


'''simple docstring'''

import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


a_						:       Any      			=				16
a_						:       Optional[int]      			=				32





def __snake_case      (   UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		List[str] = 16      ,       UpperCAmelCase_		:		Optional[int] = "bert-base-cased"	):
	lowerCamelCase_  				=						AutoTokenizer.from_pretrained(UpperCAmelCase_	)
	lowerCamelCase_  				=						load_dataset("glue"      ,       "mrpc"	)

	def tokenize_function(UpperCAmelCase_		:		Any	):
		# max_length=None => use the model max length (it's actually the default)
		lowerCamelCase_  				=						tokenizer(examples["sentence1"]      ,       examples["sentence2"]      ,       truncation=UpperCAmelCase_      ,       max_length=UpperCAmelCase_	)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	lowerCamelCase_  				=						datasets.map(
	    UpperCAmelCase_      ,       batched=UpperCAmelCase_      ,       remove_columns=["idx", "sentence1", "sentence2"]      ,       load_from_cache_file=UpperCAmelCase_	)

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	lowerCamelCase_  				=						tokenized_datasets.rename_column("label"      ,       "labels"	)

	def collate_fn(UpperCAmelCase_		:		List[str]	):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(UpperCAmelCase_      ,       padding="max_length"      ,       max_length=128      ,       return_tensors="pt"	)
		return tokenizer.pad(UpperCAmelCase_      ,       padding="longest"      ,       return_tensors="pt"	)

	# Instantiate dataloaders.
	lowerCamelCase_  				=						DataLoader(
	    tokenized_datasets["train"]      ,       shuffle=UpperCAmelCase_      ,       collate_fn=UpperCAmelCase_      ,       batch_size=UpperCAmelCase_	)
	lowerCamelCase_  				=						DataLoader(
	    tokenized_datasets["validation"]      ,       shuffle=UpperCAmelCase_      ,       collate_fn=UpperCAmelCase_      ,       batch_size=UpperCAmelCase_	)

	return train_dataloader, eval_dataloader





def __snake_case      (   UpperCAmelCase_		:		Dict      ,       UpperCAmelCase_		:		Tuple	):
	# Initialize accelerator
	lowerCamelCase_  				=						Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	lowerCamelCase_  				=						config["""lr"""]
	lowerCamelCase_  				=						int(config["num_epochs"]	)
	lowerCamelCase_  				=						int(config["seed"]	)
	lowerCamelCase_  				=						int(config["batch_size"]	)
	lowerCamelCase_  				=						args.model_name_or_path

	set_seed(UpperCAmelCase_	)
	lowerCamelCase_  				=						get_dataloaders(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_	)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	lowerCamelCase_  				=						AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase_      ,       return_dict=UpperCAmelCase_	)

	# Instantiate optimizer
	lowerCamelCase_  				=						(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	lowerCamelCase_  				=						optimizer_cls(params=model.parameters()      ,       lr=UpperCAmelCase_	)

	if accelerator.state.deepspeed_plugin is not None:
		lowerCamelCase_  				=						accelerator.state.deepspeed_plugin.deepspeed_config[
		    """gradient_accumulation_steps"""
		]
	else:
		lowerCamelCase_  				=						1
	lowerCamelCase_  				=						(len(UpperCAmelCase_	) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		lowerCamelCase_  				=						get_linear_schedule_with_warmup(
		    optimizer=UpperCAmelCase_      ,       num_warmup_steps=0      ,       num_training_steps=UpperCAmelCase_      ,       )
	else:
		lowerCamelCase_  				=						DummyScheduler(UpperCAmelCase_      ,       total_num_steps=UpperCAmelCase_      ,       warmup_num_steps=0	)

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	lowerCamelCase_  				=						accelerator.prepare(
	    UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_	)

	# We need to keep track of how many total steps we have iterated over
	lowerCamelCase_  				=						0
	# We also need to keep track of the stating epoch so files are named properly
	lowerCamelCase_  				=						0

