import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


snake_case__				= logging.get_logger(__name__)



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

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






           """simple docstring"""
           warnings.warn(
               '''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
               ''' use GLPNImageProcessor instead.'''				,				A_				,				)
           super().__init__(*A_				,				**A_						)
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				,				A_				,				A_=7				,				A_=3				,				A_=30				,				A_=4_00				,				A_=True				,				A_=None				,				A_=True				,				A_=[0.5, 0.5, 0.5]				,				A_=[0.5, 0.5, 0.5]				,				A_=True				,				A_=1 / 2_55				,				A_=True				,				) ->				List[Any]:






           """simple docstring"""
           # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
           _lowerCamelCase			    =				size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
           _lowerCamelCase			    =				parent
           _lowerCamelCase			    =				batch_size
           _lowerCamelCase			    =				num_channels
           _lowerCamelCase			    =				min_resolution
           _lowerCamelCase			    =				max_resolution
           _lowerCamelCase			    =				do_resize
           _lowerCamelCase			    =				size
           _lowerCamelCase			    =				do_normalize
           _lowerCamelCase			    =				image_mean
           _lowerCamelCase			    =				image_std
           _lowerCamelCase			    =				do_rescale
           _lowerCamelCase			    =				rescale_factor
           _lowerCamelCase			    =				do_pad



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """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 		UpperCamelCase_						(					self				,				A_				,				A_=False						) ->				List[str]:






           """simple docstring"""
           if not batched:
                 _lowerCamelCase			    =				image_inputs[0]
                 if isinstance(A_				,				Image.Image						):
                       _lowerCamelCase    ,					_lowerCamelCase			    =				image.size
                 else:
                       _lowerCamelCase    ,					_lowerCamelCase			    =				image.shape[1], image.shape[2]
                 if w < h:
                       _lowerCamelCase			    =				int(self.size['''shortest_edge'''] * h / w						)
                       _lowerCamelCase			    =				self.size['''shortest_edge''']
                 elif w > h:
                       _lowerCamelCase			    =				self.size['''shortest_edge''']
                       _lowerCamelCase			    =				int(self.size['''shortest_edge'''] * w / h						)
                 else:
                       _lowerCamelCase			    =				self.size['''shortest_edge''']
                       _lowerCamelCase			    =				self.size['''shortest_edge''']

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

           return expected_height, expected_width





@require_torch
@require_vision
class  UpperCamelCase				( __lowercase   ,  unittest.TestCase ):

     '''simple docstring'''

     A_										=  YolosImageProcessor if is_vision_available() else None



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				YolosImageProcessingTester(self						)



     @property
     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           return self.image_processor_tester.prepare_image_processor_dict()



     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.image_processing_class.from_dict(self.image_processor_dict						)
           self.assertEqual(image_processor.size				,				{'''shortest_edge''': 18, '''longest_edge''': 13_33}						)
           self.assertEqual(image_processor.do_pad				,				A_						)

           _lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           pass



     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           # Initialize image_processing
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           # create random PIL images
           _lowerCamelCase			    =				prepare_image_inputs(self.image_processor_tester				,				equal_resolution=A_						)
           for image in image_inputs:
                 self.assertIsInstance(A_				,				Image.Image						)

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

           _lowerCamelCase    ,					_lowerCamelCase			    =				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
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_				,				batched=A_						)

           _lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           # Initialize image_processing
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           # create random numpy tensors
           _lowerCamelCase			    =				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
           _lowerCamelCase			    =				image_processing(image_inputs[0]				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				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
           _lowerCamelCase			    =				image_processing(A_				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           # Initialize image_processing
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           # create random PyTorch tensors
           _lowerCamelCase			    =				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
           _lowerCamelCase			    =				image_processing(image_inputs[0]				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				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
           _lowerCamelCase			    =				image_processing(A_				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           # Initialize image_processings
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           _lowerCamelCase			    =				self.image_processing_class(do_resize=A_				,				do_normalize=A_				,				do_rescale=A_						)
           # create random PyTorch tensors
           _lowerCamelCase			    =				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
           _lowerCamelCase			    =				image_processing_a.pad(A_				,				return_tensors='''pt'''						)
           _lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           # prepare image and target
           _lowerCamelCase			    =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
           with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''				,				'''r'''						) as f:
                 _lowerCamelCase			    =				json.loads(f.read()						)

