






"""simple docstring"""




import argparse
import json

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

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def     _SCREAMING_SNAKE_CASE				(			UpperCamelCase  :						Optional[Any]     ):
  A__           =				[2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
  A__           =				True if '''large''' in model_name or '''huge''' in model_name else False
  A__           =				True if '''large''' in model_name or '''huge''' in model_name else False
  A__           =				True if '''large''' in model_name or '''huge''' in model_name else False

  if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
    if "fl3" in model_name:
      A__           =				[3, 3, 3, 3]
      A__           =				[5, 5, 5, 5]
    elif "fl4" in model_name:
      A__           =				[4, 4, 4, 4]
      A__           =				[3, 3, 3, 3]

  if "tiny" in model_name or "small" in model_name or "base" in model_name:
    A__           =				[3, 3, 3, 3]
    if "lrf" in model_name:
      A__           =				[3, 3, 3, 3]
    else:
      A__           =				[2, 2, 2, 2]

  if "tiny" in model_name:
    A__           =				96
  elif "small" in model_name:
    A__           =				96
  elif "base" in model_name:
    A__           =				128
  elif "large" in model_name:
    A__           =				192
  elif "xlarge" in model_name:
    A__           =				256
  elif "huge" in model_name:
    A__           =				352

  # set label information
  A__           =				'''huggingface/label-files'''
  if "large" in model_name or "huge" in model_name:
    A__           =				'''imagenet-22k-id2label.json'''
  else:
    A__           =				'''imagenet-1k-id2label.json'''

  A__           =				json.load(open(hf_hub_download(UpperCAmelCase_		,					UpperCAmelCase_		,					repo_type="""dataset"""     )		,					"""r"""     )     )
  A__           =				{int(UpperCAmelCase_     ): v for k, v in idalabel.items()}
  A__           =				{v: k for k, v in idalabel.items()}

  A__           =				FocalNetConfig(
      embed_dim=UpperCAmelCase_		,					depths=UpperCAmelCase_		,					focal_levels=UpperCAmelCase_		,					focal_windows=UpperCAmelCase_		,					use_conv_embed=UpperCAmelCase_		,					idalabel=UpperCAmelCase_		,					labelaid=UpperCAmelCase_		,					use_post_layernorm=UpperCAmelCase_		,					use_layerscale=UpperCAmelCase_		,					)

  return config






def     _SCREAMING_SNAKE_CASE				(			UpperCamelCase  :						Dict     ):
  if "patch_embed.proj" in name:
    A__           =				name.replace("""patch_embed.proj"""		,					"""embeddings.patch_embeddings.projection"""     )
  if "patch_embed.norm" in name:
    A__           =				name.replace("""patch_embed.norm"""		,					"""embeddings.norm"""     )
  if "layers" in name:
    A__           =				'''encoder.''' + name
  if "encoder.layers" in name:
    A__           =				name.replace("""encoder.layers"""		,					"""encoder.stages"""     )
  if "downsample.proj" in name:
    A__           =				name.replace("""downsample.proj"""		,					"""downsample.projection"""     )
  if "blocks" in name:
    A__           =				name.replace("""blocks"""		,					"""layers"""     )
  if "modulation.f.weight" in name or "modulation.f.bias" in name:
    A__           =				name.replace("""modulation.f"""		,					"""modulation.projection_in"""     )
  if "modulation.h.weight" in name or "modulation.h.bias" in name:
    A__           =				name.replace("""modulation.h"""		,					"""modulation.projection_context"""     )
  if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
    A__           =				name.replace("""modulation.proj"""		,					"""modulation.projection_out"""     )

  if name == "norm.weight":
    A__           =				'''layernorm.weight'''
  if name == "norm.bias":
    A__           =				'''layernorm.bias'''

  if "head" in name:
    A__           =				name.replace("""head"""		,					"""classifier"""     )
  else:
    A__           =				'''focalnet.''' + name

  return name






def     _SCREAMING_SNAKE_CASE				(			UpperCamelCase  :						str		,					UpperCamelCase  :						List[Any]		,					UpperCamelCase  :						Any=False     ):
  # fmt: off
  A__           =				{
      '''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
      '''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
      '''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
      '''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
      '''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
      '''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
      '''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
      '''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
      '''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
      '''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
  }
  # fmt: on

