


"""simple docstring"""






import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


__snake_case		=       (
    '''https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'''
)


__snake_case		=       logging.get_logger(__name__)  # pylint: disable=invalid-name
def       A_   (   ):






     """simple docstring"""

     _a    	=				'''https://pypi.org/pypi/diffusers/json'''
     _a    	=				json.loads(request.urlopen(_lowerCAmelCase       ).read()       )['''releases'''].keys()
     return sorted(_lowerCAmelCase,     key=lambda _lowerCAmelCase       : version.Version(_lowerCAmelCase       )       )
def       A_   (   ):






     """simple docstring"""

     if HF_MODULES_CACHE in sys.path:
          return

     sys.path.append(_lowerCAmelCase       )
     os.makedirs(_lowerCAmelCase,     exist_ok=_lowerCAmelCase       )
     _a    	=				Path(_lowerCAmelCase       ) / '''__init__.py'''
     if not init_path.exists():
          init_path.touch()
def       A_   (   _lowerCAmelCase     :					Union[str, os.PathLike]       ):






     """simple docstring"""

     init_hf_modules()
     _a    	=				Path(_lowerCAmelCase       ) / name
     # If the parent module does not exist yet, recursively create it.
     if not dynamic_module_path.parent.exists():
          create_dynamic_module(dynamic_module_path.parent       )
     os.makedirs(_lowerCAmelCase,     exist_ok=_lowerCAmelCase       )
     _a    	=				dynamic_module_path / '''__init__.py'''
     if not init_path.exists():
          init_path.touch()
def       A_   (   _lowerCAmelCase     :					Optional[Any]       ):






     """simple docstring"""

     with open(_lowerCAmelCase,     '''r''',     encoding='''utf-8'''       ) as f:
          _a    	=				f.read()

     # Imports of the form `import .xxx`
     _a    	=				re.findall('''^\s*import\s+\.(\S+)\s*$''',     _lowerCAmelCase,     flags=re.MULTILINE       )
     # Imports of the form `from .xxx import yyy`
     relative_imports += re.findall('''^\s*from\s+\.(\S+)\s+import''',     _lowerCAmelCase,     flags=re.MULTILINE       )
     # Unique-ify
     return list(set(_lowerCAmelCase       )       )
def       A_   (   _lowerCAmelCase     :					int       ):






     """simple docstring"""

     _a    	=				False
     _a    	=				[module_file]
     _a    	=				[]

     # Let's recurse through all relative imports
     while not no_change:
          _a    	=				[]
          for f in files_to_check:
               new_imports.extend(get_relative_imports(_lowerCAmelCase       )       )

          _a    	=				Path(_lowerCAmelCase       ).parent
          _a    	=				[str(module_path / m       ) for m in new_imports]
          _a    	=				[f for f in new_import_files if f not in all_relative_imports]
          _a    	=				[f'{f}.py' for f in new_import_files]

          _a    	=				len(_lowerCAmelCase       ) == 0
          all_relative_imports.extend(_lowerCAmelCase       )

     return all_relative_imports
def       A_   (   _lowerCAmelCase     :					List[Any]       ):






     """simple docstring"""

     with open(_lowerCAmelCase,     '''r''',     encoding='''utf-8'''       ) as f:
          _a    	=				f.read()

     # Imports of the form `import xxx`
     _a    	=				re.findall('''^\s*import\s+(\S+)\s*$''',     _lowerCAmelCase,     flags=re.MULTILINE       )
     # Imports of the form `from xxx import yyy`
     imports += re.findall('''^\s*from\s+(\S+)\s+import''',     _lowerCAmelCase,     flags=re.MULTILINE       )
     # Only keep the top-level module
     _a    	=				[imp.split('''.'''       )[0] for imp in imports if not imp.startswith('''.'''       )]

     # Unique-ify and test we got them all
     _a    	=				list(set(_lowerCAmelCase       )       )
     _a    	=				[]
     for imp in imports:
          try:
               importlib.import_module(_lowerCAmelCase       )
          except ImportError:
               missing_packages.append(_lowerCAmelCase       )

     if len(_lowerCAmelCase       ) > 0:
          raise ImportError(
              '''This modeling file requires the following packages that were not found in your environment: '''
              f'{", ".join(_lowerCAmelCase       )}. Run `pip install {" ".join(_lowerCAmelCase       )}`'       )

     return get_relative_imports(_lowerCAmelCase       )
def       A_   (   _lowerCAmelCase     :					Union[str, Any],     _lowerCAmelCase     :					List[str]       ):






     """simple docstring"""

