





import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
A       :   Any					= logging.getLogger(__name__)







def   __lowerCamelCase   (				__a  :int       ,	__a  :str       )						->							List[str]:






		"""simple docstring"""

		A__     =  np.argmax(_snake_case       ,	axis=1       )
		return np.sum(outputs == labels       )







def   __lowerCamelCase   (				__a  :Dict       )						->							List[str]:






		"""simple docstring"""

		with open(_snake_case       ,	encoding="""utf_8"""       ) as f:
				A__     =  csv.reader(_snake_case       )
				A__     =  []
				next(_snake_case       )  # skip the first line
				for line in tqdm(_snake_case       ):
						output.append((""" """.join(line[1:5]       ), line[5], line[6], int(line[-1]       ) - 1)       )
		return output







def   __lowerCamelCase   (				__a  :Optional[Any]       ,	__a  :str       ,	__a  :List[str]       ,	__a  :Tuple       ,	__a  :int       ,	__a  :List[str]       )						->							Tuple:






		"""simple docstring"""

		A__     =  []
		for dataset in encoded_datasets:
				A__     =  len(_snake_case       )
				A__     =  np.zeros((n_batch, 2, input_len)       ,	dtype=np.intaa       )
				A__     =  np.zeros((n_batch, 2)       ,	dtype=np.intaa       )
				A__     =  np.full((n_batch, 2, input_len)       ,	fill_value=-1_0_0       ,	dtype=np.intaa       )
				A__     =  np.zeros((n_batch,)       ,	dtype=np.intaa       )
				for (
				    i,
				    (story, conta, conta, mc_label),
				) in enumerate(_snake_case       ):
						A__     =  [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
						A__     =  [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
						A__     =  with_conta
						A__     =  with_conta
						A__     =  len(_snake_case       ) - 1
						A__     =  len(_snake_case       ) - 1
						A__     =  with_conta
						A__     =  with_conta
						A__     =  mc_label
				A__     =  (input_ids, mc_token_ids, lm_labels, mc_labels)
				tensor_datasets.append(tuple(torch.tensor(_snake_case       ) for t in all_inputs       )       )
		return tensor_datasets







def   __lowerCamelCase   (				)						->							Union[str, Any]:






		"""simple docstring"""

		A__     =  argparse.ArgumentParser()
		parser.add_argument("""--model_name"""       ,	type=_snake_case       ,	default="""openai-gpt"""       ,	help="""pretrained model name"""       )
		parser.add_argument("""--do_train"""       ,	action="""store_true"""       ,	help="""Whether to run training."""       )
		parser.add_argument("""--do_eval"""       ,	action="""store_true"""       ,	help="""Whether to run eval on the dev set."""       )
		parser.add_argument(
		    """--output_dir"""       ,	default=_snake_case       ,	type=_snake_case       ,	required=_snake_case       ,	help="""The output directory where the model predictions and checkpoints will be written."""       ,	)
		parser.add_argument("""--train_dataset"""       ,	type=_snake_case       ,	default=""""""       )
		parser.add_argument("""--eval_dataset"""       ,	type=_snake_case       ,	default=""""""       )
		parser.add_argument("""--seed"""       ,	type=_snake_case       ,	default=4_2       )
		parser.add_argument("""--num_train_epochs"""       ,	type=_snake_case       ,	default=3       )
		parser.add_argument("""--train_batch_size"""       ,	type=_snake_case       ,	default=8       )
		parser.add_argument("""--eval_batch_size"""       ,	type=_snake_case       ,	default=1_6       )
		parser.add_argument("""--adam_epsilon"""       ,	default=1E-8       ,	type=_snake_case       ,	help="""Epsilon for Adam optimizer."""       )
		parser.add_argument("""--max_grad_norm"""       ,	type=_snake_case       ,	default=1       )
		parser.add_argument(
		    """--max_steps"""       ,	default=-1       ,	type=_snake_case       ,	help=(
		        """If > 0: set total number of training                         steps to perform. Override num_train_epochs."""
		    )       ,	)
		parser.add_argument(
		    """--gradient_accumulation_steps"""       ,	type=_snake_case       ,	default=1       ,	help="""Number of updates steps to accumulate before                        performing a backward/update pass."""       ,	)
		parser.add_argument("""--learning_rate"""       ,	type=_snake_case       ,	default=6.25E-5       )
		parser.add_argument("""--warmup_steps"""       ,	default=0       ,	type=_snake_case       ,	help="""Linear warmup over warmup_steps."""       )
		parser.add_argument("""--lr_schedule"""       ,	type=_snake_case       ,	default="""warmup_linear"""       )
		parser.add_argument("""--weight_decay"""       ,	type=_snake_case       ,	default=0.01       )
		parser.add_argument("""--lm_coef"""       ,	type=_snake_case       ,	default=0.9       )
		parser.add_argument("""--n_valid"""       ,	type=_snake_case       ,	default=3_7_4       )