  A__           =				model_name_to_url[model_name]
  print("""Checkpoint URL: """		,					UpperCAmelCase_     )
  A__           =				torch.hub.load_state_dict_from_url(UpperCAmelCase_		,					map_location="""cpu"""     )['''model''']

  # rename keys
  for key in state_dict.copy().keys():
    A__           =				state_dict.pop(UpperCAmelCase_     )
    A__           =				val

  A__           =				get_focalnet_config(UpperCAmelCase_     )
  A__           =				FocalNetForImageClassification(UpperCAmelCase_     )
  model.eval()

  # load state dict
  model.load_state_dict(UpperCAmelCase_     )

  # verify conversion
  A__           =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''

  A__           =				BitImageProcessor(
      do_resize=UpperCAmelCase_		,					size={"""shortest_edge""": 256}		,					resample=PILImageResampling.BILINEAR		,					do_center_crop=UpperCAmelCase_		,					crop_size=224		,					do_normalize=UpperCAmelCase_		,					image_mean=UpperCAmelCase_		,					image_std=UpperCAmelCase_		,					)
  A__           =				Image.open(requests.get(UpperCAmelCase_		,					stream=UpperCAmelCase_     ).raw     )
  A__           =				processor(images=UpperCAmelCase_		,					return_tensors="""pt"""     )

  A__           =				transforms.Compose(
      [
          transforms.Resize(256     ),
          transforms.CenterCrop(224     ),
          transforms.ToTensor(),
          transforms.Normalize(mean=[0.485, 0.456, 0.406]		,					std=[0.229, 0.224, 0.225]     ),
      ]     )

  A__           =				image_transforms(UpperCAmelCase_     ).unsqueeze(0     )

  # verify pixel_values
  assert torch.allclose(inputs.pixel_values		,					UpperCAmelCase_		,					atol=1e-4     )

  A__           =				model(**UpperCAmelCase_     )

  A__           =				outputs.logits.argmax(-1     ).item()
  print("""Predicted class:"""		,					model.config.idalabel[predicted_class_idx]     )

  print("""First values of logits:"""		,					outputs.logits[0, :3]     )

  if model_name == "focalnet-tiny":
    A__           =				torch.tensor([0.2_166, -0.4_368, 0.2_191]     )
  elif model_name == "focalnet-tiny-lrf":
    A__           =				torch.tensor([1.1_669, 0.0_125, -0.1_695]     )
  elif model_name == "focalnet-small":
    A__           =				torch.tensor([0.4_917, -0.0_430, 0.1_341]     )
  elif model_name == "focalnet-small-lrf":
    A__           =				torch.tensor([-0.2_588, -0.5_342, -0.2_331]     )
  elif model_name == "focalnet-base":
    A__           =				torch.tensor([-0.1_655, -0.4_090, -0.1_730]     )
  elif model_name == "focalnet-base-lrf":
    A__           =				torch.tensor([0.5_306, -0.0_483, -0.3_928]     )
  assert torch.allclose(outputs.logits[0, :3]		,					UpperCAmelCase_		,					atol=1e-4     )
  print("""Looks ok!"""     )

  if pytorch_dump_folder_path is not None:
    print(F"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}"""     )
    model.save_pretrained(UpperCAmelCase_     )
    processor.save_pretrained(UpperCAmelCase_     )

  if push_to_hub:
    print(F"""Pushing model and processor of {model_name} to the hub..."""     )
    model.push_to_hub(F"""{model_name}"""     )
    processor.push_to_hub(F"""{model_name}"""     )


if __name__ == "__main__":
      lowerCamelCase__      = argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--model_name",
          default="focalnet-tiny",
          type=str,
          help="Name of the FocalNet model you'd like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
      )
      parser.add_argument(
          "--push_to_hub",
          action="store_true",
          help="Whether to push the model and processor to the hub.",
      )

      lowerCamelCase__      = parser.parse_args()
      convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






