
"""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       __magic_name__							(							unittest.TestCase			):
       def __init__(  self			,					A_			,					A_=7			,					A_=3			,					A_=30			,					A_=400			,					A_=True			,					A_=None			,					A_=True			,					A_=[0.5, 0.5, 0.5]			,					A_=[0.5, 0.5, 0.5]			,					A_=True			,					A_=1 / 255			,					A_=True			,					)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      int   			=  size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
         _lowercase:      List[str]   			=  parent
         _lowercase:      Dict   			=  batch_size
         _lowercase:      Optional[int]   			=  num_channels
         _lowercase:      Optional[Any]   			=  min_resolution
         _lowercase:      Union[str, Any]   			=  max_resolution
         _lowercase:      Tuple   			=  do_resize
         _lowercase:      Any   			=  size
         _lowercase:      List[Any]   			=  do_normalize
         _lowercase:      str   			=  image_mean
         _lowercase:      List[str]   			=  image_std
         _lowercase:      List[Any]   			=  do_rescale
         _lowercase:      Tuple   			=  rescale_factor
         _lowercase:      List[Any]   			=  do_pad
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         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 				lowercase_				(  self			,					A_			,					A_=False							)  ->						List[Any]:





         """simple docstring"""




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

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

         return expected_height, expected_width


@require_torch
@require_vision
class       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		YolosImageProcessor if is_vision_available() else None
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  YolosImageProcessingTester(self							)
       @property
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         return self.image_processor_tester.prepare_image_processor_dict()
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Tuple   			=  self.image_processing_class(**self.image_processor_dict							)
         self.assertTrue(hasattr(A_			,					'''image_mean'''							)							)
         self.assertTrue(hasattr(A_			,					'''image_std'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_normalize'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_resize'''							)							)
         self.assertTrue(hasattr(A_			,					'''size'''							)							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase:      Dict   			=  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			,					A_							)

         _lowercase:      int   			=  self.image_processing_class.from_dict(
             self.image_processor_dict			,					size=42			,					max_size=84			,					pad_and_return_pixel_mask=A_							)
         self.assertEqual(image_processor.size			,					{'''shortest_edge''': 42, '''longest_edge''': 84}							)
         self.assertEqual(image_processor.do_pad			,					A_							)
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         pass
       def 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random PIL images
         _lowercase:      int   			=  prepare_image_inputs(self.image_processor_tester			,					equal_resolution=A_							)
         for image in image_inputs:
           self.assertIsInstance(A_			,					Image.Image							)

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

         _lowercase			,					_lowercase:      Optional[Any]   			=  self.image_processor_tester.get_expected_values(A_							)

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

         # Test batched
         _lowercase			,					_lowercase:      Dict   			=  self.image_processor_tester.get_expected_values(A_			,					batched=A_							)

         _lowercase:      List[str]   			=  image_processing(A_			,					return_tensors='''pt'''							).pixel_values
         self.assertEqual(
             encoded_images.shape			,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,					)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         _lowercase:      Optional[Any]   			=  prepare_image_inputs(self.image_processor_tester			,					equal_resolution=A_			,					numpify=A_							)
         for image in image_inputs:
           self.assertIsInstance(A_			,					np.ndarray							)

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

         _lowercase			,					_lowercase:      Dict   			=  self.image_processor_tester.get_expected_values(A_							)

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

         # Test batched
         _lowercase:      int   			=  image_processing(A_			,					return_tensors='''pt'''							).pixel_values

         _lowercase			,					_lowercase:      str   			=  self.image_processor_tester.get_expected_values(A_			,					batched=A_							)

         self.assertEqual(
             encoded_images.shape			,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,					)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase:      int   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random PyTorch tensors
         _lowercase:      Any   			=  prepare_image_inputs(self.image_processor_tester			,					equal_resolution=A_			,					torchify=A_							)
         for image in image_inputs:
           self.assertIsInstance(A_			,					torch.Tensor							)

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

         _lowercase			,					_lowercase:      Dict   			=  self.image_processor_tester.get_expected_values(A_							)