     _a    	=				module_path.replace(os.path.sep,     '''.'''       )
     _a    	=				importlib.import_module(_lowerCAmelCase       )

     if class_name is None:
          return find_pipeline_class(_lowerCAmelCase       )
     return getattr(_lowerCAmelCase,     _lowerCAmelCase       )
def       A_   (   _lowerCAmelCase     :					Any       ):






     """simple docstring"""

     from ..pipelines import DiffusionPipeline

     _a    	=				dict(inspect.getmembers(_lowerCAmelCase,     inspect.isclass       )       )

     _a    	=				None
     for cls_name, cls in cls_members.items():
          if (
              cls_name != DiffusionPipeline.__name__
              and issubclass(cls,     _lowerCAmelCase       )
              and cls.__module__.split('''.'''       )[0] != "diffusers"
          ):
               if pipeline_class is not None:
                    raise ValueError(
                        f'Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'
                        f' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'
                        f' {loaded_module}.'       )
               _a    	=				cls

     return pipeline_class
def       A_   (   _lowerCAmelCase     :					Union[str, os.PathLike],     _lowerCAmelCase     :					str,     _lowerCAmelCase     :					Optional[Union[str, os.PathLike]] = None,     _lowerCAmelCase     :					bool = False,     _lowerCAmelCase     :					bool = False,     _lowerCAmelCase     :					Optional[Dict[str, str]] = None,     _lowerCAmelCase     :					Optional[Union[bool, str]] = None,     _lowerCAmelCase     :					Optional[str] = None,     _lowerCAmelCase     :					bool = False,     ):






     """simple docstring"""

     _a    	=				str(_lowerCAmelCase       )

     _a    	=				os.path.join(_lowerCAmelCase,     _lowerCAmelCase       )

     if os.path.isfile(_lowerCAmelCase       ):
          _a    	=				module_file_or_url
          _a    	=				'''local'''
     elif pretrained_model_name_or_path.count('''/'''       ) == 0:
          _a    	=				get_diffusers_versions()
          # cut ".dev0"
          _a    	=				'''v''' + '''.'''.join(__version__.split('''.'''       )[:3]       )

          # retrieve github version that matches
          if revision is None:
               _a    	=				latest_version if latest_version[1:] in available_versions else '''main'''
               logger.info(f'Defaulting to latest_version: {revision}.'       )
          elif revision in available_versions:
               _a    	=				f'v{revision}'
          elif revision == "main":
               _a    	=				revision
          else:
               raise ValueError(
                   f'`custom_revision`: {revision} does not exist. Please make sure to choose one of'
                   f' {", ".join(available_versions + ["main"]       )}.'       )

          # community pipeline on GitHub
          _a    	=				COMMUNITY_PIPELINES_URL.format(revision=_lowerCAmelCase,     pipeline=_lowerCAmelCase       )
          try:
               _a    	=				cached_download(
                   _lowerCAmelCase,     cache_dir=_lowerCAmelCase,     force_download=_lowerCAmelCase,     proxies=_lowerCAmelCase,     resume_download=_lowerCAmelCase,     local_files_only=_lowerCAmelCase,     use_auth_token=_lowerCAmelCase,     )
               _a    	=				'''git'''
               _a    	=				pretrained_model_name_or_path + '''.py'''
          except EnvironmentError:
               logger.error(f'Could not locate the {module_file} inside {pretrained_model_name_or_path}.'       )
               raise
     else:
          try:
               # Load from URL or cache if already cached
               _a    	=				hf_hub_download(
                   _lowerCAmelCase,     _lowerCAmelCase,     cache_dir=_lowerCAmelCase,     force_download=_lowerCAmelCase,     proxies=_lowerCAmelCase,     resume_download=_lowerCAmelCase,     local_files_only=_lowerCAmelCase,     use_auth_token=_lowerCAmelCase,     )
               _a    	=				os.path.join('''local''',     '''--'''.join(pretrained_model_name_or_path.split('''/'''       )       )       )
          except EnvironmentError:
               logger.error(f'Could not locate the {module_file} inside {pretrained_model_name_or_path}.'       )
               raise

    # Check we have all the requirements in our environment
     _a    	=				check_imports(_lowerCAmelCase       )

     # Now we move the module inside our cached dynamic modules.
     _a    	=				DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
     create_dynamic_module(_lowerCAmelCase       )
     _a    	=				Path(_lowerCAmelCase       ) / full_submodule
     if submodule == "local" or submodule == "git":
          # We always copy local files (we could hash the file to see if there was a change, and give them the name of
          # that hash, to only copy when there is a modification but it seems overkill for now).
          # The only reason we do the copy is to avoid putting too many folders in sys.path.
          shutil.copy(_lowerCAmelCase,     submodule_path / module_file       )
          for module_needed in modules_needed:
               _a    	=				f'{module_needed}.py'
               shutil.copy(os.path.join(_lowerCAmelCase,     _lowerCAmelCase       ),     submodule_path / module_needed       )
     else:
          # Get the commit hash
          # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
          if isinstance(_lowerCAmelCase,     _lowerCAmelCase       ):
               _a    	=				use_auth_token
          elif use_auth_token is True:
               _a    	=				HfFolder.get_token()
          else:
               _a    	=				None

