

import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def 			UpperCamelCase_(				_snake_case  :    Union[str, Any]			,	_snake_case  :    Optional[int]			,	_snake_case  :    Tuple			,	_snake_case  :    Tuple			,	_snake_case  :    Dict			,	_snake_case  :    List[str] = None			,	):




			"""simple docstring"""






			__a     ={}

			if train_file is not None:
						__a     =[train_file]
			if eval_file is not None:
						__a     =[eval_file]
			if test_file is not None:
						__a     =[test_file]

			__a     =datasets.load_dataset('csv'			,	data_files=__lowerCAmelCase      )
			__a     =list(ds[list(files.keys()      )[0]].features.keys()      )
			__a     =features_name.pop(__lowerCAmelCase      )
			__a     =list(set(ds[list(files.keys()      )[0]][label_name]      )      )
			__a     ={label: i for i, label in enumerate(__lowerCAmelCase      )}
			__a     =tokenizer.model_input_names
			__a     ={}

			if len(__lowerCAmelCase      ) == 1:
						for k in files.keys():
									__a     =ds[k].map(
									    lambda _snake_case      : tokenizer.batch_encode_plus(
									        example[features_name[0]]			,	truncation=__lowerCAmelCase			,	max_length=__lowerCAmelCase			,	padding='max_length'      )			,	batched=__lowerCAmelCase			,	)
			elif len(__lowerCAmelCase      ) == 2:
						for k in files.keys():
									__a     =ds[k].map(
									    lambda _snake_case      : tokenizer.batch_encode_plus(
									        (example[features_name[0]], example[features_name[1]])			,	truncation=__lowerCAmelCase			,	max_length=__lowerCAmelCase			,	padding='max_length'			,	)			,	batched=__lowerCAmelCase			,	)

			def gen_train():
						for ex in transformed_ds[datasets.Split.TRAIN]:
									__a     ={k: v for k, v in ex.items() if k in input_names}
									__a     =labelaid[ex[label_name]]
									yield (d, label)

			def gen_val():
						for ex in transformed_ds[datasets.Split.VALIDATION]:
									__a     ={k: v for k, v in ex.items() if k in input_names}
									__a     =labelaid[ex[label_name]]
									yield (d, label)

			def gen_test():
						for ex in transformed_ds[datasets.Split.TEST]:
									__a     ={k: v for k, v in ex.items() if k in input_names}
									__a     =labelaid[ex[label_name]]
									yield (d, label)

			__a     =(
			    tf.data.Dataset.from_generator(
			        __lowerCAmelCase			,	({k: tf.intaa for k in input_names}, tf.intaa)			,	({k: tf.TensorShape([None]      ) for k in input_names}, tf.TensorShape([]      ))			,	)
			    if datasets.Split.TRAIN in transformed_ds
			    else None
			)

			if train_ds is not None:
						__a     =train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]      )      )      )

			__a     =(
			    tf.data.Dataset.from_generator(
			        __lowerCAmelCase			,	({k: tf.intaa for k in input_names}, tf.intaa)			,	({k: tf.TensorShape([None]      ) for k in input_names}, tf.TensorShape([]      ))			,	)
			    if datasets.Split.VALIDATION in transformed_ds
			    else None
			)

			if val_ds is not None:
						__a     =val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]      )      )      )

			__a     =(
			    tf.data.Dataset.from_generator(
			        __lowerCAmelCase			,	({k: tf.intaa for k in input_names}, tf.intaa)			,	({k: tf.TensorShape([None]      ) for k in input_names}, tf.TensorShape([]      ))			,	)
			    if datasets.Split.TEST in transformed_ds
			    else None
			)

			if test_ds is not None:
						__a     =test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]      )      )      )

			return train_ds, val_ds, test_ds, labelaid


_lowerCAmelCase   :     Optional[Any]    							=     logging.getLogger(__name__)






@dataclass
class       __magic_name__       :
			SCREAMING_SNAKE_CASE				  =		field(metadata={'help': 'Which column contains the label'} )
			SCREAMING_SNAKE_CASE				  =		field(default=__UpperCamelCase    ,       metadata={'help': 'The path of the training file'} )
			SCREAMING_SNAKE_CASE				  =		field(default=__UpperCamelCase    ,       metadata={'help': 'The path of the development file'} )
			SCREAMING_SNAKE_CASE				  =		field(default=__UpperCamelCase    ,       metadata={'help': 'The path of the test file'} )
			SCREAMING_SNAKE_CASE				  =		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.'
			        )
			    }    ,       )
			SCREAMING_SNAKE_CASE				  =		field(
			    default=__UpperCamelCase    ,       metadata={'help': 'Overwrite the cached training and evaluation sets'} )






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






def 			UpperCamelCase_(				):




			"""simple docstring"""






			__a     =HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)      )
			__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.'      )

