




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BertTokenizer,
    ViltConfig,
    ViltForImageAndTextRetrieval,
    ViltForImagesAndTextClassification,
    ViltForMaskedLM,
    ViltForQuestionAnswering,
    ViltImageProcessor,
    ViltProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase	:		int							       =   logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_=False ,				lowercase_=False ,				lowercase_=False						)   ->       Optional[Any]:



       """simple docstring"""

       A__       =    []
       for i in range(config.num_hidden_layers						):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append((f"""transformer.blocks.{i}.norm1.weight""", f"""vilt.encoder.layer.{i}.layernorm_before.weight""")						)
              rename_keys.append((f"""transformer.blocks.{i}.norm1.bias""", f"""vilt.encoder.layer.{i}.layernorm_before.bias""")						)
              rename_keys.append(
                  (f"""transformer.blocks.{i}.attn.proj.weight""", f"""vilt.encoder.layer.{i}.attention.output.dense.weight""")						)
              rename_keys.append(
                  (f"""transformer.blocks.{i}.attn.proj.bias""", f"""vilt.encoder.layer.{i}.attention.output.dense.bias""")						)
              rename_keys.append((f"""transformer.blocks.{i}.norm2.weight""", f"""vilt.encoder.layer.{i}.layernorm_after.weight""")						)
              rename_keys.append((f"""transformer.blocks.{i}.norm2.bias""", f"""vilt.encoder.layer.{i}.layernorm_after.bias""")						)
              rename_keys.append(
                  (f"""transformer.blocks.{i}.mlp.fc1.weight""", f"""vilt.encoder.layer.{i}.intermediate.dense.weight""")						)
              rename_keys.append((f"""transformer.blocks.{i}.mlp.fc1.bias""", f"""vilt.encoder.layer.{i}.intermediate.dense.bias""")						)
              rename_keys.append((f"""transformer.blocks.{i}.mlp.fc2.weight""", f"""vilt.encoder.layer.{i}.output.dense.weight""")						)
              rename_keys.append((f"""transformer.blocks.{i}.mlp.fc2.bias""", f"""vilt.encoder.layer.{i}.output.dense.bias""")						)

       # embeddings
       rename_keys.extend(
           [
               # text embeddings
               ('''text_embeddings.word_embeddings.weight''', '''vilt.embeddings.text_embeddings.word_embeddings.weight'''),
               (
                   '''text_embeddings.position_embeddings.weight''',
                   '''vilt.embeddings.text_embeddings.position_embeddings.weight''',
               ),
               ('''text_embeddings.position_ids''', '''vilt.embeddings.text_embeddings.position_ids'''),
               (
                   '''text_embeddings.token_type_embeddings.weight''',
                   '''vilt.embeddings.text_embeddings.token_type_embeddings.weight''',
               ),
               ('''text_embeddings.LayerNorm.weight''', '''vilt.embeddings.text_embeddings.LayerNorm.weight'''),
               ('''text_embeddings.LayerNorm.bias''', '''vilt.embeddings.text_embeddings.LayerNorm.bias'''),
               # patch embeddings
               ('''transformer.cls_token''', '''vilt.embeddings.cls_token'''),
               ('''transformer.patch_embed.proj.weight''', '''vilt.embeddings.patch_embeddings.projection.weight'''),
               ('''transformer.patch_embed.proj.bias''', '''vilt.embeddings.patch_embeddings.projection.bias'''),
               ('''transformer.pos_embed''', '''vilt.embeddings.position_embeddings'''),
               # token type embeddings
               ('''token_type_embeddings.weight''', '''vilt.embeddings.token_type_embeddings.weight'''),
           ]						)

       # final layernorm + pooler
       rename_keys.extend(
           [
               ('''transformer.norm.weight''', '''vilt.layernorm.weight'''),
               ('''transformer.norm.bias''', '''vilt.layernorm.bias'''),
               ('''pooler.dense.weight''', '''vilt.pooler.dense.weight'''),
               ('''pooler.dense.bias''', '''vilt.pooler.dense.bias'''),
           ]						)