	# Now we train the model
	lowerCamelCase_  				=						evaluate.load("glue"      ,       "mrpc"	)
	lowerCamelCase_  				=						0
	lowerCamelCase_  				=						{}
	for epoch in range(UpperCAmelCase_      ,       UpperCAmelCase_	):
		model.train()
		for step, batch in enumerate(UpperCAmelCase_	):
			lowerCamelCase_  				=						model(**UpperCAmelCase_	)
			lowerCamelCase_  				=						outputs.loss
			lowerCamelCase_  				=						loss / gradient_accumulation_steps
			accelerator.backward(UpperCAmelCase_	)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

		model.eval()
		lowerCamelCase_  				=						0
		for step, batch in enumerate(UpperCAmelCase_	):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device	)
			with torch.no_grad():
				lowerCamelCase_  				=						model(**UpperCAmelCase_	)
			lowerCamelCase_  				=						outputs.logits.argmax(dim=-1	)
			# It is slightly faster to call this once, than multiple times
			lowerCamelCase_  				=						accelerator.gather(
			    (predictions, batch["labels"])	)  # If we are in a multiprocess environment, the last batch has duplicates
			if accelerator.use_distributed:
				if step == len(UpperCAmelCase_	) - 1:
					lowerCamelCase_  				=						predictions[: len(eval_dataloader.dataset	) - samples_seen]
					lowerCamelCase_  				=						references[: len(eval_dataloader.dataset	) - samples_seen]
				else:
					samples_seen += references.shape[0]
			metric.add_batch(
			    predictions=UpperCAmelCase_      ,       references=UpperCAmelCase_      ,       )

		lowerCamelCase_  				=						metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'''epoch {epoch}:'''      ,       UpperCAmelCase_	)
		lowerCamelCase_  				=						eval_metric["""accuracy"""]

		if best_performance < eval_metric["accuracy"]:
			lowerCamelCase_  				=						eval_metric["""accuracy"""]

	if args.performance_lower_bound is not None:
		assert (
		    args.performance_lower_bound <= best_performance
		), F'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
		with open(os.path.join(args.output_dir      ,       "all_results.json"	)      ,       "w"	) as f:
			json.dump(UpperCAmelCase_      ,       UpperCAmelCase_	)





def __snake_case      (   ):
	lowerCamelCase_  				=						argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."	)
	parser.add_argument(
	    "--model_name_or_path"      ,       type=UpperCAmelCase_      ,       default="bert-base-cased"      ,       help="Path to pretrained model or model identifier from huggingface.co/models."      ,       required=UpperCAmelCase_      ,       )
	parser.add_argument(
	    "--output_dir"      ,       type=UpperCAmelCase_      ,       default="."      ,       help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."      ,       )
	parser.add_argument(
	    "--performance_lower_bound"      ,       type=UpperCAmelCase_      ,       default=UpperCAmelCase_      ,       help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."      ,       )
	parser.add_argument(
	    "--num_epochs"      ,       type=UpperCAmelCase_      ,       default=3      ,       help="Number of train epochs."      ,       )
	lowerCamelCase_  				=						parser.parse_args()
	lowerCamelCase_  				=						{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
	training_function(UpperCAmelCase_      ,       UpperCAmelCase_	)


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 DetrImageProcessor





class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase=7    ,						UpperCamelCase=3    ,						UpperCamelCase=30    ,						UpperCamelCase=400    ,						UpperCamelCase=True    ,						UpperCamelCase=None    ,						UpperCamelCase=True    ,						UpperCamelCase=1 / 255    ,						UpperCamelCase=True    ,						UpperCamelCase=[0.5, 0.5, 0.5]    ,						UpperCamelCase=[0.5, 0.5, 0.5]    ,						UpperCamelCase=True    ,						):







