
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase											=    logging.get_logger()


@dataclass
class 			lowercase_		:
       __lowerCamelCase				       =						42
       __lowerCamelCase				       =						field(default_factory=A       )
       __lowerCamelCase				       =						field(default_factory=A       )


       def 	_snake_case				(    self						,       __A						,       __A						,       __A				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Tuple		=len(list(m.modules()				)				) == 1 or isinstance(__A						,       nn.Convad				) or isinstance(__A						,       nn.BatchNormad				)
              if has_not_submodules:
                     self.traced.append(__A				)


       def __call__(    self						,       __A				)						->    List[Any]:
              for m in self.module.modules():
                     self.handles.append(m.register_forward_hook(self._forward_hook				)				)
              self.module(__A				)
              [x.remove() for x in self.handles]
              return self





       @property
       def 	_snake_case				(    self				)						->    Optional[int]:
              # check the len of the state_dict keys to see if we have learnable params
              return list(filter(lambda __A				: len(list(x.state_dict().keys()				)				) > 0						,       self.traced				)				)






@dataclass
class 			lowercase_		:
       __lowerCamelCase				       =						42
       __lowerCamelCase				       =						42
       __lowerCamelCase				       =						0
       __lowerCamelCase				       =						field(default_factory=A       )
       __lowerCamelCase				       =						field(default_factory=A       )


       def __call__(    self						,       __A				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=Tracker(self.dest				)(__A				).parametrized
              SCREAMING_SNAKE_CASE_							:				Dict		=Tracker(self.src				)(__A				).parametrized

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=list(filter(lambda __A				: type(__A				) not in self.src_skip						,       __A				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=list(filter(lambda __A				: type(__A				) not in self.dest_skip						,       __A				)				)

              if len(__A				) != len(__A				):
                     raise Exception(
                         F'Numbers of operations are different. Source module has {len(__A				)} operations while'
                         F' destination module has {len(__A				)}.'				)

              for dest_m, src_m in zip(__A						,       __A				):
                     dest_m.load_state_dict(src_m.state_dict()				)
                     if self.verbose == 1:
                            print(F'Transfered from={src_m} to={dest_m}'				)






def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     ResNetConfig			,							UpperCAmelCase_	:     Path			,							UpperCAmelCase_	:     bool = True				)			->						List[str]:
       print(f'Converting {name}...'				)
       with torch.no_grad():
              SCREAMING_SNAKE_CASE_							:				Any		=timm.create_model(UpperCAmelCase_			,							pretrained=UpperCAmelCase_				).eval()
              SCREAMING_SNAKE_CASE_							:				str		=ResNetForImageClassification(UpperCAmelCase_				).eval()
              SCREAMING_SNAKE_CASE_							:				Dict		=ModuleTransfer(src=UpperCAmelCase_			,							dest=UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.randn((1, 3, 2_2_4, 2_2_4)				)
              module_transfer(UpperCAmelCase_				)

       assert torch.allclose(from_model(UpperCAmelCase_				)			,							our_model(UpperCAmelCase_				).logits				), "The model logits don't match the original one."

       SCREAMING_SNAKE_CASE_							:				Dict		=f'resnet{"-".join(name.split("resnet"				)				)}'
       print(UpperCAmelCase_				)

       if push_to_hub:
              our_model.push_to_hub(
                  repo_path_or_name=save_directory / checkpoint_name			,							commit_message='''Add model'''			,							use_temp_dir=UpperCAmelCase_			,							)

              # we can use the convnext one
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''				)
              image_processor.push_to_hub(
                  repo_path_or_name=save_directory / checkpoint_name			,							commit_message='''Add image processor'''			,							use_temp_dir=UpperCAmelCase_			,							)

              print(f'Pushed {checkpoint_name}'				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Path			,							UpperCAmelCase_	:     str = None			,							UpperCAmelCase_	:     bool = True				)			->						Any:
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		='''imagenet-1k-id2label.json'''
       SCREAMING_SNAKE_CASE_							:				List[str]		=1_0_0_0
       SCREAMING_SNAKE_CASE_							:				List[str]		=(1, num_labels)

       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		='''huggingface/label-files'''
       SCREAMING_SNAKE_CASE_							:				Any		=num_labels
       SCREAMING_SNAKE_CASE_							:				Tuple		=json.load(open(hf_hub_download(UpperCAmelCase_			,							UpperCAmelCase_			,							repo_type='''dataset'''				)			,							'''r'''				)				)
       SCREAMING_SNAKE_CASE_							:				List[Any]		={int(UpperCAmelCase_				): v for k, v in idalabel.items()}

       SCREAMING_SNAKE_CASE_							:				List[str]		=idalabel
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		={v: k for k, v in idalabel.items()}

       SCREAMING_SNAKE_CASE_							:				str		=partial(UpperCAmelCase_			,							num_labels=UpperCAmelCase_			,							idalabel=UpperCAmelCase_			,							labelaid=UpperCAmelCase_				)

       SCREAMING_SNAKE_CASE_							:				List[Any]		={
           '''resnet18''': ImageNetPreTrainedConfig(
               depths=[2, 2, 2, 2]			,							hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]			,							layer_type='''basic'''				),
           '''resnet26''': ImageNetPreTrainedConfig(
               depths=[2, 2, 2, 2]			,							hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]			,							layer_type='''bottleneck'''				),
           '''resnet34''': ImageNetPreTrainedConfig(
               depths=[3, 4, 6, 3]			,							hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]			,							layer_type='''basic'''				),
           '''resnet50''': ImageNetPreTrainedConfig(
               depths=[3, 4, 6, 3]			,							hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]			,							layer_type='''bottleneck'''				),
           '''resnet101''': ImageNetPreTrainedConfig(
               depths=[3, 4, 2_3, 3]			,							hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]			,							layer_type='''bottleneck'''				),
           '''resnet152''': ImageNetPreTrainedConfig(
               depths=[3, 8, 3_6, 3]			,							hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]			,							layer_type='''bottleneck'''				),
       }

       if model_name:
              convert_weight_and_push(UpperCAmelCase_			,							names_to_config[model_name]			,							UpperCAmelCase_			,							UpperCAmelCase_				)
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_				)
       return config, expected_shape


if __name__ == "__main__":
 _lowercase											=    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default=None,
     type=str,
     help=(
         """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
         """ currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""",
     default=None,
     type=Path,
     required=True,
     help="""Path to the output PyTorch model directory.""",
 )
 parser.add_argument(
     """--push_to_hub""",
     default=True,
     type=bool,
     required=False,
     help="""If True, push model and image processor to the hub.""",
 )

 _lowercase											=    parser.parse_args()
 _lowercase											=    args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 convert_weights_and_push(pytorch_dump_folder_path, args.model_name, 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				)				)