       # classifier head(s)
       if vqa_model:
              # classification head
              rename_keys.extend(
                  [
                      ('''vqa_classifier.0.weight''', '''classifier.0.weight'''),
                      ('''vqa_classifier.0.bias''', '''classifier.0.bias'''),
                      ('''vqa_classifier.1.weight''', '''classifier.1.weight'''),
                      ('''vqa_classifier.1.bias''', '''classifier.1.bias'''),
                      ('''vqa_classifier.3.weight''', '''classifier.3.weight'''),
                      ('''vqa_classifier.3.bias''', '''classifier.3.bias'''),
                  ]						)
       elif nlvr_model:
              # classification head
              rename_keys.extend(
                  [
                      ('''nlvr2_classifier.0.weight''', '''classifier.0.weight'''),
                      ('''nlvr2_classifier.0.bias''', '''classifier.0.bias'''),
                      ('''nlvr2_classifier.1.weight''', '''classifier.1.weight'''),
                      ('''nlvr2_classifier.1.bias''', '''classifier.1.bias'''),
                      ('''nlvr2_classifier.3.weight''', '''classifier.3.weight'''),
                      ('''nlvr2_classifier.3.bias''', '''classifier.3.bias'''),
                  ]						)
       else:
              pass

       return rename_keys







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Optional[int]:



       """simple docstring"""

       for i in range(config.num_hidden_layers						):
              A__       =    '''vilt.'''
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              A__       =    state_dict.pop(f"""transformer.blocks.{i}.attn.qkv.weight"""						)
              A__       =    state_dict.pop(f"""transformer.blocks.{i}.attn.qkv.bias"""						)
              # next, add query, keys and values (in that order) to the state dict
              A__       =    in_proj_weight[
                  : config.hidden_size, :
              ]
              A__       =    in_proj_bias[: config.hidden_size]
              A__       =    in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              A__       =    in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              A__       =    in_proj_weight[
                  -config.hidden_size :, :
              ]
              A__       =    in_proj_bias[-config.hidden_size :]







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Optional[int]:



       """simple docstring"""

       A__       =    ['''head.weight''', '''head.bias''']
       for k in ignore_keys:
              state_dict.pop(lowercase_ ,				lowercase_						)







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



       """simple docstring"""

       A__       =    dct.pop(lowercase_						)
       A__       =    val







@torch.no_grad()
def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Optional[int]:



       """simple docstring"""

       A__       =    ViltConfig(image_size=384 ,				patch_size=32 ,				tie_word_embeddings=lowercase_						)
       A__       =    False
       A__       =    False
       A__       =    False
       A__       =    False
       if "vqa" in checkpoint_url:
              A__       =    True
              A__       =    3_129
              A__       =    '''huggingface/label-files'''
              A__       =    '''vqa2-id2label.json'''
              A__       =    json.load(open(hf_hub_download(lowercase_ ,				lowercase_ ,				repo_type='''dataset'''						) ,				'''r'''						)						)
              A__       =    {int(lowercase_						): v for k, v in idalabel.items()}
              A__       =    idalabel
              A__       =    {v: k for k, v in idalabel.items()}
              A__       =    ViltForQuestionAnswering(lowercase_						)
       elif "nlvr" in checkpoint_url:
              A__       =    True
              A__       =    2
              A__       =    {0: '''False''', 1: '''True'''}
              A__       =    {v: k for k, v in config.idalabel.items()}
              A__       =    3
              A__       =    ViltForImagesAndTextClassification(lowercase_						)
       elif "irtr" in checkpoint_url:
              A__       =    True
              A__       =    ViltForImageAndTextRetrieval(lowercase_						)
       elif "mlm_itm" in checkpoint_url:
              A__       =    True
              A__       =    ViltForMaskedLM(lowercase_						)
       else:
              raise ValueError('''Unknown model type'''						)

       # load state_dict of original model, remove and rename some keys
       A__       =    torch.hub.load_state_dict_from_url(lowercase_ ,				map_location='''cpu'''						)['''state_dict''']
       A__       =    create_rename_keys(lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)
       for src, dest in rename_keys:
              rename_key(lowercase_ ,				lowercase_ ,				lowercase_						)
       read_in_q_k_v(lowercase_ ,				lowercase_						)
       if mlm_model or irtr_model:
              A__       =    ['''itm_score.fc.weight''', '''itm_score.fc.bias''']
              for k in ignore_keys:
                     state_dict.pop(lowercase_ ,				lowercase_						)

    # load state dict into HuggingFace model
       model.eval()
       if mlm_model:
              A__ ,							A__       =    model.load_state_dict(lowercase_ ,				strict=lowercase_						)
              assert missing_keys == ["mlm_score.decoder.bias"]
       else:
              model.load_state_dict(lowercase_						)

       # Define processor
       A__       =    ViltImageProcessor(size=384						)
       A__       =    BertTokenizer.from_pretrained('''bert-base-uncased'''						)
       A__       =    ViltProcessor(lowercase_ ,				lowercase_						)