   """simple docstring"""
   # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
   lowerCamelCase_  				=						size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
   lowerCamelCase_  				=						parent
   lowerCamelCase_  				=						batch_size
   lowerCamelCase_  				=						num_channels
   lowerCamelCase_  				=						min_resolution
   lowerCamelCase_  				=						max_resolution
   lowerCamelCase_  				=						do_resize
   lowerCamelCase_  				=						size
   lowerCamelCase_  				=						do_rescale
   lowerCamelCase_  				=						rescale_factor
   lowerCamelCase_  				=						do_normalize
   lowerCamelCase_  				=						image_mean
   lowerCamelCase_  				=						image_std
   lowerCamelCase_  				=						do_pad






  def 						snake_case	(					self  ):







   """simple docstring"""
   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 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase=False  ):







   """simple docstring"""
   if not batched:
    lowerCamelCase_  				=						image_inputs[0]
    if isinstance(UpperCamelCase    ,						Image.Image  ):
     lowerCamelCase_				,lowerCamelCase_  				=						image.size
    else:
     lowerCamelCase_				,lowerCamelCase_  				=						image.shape[1], image.shape[2]
    if w < h:
     lowerCamelCase_  				=						int(self.size["shortest_edge"] * h / w  )
     lowerCamelCase_  				=						self.size["shortest_edge"]
    elif w > h:
     lowerCamelCase_  				=						self.size["shortest_edge"]
     lowerCamelCase_  				=						int(self.size["shortest_edge"] * w / h  )
    else:
     lowerCamelCase_  				=						self.size["shortest_edge"]
     lowerCamelCase_  				=						self.size["shortest_edge"]

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

   return expected_height, expected_width





@require_torch
@require_vision
class   snake_case					(					lowercase    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						DetrImageProcessor if is_vision_available() else None






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						DetrImageProcessingTester(self  )






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return self.image_processor_tester.prepare_image_processor_dict()






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"image_mean"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"image_std"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"do_normalize"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"do_rescale"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"rescale_factor"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"do_resize"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"size"  )  )
   self.assertTrue(hasattr(UpperCamelCase    ,						"do_pad"  )  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.image_processing_class.from_dict(self.image_processor_dict  )
   self.assertEqual(image_processor.size    ,						{"shortest_edge": 18, "longest_edge": 1333}  )
   self.assertEqual(image_processor.do_pad    ,						UpperCamelCase  )

   lowerCamelCase_  				=						self.image_processing_class.from_dict(
       self.image_processor_dict    ,						size=42    ,						max_size=84    ,						pad_and_return_pixel_mask=UpperCamelCase  )
   self.assertEqual(image_processor.size    ,						{"shortest_edge": 42, "longest_edge": 84}  )
   self.assertEqual(image_processor.do_pad    ,						UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self  ):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
   # create random PIL images
   lowerCamelCase_  				=						prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase  )
   for image in image_inputs:
    self.assertIsInstance(UpperCamelCase    ,						Image.Image  )

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

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase  )

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

   # Test batched
   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase    ,						batched=UpperCamelCase  )

   lowerCamelCase_  				=						image_processing(UpperCamelCase    ,						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 						snake_case	(					self  ):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
   # create random numpy tensors
   lowerCamelCase_  				=						prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase    ,						numpify=UpperCamelCase  )
   for image in image_inputs:
    self.assertIsInstance(UpperCamelCase    ,						np.ndarray  )

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

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase  )

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

   # Test batched
   lowerCamelCase_  				=						image_processing(UpperCamelCase    ,						return_tensors="pt"  ).pixel_values

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase    ,						batched=UpperCamelCase  )

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






  def 						snake_case	(					self  ):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
   # create random PyTorch tensors
   lowerCamelCase_  				=						prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase    ,						torchify=UpperCamelCase  )
   for image in image_inputs:
    self.assertIsInstance(UpperCamelCase    ,						torch.Tensor  )

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

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase  )