			# Setup logging
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'			,	datefmt='%m/%d/%Y %H:%M:%S'			,	level=logging.INFO			,	)
			logger.info(
			    F'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1      )}, '
			    F'16-bits training: {training_args.fpaa}'      )
			logger.info(F'Training/evaluation parameters {training_args}'      )

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

			__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			,	)

			__a ,       __a ,       __a ,       __a     =get_tfds(
			    train_file=data_args.train_file			,	eval_file=data_args.dev_file			,	test_file=data_args.test_file			,	tokenizer=__lowerCAmelCase			,	label_column_id=data_args.label_column_id			,	max_seq_length=data_args.max_seq_length			,	)

			__a     =AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,	num_labels=len(__lowerCAmelCase      )			,	labelaid=__lowerCAmelCase			,	idalabel={id: label for label, id in labelaid.items()}			,	finetuning_task='text-classification'			,	cache_dir=model_args.cache_dir			,	)

			with training_args.strategy.scope():
						__a     =TFAutoModelForSequenceClassification.from_pretrained(
						    model_args.model_name_or_path			,	from_pt=bool('.bin' in model_args.model_name_or_path      )			,	config=__lowerCAmelCase			,	cache_dir=model_args.cache_dir			,	)

			def compute_metrics(_snake_case  :    Any      ) -> Dict:
						__a     =np.argmax(p.predictions			,	axis=1      )

						return {"acc": (preds == p.label_ids).mean()}

			# Initialize our Trainer
			__a     =TFTrainer(
			    model=__lowerCAmelCase			,	args=__lowerCAmelCase			,	train_dataset=__lowerCAmelCase			,	eval_dataset=__lowerCAmelCase			,	compute_metrics=__lowerCAmelCase			,	)

			# Training
			if training_args.do_train:
						trainer.train()
						trainer.save_model()
						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'      )

						with open(__lowerCAmelCase			,	'w'      ) as writer:
									logger.info('***** Eval results *****'      )

									for key, value in result.items():
												logger.info(F'  {key} = {value}'      )
												writer.write(F'{key} = {value}\n'      )

									results.update(__lowerCAmelCase      )

			return results


if __name__ == "__main__":
						main()








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 DetrImageProcessor




class        _SCREAMING_SNAKE_CASE	(   unittest.TestCase			):
      def __init__(      self		,						lowerCamelCase		,						lowerCamelCase=7		,						lowerCamelCase=3		,						lowerCamelCase=30		,						lowerCamelCase=4_00		,						lowerCamelCase=True		,						lowerCamelCase=None		,						lowerCamelCase=True		,						lowerCamelCase=1 / 2_55		,						lowerCamelCase=True		,						lowerCamelCase=[0.5, 0.5, 0.5]		,						lowerCamelCase=[0.5, 0.5, 0.5]		,						lowerCamelCase=True		,						):
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          snake_case__    =					size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
          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_rescale
          snake_case__    =					rescale_factor
          snake_case__    =					do_normalize
          snake_case__    =					image_mean
          snake_case__    =					image_std
          snake_case__    =					do_pad
      def A_      (      self			):
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_rescale": self.do_rescale,
              "rescale_factor": self.rescale_factor,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_pad": self.do_pad,
          }



      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase=False			):
          if not batched:
              snake_case__    =					image_inputs[0]
              if isinstance(lowerCamelCase		,						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(lowerCamelCase		,						key=lambda lowerCamelCase			: item[0]			)[0]
              snake_case__    =					max(lowerCamelCase		,						key=lambda lowerCamelCase			: item[1]			)[1]

          return expected_height, expected_width





@require_torch
@require_vision
class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase							,       unittest.TestCase			):
      _A    :		Optional[Any]     						=  DetrImageProcessor if is_vision_available() else None
      def A_      (      self			):
          snake_case__    =					DetrImageProcessingTester(self			)
      @property
      def A_      (      self			):
          return self.image_processor_tester.prepare_image_processor_dict()
      def A_      (      self			):
          snake_case__    =					self.image_processing_class(**self.image_processor_dict			)
          self.assertTrue(hasattr(lowerCamelCase		,						"image_mean"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"image_std"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"do_normalize"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"do_rescale"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"rescale_factor"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"do_resize"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"size"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"do_pad"			)			)
      def A_      (      self			):
          snake_case__    =					self.image_processing_class.from_dict(self.image_processor_dict			)
          self.assertEqual(image_processor.size		,						{"shortest_edge": 18, "longest_edge": 13_33}			)
          self.assertEqual(image_processor.do_pad		,						lowerCamelCase			)

          snake_case__    =					self.image_processing_class.from_dict(
              self.image_processor_dict		,						size=42		,						max_size=84		,						pad_and_return_pixel_mask=lowerCamelCase			)
          self.assertEqual(image_processor.size		,						{"shortest_edge": 42, "longest_edge": 84}			)
          self.assertEqual(image_processor.do_pad		,						lowerCamelCase			)
      def A_      (      self			):
          pass
      def A_      (      self			):
          # Initialize image_processing
          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=lowerCamelCase			)
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,						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(lowerCamelCase			)