       # Forward pass on example inputs (image + text)
       if nlvr_model:
              A__       =    Image.open(requests.get('''https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg''' ,				stream=lowercase_						).raw						)
              A__       =    Image.open(requests.get('''https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg''' ,				stream=lowercase_						).raw						)
              A__       =    (
                  '''The left image contains twice the number of dogs as the right image, and at least two dogs in total are'''
                  ''' standing.'''
              )
              A__       =    processor(lowercase_ ,				lowercase_ ,				return_tensors='''pt'''						)
              A__       =    processor(lowercase_ ,				lowercase_ ,				return_tensors='''pt'''						)
              A__       =    model(
                  input_ids=encoding_a.input_ids ,				pixel_values=encoding_a.pixel_values ,				pixel_values_a=encoding_a.pixel_values ,				)
       else:
              A__       =    Image.open(requests.get('''http://images.cocodataset.org/val2017/000000039769.jpg''' ,				stream=lowercase_						).raw						)
              if mlm_model:
                     A__       =    '''a bunch of [MASK] laying on a [MASK].'''
              else:
                     A__       =    '''How many cats are there?'''
              A__       =    processor(lowercase_ ,				lowercase_ ,				return_tensors='''pt'''						)
              A__       =    model(**lowercase_						)

       # Verify outputs
       if mlm_model:
              A__       =    torch.Size([1, 11, 30_522]						)
              A__       =    torch.tensor([-12.50_61, -12.51_23, -12.51_74]						)
              assert outputs.logits.shape == expected_shape
              assert torch.allclose(outputs.logits[0, 0, :3] ,				lowercase_ ,				atol=1E-4						)

              # verify masked token prediction equals "cats"
              A__       =    outputs.logits[0, 4, :].argmax(-1						).item()
              assert tokenizer.decode([predicted_id]						) == "cats"
       elif vqa_model:
              A__       =    torch.Size([1, 3_129]						)
              A__       =    torch.tensor([-15.94_95, -18.14_72, -10.30_41]						)
              assert torch.allclose(outputs.logits[0, :3] ,				lowercase_ ,				atol=1E-4						)
              assert outputs.logits.shape == expected_shape
              assert torch.allclose(outputs.logits[0, 0, :3] ,				lowercase_ ,				atol=1E-4						)

              # verify vqa prediction equals "2"
              A__       =    outputs.logits.argmax(-1						).item()
              assert model.config.idalabel[predicted_idx] == "2"
       elif nlvr_model:
              A__       =    torch.Size([1, 2]						)
              A__       =    torch.tensor([-2.87_21, 2.12_91]						)
              assert torch.allclose(outputs.logits[0, :3] ,				lowercase_ ,				atol=1E-4						)
              assert outputs.logits.shape == expected_shape

       Path(lowercase_						).mkdir(exist_ok=lowercase_						)
       print(f"""Saving model and processor to {pytorch_dump_folder_path}"""						)
       model.save_pretrained(lowercase_						)
       processor.save_pretrained(lowercase_						)


if __name__ == "__main__":
  _lowerCamelCase	:		str							       =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt""",
      type=str,
      help="""URL of the checkpoint you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  _lowerCamelCase	:		Optional[Any]							       =   parser.parse_args()
  convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








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 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    def __init__(			self      :	List[str]					, UpperCAmelCase__      :	Optional[int]					, UpperCAmelCase__      :	int=7					, UpperCAmelCase__      :	Dict=3					, UpperCAmelCase__      :	List[Any]=30					, UpperCAmelCase__      :	Any=400					, UpperCAmelCase__      :	Optional[Any]=True					, UpperCAmelCase__      :	List[str]=None					, UpperCAmelCase__      :	Any=True					, UpperCAmelCase__      :	Optional[Any]=[0.5, 0.5, 0.5]					, UpperCAmelCase__      :	Any=[0.5, 0.5, 0.5]					, UpperCAmelCase__      :	List[str]=True					, UpperCAmelCase__      :	Optional[int]=1 / 255					, UpperCAmelCase__      :	Optional[Any]=True					, )						->str:





           '''simple docstring'''

           A__       =    size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
           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        SCREAMING_SNAKE_CASE							(			self      :	Any)						->List[str]:





           '''simple docstring'''

           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]					, UpperCAmelCase__      :	List[str]					, UpperCAmelCase__      :	int=False)						->Optional[Any]:





