

"""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 lowerCamelCase__   (				unittest.TestCase    ):



    '''simple docstring'''




    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_=7 ,lowerCamelCase_=3 ,lowerCamelCase_=3_0 ,lowerCamelCase_=4_0_0 ,lowerCamelCase_=True ,lowerCamelCase_=None ,lowerCamelCase_=True ,lowerCamelCase_=[0.5, 0.5, 0.5] ,lowerCamelCase_=[0.5, 0.5, 0.5] ,lowerCamelCase_=True ,lowerCamelCase_=1 / 2_5_5 ,lowerCamelCase_=True ,)    ->      List[str]:
          A				  =		size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
          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 ,lowerCamelCase_ ,lowerCamelCase_=False    )    ->      Any:

          if not batched:
                A				  =		image_inputs[0]
                if isinstance(snake_case__ ,Image.Image    ):
                      A				  =		image.size
                else:
                      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				  =		self.get_expected_values([image]    )
                      expected_values.append((expected_height, expected_width)    )
                A				  =		max(snake_case__ ,key=lambda lowerCamelCase_    : item[0]    )[0]
                A				  =		max(snake_case__ ,key=lambda lowerCamelCase_    : item[1]    )[1]

          return expected_height, expected_width





@require_torch
@require_vision
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE_     ,   unittest.TestCase    ):



    '''simple docstring'''




    _lowerCamelCase					=   YolosImageProcessor if is_vision_available() else None




    def UpperCamelCase__		( self    )    ->      Tuple:
          A				  =		YolosImageProcessingTester(self    )




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




    def UpperCamelCase__		( self    )    ->      Any:
          A				  =		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 UpperCamelCase__		( self    )    ->      Any:
          A				  =		self.image_processing_class.from_dict(self.image_processor_dict    )
          self.assertEqual(image_processor.size ,{"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}    )
          self.assertEqual(image_processor.do_pad ,snake_case__    )

          A				  =		self.image_processing_class.from_dict(
              self.image_processor_dict ,size=4_2 ,max_size=8_4 ,pad_and_return_pixel_mask=snake_case__    )
          self.assertEqual(image_processor.size ,{"""shortest_edge""": 4_2, """longest_edge""": 8_4}    )
          self.assertEqual(image_processor.do_pad ,snake_case__    )




    def UpperCamelCase__		( self    )    ->      Tuple:
          pass




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          A				  =		self.image_processing_class(**self.image_processor_dict    )
          # create random PIL images
          A				  =		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
          A				  =		image_processing(image_inputs[0] ,return_tensors="""pt"""    ).pixel_values

          A				  =		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
          A				  =		self.image_processor_tester.get_expected_values(snake_case__ ,batched=snake_case__    )

          A				  =		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 UpperCamelCase__		( self    )    ->      Dict:
          A				  =		self.image_processing_class(**self.image_processor_dict    )
          # create random numpy tensors
          A				  =		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
          A				  =		image_processing(image_inputs[0] ,return_tensors="""pt"""    ).pixel_values

          A				  =		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
          A				  =		image_processing(snake_case__ ,return_tensors="""pt"""    ).pixel_values

          A				  =		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 UpperCamelCase__		( self    )    ->      List[str]:
          A				  =		self.image_processing_class(**self.image_processor_dict    )
          # create random PyTorch tensors
          A				  =		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
          A				  =		image_processing(image_inputs[0] ,return_tensors="""pt"""    ).pixel_values

          A				  =		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
          A				  =		image_processing(snake_case__ ,return_tensors="""pt"""    ).pixel_values

          A				  =		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 UpperCamelCase__		( self    )    ->      List[Any]:
          A				  =		self.image_processing_class(**self.image_processor_dict    )
          A				  =		self.image_processing_class(do_resize=snake_case__ ,do_normalize=snake_case__ ,do_rescale=snake_case__    )
          # create random PyTorch tensors
          A				  =		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
          A				  =		image_processing_a.pad(snake_case__ ,return_tensors="""pt"""    )
          A				  =		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 UpperCamelCase__		( self    )    ->      Dict:
          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': 3_9_7_6_9, 'annotations': target}

