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


_lowercase							:      Union[str, Any]      							=1_6
_lowercase							:      Union[str, Any]      							=3_2



def A__  (			lowercase:     Tuple,		lowercase:     Tuple = 16,		lowercase:     List[Any] = "bert-base-cased"					)				->		Dict:
       A						:     Union[str, Any]         =AutoTokenizer.from_pretrained(lowercase					)
       A						:     Any         =load_dataset('glue',		'mrpc'					)

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

       # Apply the method we just defined to all the examples in all the splits of the dataset
       A						:     List[str]         =datasets.map(
           lowercase,		batched=lowercase,		remove_columns=['idx', 'sentence1', 'sentence2'],		load_from_cache_file=lowercase					)

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

       def collate_fn(lowercase:     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(lowercase,		padding='max_length',		max_length=128,		return_tensors='pt'					)
              return tokenizer.pad(lowercase,		padding='longest',		return_tensors='pt'					)

       # Instantiate dataloaders.
       A						:     Dict         =DataLoader(
           tokenized_datasets['train'],		shuffle=lowercase,		collate_fn=lowercase,		batch_size=lowercase					)
       A						:     Tuple         =DataLoader(
           tokenized_datasets['validation'],		shuffle=lowercase,		collate_fn=lowercase,		batch_size=lowercase					)

       return train_dataloader, eval_dataloader



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

       A						:     str         =metric.compute()
       return eval_metric["accuracy"]



def A__  (			lowercase:     Dict,		lowercase:     Dict					)				->		Union[str, Any]:
       # Initialize accelerator
       A						:     Union[str, Any]         =Accelerator()

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

       set_seed(lowercase					)
       A						:     Optional[int]         =get_dataloaders(lowercase,		lowercase,		lowercase					)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       A						:     List[Any]         =AutoModelForSequenceClassification.from_pretrained(lowercase,		return_dict=lowercase					)

       # Instantiate optimizer
       A						:     Dict         =(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       A						:     Tuple         =optimizer_cls(params=model.parameters(),		lr=lowercase					)

       if accelerator.state.deepspeed_plugin is not None:
              A						:     Dict         =accelerator.state.deepspeed_plugin.deepspeed_config[
                  """gradient_accumulation_steps"""
              ]
       else:
              A						:     Optional[Any]         =1
       A						:     str         =(len(lowercase					) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              A						:     Any         =get_linear_schedule_with_warmup(
                  optimizer=lowercase,		num_warmup_steps=0,		num_training_steps=lowercase,		)
       else:
              A						:     Any         =DummyScheduler(lowercase,		total_num_steps=lowercase,		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.
       A						:     str         =accelerator.prepare(
           lowercase,		lowercase,		lowercase,		lowercase,		lowercase					)

       # We need to keep track of how many total steps we have iterated over
       A						:     str         =0
       # We also need to keep track of the stating epoch so files are named properly
       A						:     int         =0
       A						:     int         =evaluate.load('glue',		'mrpc'					)
       A						:     List[Any]         =num_epochs

       if args.partial_train_epoch is not None:
              A						:     int         =args.partial_train_epoch

       if args.resume_from_checkpoint:
              accelerator.load_state(args.resume_from_checkpoint					)
              A						:     int         =args.resume_from_checkpoint.split('epoch_'					)[1]
              A						:     Optional[int]         =""""""
              for char in epoch_string:
                     if char.isdigit():
                            state_epoch_num += char
                     else:
                            break
              A						:     int         =int(lowercase					) + 1
              A						:     Dict         =evaluation_loop(lowercase,		lowercase,		lowercase,		lowercase					)
              accelerator.print('resumed checkpoint performance:',		lowercase					)
              accelerator.print('resumed checkpoint\'s scheduler\'s lr:',		lr_scheduler.get_lr()[0]					)
              accelerator.print('resumed optimizers\'s lr:',		optimizer.param_groups[0]['lr']					)
              with open(os.path.join(args.output_dir,		F'state_{starting_epoch-1}.json'					),		'r'					) as f:
                     A						:     List[Any]         =json.load(lowercase					)
                     assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["lr"] == lr_scheduler.get_lr()[0]
                     ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                     ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                     assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                     return

