





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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase 				=     logging.get_logger(__name__)



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	YolosConfig()

   # size of the architecture
   if "yolos_ti" in yolos_name:
      A_     :				Dict			      =	1_9_2
      A_     :				Tuple			      =	7_6_8
      A_     :				Dict			      =	1_2
      A_     :				int			      =	3
      A_     :				Tuple			      =	[8_0_0, 1_3_3_3]
      A_     :				Any			      =	False
   elif yolos_name == "yolos_s_dWr":
      A_     :				Optional[Any]			      =	3_3_0
      A_     :				List[str]			      =	1_4
      A_     :				Dict			      =	6
      A_     :				Tuple			      =	1_3_2_0
   elif "yolos_s" in yolos_name:
      A_     :				List[Any]			      =	3_8_4
      A_     :				List[Any]			      =	1_5_3_6
      A_     :				int			      =	1_2
      A_     :				int			      =	6
   elif "yolos_b" in yolos_name:
      A_     :				Tuple			      =	[8_0_0, 1_3_4_4]

   A_     :				str			      =	9_1
   A_     :				Optional[int]			      =	"""huggingface/label-files"""
   A_     :				List[Any]			      =	"""coco-detection-id2label.json"""
   A_     :				Optional[int]			      =	json.load(open(hf_hub_download(_lowerCAmelCase  ,_lowerCAmelCase  ,repo_type="""dataset"""     )  ,"""r"""     )     )
   A_     :				Optional[int]			      =	{int(_lowerCAmelCase     ): v for k, v in idalabel.items()}
   A_     :				Dict			      =	idalabel
   A_     :				Dict			      =	{v: k for k, v in idalabel.items()}

   return config



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase = False     ):
   '''simple docstring'''


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



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   if "backbone" in name:
      A_     :				Tuple			      =	name.replace("""backbone"""  ,"""vit"""     )
   if "cls_token" in name:
      A_     :				List[str]			      =	name.replace("""cls_token"""  ,"""embeddings.cls_token"""     )
   if "det_token" in name:
      A_     :				List[str]			      =	name.replace("""det_token"""  ,"""embeddings.detection_tokens"""     )
   if "mid_pos_embed" in name:
      A_     :				int			      =	name.replace("""mid_pos_embed"""  ,"""encoder.mid_position_embeddings"""     )
   if "pos_embed" in name:
      A_     :				Optional[int]			      =	name.replace("""pos_embed"""  ,"""embeddings.position_embeddings"""     )
   if "patch_embed.proj" in name:
      A_     :				int			      =	name.replace("""patch_embed.proj"""  ,"""embeddings.patch_embeddings.projection"""     )
   if "blocks" in name:
      A_     :				Union[str, Any]			      =	name.replace("""blocks"""  ,"""encoder.layer"""     )
   if "attn.proj" in name:
      A_     :				Dict			      =	name.replace("""attn.proj"""  ,"""attention.output.dense"""     )
   if "attn" in name:
      A_     :				int			      =	name.replace("""attn"""  ,"""attention.self"""     )
   if "norm1" in name:
      A_     :				List[Any]			      =	name.replace("""norm1"""  ,"""layernorm_before"""     )
   if "norm2" in name:
      A_     :				str			      =	name.replace("""norm2"""  ,"""layernorm_after"""     )
   if "mlp.fc1" in name:
      A_     :				int			      =	name.replace("""mlp.fc1"""  ,"""intermediate.dense"""     )
   if "mlp.fc2" in name:
      A_     :				Any			      =	name.replace("""mlp.fc2"""  ,"""output.dense"""     )
   if "class_embed" in name:
      A_     :				Dict			      =	name.replace("""class_embed"""  ,"""class_labels_classifier"""     )
   if "bbox_embed" in name:
      A_     :				List[str]			      =	name.replace("""bbox_embed"""  ,"""bbox_predictor"""     )
   if "vit.norm" in name:
      A_     :				int			      =	name.replace("""vit.norm"""  ,"""vit.layernorm"""     )

   return name



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   for key in orig_state_dict.copy().keys():
      A_     :				int			      =	orig_state_dict.pop(_lowerCAmelCase     )