		parser.add_argument("""--server_ip"""       ,	type=_snake_case       ,	default=""""""       ,	help="""Can be used for distant debugging."""       )
		parser.add_argument("""--server_port"""       ,	type=_snake_case       ,	default=""""""       ,	help="""Can be used for distant debugging."""       )
		A__     =  parser.parse_args()
		print(_snake_case       )

		if args.server_ip and args.server_port:
				# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
				import ptvsd

				print("""Waiting for debugger attach"""       )
				ptvsd.enable_attach(address=(args.server_ip, args.server_port)       ,	redirect_output=_snake_case       )
				ptvsd.wait_for_attach()

		random.seed(args.seed       )
		np.random.seed(args.seed       )
		torch.manual_seed(args.seed       )
		torch.cuda.manual_seed_all(args.seed       )

		A__     =  torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""       )
		A__     =  torch.cuda.device_count()
		logger.info("""device: {}, n_gpu {}""".format(_snake_case       ,	_snake_case       )       )

		if not args.do_train and not args.do_eval:
				raise ValueError("""At least one of `do_train` or `do_eval` must be True."""       )

		if not os.path.exists(args.output_dir       ):
				os.makedirs(args.output_dir       )

		# Load tokenizer and model
		# This loading functions also add new tokens and embeddings called `special tokens`
		# These new embeddings will be fine-tuned on the RocStories dataset
		A__     =  ["""_start_""", """_delimiter_""", """_classify_"""]
		A__     =  OpenAIGPTTokenizer.from_pretrained(args.model_name       )
		tokenizer.add_tokens(_snake_case       )
		A__     =  tokenizer.convert_tokens_to_ids(_snake_case       )
		A__     =  OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name       )
		model.resize_token_embeddings(len(_snake_case       )       )
		model.to(_snake_case       )

		# Load and encode the datasets
		def tokenize_and_encode(__a  :Tuple       ):
				if isinstance(_snake_case       ,	_snake_case       ):
						return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_snake_case       )       )
				elif isinstance(_snake_case       ,	_snake_case       ):
						return obj
				return [tokenize_and_encode(_snake_case       ) for o in obj]

		logger.info("""Encoding dataset..."""       )
		A__     =  load_rocstories_dataset(args.train_dataset       )
		A__     =  load_rocstories_dataset(args.eval_dataset       )
		A__     =  (train_dataset, eval_dataset)
		A__     =  tokenize_and_encode(_snake_case       )

		# Compute the max input length for the Transformer
		A__     =  model.config.n_positions // 2 - 2
		A__     =  max(
		    len(story[:max_length]       ) + max(len(conta[:max_length]       )       ,	len(conta[:max_length]       )       ) + 3
		    for dataset in encoded_datasets
		    for story, conta, conta, _ in dataset       )
		A__     =  min(_snake_case       ,	model.config.n_positions       )  # Max size of input for the pre-trained model

		# Prepare inputs tensors and dataloaders
		A__     =  pre_process_datasets(_snake_case       ,	_snake_case       ,	_snake_case       ,	*_snake_case       )
		A__ ,     A__     =  tensor_datasets[0], tensor_datasets[1]

		A__     =  TensorDataset(*_snake_case       )
		A__     =  RandomSampler(_snake_case       )
		A__     =  DataLoader(_snake_case       ,	sampler=_snake_case       ,	batch_size=args.train_batch_size       )

		A__     =  TensorDataset(*_snake_case       )
		A__     =  SequentialSampler(_snake_case       )
		A__     =  DataLoader(_snake_case       ,	sampler=_snake_case       ,	batch_size=args.eval_batch_size       )

		# Prepare optimizer
		if args.do_train:
				if args.max_steps > 0:
						A__     =  args.max_steps
						A__     =  args.max_steps // (len(_snake_case       ) // args.gradient_accumulation_steps) + 1
				else:
						A__     =  len(_snake_case       ) // args.gradient_accumulation_steps * args.num_train_epochs

