



'''simple docstring'''





import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")





def 						__snake_case(				_lowerCAmelCase		)		-> int:
      snake_case__  :    Optional[int]    						=      test_file.split(os.path.sep		)
      if components[0:2] != ["tests", "models"]:
            raise ValueError(
                """`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got """
                f"{test_file} instead."		)
      snake_case__  :    Dict    						=      components[-1]
      if not test_fn.endswith("""py"""		):
            raise ValueError(f"`test_file` should be a python file. Got {test_fn} instead."		)
      if not test_fn.startswith("""test_modeling_"""		):
            raise ValueError(
                f"`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."		)

      snake_case__  :    int    						=      components[:-1] + [test_fn.replace(""".py"""     ,   """"""		)]
      snake_case__  :    int    						=      """.""".join(_lowerCAmelCase		)

      return test_module_path





def 						__snake_case(				_lowerCAmelCase		)		-> List[str]:
      snake_case__  :    str    						=      get_module_path(_lowerCAmelCase		)
      snake_case__  :    Union[str, Any]    						=      importlib.import_module(_lowerCAmelCase		)

      return test_module





def 						__snake_case(				_lowerCAmelCase		)		-> int:
      snake_case__  :    List[Any]    						=      []
      snake_case__  :    Optional[int]    						=      get_test_module(_lowerCAmelCase		)
      for attr in dir(_lowerCAmelCase		):
            if attr.endswith("""ModelTester"""		):
                  tester_classes.append(getattr(_lowerCAmelCase     ,   _lowerCAmelCase		)		)

    # sort with class names
      return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase		: x.__name__		)





def 						__snake_case(				_lowerCAmelCase		)		-> Dict:
      snake_case__  :    List[str]    						=      []
      snake_case__  :    Any    						=      get_test_module(_lowerCAmelCase		)
      for attr in dir(_lowerCAmelCase		):
            snake_case__  :    Dict    						=      getattr(_lowerCAmelCase     ,   _lowerCAmelCase		)
            # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
            # `all_model_classes` is not empty (which also excludes other special classes).
            snake_case__  :    List[str]    						=      getattr(_lowerCAmelCase     ,   """all_model_classes"""     ,   []		)
            if len(_lowerCAmelCase		) > 0:
                  test_classes.append(_lowerCAmelCase		)

    # sort with class names
      return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase		: x.__name__		)





def 						__snake_case(				_lowerCAmelCase		)		-> Dict:
      snake_case__  :    Any    						=      get_test_classes(_lowerCAmelCase		)
      snake_case__  :    Optional[Any]    						=      set()
      for test_class in test_classes:
            model_classes.update(test_class.all_model_classes		)

      # sort with class names
      return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase		: x.__name__		)





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[Any]:
      snake_case__  :    Optional[int]    						=      test_class()
      if hasattr(_lowerCAmelCase     ,   """setUp"""		):
            test.setUp()

      snake_case__  :    Any    						=      None
      if hasattr(_lowerCAmelCase     ,   """model_tester"""		):
            # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
            if test.model_tester is not None:
                  snake_case__  :    Tuple    						=      test.model_tester.__class__

      return model_tester





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Dict:
      snake_case__  :    Union[str, Any]    						=      get_test_classes(_lowerCAmelCase		)

      snake_case__  :    str    						=      []
      for test_class in test_classes:
            if model_class in test_class.all_model_classes:
                  target_test_classes.append(_lowerCAmelCase		)

    # sort with class names
      return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase		: x.__name__		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Tuple:
      snake_case__  :    Optional[Any]    						=      get_test_classes_for_model(_lowerCAmelCase     ,   _lowerCAmelCase		)

      snake_case__  :    Union[str, Any]    						=      []
      for test_class in test_classes:
            snake_case__  :    Tuple    						=      get_model_tester_from_test_class(_lowerCAmelCase		)
            if tester_class is not None:
                  tester_classes.append(_lowerCAmelCase		)