    # Now we train the model
       A						:     Dict         ={}
       for epoch in range(lowercase,		lowercase					):
              model.train()
              for step, batch in enumerate(lowercase					):
                     A						:     Union[str, Any]         =model(**lowercase					)
                     A						:     str         =outputs.loss
                     A						:     List[str]         =loss / gradient_accumulation_steps
                     accelerator.backward(lowercase					)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1
              A						:     int         =F'epoch_{epoch}'
              A						:     Optional[int]         =os.path.join(args.output_dir,		lowercase					)
              accelerator.save_state(lowercase					)
              A						:     Optional[int]         =evaluation_loop(lowercase,		lowercase,		lowercase,		lowercase					)
              A						:     Optional[Any]         =accuracy
              A						:     str         =lr_scheduler.get_lr()[0]
              A						:     List[str]         =optimizer.param_groups[0]["""lr"""]
              A						:     Union[str, Any]         =epoch
              A						:     str         =overall_step
              accelerator.print(F'epoch {epoch}:',		lowercase					)

              accelerator.wait_for_everyone()
              if accelerator.is_main_process:
                     with open(os.path.join(args.output_dir,		F'state_{epoch}.json'					),		'w'					) as f:
                            json.dump(lowercase,		lowercase					)



def A__  (			)				->		Tuple:
       A						:     Tuple         =argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'					)
       parser.add_argument(
           '--model_name_or_path',		type=lowercase,		default='bert-base-cased',		help='Path to pretrained model or model identifier from huggingface.co/models.',		required=lowercase,		)
       parser.add_argument(
           '--output_dir',		type=lowercase,		default='.',		help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.',		)
       parser.add_argument(
           '--resume_from_checkpoint',		type=lowercase,		default=lowercase,		help='If the training should continue from a checkpoint folder.',		)
       parser.add_argument(
           '--partial_train_epoch',		type=lowercase,		default=lowercase,		help='If passed, the training will stop after this number of epochs.',		)
       parser.add_argument(
           '--num_epochs',		type=lowercase,		default=2,		help='Number of train epochs.',		)
       A						:     List[str]         =parser.parse_args()
       A						:     List[str]         ={"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

       training_function(lowercase,		lowercase					)


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 ConditionalDetrImageProcessor



class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  def __init__(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Tuple=7      ,	SCREAMING_SNAKE_CASE__    :					Dict=3      ,	SCREAMING_SNAKE_CASE__    :					Tuple=30      ,	SCREAMING_SNAKE_CASE__    :					int=4_00      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					Dict=None      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=True      ,	SCREAMING_SNAKE_CASE__    :					Dict=[0.5, 0.5, 0.5]      ,	SCREAMING_SNAKE_CASE__    :					str=[0.5, 0.5, 0.5]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=True      ,	SCREAMING_SNAKE_CASE__    :					Any=1 / 2_55      ,	SCREAMING_SNAKE_CASE__    :					int=True      ,	)    ->				Optional[int]:
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         A						:     Optional[Any]         =size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
         A						:     Union[str, Any]         =parent
         A						:     Union[str, Any]         =batch_size
         A						:     Union[str, Any]         =num_channels
         A						:     int         =min_resolution
         A						:     List[Any]         =max_resolution
         A						:     Dict         =do_resize
         A						:     Tuple         =size
         A						:     List[str]         =do_normalize
         A						:     List[Any]         =image_mean
         A						:     Dict         =image_std
         A						:     Any         =do_rescale
         A						:     List[str]         =rescale_factor
         A						:     Optional[Any]         =do_pad







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

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

         return expected_height, expected_width







@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_   ,						unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     List[Any]            =				ConditionalDetrImageProcessor if is_vision_available() else None







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Tuple:
         A						:     str         =ConditionalDetrImageProcessingTester(self     )







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				int:
         return self.image_processor_tester.prepare_image_processor_dict()







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				Tuple:
         A						:     Tuple         =self.image_processing_class(**self.image_processor_dict     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	'image_mean'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	'image_std'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	'do_normalize'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	'do_resize'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,	'size'     )     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				int:
         A						:     int         =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      ,	SCREAMING_SNAKE_CASE__     )

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Optional[int]:
         pass







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Union[str, Any]:
         # Initialize image_processing
         A						:     Union[str, Any]         =self.image_processing_class(**self.image_processor_dict     )
         # create random PIL images
         A						:     Tuple         =prepare_image_inputs(self.image_processor_tester      ,	equal_resolution=SCREAMING_SNAKE_CASE__     )
         for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,	Image.Image     )

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

         A  ,  A						:     List[str]         =self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__     )

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

         # Test batched
         A  ,  A						:     Union[str, Any]         =self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__      ,	batched=SCREAMING_SNAKE_CASE__     )

         A						:     Union[str, Any]         =image_processing(SCREAMING_SNAKE_CASE__      ,	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 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				int:
         # Initialize image_processing
         A						:     Optional[Any]         =self.image_processing_class(**self.image_processor_dict     )
         # create random numpy tensors
         A						:     str         =prepare_image_inputs(self.image_processor_tester      ,	equal_resolution=SCREAMING_SNAKE_CASE__      ,	numpify=SCREAMING_SNAKE_CASE__     )
         for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,	np.ndarray     )