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 YolosImageProcessor


class 			lowercase_		(      unittest.TestCase       ):


       def __init__(    self						,       __A						,       __A=7						,       __A=3						,       __A=30						,       __A=400						,       __A=True						,       __A=None						,       __A=True						,       __A=[0.5, 0.5, 0.5]						,       __A=[0.5, 0.5, 0.5]						,       __A=True						,       __A=1 / 255						,       __A=True						,       )						->    Tuple:
              # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
              SCREAMING_SNAKE_CASE_							:				Any		=size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
              SCREAMING_SNAKE_CASE_							:				Dict		=parent
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=batch_size
              SCREAMING_SNAKE_CASE_							:				List[Any]		=num_channels
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=min_resolution
              SCREAMING_SNAKE_CASE_							:				str		=max_resolution
              SCREAMING_SNAKE_CASE_							:				int		=do_resize
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=size
              SCREAMING_SNAKE_CASE_							:				str		=do_normalize
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=image_mean
              SCREAMING_SNAKE_CASE_							:				Any		=image_std
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=do_rescale
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=rescale_factor
              SCREAMING_SNAKE_CASE_							:				str		=do_pad


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

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

              return expected_height, expected_width






@require_torch
@require_vision
class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						YolosImageProcessor if is_vision_available() else None


       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				str		=YolosImageProcessingTester(self				)


       @property
       def 	_snake_case				(    self				)						->    Dict:
              return self.image_processor_tester.prepare_image_processor_dict()


       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.image_processing_class(**self.image_processor_dict				)
              self.assertTrue(hasattr(__A						,       '''image_mean'''				)				)
              self.assertTrue(hasattr(__A						,       '''image_std'''				)				)
              self.assertTrue(hasattr(__A						,       '''do_normalize'''				)				)
              self.assertTrue(hasattr(__A						,       '''do_resize'''				)				)
              self.assertTrue(hasattr(__A						,       '''size'''				)				)


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				str		=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						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.image_processing_class.from_dict(
                  self.image_processor_dict						,       size=42						,       max_size=84						,       pad_and_return_pixel_mask=__A				)
              self.assertEqual(image_processor.size						,       {'''shortest_edge''': 42, '''longest_edge''': 84}				)
              self.assertEqual(image_processor.do_pad						,       __A				)


       def 	_snake_case				(    self				)						->    List[str]:
              pass


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

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

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.image_processor_tester.get_expected_values(__A				)

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

              # Test batched
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[str]		=self.image_processor_tester.get_expected_values(__A						,       batched=__A				)

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

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

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.image_processor_tester.get_expected_values(__A				)

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

              # Test batched
              SCREAMING_SNAKE_CASE_							:				Dict		=image_processing(__A						,       return_tensors='''pt'''				).pixel_values

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.image_processor_tester.get_expected_values(__A						,       batched=__A				)

              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				)						->    Dict:
              # Initialize image_processing
              SCREAMING_SNAKE_CASE_							:				str		=self.image_processing_class(**self.image_processor_dict				)
              # create random PyTorch tensors
              SCREAMING_SNAKE_CASE_							:				Dict		=prepare_image_inputs(self.image_processor_tester						,       equal_resolution=__A						,       torchify=__A				)
              for image in image_inputs:
                     self.assertIsInstance(__A						,       torch.Tensor				)

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

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.image_processor_tester.get_expected_values(__A				)

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

              # Test batched
              SCREAMING_SNAKE_CASE_							:				str		=image_processing(__A						,       return_tensors='''pt'''				).pixel_values

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.image_processor_tester.get_expected_values(__A						,       batched=__A				)