    # sort with class names
      return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase		: x.__name__		)





def 						__snake_case(				_lowerCAmelCase		)		-> Union[str, Any]:
      snake_case__  :    Optional[Any]    						=      get_test_classes(_lowerCAmelCase		)
      snake_case__  :    Union[str, Any]    						=      {test_class: get_model_tester_from_test_class(_lowerCAmelCase		) for test_class in test_classes}
      return test_tester_mapping





def 						__snake_case(				_lowerCAmelCase		)		-> int:
      snake_case__  :    Any    						=      get_model_classes(_lowerCAmelCase		)
      snake_case__  :    Any    						=      {
          model_class: get_test_classes_for_model(_lowerCAmelCase     ,   _lowerCAmelCase		) for model_class in model_classes
      }
      return model_test_mapping





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    Union[str, Any]    						=      get_model_classes(_lowerCAmelCase		)
      snake_case__  :    str    						=      {
          model_class: get_tester_classes_for_model(_lowerCAmelCase     ,   _lowerCAmelCase		) for model_class in model_classes
      }
      return model_to_tester_mapping





def 						__snake_case(				_lowerCAmelCase		)		-> int:
      if isinstance(_lowerCAmelCase     ,   _lowerCAmelCase		):
            return o
      elif isinstance(_lowerCAmelCase     ,   _lowerCAmelCase		):
            return o.__name__
      elif isinstance(_lowerCAmelCase     ,   (list, tuple)		):
            return [to_json(_lowerCAmelCase		) for x in o]
      elif isinstance(_lowerCAmelCase     ,   _lowerCAmelCase		):
            return {to_json(_lowerCAmelCase		): to_json(_lowerCAmelCase		) for k, v in o.items()}
      else:
            return o

"""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 YolosImageProcessor


class     UpperCAmelCase						(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    ,			):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       lowercase__:   str					=      size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
       lowercase__:   Optional[Any]					=      parent
       lowercase__:   List[Any]					=      batch_size
       lowercase__:   Tuple					=      num_channels
       lowercase__:   Optional[Any]					=      min_resolution
       lowercase__:   Dict					=      max_resolution
       lowercase__:   Optional[int]					=      do_resize
       lowercase__:   Any					=      size
       lowercase__:   Optional[Any]					=      do_normalize
       lowercase__:   Union[str, Any]					=      image_mean
       lowercase__:   Tuple					=      image_std
       lowercase__:   str					=      do_rescale
       lowercase__:   Any					=      rescale_factor
       lowercase__:   List[Any]					=      do_pad






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

       else:
          lowercase__:   Optional[int]					=      []
          for image in image_inputs:
             lowercase__, lowercase__:   int					=      self.get_expected_values([image]							)
             expected_values.append((expected_height, expected_width)							)
          lowercase__:   Union[str, Any]					=      max(_UpperCAmelCase    ,			key=lambda _UpperCAmelCase							: item[0]							)[0]
          lowercase__:   Dict					=      max(_UpperCAmelCase    ,			key=lambda _UpperCAmelCase							: item[1]							)[1]

       return expected_height, expected_width



@require_torch
@require_vision
class     UpperCAmelCase						(_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Optional[int]						    = YolosImageProcessor if is_vision_available() else None






    def _snake_case			(   self							):
       lowercase__:   int					=      YolosImageProcessingTester(self							)






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






    def _snake_case			(   self							):
       lowercase__:   List[str]					=      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    ,			'''size'''							)							)






    def _snake_case			(   self							):
       lowercase__:   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    ,			_UpperCAmelCase							)

       lowercase__:   Any					=      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 _snake_case			(   self							):
       pass






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

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

       lowercase__, lowercase__:   Optional[int]					=      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
       lowercase__, lowercase__:   Any					=      self.image_processor_tester.get_expected_values(_UpperCAmelCase    ,			batched=_UpperCAmelCase							)

       lowercase__:   int					=      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 _snake_case			(   self							):
       # Initialize image_processing
       lowercase__:   List[str]					=      self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       lowercase__:   Optional[Any]					=      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
       lowercase__:   List[Any]					=      image_processing(image_inputs[0]    ,			return_tensors='''pt'''							).pixel_values

