


'''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 			a       (	unittest.TestCase ):


     """simple docstring"""






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

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

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

          return expected_height, expected_width



@require_torch
@require_vision
class 			a       (	_a			,						unittest.TestCase ):


     """simple docstring"""






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

     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							Union[str, Any]:
          __UpperCAmelCase			:				List[str]													=    YolosImageProcessingTester(self    )

     @property
     def 				lowerCamelCase__				(       self  :	Dict    )		->							Optional[int]:
          return self.image_processor_tester.prepare_image_processor_dict()

     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							int:
          __UpperCAmelCase			:				Optional[int]													=    self.image_processing_class(**self.image_processor_dict    )
          self.assertTrue(hasattr(snake_case							,				'''image_mean'''    )    )
          self.assertTrue(hasattr(snake_case							,				'''image_std'''    )    )
          self.assertTrue(hasattr(snake_case							,				'''do_normalize'''    )    )
          self.assertTrue(hasattr(snake_case							,				'''do_resize'''    )    )
          self.assertTrue(hasattr(snake_case							,				'''size'''    )    )

     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							List[Any]:
          __UpperCAmelCase			:				Tuple													=    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							,				snake_case    )

          __UpperCAmelCase			:				str													=    self.image_processing_class.from_dict(
              self.image_processor_dict							,				size=42							,				max_size=84							,				pad_and_return_pixel_mask=snake_case    )
          self.assertEqual(image_processor.size							,				{'''shortest_edge''': 42, '''longest_edge''': 84}    )
          self.assertEqual(image_processor.do_pad							,				snake_case    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Any:
          pass

     def 				lowerCamelCase__				(       self  :	Dict    )		->							Dict:
          # Initialize image_processing
          __UpperCAmelCase			:				List[Any]													=    self.image_processing_class(**self.image_processor_dict    )
          # create random PIL images
          __UpperCAmelCase			:				Optional[Any]													=    prepare_image_inputs(self.image_processor_tester							,				equal_resolution=snake_case    )
          for image in image_inputs:
               self.assertIsInstance(snake_case							,				Image.Image    )

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

          __UpperCAmelCase							,     __UpperCAmelCase			:				str													=    self.image_processor_tester.get_expected_values(snake_case    )

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

          # Test batched
          __UpperCAmelCase							,     __UpperCAmelCase			:				str													=    self.image_processor_tester.get_expected_values(snake_case							,				batched=snake_case    )

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

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

          __UpperCAmelCase							,     __UpperCAmelCase			:				Optional[Any]													=    self.image_processor_tester.get_expected_values(snake_case    )

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

          # Test batched
          __UpperCAmelCase			:				Any													=    image_processing(snake_case							,				return_tensors='''pt'''    ).pixel_values

          __UpperCAmelCase							,     __UpperCAmelCase			:				Dict													=    self.image_processor_tester.get_expected_values(snake_case							,				batched=snake_case    )

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

     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Optional[Any]:
          # Initialize image_processing
          __UpperCAmelCase			:				int													=    self.image_processing_class(**self.image_processor_dict    )
          # create random PyTorch tensors
          __UpperCAmelCase			:				Optional[int]													=    prepare_image_inputs(self.image_processor_tester							,				equal_resolution=snake_case							,				torchify=snake_case    )
          for image in image_inputs:
               self.assertIsInstance(snake_case							,				torch.Tensor    )

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

          __UpperCAmelCase							,     __UpperCAmelCase			:				str													=    self.image_processor_tester.get_expected_values(snake_case    )

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

          # Test batched
          __UpperCAmelCase			:				Any													=    image_processing(snake_case							,				return_tensors='''pt'''    ).pixel_values

          __UpperCAmelCase							,     __UpperCAmelCase			:				Tuple													=    self.image_processor_tester.get_expected_values(snake_case							,				batched=snake_case    )

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

     def 				lowerCamelCase__				(       self  :	int    )		->							Optional[Any]:
          # Initialize image_processings
          __UpperCAmelCase			:				Optional[int]													=    self.image_processing_class(**self.image_processor_dict    )
          __UpperCAmelCase			:				Union[str, Any]													=    self.image_processing_class(do_resize=snake_case							,				do_normalize=snake_case							,				do_rescale=snake_case    )
          # create random PyTorch tensors
          __UpperCAmelCase			:				Any													=    prepare_image_inputs(self.image_processor_tester							,				equal_resolution=snake_case							,				torchify=snake_case    )
          for image in image_inputs:
               self.assertIsInstance(snake_case							,				torch.Tensor    )