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

         # Test batched
         _lowercase:      Union[str, Any]   			=  image_processing(A_			,					return_tensors='''pt'''							).pixel_values

         _lowercase			,					_lowercase:      Optional[int]   			=  self.image_processor_tester.get_expected_values(A_			,					batched=A_							)

         self.assertEqual(
             encoded_images.shape			,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,					)
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  self.image_processing_class(**self.image_processor_dict							)
         _lowercase:      int   			=  self.image_processing_class(do_resize=A_			,					do_normalize=A_			,					do_rescale=A_							)
         # create random PyTorch tensors
         _lowercase:      Optional[Any]   			=  prepare_image_inputs(self.image_processor_tester			,					equal_resolution=A_			,					torchify=A_							)
         for image in image_inputs:
           self.assertIsInstance(A_			,					torch.Tensor							)

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

         self.assertTrue(
             torch.allclose(encoded_images_with_method['''pixel_values''']			,					encoded_images['''pixel_values''']			,					atol=1E-4							)							)
       @slow
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _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:      int   			=  json.loads(f.read()							)

         _lowercase:      Union[str, Any]   			=  {'''image_id''': 3_9769, '''annotations''': target}

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

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

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

         # verify area
         _lowercase:      Optional[Any]   			=  torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]							)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']			,					A_							)							)
         # verify boxes
         _lowercase:      List[str]   			=  torch.Size([6, 4]							)
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,					A_							)
         _lowercase:      Dict   			=  torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]							)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]			,					A_			,					atol=1E-3							)							)
         # verify image_id
         _lowercase:      Tuple   			=  torch.tensor([3_9769]							)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,					A_							)							)
         # verify is_crowd
         _lowercase:      Any   			=  torch.tensor([0, 0, 0, 0, 0, 0]							)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,					A_							)							)
         # verify class_labels
         _lowercase:      List[Any]   			=  torch.tensor([75, 75, 63, 65, 17, 17]							)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,					A_							)							)
         # verify orig_size
         _lowercase:      Optional[int]   			=  torch.tensor([480, 640]							)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,					A_							)							)
         # verify size
         _lowercase:      List[Any]   			=  torch.tensor([800, 1066]							)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,					A_							)							)



       @slow
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
         with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''			,					'''r'''							) as f:
           _lowercase:      str   			=  json.loads(f.read()							)

         _lowercase:      List[Any]   			=  {'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

         _lowercase:      str   			=  pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''							)

         # encode them
         _lowercase:      Tuple   			=  YolosImageProcessor(format='''coco_panoptic'''							)
         _lowercase:      Dict   			=  image_processing(images=A_			,					annotations=A_			,					masks_path=A_			,					return_tensors='''pt'''							)

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

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

         # verify area
         _lowercase:      List[str]   			=  torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]							)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']			,					A_							)							)
         # verify boxes
         _lowercase:      List[Any]   			=  torch.Size([6, 4]							)
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,					A_							)
         _lowercase:      Union[str, Any]   			=  torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]							)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]			,					A_			,					atol=1E-3							)							)
         # verify image_id
         _lowercase:      Optional[int]   			=  torch.tensor([3_9769]							)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,					A_							)							)
         # verify is_crowd
         _lowercase:      Tuple   			=  torch.tensor([0, 0, 0, 0, 0, 0]							)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,					A_							)							)
         # verify class_labels
         _lowercase:      Tuple   			=  torch.tensor([17, 17, 63, 75, 75, 93]							)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,					A_							)							)
         # verify masks
         _lowercase:      Union[str, Any]   			=  82_2873
         self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()			,					A_							)
         # verify orig_size
         _lowercase:      List[Any]   			=  torch.tensor([480, 640]							)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,					A_							)							)
         # verify size
         _lowercase:      Dict   			=  torch.tensor([800, 1066]							)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,					A_							)							)


"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


A__ :       List[Any]				= logging.get_logger(__name__)


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       def __init__(  self			,					*A_			,					**A_							)  ->						None:





         """simple docstring"""




         warnings.warn(
             '''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
             ''' Please use SegformerImageProcessor instead.'''			,					A_			,					)
         super().__init__(*A_			,					**A_							)
