[str, Any]          =						torch.load(lowercase_						)
            lowercase	:      List[Any]          =						torch.tensor([f.input_ids for f in features]							,  dtype=torch.long						)
            lowercase	:      Dict          =						torch.tensor([f.attention_mask for f in features]							,  dtype=torch.long						)
            lowercase	:      Optional[int]          =						torch.tensor([f.token_type_ids for f in features]							,  dtype=torch.long						)
            if self.hparams.glue_output_mode == "classification":
                  lowercase	:      Optional[Any]          =						torch.tensor([f.label for f in features]							,  dtype=torch.long						)
            elif self.hparams.glue_output_mode == "regression":
                  lowercase	:      List[Any]          =						torch.tensor([f.label for f in features]							,  dtype=torch.float						)

            return DataLoader(
                TensorDataset(lowercase_							,  lowercase_							,  lowercase_							,  lowercase_						)							,  batch_size=lowercase_							,  shuffle=lowercase_							,  )


      def 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__						):
            lowercase	:      Optional[int]          =						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

            if self.config.model_type not in ["distilbert", "bart"]:
                  lowercase	:      int          =						batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

            lowercase	:      int          =						self(**lowercase_						)
            lowercase	:      str          =						outputs[:2]
            lowercase	:      Optional[Any]          =						logits.detach().cpu().numpy()
            lowercase	:      Union[str, Any]          =						inputs['''labels'''].detach().cpu().numpy()

            return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}


      def 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__						):
            lowercase	:      int          =						torch.stack([x['''val_loss'''] for x in outputs]						).mean().detach().cpu().item()
            lowercase	:      Any          =						np.concatenate([x['''pred'''] for x in outputs]							,  axis=0						)

            if self.hparams.glue_output_mode == "classification":
                  lowercase	:      Any          =						np.argmax(lowercase_							,  axis=1						)
            elif self.hparams.glue_output_mode == "regression":
                  lowercase	:      Optional[Any]          =						np.squeeze(lowercase_						)

            lowercase	:      Union[str, Any]          =						np.concatenate([x['''target'''] for x in outputs]							,  axis=0						)
            lowercase	:      Tuple          =						[[] for _ in range(out_label_ids.shape[0]						)]
            lowercase	:      str          =						[[] for _ in range(out_label_ids.shape[0]						)]

            lowercase	:      Any          =						{**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task							,  lowercase_							,  lowercase_						)}

            lowercase	:      Optional[int]          =						dict(results.items()						)
            lowercase	:      Any          =						results
            return ret, preds_list, out_label_list


      def 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__						):
            lowercase	:      List[str]          =						self._eval_end(lowercase_						)
            lowercase	:      str          =						ret['''log''']
            return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


      def 				__lowerCamelCase	(							self							,  SCREAMING_SNAKE_CASE__						):
            lowercase	:      List[str]          =						self._eval_end(lowercase_						)
            lowercase	:      List[str]          =						ret['''log''']
            # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
            return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






      @staticmethod
      def 				__lowerCamelCase	(							SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__						):
            BaseTransformer.add_model_specific_args(lowercase_							,  lowercase_						)
            parser.add_argument(
                '''--max_seq_length'''							,  default=128							,  type=lowercase_							,  help=(
                    '''The maximum total input sequence length after tokenization. Sequences longer '''
                    '''than this will be truncated, sequences shorter will be padded.'''
                )							,  )

            parser.add_argument(
                '''--task'''							,  default=''''''							,  type=lowercase_							,  required=lowercase_							,  help='''The GLUE task to run'''							,  )
            parser.add_argument(
                '''--gpus'''							,  default=0							,  type=lowercase_							,  help='''The number of GPUs allocated for this, it is by default 0 meaning none'''							,  )

            parser.add_argument(
                '''--overwrite_cache'''							,  action='''store_true'''							,  help='''Overwrite the cached training and evaluation sets'''						)

            return parser





def 	__lowercase		(  )			->Optional[Any]:







      """simple docstring"""

      lowercase	:      str          =						argparse.ArgumentParser()
      add_generic_args(A_,			os.getcwd()   )
      lowercase	:      int          =						GLUETransformer.add_model_specific_args(A_,			os.getcwd()   )
      lowercase	:      Tuple          =						parser.parse_args()

      # If output_dir not provided, a folder will be generated in pwd
      if args.output_dir is None:
            lowercase	:      Tuple          =						os.path.join(
                '''./results''',			f"""{args.task}_{time.strftime('%Y%m%d_%H%M%S'   )}""",			)
            os.makedirs(args.output_dir   )

      lowercase	:      Tuple          =						GLUETransformer(A_   )
      lowercase	:      Tuple          =						generic_train(A_,			A_   )