           _lowerCamelCase			    =				{'''image_id''': 3_97_69, '''annotations''': target}

           # encode them
           _lowerCamelCase			    =				YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''						)
           _lowerCamelCase			    =				image_processing(images=A_				,				annotations=A_				,				return_tensors='''pt'''						)

           # verify pixel values
           _lowerCamelCase			    =				torch.Size([1, 3, 8_00, 10_66]						)
           self.assertEqual(encoding['''pixel_values'''].shape				,				A_						)

           _lowerCamelCase			    =				torch.tensor([0.2796, 0.3138, 0.3481]						)
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]				,				A_				,				atol=1E-4						)						)

           # verify area
           _lowerCamelCase			    =				torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,				A_						)						)
           # verify boxes
           _lowerCamelCase			    =				torch.Size([6, 4]						)
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,				A_						)
           _lowerCamelCase			    =				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,				A_				,				atol=1E-3						)						)
           # verify image_id
           _lowerCamelCase			    =				torch.tensor([3_97_69]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,				A_						)						)
           # verify is_crowd
           _lowerCamelCase			    =				torch.tensor([0, 0, 0, 0, 0, 0]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,				A_						)						)
           # verify class_labels
           _lowerCamelCase			    =				torch.tensor([75, 75, 63, 65, 17, 17]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,				A_						)						)
           # verify orig_size
           _lowerCamelCase			    =				torch.tensor([4_80, 6_40]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,				A_						)						)
           # verify size
           _lowerCamelCase			    =				torch.tensor([8_00, 10_66]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,				A_						)						)





     @slow
     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           # prepare image, target and masks_path
           _lowerCamelCase			    =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
           with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''				,				'''r'''						) as f:
                 _lowerCamelCase			    =				json.loads(f.read()						)

           _lowerCamelCase			    =				{'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

           _lowerCamelCase			    =				pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''						)

           # encode them
           _lowerCamelCase			    =				YolosImageProcessor(format='''coco_panoptic'''						)
           _lowerCamelCase			    =				image_processing(images=A_				,				annotations=A_				,				masks_path=A_				,				return_tensors='''pt'''						)

           # verify pixel values
           _lowerCamelCase			    =				torch.Size([1, 3, 8_00, 10_66]						)
           self.assertEqual(encoding['''pixel_values'''].shape				,				A_						)

           _lowerCamelCase			    =				torch.tensor([0.2796, 0.3138, 0.3481]						)
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]				,				A_				,				atol=1E-4						)						)

           # verify area
           _lowerCamelCase			    =				torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,				A_						)						)
           # verify boxes
           _lowerCamelCase			    =				torch.Size([6, 4]						)
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,				A_						)
           _lowerCamelCase			    =				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,				A_				,				atol=1E-3						)						)
           # verify image_id
           _lowerCamelCase			    =				torch.tensor([3_97_69]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,				A_						)						)
           # verify is_crowd
           _lowerCamelCase			    =				torch.tensor([0, 0, 0, 0, 0, 0]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,				A_						)						)
           # verify class_labels
           _lowerCamelCase			    =				torch.tensor([17, 17, 63, 75, 75, 93]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,				A_						)						)
           # verify masks
           _lowerCamelCase			    =				82_28_73
           self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()				,				A_						)
           # verify orig_size
           _lowerCamelCase			    =				torch.tensor([4_80, 6_40]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,				A_						)						)
           # verify size
           _lowerCamelCase			    =				torch.tensor([8_00, 10_66]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,				A_						)						)