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

   # Test batched
   lowerCamelCase_  				=						image_processing(UpperCamelCase    ,						return_tensors="pt"  ).pixel_values

   lowerCamelCase_				,lowerCamelCase_  				=						self.image_processor_tester.get_expected_values(UpperCamelCase    ,						batched=UpperCamelCase  )

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






  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   # prepare image and target
   lowerCamelCase_  				=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
   with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"    ,						"r"  ) as f:
    lowerCamelCase_  				=						json.loads(f.read()  )

   lowerCamelCase_  				=						{"image_id": 3_9769, "annotations": target}

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

   # verify pixel values
   lowerCamelCase_  				=						torch.Size([1, 3, 800, 1066]  )
   self.assertEqual(encoding["pixel_values"].shape    ,						UpperCamelCase  )

   lowerCamelCase_  				=						torch.tensor([0.2_796, 0.3_138, 0.3_481]  )
   self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]    ,						UpperCamelCase    ,						atol=1e-4  )  )

   # verify area
   lowerCamelCase_  				=						torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["area"]    ,						UpperCamelCase  )  )
   # verify boxes
   lowerCamelCase_  				=						torch.Size([6, 4]  )
   self.assertEqual(encoding["labels"][0]["boxes"].shape    ,						UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]    ,						UpperCamelCase    ,						atol=1e-3  )  )
   # verify image_id
   lowerCamelCase_  				=						torch.tensor([3_9769]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]    ,						UpperCamelCase  )  )
   # verify is_crowd
   lowerCamelCase_  				=						torch.tensor([0, 0, 0, 0, 0, 0]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]    ,						UpperCamelCase  )  )
   # verify class_labels
   lowerCamelCase_  				=						torch.tensor([75, 75, 63, 65, 17, 17]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]    ,						UpperCamelCase  )  )
   # verify orig_size
   lowerCamelCase_  				=						torch.tensor([480, 640]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,						UpperCamelCase  )  )
   # verify size
   lowerCamelCase_  				=						torch.tensor([800, 1066]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,						UpperCamelCase  )  )







  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   # prepare image, target and masks_path
   lowerCamelCase_  				=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
   with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"    ,						"r"  ) as f:
    lowerCamelCase_  				=						json.loads(f.read()  )

   lowerCamelCase_  				=						{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

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

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

   # verify pixel values
   lowerCamelCase_  				=						torch.Size([1, 3, 800, 1066]  )
   self.assertEqual(encoding["pixel_values"].shape    ,						UpperCamelCase  )

   lowerCamelCase_  				=						torch.tensor([0.2_796, 0.3_138, 0.3_481]  )
   self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]    ,						UpperCamelCase    ,						atol=1e-4  )  )

   # verify area
   lowerCamelCase_  				=						torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["area"]    ,						UpperCamelCase  )  )
   # verify boxes
   lowerCamelCase_  				=						torch.Size([6, 4]  )
   self.assertEqual(encoding["labels"][0]["boxes"].shape    ,						UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]    ,						UpperCamelCase    ,						atol=1e-3  )  )
   # verify image_id
   lowerCamelCase_  				=						torch.tensor([3_9769]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]    ,						UpperCamelCase  )  )
   # verify is_crowd
   lowerCamelCase_  				=						torch.tensor([0, 0, 0, 0, 0, 0]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]    ,						UpperCamelCase  )  )
   # verify class_labels
   lowerCamelCase_  				=						torch.tensor([17, 17, 63, 75, 75, 93]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]    ,						UpperCamelCase  )  )
   # verify masks
   lowerCamelCase_  				=						82_2873
   self.assertEqual(encoding["labels"][0]["masks"].sum().item()    ,						UpperCamelCase  )
   # verify orig_size
   lowerCamelCase_  				=						torch.tensor([480, 640]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]    ,						UpperCamelCase  )  )
   # verify size
   lowerCamelCase_  				=						torch.tensor([800, 1066]  )
   self.assertTrue(torch.allclose(encoding["labels"][0]["size"]    ,						UpperCamelCase  )  )