      if "qkv" in key:
         A_     :				int			      =	key.split("""."""     )
         A_     :				Optional[int]			      =	int(key_split[2]     )
         A_     :				int			      =	model.vit.encoder.layer[layer_num].attention.attention.all_head_size
         if "weight" in key:
            A_     :				Union[str, Any]			      =	val[:dim, :]
            A_     :				Any			      =	val[
                dim : dim * 2, :
            ]
            A_     :				Any			      =	val[-dim:, :]
         else:
            A_     :				Optional[int]			      =	val[:dim]
            A_     :				Dict			      =	val[dim : dim * 2]
            A_     :				Union[str, Any]			      =	val[-dim:]
      else:
         A_     :				Union[str, Any]			      =	val

   return orig_state_dict



def     _lowerCAmelCase		(  ):
   '''simple docstring'''


   A_     :				Dict			      =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   A_     :				int			      =	Image.open(requests.get(_lowerCAmelCase  ,stream=_lowerCAmelCase     ).raw     )
   return im



@torch.no_grad()
def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase = False     ):
   '''simple docstring'''


   A_     :				Optional[Any]			      =	get_yolos_config(_lowerCAmelCase     )

   # load original state_dict
   A_     :				List[str]			      =	torch.load(_lowerCAmelCase  ,map_location="""cpu"""     )["""model"""]

   # load 🤗 model
   A_     :				List[str]			      =	YolosForObjectDetection(_lowerCAmelCase     )
   model.eval()
   A_     :				str			      =	convert_state_dict(_lowerCAmelCase  ,_lowerCAmelCase     )
   model.load_state_dict(_lowerCAmelCase     )

   # Check outputs on an image, prepared by YolosImageProcessor
   A_     :				int			      =	8_0_0 if yolos_name != """yolos_ti""" else 5_1_2
   A_     :				List[str]			      =	YolosImageProcessor(format="""coco_detection"""  ,size=_lowerCAmelCase     )
   A_     :				Optional[int]			      =	image_processor(images=prepare_img()  ,return_tensors="""pt"""     )
   A_     :				List[str]			      =	model(**_lowerCAmelCase     )
   A_     :				Tuple			      =	outputs.logits, outputs.pred_boxes

   A_     :				Optional[int]			      =	None, None
   if yolos_name == "yolos_ti":
      A_     :				str			      =	torch.tensor(
          [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]     )
      A_     :				Union[str, Any]			      =	torch.tensor(
          [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]     )
   elif yolos_name == "yolos_s_200_pre":
      A_     :				Optional[Any]			      =	torch.tensor(
          [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]     )
      A_     :				Optional[int]			      =	torch.tensor(
          [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]     )
   elif yolos_name == "yolos_s_300_pre":
      A_     :				str			      =	torch.tensor(
          [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]     )
      A_     :				Tuple			      =	torch.tensor(
          [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]     )
   elif yolos_name == "yolos_s_dWr":
      A_     :				Optional[int]			      =	torch.tensor(
          [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]     )
      A_     :				int			      =	torch.tensor(
          [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]     )
   elif yolos_name == "yolos_base":
      A_     :				Optional[int]			      =	torch.tensor(
          [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]     )
      A_     :				List[Any]			      =	torch.tensor(
          [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]     )
   else:
      raise ValueError(f"""Unknown yolos_name: {yolos_name}"""     )

   assert torch.allclose(logits[0, :3, :3]  ,_lowerCAmelCase  ,atol=1e-4     )
   assert torch.allclose(pred_boxes[0, :3, :3]  ,_lowerCAmelCase  ,atol=1e-4     )

   Path(_lowerCAmelCase     ).mkdir(exist_ok=_lowerCAmelCase     )
   print(f"""Saving model {yolos_name} to {pytorch_dump_folder_path}"""     )
   model.save_pretrained(_lowerCAmelCase     )
   print(f"""Saving image processor to {pytorch_dump_folder_path}"""     )
   image_processor.save_pretrained(_lowerCAmelCase     )

   if push_to_hub:
      A_     :				Dict			      =	{
          """yolos_ti""": """yolos-tiny""",
          """yolos_s_200_pre""": """yolos-small""",
          """yolos_s_300_pre""": """yolos-small-300""",
          """yolos_s_dWr""": """yolos-small-dwr""",
          """yolos_base""": """yolos-base""",
      }