				A__     =  list(model.named_parameters()       )
				A__     =  ["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
				A__     =  [
				    {
				        """params""": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay       )],
				        """weight_decay""": args.weight_decay,
				    },
				    {"""params""": [p for n, p in param_optimizer if any(nd in n for nd in no_decay       )], """weight_decay""": 0.0},
				]
				A__     =  AdamW(_snake_case       ,	lr=args.learning_rate       ,	eps=args.adam_epsilon       )
				A__     =  get_linear_schedule_with_warmup(
				    _snake_case       ,	num_warmup_steps=args.warmup_steps       ,	num_training_steps=_snake_case       )

		if args.do_train:
				A__ ,     A__ ,     A__     =  0, 0, None
				model.train()
				for _ in trange(int(args.num_train_epochs       )       ,	desc="""Epoch"""       ):
						A__     =  0
						A__     =  0
						A__     =  tqdm(_snake_case       ,	desc="""Training"""       )
						for step, batch in enumerate(_snake_case       ):
								A__     =  tuple(t.to(_snake_case       ) for t in batch       )
								A__ ,     A__ ,     A__ ,     A__     =  batch
								A__     =  model(_snake_case       ,	mc_token_ids=_snake_case       ,	lm_labels=_snake_case       ,	mc_labels=_snake_case       )
								A__     =  args.lm_coef * losses[0] + losses[1]
								loss.backward()
								optimizer.step()
								scheduler.step()
								optimizer.zero_grad()
								tr_loss += loss.item()
								A__     =  (
								    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
								)
								nb_tr_steps += 1
								A__     =  """Training loss: {:.2e} lr: {:.2e}""".format(_snake_case       ,	scheduler.get_lr()[0]       )

    # Save a trained model
		if args.do_train:
				# Save a trained model, configuration and tokenizer
				A__     =  model.module if hasattr(_snake_case       ,	"""module"""       ) else model  # Only save the model itself

				# If we save using the predefined names, we can load using `from_pretrained`
				A__     =  os.path.join(args.output_dir       ,	_snake_case       )
				A__     =  os.path.join(args.output_dir       ,	_snake_case       )

				torch.save(model_to_save.state_dict()       ,	_snake_case       )
				model_to_save.config.to_json_file(_snake_case       )
				tokenizer.save_vocabulary(args.output_dir       )

				# Load a trained model and vocabulary that you have fine-tuned
				A__     =  OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir       )
				A__     =  OpenAIGPTTokenizer.from_pretrained(args.output_dir       )
				model.to(_snake_case       )

		if args.do_eval:
				model.eval()
				A__ ,     A__     =  0, 0
				A__ ,     A__     =  0, 0
				for batch in tqdm(_snake_case       ,	desc="""Evaluating"""       ):
						A__     =  tuple(t.to(_snake_case       ) for t in batch       )
						A__ ,     A__ ,     A__ ,     A__     =  batch
						with torch.no_grad():
								A__ ,     A__ ,     A__ ,     A__     =  model(
								    _snake_case       ,	mc_token_ids=_snake_case       ,	lm_labels=_snake_case       ,	mc_labels=_snake_case       )

						A__     =  mc_logits.detach().cpu().numpy()
						A__     =  mc_labels.to("""cpu"""       ).numpy()
						A__     =  accuracy(_snake_case       ,	_snake_case       )

						eval_loss += mc_loss.mean().item()
						eval_accuracy += tmp_eval_accuracy

						nb_eval_examples += input_ids.size(0       )
						nb_eval_steps += 1

				A__     =  eval_loss / nb_eval_steps
				A__     =  eval_accuracy / nb_eval_examples
				A__     =  tr_loss / nb_tr_steps if args.do_train else None
				A__     =  {"""eval_loss""": eval_loss, """eval_accuracy""": eval_accuracy, """train_loss""": train_loss}

				A__     =  os.path.join(args.output_dir       ,	"""eval_results.txt"""       )
				with open(_snake_case       ,	"""w"""       ) as writer:
						logger.info("""***** Eval results *****"""       )
						for key in sorted(result.keys()       ):
								logger.info("""  %s = %s"""       ,	_snake_case       ,	str(result[key]       )       )
								writer.write("""%s = %s\n""" % (key, str(result[key]       ))       )


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 DeformableDetrImageProcessor


class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

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

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

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

    return expected_height, expected_width


@require_torch
@require_vision
class 	lowerCamelCase__	(       _A							,  unittest.TestCase):



 """simple docstring"""

 a__					: Any =  DeformableDetrImageProcessor if is_vision_available() else None

 def     snake_case_      (    self					:					Optional[int]      )   ->	Any:
    _A      =       DeformableDetrImageProcessingTester(self      )