          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(lowerCamelCase		,						batched=lowerCamelCase			)

          snake_case__    =					image_processing(lowerCamelCase		,						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			):
          # Initialize image_processing
          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=lowerCamelCase		,						numpify=lowerCamelCase			)
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,						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(lowerCamelCase			)

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

          # Test batched
          snake_case__    =					image_processing(lowerCamelCase		,						return_tensors="pt"			).pixel_values

          snake_case__    ,       snake_case__    =					self.image_processor_tester.get_expected_values(lowerCamelCase		,						batched=lowerCamelCase			)

          self.assertEqual(
              encoded_images.shape		,						(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )		,						)
      def A_      (      self			):
          # Initialize image_processing
          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=lowerCamelCase		,						torchify=lowerCamelCase			)
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,						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(lowerCamelCase			)

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

          # Test batched
          snake_case__    =					image_processing(lowerCamelCase		,						return_tensors="pt"			).pixel_values

          snake_case__    ,       snake_case__    =					self.image_processor_tester.get_expected_values(lowerCamelCase		,						batched=lowerCamelCase			)

          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			):
          # prepare image and target
          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": 3_97_69, "annotations": target}

          # encode them
          snake_case__    =					DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"			)
          snake_case__    =					image_processing(images=lowerCamelCase		,						annotations=lowerCamelCase		,						return_tensors="pt"			)

          # verify pixel values
          snake_case__    =					torch.Size([1, 3, 8_00, 10_66]			)
          self.assertEqual(encoding["pixel_values"].shape		,						lowerCamelCase			)

          snake_case__    =					torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
          self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,						lowerCamelCase		,						atol=1e-4			)			)

          # verify area
          snake_case__    =					torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,						lowerCamelCase			)			)
          # verify boxes
          snake_case__    =					torch.Size([6, 4]			)
          self.assertEqual(encoding["labels"][0]["boxes"].shape		,						lowerCamelCase			)
          snake_case__    =					torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,						lowerCamelCase		,						atol=1e-3			)			)
          # verify image_id
          snake_case__    =					torch.tensor([3_97_69]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,						lowerCamelCase			)			)
          # verify is_crowd
          snake_case__    =					torch.tensor([0, 0, 0, 0, 0, 0]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,						lowerCamelCase			)			)
          # verify class_labels
          snake_case__    =					torch.tensor([75, 75, 63, 65, 17, 17]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,						lowerCamelCase			)			)
          # verify orig_size
          snake_case__    =					torch.tensor([4_80, 6_40]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,						lowerCamelCase			)			)
          # verify size
          snake_case__    =					torch.tensor([8_00, 10_66]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,						lowerCamelCase			)			)



      @slow
      def A_      (      self			):
          # prepare image, target and masks_path
          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": 3_97_69, "segments_info": target}

          snake_case__    =					pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"			)

          # encode them
          snake_case__    =					DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"			)
          snake_case__    =					image_processing(images=lowerCamelCase		,						annotations=lowerCamelCase		,						masks_path=lowerCamelCase		,						return_tensors="pt"			)

          # verify pixel values
          snake_case__    =					torch.Size([1, 3, 8_00, 10_66]			)
          self.assertEqual(encoding["pixel_values"].shape		,						lowerCamelCase			)

          snake_case__    =					torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
          self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,						lowerCamelCase		,						atol=1e-4			)			)

          # verify area
          snake_case__    =					torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,						lowerCamelCase			)			)
          # verify boxes
          snake_case__    =					torch.Size([6, 4]			)
          self.assertEqual(encoding["labels"][0]["boxes"].shape		,						lowerCamelCase			)
          snake_case__    =					torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,						lowerCamelCase		,						atol=1e-3			)			)
          # verify image_id
          snake_case__    =					torch.tensor([3_97_69]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,						lowerCamelCase			)			)
          # verify is_crowd
          snake_case__    =					torch.tensor([0, 0, 0, 0, 0, 0]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,						lowerCamelCase			)			)
          # verify class_labels
          snake_case__    =					torch.tensor([17, 17, 63, 75, 75, 93]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,						lowerCamelCase			)			)
          # verify masks
          snake_case__    =					82_28_73
          self.assertEqual(encoding["labels"][0]["masks"].sum().item()		,						lowerCamelCase			)
          # verify orig_size
          snake_case__    =					torch.tensor([4_80, 6_40]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,						lowerCamelCase			)			)
          # verify size
          snake_case__    =					torch.tensor([8_00, 10_66]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,						lowerCamelCase			)			)