      print("""Pushing to the hub..."""     )
      A_     :				str			      =	model_mapping[yolos_name]
      image_processor.push_to_hub(_lowerCAmelCase  ,organization="""hustvl"""     )
      model.push_to_hub(_lowerCAmelCase  ,organization="""hustvl"""     )


if __name__ == "__main__":
      _lowerCAmelCase 				=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--yolos_name""",
          default="""yolos_s_200_pre""",
          type=str,
          help=(
              """Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"""
              """ 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."""
          ),
      )
      parser.add_argument(
          """--checkpoint_path""", default=None, type=str, help="""Path to the original state dict (.pth file)."""
      )
      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 or not to push the converted model to the 🤗 hub."""
      )

      _lowerCAmelCase 				=     parser.parse_args()
      convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, 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 DetaImageProcessor

class    _UpperCAmelCase							(		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      ,       ):
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         A_     :				Optional[int]			      =	size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
         A_     :				str			      =	parent
         A_     :				Dict			      =	batch_size
         A_     :				str			      =	num_channels
         A_     :				List[str]			      =	min_resolution
         A_     :				List[Any]			      =	max_resolution
         A_     :				Optional[Any]			      =	do_resize
         A_     :				int			      =	size
         A_     :				List[Any]			      =	do_normalize
         A_     :				List[Any]			      =	image_mean
         A_     :				Dict			      =	image_std
         A_     :				Tuple			      =	do_rescale
         A_     :				List[Any]			      =	rescale_factor
         A_     :				Tuple			      =	do_pad
      def 		_lowerCamelCase      (		self       ):
         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 		_lowerCamelCase      (		self      ,       a__      ,       a__=False       ):
         if not batched:
            A_     :				List[str]			      =	image_inputs[0]
            if isinstance(a__      ,       Image.Image       ):
               A_      ,				A_     :				Tuple			      =	image.size
            else:
               A_      ,				A_     :				List[Any]			      =	image.shape[1], image.shape[2]
            if w < h:
               A_     :				List[str]			      =	int(self.size["""shortest_edge"""] * h / w       )
               A_     :				Tuple			      =	self.size["""shortest_edge"""]
            elif w > h:
               A_     :				Tuple			      =	self.size["""shortest_edge"""]
               A_     :				Union[str, Any]			      =	int(self.size["""shortest_edge"""] * w / h       )
            else:
               A_     :				Optional[Any]			      =	self.size["""shortest_edge"""]
               A_     :				int			      =	self.size["""shortest_edge"""]

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

         return expected_height, expected_width






@require_torch
@require_vision
class    _UpperCAmelCase							(		_lowerCamelCase			,       unittest.TestCase     ):
      a						       =							DetaImageProcessor if is_vision_available() else None
      def 		_lowerCamelCase      (		self       ):
         A_     :				str			      =	DetaImageProcessingTester(self       )
      @property
      def 		_lowerCamelCase      (		self       ):
         return self.image_processor_tester.prepare_image_processor_dict()
      def 		_lowerCamelCase      (		self       ):
         A_     :				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__      ,       """do_rescale"""       )       )
         self.assertTrue(hasattr(a__      ,       """do_pad"""       )       )
         self.assertTrue(hasattr(a__      ,       """size"""       )       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size      ,       {"""shortest_edge""": 18, """longest_edge""": 1333}       )
         self.assertEqual(image_processor.do_pad      ,       a__       )
      def 		_lowerCamelCase      (		self       ):
         pass
      def 		_lowerCamelCase      (		self       ):
         # Initialize image_processing
         A_     :				List[Any]			      =	self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         A_     :				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
         A_     :				List[Any]			      =	image_processing(image_inputs[0]      ,       return_tensors="""pt"""       ).pixel_values

         A_      ,				A_     :				Optional[int]			      =	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
         A_      ,				A_     :				Optional[Any]			      =	self.image_processor_tester.get_expected_values(a__      ,       batched=a__       )

         A_     :				Optional[int]			      =	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 		_lowerCamelCase      (		self       ):
         # Initialize image_processing
         A_     :				Any			      =	self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         A_     :				int			      =	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
         A_     :				Union[str, Any]			      =	image_processing(image_inputs[0]      ,       return_tensors="""pt"""       ).pixel_values