          # encode them
          A				  =		YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""    )
          A				  =		image_processing(images=snake_case__ ,annotations=snake_case__ ,return_tensors="""pt"""    )

          # verify pixel values
          A				  =		torch.Size([1, 3, 8_0_0, 1_0_6_6]    )
          self.assertEqual(encoding["""pixel_values"""].shape ,snake_case__    )

          A				  =		torch.tensor([0.27_96, 0.31_38, 0.34_81]    )
          self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] ,snake_case__ ,atol=1E-4    )    )

          # verify area
          A				  =		torch.tensor([58_87.96_00, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]    )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] ,snake_case__    )    )
          # verify boxes
          A				  =		torch.Size([6, 4]    )
          self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape ,snake_case__    )
          A				  =		torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]    )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] ,snake_case__ ,atol=1E-3    )    )
          # verify image_id
          A				  =		torch.tensor([3_9_7_6_9]    )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] ,snake_case__    )    )
          # verify is_crowd
          A				  =		torch.tensor([0, 0, 0, 0, 0, 0]    )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] ,snake_case__    )    )
          # verify class_labels
          A				  =		torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]    )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] ,snake_case__    )    )
          # verify orig_size
          A				  =		torch.tensor([4_8_0, 6_4_0]    )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] ,snake_case__    )    )
          # verify size
          A				  =		torch.tensor([8_0_0, 1_0_6_6]    )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] ,snake_case__    )    )




    @slow
    def UpperCamelCase__		( self    )    ->      int:
          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': 3_9_7_6_9, 'segments_info': target}

          A				  =		pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""    )

          # encode them
          A				  =		YolosImageProcessor(format="""coco_panoptic"""    )
          A				  =		image_processing(images=snake_case__ ,annotations=snake_case__ ,masks_path=snake_case__ ,return_tensors="""pt"""    )

          # verify pixel values
          A				  =		torch.Size([1, 3, 8_0_0, 1_0_6_6]    )
          self.assertEqual(encoding["""pixel_values"""].shape ,snake_case__    )

          A				  =		torch.tensor([0.27_96, 0.31_38, 0.34_81]    )
          self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] ,snake_case__ ,atol=1E-4    )    )

          # verify area
          A				  =		torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 58_79.65_62, 76_34.11_47]    )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] ,snake_case__    )    )
          # verify boxes
          A				  =		torch.Size([6, 4]    )
          self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape ,snake_case__    )
          A				  =		torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]    )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] ,snake_case__ ,atol=1E-3    )    )
          # verify image_id
          A				  =		torch.tensor([3_9_7_6_9]    )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] ,snake_case__    )    )
          # verify is_crowd
          A				  =		torch.tensor([0, 0, 0, 0, 0, 0]    )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] ,snake_case__    )    )
          # verify class_labels
          A				  =		torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]    )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] ,snake_case__    )    )
          # verify masks
          A				  =		8_2_2_8_7_3
          self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() ,snake_case__    )
          # verify orig_size
          A				  =		torch.tensor([4_8_0, 6_4_0]    )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] ,snake_case__    )    )
          # verify size
          A				  =		torch.tensor([8_0_0, 1_0_6_6]    )
          self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] ,snake_case__    )    )








'''simple docstring'''







from ..utils import DummyObject, requires_backends
class 						UpperCamelCase__       (			metaclass=SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     ["torch", "torchsde"]







     def __init__(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            requires_backends(self   ,						['torch', 'torchsde'] )







     @classmethod
     def a						(			cls   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            requires_backends(cls   ,						['torch', 'torchsde'] )







     @classmethod
     def a						(			cls   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            requires_backends(cls   ,						['torch', 'torchsde'] )