      # Optionally, predict on dev set and write to output_dir
      if args.do_predict:
            lowercase	:      Union[str, Any]          =						sorted(glob.glob(os.path.join(args.output_dir,			'''checkpoint-epoch=*.ckpt'''   ),			recursive=A_   )   )
            lowercase	:      Dict          =						model.load_from_checkpoint(checkpoints[-1]   )
            return trainer.test(A_   )


if __name__ == "__main__":
  main()




"""simple docstring"""





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 DetaImageProcessor







class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def __init__(       self			:				List[str]   ,lowercase_			:				Dict   ,lowercase_			:				Dict=7   ,lowercase_			:				Optional[int]=3   ,lowercase_			:				int=3_0   ,lowercase_			:				Optional[Any]=4_0_0   ,lowercase_			:				Any=True   ,lowercase_			:				List[str]=None   ,lowercase_			:				str=True   ,lowercase_			:				List[Any]=[0.5, 0.5, 0.5]   ,lowercase_			:				List[str]=[0.5, 0.5, 0.5]   ,lowercase_			:				Any=True   ,lowercase_			:				Union[str, Any]=1 / 2_5_5   ,lowercase_			:				str=True   ,):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						lowerCAmelCase__	:						str  				=	size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
						lowerCAmelCase__	:						Any  				=	parent
						lowerCAmelCase__	:						Tuple  				=	batch_size
						lowerCAmelCase__	:						List[str]  				=	num_channels
						lowerCAmelCase__	:						Optional[Any]  				=	min_resolution
						lowerCAmelCase__	:						Union[str, Any]  				=	max_resolution
						lowerCAmelCase__	:						Optional[int]  				=	do_resize
						lowerCAmelCase__	:						str  				=	size
						lowerCAmelCase__	:						Union[str, Any]  				=	do_normalize
						lowerCAmelCase__	:						List[str]  				=	image_mean
						lowerCAmelCase__	:						str  				=	image_std
						lowerCAmelCase__	:						Optional[Any]  				=	do_rescale
						lowerCAmelCase__	:						Union[str, Any]  				=	rescale_factor
						lowerCAmelCase__	:						Optional[Any]  				=	do_pad







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						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        __lowerCAmelCase (       self			:				List[str]   ,lowercase_			:				List[Any]   ,lowercase_			:				int=False		):
						if not batched:
							lowerCAmelCase__	:						Tuple  				=	image_inputs[0]
							if isinstance(lowercase_   ,Image.Image		):
								lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	image.size
							else:
								lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	image.shape[1], image.shape[2]
							if w < h:
								lowerCAmelCase__	:						Any  				=	int(self.size['''shortest_edge'''] * h / w		)
								lowerCAmelCase__	:						str  				=	self.size['''shortest_edge''']
							elif w > h:
								lowerCAmelCase__	:						Union[str, Any]  				=	self.size['''shortest_edge''']
								lowerCAmelCase__	:						Dict  				=	int(self.size['''shortest_edge'''] * w / h		)
							else:
								lowerCAmelCase__	:						List[str]  				=	self.size['''shortest_edge''']
								lowerCAmelCase__	:						str  				=	self.size['''shortest_edge''']

						else:
							lowerCAmelCase__	:						Optional[Any]  				=	[]
							for image in image_inputs:
								lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	self.get_expected_values([image]		)
								expected_values.append((expected_height, expected_width)		)
							lowerCAmelCase__	:						List[str]  				=	max(lowercase_   ,key=lambda lowercase_		: item[0]		)[0]
							lowerCAmelCase__	:						Any  				=	max(lowercase_   ,key=lambda lowercase_		: item[1]		)[1]

						return expected_height, expected_width







@require_torch
@require_vision
class 				SCREAMING_SNAKE_CASE					(		a_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       DetaImageProcessor if is_vision_available() else None







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Optional[Any]  				=	DetaImageProcessingTester(self		)







					@property
					def        __lowerCAmelCase (       self			:				Any		):
						return self.image_processor_tester.prepare_image_processor_dict()







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						str  				=	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_   ,'''do_rescale'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_pad'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''size'''		)		)







					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						Dict  				=	self.image_processing_class.from_dict(self.image_processor_dict		)
						self.assertEqual(image_processor.size   ,{'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}		)
						self.assertEqual(image_processor.do_pad   ,lowercase_		)







					def        __lowerCAmelCase (       self			:				List[str]		):
						pass







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						# Initialize image_processing
						lowerCAmelCase__	:						str  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						lowerCAmelCase__	:						Union[str, Any]  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,Image.Image		)