          # Test whether the method "pad" and calling the image processor return the same tensors
          __UpperCAmelCase			:				str													=    image_processing_a.pad(snake_case							,				return_tensors='''pt'''    )
          __UpperCAmelCase			:				Any													=    image_processing_a(snake_case							,				return_tensors='''pt'''    )

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

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

          __UpperCAmelCase			:				str													=    {'''image_id''': 3_9769, '''annotations''': target}

          # encode them
          __UpperCAmelCase			:				str													=    YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''    )
          __UpperCAmelCase			:				List[Any]													=    image_processing(images=snake_case							,				annotations=snake_case							,				return_tensors='''pt'''    )

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

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

          # verify area
          __UpperCAmelCase			:				List[str]													=    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''']							,				snake_case    )    )
          # verify boxes
          __UpperCAmelCase			:				Tuple													=    torch.Size([6, 4]    )
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,				snake_case    )
          __UpperCAmelCase			:				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]							,				snake_case							,				atol=1E-3    )    )
          # verify image_id
          __UpperCAmelCase			:				Dict													=    torch.tensor([3_9769]    )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,				snake_case    )    )
          # verify is_crowd
          __UpperCAmelCase			:				Dict													=    torch.tensor([0, 0, 0, 0, 0, 0]    )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,				snake_case    )    )
          # verify class_labels
          __UpperCAmelCase			:				List[Any]													=    torch.tensor([75, 75, 63, 65, 17, 17]    )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,				snake_case    )    )
          # verify orig_size
          __UpperCAmelCase			:				str													=    torch.tensor([480, 640]    )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,				snake_case    )    )
          # verify size
          __UpperCAmelCase			:				Optional[int]													=    torch.tensor([800, 1066]    )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,				snake_case    )    )






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

          __UpperCAmelCase			:				str													=    {'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

          __UpperCAmelCase			:				int													=    pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''    )

          # encode them
          __UpperCAmelCase			:				Any													=    YolosImageProcessor(format='''coco_panoptic'''    )
          __UpperCAmelCase			:				str													=    image_processing(images=snake_case							,				annotations=snake_case							,				masks_path=snake_case							,				return_tensors='''pt'''    )

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

          __UpperCAmelCase			:				int													=    torch.tensor([0.2_796, 0.3_138, 0.3_481]    )
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							,				snake_case							,				atol=1E-4    )    )

          # verify area
          __UpperCAmelCase			:				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''']							,				snake_case    )    )
          # verify boxes
          __UpperCAmelCase			:				Optional[Any]													=    torch.Size([6, 4]    )
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,				snake_case    )
          __UpperCAmelCase			:				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]							,				snake_case							,				atol=1E-3    )    )
          # verify image_id
          __UpperCAmelCase			:				Optional[int]													=    torch.tensor([3_9769]    )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,				snake_case    )    )
          # verify is_crowd
          __UpperCAmelCase			:				Any													=    torch.tensor([0, 0, 0, 0, 0, 0]    )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,				snake_case    )    )
          # verify class_labels
          __UpperCAmelCase			:				List[Any]													=    torch.tensor([17, 17, 63, 75, 75, 93]    )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,				snake_case    )    )
          # verify masks
          __UpperCAmelCase			:				int													=    82_2873
          self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()							,				snake_case    )
          # verify orig_size
          __UpperCAmelCase			:				Tuple													=    torch.tensor([480, 640]    )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,				snake_case    )    )
          # verify size
          __UpperCAmelCase			:				Tuple													=    torch.tensor([800, 1066]    )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,				snake_case    )    )



'''simple docstring'''



from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__UpperCAmelCase    :Optional[int]				       =     TypeVar("KEY")
__UpperCAmelCase    :Tuple				       =     TypeVar("VAL")



@dataclass(frozen=_a			,						slots=_a )
class 			a       (	Generic[KEY, VAL] ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				KEY
     SCREAMING_SNAKE_CASE :				VAL



class 			a       (	_Item ):


     """simple docstring"""






     def __init__(       self  :	Optional[int]    )		->							None:
          super().__init__(snake_case							,				snake_case    )






     def __bool__(       self  :	Dict    )		->							bool:
          return False



__UpperCAmelCase    :Optional[Any]				       =     _DeletedItem()



class 			a       (	MutableMapping[KEY, VAL] ):