           '''simple docstring'''

           if not batched:
                  A__       =    image_inputs[0]
                  if isinstance(UpperCAmelCase__					, 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(UpperCAmelCase__					, key=lambda UpperCAmelCase__: item[0])[0]
                  A__       =    max(UpperCAmelCase__					, key=lambda UpperCAmelCase__: item[1])[1]

           return expected_height, expected_width


@require_torch
@require_vision
class 				UpperCamelCase_    ( UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      DeformableDetrImageProcessor if is_vision_available() else None


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Tuple:





           '''simple docstring'''

           A__       =    DeformableDetrImageProcessingTester(self)


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Any:





           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->List[str]:





           '''simple docstring'''

           A__       =    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_resize'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''do_rescale'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''do_pad'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''size'''))


    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->int:





           '''simple docstring'''

           A__       =    self.image_processing_class.from_dict(self.image_processor_dict)
           self.assertEqual(image_processor.size					, {'''shortest_edge''': 18, '''longest_edge''': 1_333})
           self.assertEqual(image_processor.do_pad					, UpperCAmelCase__)

           A__       =    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        SCREAMING_SNAKE_CASE							(			self      :	Any)						->List[str]:





           '''simple docstring'''

           pass


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->Dict:





           '''simple docstring'''

           A__       =    self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           A__       =    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase__)
           for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase__					, 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(UpperCAmelCase__)

           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(UpperCAmelCase__					, batched=UpperCAmelCase__)

           A__       =    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        SCREAMING_SNAKE_CASE							(			self      :	int)						->Optional[int]:





           '''simple docstring'''

           A__       =    self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           A__       =    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
           A__       =    image_processing(image_inputs[0]					, return_tensors='''pt''').pixel_values

           A__ ,							A__       =    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
           A__       =    image_processing(UpperCAmelCase__					, return_tensors='''pt''').pixel_values

           A__ ,							A__       =    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        SCREAMING_SNAKE_CASE							(			self      :	int)						->Tuple:





           '''simple docstring'''

           A__       =    self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           A__       =    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
           A__       =    image_processing(image_inputs[0]					, return_tensors='''pt''').pixel_values

           A__ ,							A__       =    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
           A__       =    image_processing(UpperCAmelCase__					, return_tensors='''pt''').pixel_values

           A__ ,							A__       =    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        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->List[str]:





           '''simple docstring'''

           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''': 39_769, '''annotations''': target}

           # encode them
           A__       =    DeformableDetrImageProcessor()
           A__       =    image_processing(images=UpperCAmelCase__					, annotations=UpperCAmelCase__					, return_tensors='''pt''')

           # verify pixel values
           A__       =    torch.Size([1, 3, 800, 1_066])
           self.assertEqual(encoding['''pixel_values'''].shape					, UpperCAmelCase__)

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

           # verify area
           A__       =    torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']					, UpperCAmelCase__))
           # verify boxes
           A__       =    torch.Size([6, 4])
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape					, UpperCAmelCase__)
           A__       =    torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]					, UpperCAmelCase__					, atol=1e-3))
           # verify image_id
           A__       =    torch.tensor([39_769])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']					, UpperCAmelCase__))
           # verify is_crowd
           A__       =    torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']					, UpperCAmelCase__))
           # verify class_labels
           A__       =    torch.tensor([75, 75, 63, 65, 17, 17])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']					, UpperCAmelCase__))
           # verify orig_size
           A__       =    torch.tensor([480, 640])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']					, UpperCAmelCase__))
           # verify size
           A__       =    torch.tensor([800, 1_066])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']					, UpperCAmelCase__))





    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->Optional[int]:





           '''simple docstring'''

           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''': 39_769, '''segments_info''': target}

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

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

           # verify pixel values
           A__       =    torch.Size([1, 3, 800, 1_066])
           self.assertEqual(encoding['''pixel_values'''].shape					, UpperCAmelCase__)

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

           # verify area
           A__       =    torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']					, UpperCAmelCase__))
           # verify boxes
           A__       =    torch.Size([6, 4])
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape					, UpperCAmelCase__)
           A__       =    torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]					, UpperCAmelCase__					, atol=1e-3))
           # verify image_id
           A__       =    torch.tensor([39_769])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']					, UpperCAmelCase__))
           # verify is_crowd
           A__       =    torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']					, UpperCAmelCase__))
           # verify class_labels
           A__       =    torch.tensor([17, 17, 63, 75, 75, 93])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']					, UpperCAmelCase__))
           # verify masks
           A__       =    822_873
           self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()					, UpperCAmelCase__)
           # verify orig_size
           A__       =    torch.tensor([480, 640])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']					, UpperCAmelCase__))
           # verify size
           A__       =    torch.tensor([800, 1_066])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']					, UpperCAmelCase__))