              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				)						->    List[Any]:
              # Initialize image_processings
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.image_processing_class(**self.image_processor_dict				)
              SCREAMING_SNAKE_CASE_							:				str		=self.image_processing_class(do_resize=__A						,       do_normalize=__A						,       do_rescale=__A				)
              # create random PyTorch tensors
              SCREAMING_SNAKE_CASE_							:				Dict		=prepare_image_inputs(self.image_processor_tester						,       equal_resolution=__A						,       torchify=__A				)
              for image in image_inputs:
                     self.assertIsInstance(__A						,       torch.Tensor				)

              # Test whether the method "pad" and calling the image processor return the same tensors
              SCREAMING_SNAKE_CASE_							:				List[Any]		=image_processing_a.pad(__A						,       return_tensors='''pt'''				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=image_processing_a(__A						,       return_tensors='''pt'''				)

              self.assertTrue(
                  torch.allclose(encoded_images_with_method['''pixel_values''']						,       encoded_images['''pixel_values''']						,       atol=1e-4				)				)


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

              SCREAMING_SNAKE_CASE_							:				Dict		={'''image_id''': 39_769, '''annotations''': target}

              # encode them
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''				)
              SCREAMING_SNAKE_CASE_							:				Dict		=image_processing(images=__A						,       annotations=__A						,       return_tensors='''pt'''				)

              # verify pixel values
              SCREAMING_SNAKE_CASE_							:				Dict		=torch.Size([1, 3, 800, 1_066]				)
              self.assertEqual(encoding['''pixel_values'''].shape						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.tensor([0.2_796, 0.3_138, 0.3_481]				)
              self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]						,       __A						,       atol=1e-4				)				)

              # verify area
              SCREAMING_SNAKE_CASE_							:				Tuple		=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''']						,       __A				)				)
              # verify boxes
              SCREAMING_SNAKE_CASE_							:				Dict		=torch.Size([6, 4]				)
              self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,       __A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]						,       __A						,       atol=1e-3				)				)
              # verify image_id
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.tensor([39_769]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,       __A				)				)
              # verify is_crowd
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.tensor([0, 0, 0, 0, 0, 0]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,       __A				)				)
              # verify class_labels
              SCREAMING_SNAKE_CASE_							:				str		=torch.tensor([75, 75, 63, 65, 17, 17]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,       __A				)				)
              # verify orig_size
              SCREAMING_SNAKE_CASE_							:				str		=torch.tensor([480, 640]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,       __A				)				)
              # verify size
              SCREAMING_SNAKE_CASE_							:				Dict		=torch.tensor([800, 1_066]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,       __A				)				)





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

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		={'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

              SCREAMING_SNAKE_CASE_							:				Tuple		=pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''				)

              # encode them
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=YolosImageProcessor(format='''coco_panoptic'''				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=image_processing(images=__A						,       annotations=__A						,       masks_path=__A						,       return_tensors='''pt'''				)

              # verify pixel values
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.Size([1, 3, 800, 1_066]				)
              self.assertEqual(encoding['''pixel_values'''].shape						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.tensor([0.2_796, 0.3_138, 0.3_481]				)
              self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]						,       __A						,       atol=1e-4				)				)

              # verify area
              SCREAMING_SNAKE_CASE_							:				List[Any]		=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''']						,       __A				)				)
              # verify boxes
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.Size([6, 4]				)
              self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,       __A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]						,       __A						,       atol=1e-3				)				)
              # verify image_id
              SCREAMING_SNAKE_CASE_							:				Any		=torch.tensor([39_769]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,       __A				)				)
              # verify is_crowd
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.tensor([0, 0, 0, 0, 0, 0]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,       __A				)				)
              # verify class_labels
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.tensor([17, 17, 63, 75, 75, 93]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,       __A				)				)
              # verify masks
              SCREAMING_SNAKE_CASE_							:				List[Any]		=822_873
              self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()						,       __A				)
              # verify orig_size
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.tensor([480, 640]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,       __A				)				)
              # verify size
              SCREAMING_SNAKE_CASE_							:				Any		=torch.tensor([800, 1_066]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,       __A				)				)