     """simple docstring"""






     def __init__(       self  :	str							,				snake_case  :	int = 8							,				snake_case  :	float = 0.75    )		->							None:
          __UpperCAmelCase			:				Dict													=    initial_block_size
          __UpperCAmelCase			:				list[_Item | None]													=    [None] * initial_block_size
          assert 0.0 < capacity_factor < 1.0
          __UpperCAmelCase			:				str													=    capacity_factor
          __UpperCAmelCase			:				Optional[int]													=    0

     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	KEY    )		->							int:
          return hash(snake_case    ) % len(self._buckets    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	int    )		->							int:
          return (ind + 1) % len(self._buckets    )

     def 				lowerCamelCase__				(       self  :	List[str]							,				snake_case  :	int							,				snake_case  :	KEY							,				snake_case  :	VAL    )		->							bool:
          __UpperCAmelCase			:				int													=    self._buckets[ind]
          if not stored:
               __UpperCAmelCase			:				List[str]													=    _Item(snake_case							,				snake_case    )
               self._len += 1
               return True
          elif stored.key == key:
               __UpperCAmelCase			:				int													=    _Item(snake_case							,				snake_case    )
               return True
          else:
               return False

     def 				lowerCamelCase__				(       self  :	str    )		->							bool:
          __UpperCAmelCase			:				Union[str, Any]													=    len(self._buckets    ) * self._capacity_factor
          return len(self    ) >= int(snake_case    )

     def 				lowerCamelCase__				(       self  :	List[str]    )		->							bool:
          if len(self._buckets    ) <= self._initial_block_size:
               return False
          __UpperCAmelCase			:				List[str]													=    len(self._buckets    ) * self._capacity_factor / 2
          return len(self    ) < limit

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	int    )		->							None:
          __UpperCAmelCase			:				Union[str, Any]													=    self._buckets
          __UpperCAmelCase			:				Union[str, Any]													=    [None] * new_size
          __UpperCAmelCase			:				List[str]													=    0
          for item in old_buckets:
               if item:
                    self._add_item(item.key							,				item.val    )

     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							None:
          self._resize(len(self._buckets    ) * 2    )

     def 				lowerCamelCase__				(       self  :	List[str]    )		->							None:
          self._resize(len(self._buckets    ) // 2    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	KEY    )		->							Iterator[int]:
          __UpperCAmelCase			:				Tuple													=    self._get_bucket_index(snake_case    )
          for _ in range(len(self._buckets    )    ):
               yield ind
               __UpperCAmelCase			:				Dict													=    self._get_next_ind(snake_case    )

     def 				lowerCamelCase__				(       self  :	str							,				snake_case  :	KEY							,				snake_case  :	VAL    )		->							None:
          for ind in self._iterate_buckets(snake_case    ):
               if self._try_set(snake_case							,				snake_case							,				snake_case    ):
                    break

     def __setitem__(       self  :	List[str]							,				snake_case  :	KEY							,				snake_case  :	VAL    )		->							None:
          if self._is_full():
               self._size_up()

          self._add_item(snake_case							,				snake_case    )

     def __delitem__(       self  :	Any							,				snake_case  :	KEY    )		->							None:
          for ind in self._iterate_buckets(snake_case    ):
               __UpperCAmelCase			:				List[Any]													=    self._buckets[ind]
               if item is None:
                    raise KeyError(snake_case    )
               if item is _deleted:
                    continue
               if item.key == key:
                    __UpperCAmelCase			:				Optional[Any]													=    _deleted
                    self._len -= 1
                    break
          if self._is_sparse():
               self._size_down()

     def __getitem__(       self  :	List[str]							,				snake_case  :	KEY    )		->							VAL:
          for ind in self._iterate_buckets(snake_case    ):
               __UpperCAmelCase			:				Optional[int]													=    self._buckets[ind]
               if item is None:
                    break
               if item is _deleted:
                    continue
               if item.key == key:
                    return item.val
          raise KeyError(snake_case    )

     def __len__(       self  :	List[Any]    )		->							int:
          return self._len

     def __iter__(       self  :	str    )		->							Iterator[KEY]:
          yield from (item.key for item in self._buckets if item)






     def __repr__(       self  :	Optional[Any]    )		->							str:
          __UpperCAmelCase			:				str													=    ''' ,'''.join(
              f'{item.key}: {item.val}' for item in self._buckets if item    )
          return f'HashMap({val_string})'