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

 def     snake_case_      (    self					:					Optional[int]      )   ->	List[str]:
    _A      =       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_resize'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''do_rescale'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''do_pad'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''size'''      )      )

 def     snake_case_      (    self					:					List[str]      )   ->	int:
    _A      =       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      )

    _A      =       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     snake_case_      (    self					:					Any      )   ->	Union[str, Any]:
    pass

 def     snake_case_      (    self					:					List[str]      )   ->	Optional[int]:
    # Initialize image_processing
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    # create random PIL images
    _A      =       prepare_image_inputs(self.image_processor_tester			,						equal_resolution=__lowerCAmelCase      )
    for image in image_inputs:
       self.assertIsInstance(__lowerCAmelCase			,						Image.Image      )

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

    _A  ,    _A      =       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
    _A  ,    _A      =       self.image_processor_tester.get_expected_values(__lowerCAmelCase			,						batched=__lowerCAmelCase      )

    _A      =       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     snake_case_      (    self					:					Tuple      )   ->	int:
    # Initialize image_processing
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    # create random numpy tensors
    _A      =       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
    _A      =       image_processing(image_inputs[0]			,						return_tensors='''pt'''      ).pixel_values

    _A  ,    _A      =       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
    _A      =       image_processing(__lowerCAmelCase			,						return_tensors='''pt'''      ).pixel_values

    _A  ,    _A      =       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     snake_case_      (    self					:					Optional[Any]      )   ->	int:
    # Initialize image_processing
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    # create random PyTorch tensors
    _A      =       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
    _A      =       image_processing(image_inputs[0]			,						return_tensors='''pt'''      ).pixel_values

    _A  ,    _A      =       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
    _A      =       image_processing(__lowerCAmelCase			,						return_tensors='''pt'''      ).pixel_values

    _A  ,    _A      =       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     snake_case_      (    self					:					Optional[Any]      )   ->	Optional[int]:
    # prepare image and target
    _A      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''			,						'''r'''      ) as f:
       _A      =       json.loads(f.read()      )

    _A      =       {'''image_id''': 3_97_69, '''annotations''': target}

    # encode them
    _A      =       DeformableDetrImageProcessor()
    _A      =       image_processing(images=__lowerCAmelCase			,						annotations=__lowerCAmelCase			,						return_tensors='''pt'''      )

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

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

    # verify area
    _A      =       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''']			,						__lowerCAmelCase      )      )
    # verify boxes
    _A      =       torch.Size([6, 4]      )
    self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,						__lowerCAmelCase      )
    _A      =       torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]			,						__lowerCAmelCase			,						atol=1E-3      )      )
    # verify image_id
    _A      =       torch.tensor([3_97_69]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,						__lowerCAmelCase      )      )
    # verify is_crowd
    _A      =       torch.tensor([0, 0, 0, 0, 0, 0]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,						__lowerCAmelCase      )      )
    # verify class_labels
    _A      =       torch.tensor([75, 75, 63, 65, 17, 17]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,						__lowerCAmelCase      )      )
    # verify orig_size
    _A      =       torch.tensor([4_80, 6_40]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,						__lowerCAmelCase      )      )
    # verify size
    _A      =       torch.tensor([8_00, 10_66]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,						__lowerCAmelCase      )      )

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

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

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

    # encode them
    _A      =       DeformableDetrImageProcessor(format='''coco_panoptic'''      )
    _A      =       image_processing(images=__lowerCAmelCase			,						annotations=__lowerCAmelCase			,						masks_path=__lowerCAmelCase			,						return_tensors='''pt'''      )

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

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

    # verify area
    _A      =       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''']			,						__lowerCAmelCase      )      )
    # verify boxes
    _A      =       torch.Size([6, 4]      )
    self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,						__lowerCAmelCase      )
    _A      =       torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]			,						__lowerCAmelCase			,						atol=1E-3      )      )
    # verify image_id
    _A      =       torch.tensor([3_97_69]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,						__lowerCAmelCase      )      )
    # verify is_crowd
    _A      =       torch.tensor([0, 0, 0, 0, 0, 0]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,						__lowerCAmelCase      )      )
    # verify class_labels
    _A      =       torch.tensor([17, 17, 63, 75, 75, 93]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,						__lowerCAmelCase      )      )
    # verify masks
    _A      =       82_28_73
    self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()			,						__lowerCAmelCase      )
    # verify orig_size
    _A      =       torch.tensor([4_80, 6_40]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,						__lowerCAmelCase      )      )
    # verify size
    _A      =       torch.tensor([8_00, 10_66]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,						__lowerCAmelCase      )      )