       lowercase__, lowercase__:   str					=      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
       lowercase__:   Dict					=      image_processing(_UpperCAmelCase    ,			return_tensors='''pt'''							).pixel_values

       lowercase__, lowercase__:   str					=      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 _snake_case			(   self							):
       # Initialize image_processing
       lowercase__:   Any					=      self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       lowercase__:   Optional[Any]					=      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
       lowercase__:   Optional[int]					=      image_processing(image_inputs[0]    ,			return_tensors='''pt'''							).pixel_values

       lowercase__, lowercase__:   int					=      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
       lowercase__:   List[str]					=      image_processing(_UpperCAmelCase    ,			return_tensors='''pt'''							).pixel_values

       lowercase__, lowercase__:   List[Any]					=      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 _snake_case			(   self							):
       # Initialize image_processings
       lowercase__:   Union[str, Any]					=      self.image_processing_class(**self.image_processor_dict							)
       lowercase__:   Optional[Any]					=      self.image_processing_class(do_resize=_UpperCAmelCase    ,			do_normalize=_UpperCAmelCase    ,			do_rescale=_UpperCAmelCase							)
       # create random PyTorch tensors
       lowercase__:   Optional[Any]					=      prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=_UpperCAmelCase    ,			torchify=_UpperCAmelCase							)
       for image in image_inputs:
          self.assertIsInstance(_UpperCAmelCase    ,			torch.Tensor							)

       # Test whether the method "pad" and calling the image processor return the same tensors
       lowercase__:   List[str]					=      image_processing_a.pad(_UpperCAmelCase    ,			return_tensors='''pt'''							)
       lowercase__:   Tuple					=      image_processing_a(_UpperCAmelCase    ,			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							):
       # prepare image and target
       lowercase__:   Tuple					=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
       with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''    ,			'''r'''							) as f:
          lowercase__:   Any					=      json.loads(f.read()							)

       lowercase__:   Dict					=      {'''image_id''': 39769, '''annotations''': target}

       # encode them
       lowercase__:   Dict					=      YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''							)
       lowercase__:   Any					=      image_processing(images=_UpperCAmelCase    ,			annotations=_UpperCAmelCase    ,			return_tensors='''pt'''							)

       # verify pixel values
       lowercase__:   Optional[Any]					=      torch.Size([1, 3, 800, 1066]							)
       self.assertEqual(encoding['''pixel_values'''].shape    ,			_UpperCAmelCase							)

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

       # verify area
       lowercase__:   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''']    ,			_UpperCAmelCase							)							)
       # verify boxes
       lowercase__:   str					=      torch.Size([6, 4]							)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,			_UpperCAmelCase							)
       lowercase__:   List[Any]					=      torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,			_UpperCAmelCase    ,			atol=1e-3							)							)
       # verify image_id
       lowercase__:   Optional[int]					=      torch.tensor([39769]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,			_UpperCAmelCase							)							)
       # verify is_crowd
       lowercase__:   Optional[int]					=      torch.tensor([0, 0, 0, 0, 0, 0]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,			_UpperCAmelCase							)							)
       # verify class_labels
       lowercase__:   Dict					=      torch.tensor([75, 75, 63, 65, 17, 17]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,			_UpperCAmelCase							)							)
       # verify orig_size
       lowercase__:   List[Any]					=      torch.tensor([480, 640]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,			_UpperCAmelCase							)							)
       # verify size
       lowercase__:   List[Any]					=      torch.tensor([800, 1066]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,			_UpperCAmelCase							)							)






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

       lowercase__:   List[Any]					=      {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

       lowercase__:   Dict					=      pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''							)

       # encode them
       lowercase__:   Union[str, Any]					=      YolosImageProcessor(format='''coco_panoptic'''							)
       lowercase__:   Optional[Any]					=      image_processing(images=_UpperCAmelCase    ,			annotations=_UpperCAmelCase    ,			masks_path=_UpperCAmelCase    ,			return_tensors='''pt'''							)

       # verify pixel values
       lowercase__:   Optional[int]					=      torch.Size([1, 3, 800, 1066]							)
       self.assertEqual(encoding['''pixel_values'''].shape    ,			_UpperCAmelCase							)

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

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