						# Test not batched input
						lowerCAmelCase__	:						List[Any]  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Tuple  				=	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
						lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	self.image_processor_tester.get_expected_values(lowercase_   ,batched=lowercase_		)

						lowerCAmelCase__	:						Optional[int]  				=	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        __lowerCAmelCase (       self			:				Dict		):
						# Initialize image_processing
						lowerCAmelCase__	:						Optional[Any]  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						lowerCAmelCase__	:						Tuple  				=	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
						lowerCAmelCase__	:						List[str]  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	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
						lowerCAmelCase__	:						str  				=	image_processing(lowercase_   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	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        __lowerCAmelCase (       self			:				Union[str, Any]		):
						# Initialize image_processing
						lowerCAmelCase__	:						Tuple  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						lowerCAmelCase__	:						Any  				=	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
						lowerCAmelCase__	:						List[Any]  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	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
						lowerCAmelCase__	:						str  				=	image_processing(lowercase_   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	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        __lowerCAmelCase (       self			:				Tuple		):
						# prepare image and target
						lowerCAmelCase__	:						Tuple  				=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
						with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''   ,'''r'''		) as f:
							lowerCAmelCase__	:						Union[str, Any]  				=	json.loads(f.read()		)

						lowerCAmelCase__	:						str  				=	{'''image_id''': 3_9_7_6_9, '''annotations''': target}

						# encode them
						lowerCAmelCase__	:						Optional[Any]  				=	DetaImageProcessor()
						lowerCAmelCase__	:						Optional[int]  				=	image_processing(images=lowercase_   ,annotations=lowercase_   ,return_tensors='''pt'''		)

						# verify pixel values
						lowerCAmelCase__	:						Dict  				=	torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
						self.assertEqual(encoding['''pixel_values'''].shape   ,lowercase_		)

						lowerCAmelCase__	:						Any  				=	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
						lowerCAmelCase__	:						Tuple  				=	torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,lowercase_		)		)
						# verify boxes
						lowerCAmelCase__	:						List[str]  				=	torch.Size([6, 4]		)
						self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,lowercase_		)
						lowerCAmelCase__	:						Tuple  				=	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
						lowerCAmelCase__	:						Optional[int]  				=	torch.tensor([3_9_7_6_9]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,lowercase_		)		)
						# verify is_crowd
						lowerCAmelCase__	:						List[str]  				=	torch.tensor([0, 0, 0, 0, 0, 0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,lowercase_		)		)
						# verify class_labels
						lowerCAmelCase__	:						Any  				=	torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,lowercase_		)		)
						# verify orig_size
						lowerCAmelCase__	:						int  				=	torch.tensor([4_8_0, 6_4_0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,lowercase_		)		)
						# verify size
						lowerCAmelCase__	:						str  				=	torch.tensor([8_0_0, 1_0_6_6]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,lowercase_		)		)







					@slow
					def        __lowerCAmelCase (       self			:				Any		):
						# prepare image, target and masks_path
						lowerCAmelCase__	:						Dict  				=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
						with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''   ,'''r'''		) as f:
							lowerCAmelCase__	:						str  				=	json.loads(f.read()		)

						lowerCAmelCase__	:						Tuple  				=	{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

						lowerCAmelCase__	:						Optional[Any]  				=	pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''		)

						# encode them
						lowerCAmelCase__	:						str  				=	DetaImageProcessor(format='''coco_panoptic'''		)
						lowerCAmelCase__	:						Optional[int]  				=	image_processing(images=lowercase_   ,annotations=lowercase_   ,masks_path=lowercase_   ,return_tensors='''pt'''		)

						# verify pixel values
						lowerCAmelCase__	:						Any  				=	torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
						self.assertEqual(encoding['''pixel_values'''].shape   ,lowercase_		)

						lowerCAmelCase__	:						int  				=	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
						lowerCAmelCase__	:						Tuple  				=	torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,lowercase_		)		)
						# verify boxes
						lowerCAmelCase__	:						Optional[Any]  				=	torch.Size([6, 4]		)
						self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,lowercase_		)
						lowerCAmelCase__	:						str  				=	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
						lowerCAmelCase__	:						Union[str, Any]  				=	torch.tensor([3_9_7_6_9]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,lowercase_		)		)
						# verify is_crowd
						lowerCAmelCase__	:						Union[str, Any]  				=	torch.tensor([0, 0, 0, 0, 0, 0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,lowercase_		)		)
						# verify class_labels
						lowerCAmelCase__	:						List[str]  				=	torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,lowercase_		)		)
						# verify masks
						lowerCAmelCase__	:						Optional[int]  				=	8_2_2_8_7_3
						self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()   ,lowercase_		)
						# verify orig_size
						lowerCAmelCase__	:						List[Any]  				=	torch.tensor([4_8_0, 6_4_0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,lowercase_		)		)
						# verify size
						lowerCAmelCase__	:						Optional[Any]  				=	torch.tensor([8_0_0, 1_0_6_6]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,lowercase_		)		)