          _a    	=				model_info(_lowerCAmelCase,     revision=_lowerCAmelCase,     token=_lowerCAmelCase       ).sha

          # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
          # benefit of versioning.
          _a    	=				submodule_path / commit_hash
          _a    	=				full_submodule + os.path.sep + commit_hash
          create_dynamic_module(_lowerCAmelCase       )

          if not (submodule_path / module_file).exists():
               shutil.copy(_lowerCAmelCase,     submodule_path / module_file       )
          # Make sure we also have every file with relative
          for module_needed in modules_needed:
               if not (submodule_path / module_needed).exists():
                    get_cached_module_file(
                        _lowerCAmelCase,     f'{module_needed}.py',     cache_dir=_lowerCAmelCase,     force_download=_lowerCAmelCase,     resume_download=_lowerCAmelCase,     proxies=_lowerCAmelCase,     use_auth_token=_lowerCAmelCase,     revision=_lowerCAmelCase,     local_files_only=_lowerCAmelCase,     )
     return os.path.join(_lowerCAmelCase,     _lowerCAmelCase       )

def       A_   (   _lowerCAmelCase     :					Union[str, os.PathLike],     _lowerCAmelCase     :					str,     _lowerCAmelCase     :					Optional[str] = None,     _lowerCAmelCase     :					Optional[Union[str, os.PathLike]] = None,     _lowerCAmelCase     :					bool = False,     _lowerCAmelCase     :					bool = False,     _lowerCAmelCase     :					Optional[Dict[str, str]] = None,     _lowerCAmelCase     :					Optional[Union[bool, str]] = None,     _lowerCAmelCase     :					Optional[str] = None,     _lowerCAmelCase     :					bool = False,     **_lowerCAmelCase     :					Any,     ):






     """simple docstring"""

     _a    	=				get_cached_module_file(
         _lowerCAmelCase,     _lowerCAmelCase,     cache_dir=_lowerCAmelCase,     force_download=_lowerCAmelCase,     resume_download=_lowerCAmelCase,     proxies=_lowerCAmelCase,     use_auth_token=_lowerCAmelCase,     revision=_lowerCAmelCase,     local_files_only=_lowerCAmelCase,     )
     return get_class_in_module(_lowerCAmelCase,     final_module.replace('''.py''',     ''''''       )       )



"""simple docstring"""






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import DeformableDetrImageProcessor


class     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=7    ,				__UpperCAmelCase=3    ,				__UpperCAmelCase=30    ,				__UpperCAmelCase=400    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=[0.5, 0.5, 0.5]    ,				__UpperCAmelCase=[0.5, 0.5, 0.5]    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=1 / 255    ,				__UpperCAmelCase=True    ,				)       ->							Optional[int]:
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          _a    	=				size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
          _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 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_rescale": self.do_rescale,
              "rescale_factor": self.rescale_factor,
              "do_pad": self.do_pad,
          }





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=False							)       ->							List[str]:
          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     __lowerCamelCase  (					a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	List[str]    =		DeformableDetrImageProcessor if is_vision_available() else None


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				DeformableDetrImageProcessingTester(self							)


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


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _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 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				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    ,				__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 							_UpperCAmelCase      (      self							)       ->							int:
          pass


     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          # Initialize image_processing
          _a    	=				self.image_processing_class(**self.image_processor_dict							)
          # create random PIL images
          _a    	=				prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=__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 							_UpperCAmelCase      (      self							)       ->							int:
          # Initialize image_processing
          _a    	=				self.image_processing_class(**self.image_processor_dict							)
          # create random numpy tensors
          _a    	=				prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=__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 							_UpperCAmelCase      (      self							)       ->							Dict:
          # Initialize image_processing
          _a    	=				self.image_processing_class(**self.image_processor_dict							)
          # create random PyTorch tensors
          _a    	=				prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=__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 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          # prepare image and target
          _a    	=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
          with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''    ,				'''r'''							) as f:
               _a    	=				json.loads(f.read()							)

          _a    	=				{'''image_id''': 39769, '''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, 1066]							)
          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([39769]							)
          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, 1066]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,				__UpperCAmelCase							)							)





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

          _a    	=				{'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''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, 1066]							)
          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([39769]							)
          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    	=				822873
          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, 1066]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,				__UpperCAmelCase							)							)