         A_      ,				A_     :				Tuple			      =	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
         A_     :				Optional[int]			      =	image_processing(a__      ,       return_tensors="""pt"""       ).pixel_values

         A_      ,				A_     :				Union[str, 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 		_lowerCamelCase      (		self       ):
         # Initialize image_processing
         A_     :				List[str]			      =	self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         A_     :				Union[str, Any]			      =	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
         A_     :				Optional[Any]			      =	image_processing(image_inputs[0]      ,       return_tensors="""pt"""       ).pixel_values

         A_      ,				A_     :				str			      =	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
         A_     :				str			      =	image_processing(a__      ,       return_tensors="""pt"""       ).pixel_values

         A_      ,				A_     :				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,
             )      ,       )
      @slow
      def 		_lowerCamelCase      (		self       ):
         # prepare image and target
         A_     :				int			      =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
         with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""      ,       """r"""       ) as f:
            A_     :				int			      =	json.loads(f.read()       )

         A_     :				Union[str, Any]			      =	{"""image_id""": 39769, """annotations""": target}

         # encode them
         A_     :				str			      =	DetaImageProcessor()
         A_     :				int			      =	image_processing(images=a__      ,       annotations=a__      ,       return_tensors="""pt"""       )

         # verify pixel values
         A_     :				List[str]			      =	torch.Size([1, 3, 800, 1066]       )
         self.assertEqual(encoding["""pixel_values"""].shape      ,       a__       )

         A_     :				Tuple			      =	torch.tensor([0.2796, 0.3138, 0.3481]       )
         self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,       a__      ,       atol=1E-4       )       )

         # verify area
         A_     :				int			      =	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"""]      ,       a__       )       )
         # verify boxes
         A_     :				str			      =	torch.Size([6, 4]       )
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,       a__       )
         A_     :				List[Any]			      =	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,       a__      ,       atol=1E-3       )       )
         # verify image_id
         A_     :				Optional[Any]			      =	torch.tensor([39769]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,       a__       )       )
         # verify is_crowd
         A_     :				str			      =	torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,       a__       )       )
         # verify class_labels
         A_     :				Optional[int]			      =	torch.tensor([75, 75, 63, 65, 17, 17]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,       a__       )       )
         # verify orig_size
         A_     :				Dict			      =	torch.tensor([480, 640]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,       a__       )       )
         # verify size
         A_     :				Tuple			      =	torch.tensor([800, 1066]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,       a__       )       )






      @slow
      def 		_lowerCamelCase      (		self       ):
         # prepare image, target and masks_path
         A_     :				Any			      =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
         with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""      ,       """r"""       ) as f:
            A_     :				Any			      =	json.loads(f.read()       )

         A_     :				Any			      =	{"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

         A_     :				Tuple			      =	pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""       )

         # encode them
         A_     :				str			      =	DetaImageProcessor(format="""coco_panoptic"""       )
         A_     :				Tuple			      =	image_processing(images=a__      ,       annotations=a__      ,       masks_path=a__      ,       return_tensors="""pt"""       )

         # verify pixel values
         A_     :				str			      =	torch.Size([1, 3, 800, 1066]       )
         self.assertEqual(encoding["""pixel_values"""].shape      ,       a__       )

         A_     :				Tuple			      =	torch.tensor([0.2796, 0.3138, 0.3481]       )
         self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,       a__      ,       atol=1E-4       )       )

         # verify area
         A_     :				Dict			      =	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"""]      ,       a__       )       )
         # verify boxes
         A_     :				List[Any]			      =	torch.Size([6, 4]       )
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,       a__       )
         A_     :				List[str]			      =	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,       a__      ,       atol=1E-3       )       )
         # verify image_id
         A_     :				int			      =	torch.tensor([39769]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,       a__       )       )
         # verify is_crowd
         A_     :				str			      =	torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,       a__       )       )
         # verify class_labels
         A_     :				Union[str, Any]			      =	torch.tensor([17, 17, 63, 75, 75, 93]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,       a__       )       )
         # verify masks
         A_     :				Tuple			      =	822873
         self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()      ,       a__       )
         # verify orig_size
         A_     :				Union[str, Any]			      =	torch.tensor([480, 640]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,       a__       )       )
         # verify size
         A_     :				Union[str, Any]			      =	torch.tensor([800, 1066]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,       a__       )       )