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

         A  ,  A						:     Any         =self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__     )

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

         # Test batched
         A						:     Tuple         =image_processing(SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     ).pixel_values

         A  ,  A						:     Optional[int]         =self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__      ,	batched=SCREAMING_SNAKE_CASE__     )

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				List[str]:
         # Initialize image_processing
         A						:     Optional[Any]         =self.image_processing_class(**self.image_processor_dict     )
         # create random PyTorch tensors
         A						:     Any         =prepare_image_inputs(self.image_processor_tester      ,	equal_resolution=SCREAMING_SNAKE_CASE__      ,	torchify=SCREAMING_SNAKE_CASE__     )
         for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE__      ,	torch.Tensor     )

         # Test not batched input
         A						:     Optional[int]         =image_processing(image_inputs[0]      ,	return_tensors='pt'     ).pixel_values

         A  ,  A						:     Tuple         =self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__     )

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

         # Test batched
         A						:     Tuple         =image_processing(SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     ).pixel_values

         A  ,  A						:     int         =self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__      ,	batched=SCREAMING_SNAKE_CASE__     )

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







  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				Union[str, Any]:
         # prepare image and target
         A						:     Union[str, Any]         =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
         with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'      ,	'r'     ) as f:
                A						:     List[Any]         =json.loads(f.read()     )

         A						:     Any         ={'image_id': 3_97_69, 'annotations': target}

         # encode them
         A						:     str         =ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'     )
         A						:     Any         =image_processing(images=SCREAMING_SNAKE_CASE__      ,	annotations=SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     )

         # verify pixel values
         A						:     Optional[Any]         =torch.Size([1, 3, 8_00, 10_66]     )
         self.assertEqual(encoding['pixel_values'].shape      ,	SCREAMING_SNAKE_CASE__     )

         A						:     List[str]         =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]      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-4     )     )

         # verify area
         A						:     Dict         =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']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify boxes
         A						:     str         =torch.Size([6, 4]     )
         self.assertEqual(encoding['labels'][0]['boxes'].shape      ,	SCREAMING_SNAKE_CASE__     )
         A						:     Union[str, Any]         =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]      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-3     )     )
         # verify image_id
         A						:     Dict         =torch.tensor([3_97_69]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify is_crowd
         A						:     List[str]         =torch.tensor([0, 0, 0, 0, 0, 0]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify class_labels
         A						:     Union[str, Any]         =torch.tensor([75, 75, 63, 65, 17, 17]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify orig_size
         A						:     List[Any]         =torch.tensor([4_80, 6_40]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify size
         A						:     int         =torch.tensor([8_00, 10_66]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,	SCREAMING_SNAKE_CASE__     )     )







  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Tuple:
         # prepare image, target and masks_path
         A						:     List[str]         =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
         with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'      ,	'r'     ) as f:
                A						:     Optional[int]         =json.loads(f.read()     )

         A						:     int         ={'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

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

         # encode them
         A						:     List[Any]         =ConditionalDetrImageProcessor(format='coco_panoptic'     )
         A						:     Union[str, Any]         =image_processing(images=SCREAMING_SNAKE_CASE__      ,	annotations=SCREAMING_SNAKE_CASE__      ,	masks_path=SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     )

         # verify pixel values
         A						:     Dict         =torch.Size([1, 3, 8_00, 10_66]     )
         self.assertEqual(encoding['pixel_values'].shape      ,	SCREAMING_SNAKE_CASE__     )

         A						:     Dict         =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]      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-4     )     )

         # verify area
         A						:     Optional[int]         =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']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify boxes
         A						:     List[Any]         =torch.Size([6, 4]     )
         self.assertEqual(encoding['labels'][0]['boxes'].shape      ,	SCREAMING_SNAKE_CASE__     )
         A						:     Any         =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]      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-3     )     )
         # verify image_id
         A						:     List[Any]         =torch.tensor([3_97_69]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify is_crowd
         A						:     Any         =torch.tensor([0, 0, 0, 0, 0, 0]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify class_labels
         A						:     str         =torch.tensor([17, 17, 63, 75, 75, 93]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify masks
         A						:     int         =82_28_73
         self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,	SCREAMING_SNAKE_CASE__     )
         # verify orig_size
         A						:     Any         =torch.tensor([4_80, 6_40]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,	SCREAMING_SNAKE_CASE__     )     )
         # verify size
         A						:     str         =torch.tensor([8_00, 10_66]     )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,	SCREAMING_SNAKE_CASE__     )     )






