



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     ):




     def __init__(				self ,						a__ ,						a__=7 ,						a__=3 ,						a__=3_0 ,						a__=4_0_0 ,						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_5_5 ,						a__=True ,						):
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         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 		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 ,						a__ ,						a__=False):
         if not batched:
             A__		 =					image_inputs[0]
             if isinstance(a__ ,						Image.Image):
                 A__ ,	A__		 =					image.size
             else:
                 A__ ,	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__ ,	A__		 =					self.get_expected_values([image])
                 expected_values.append((expected_height, expected_width))
             A__		 =					max(a__ ,						key=lambda a__: item[0])[0]
             A__		 =					max(a__ ,						key=lambda a__: item[1])[1]

         return expected_height, expected_width







@require_torch
@require_vision
class     _UpperCAmelCase			(       A__			,      unittest.TestCase     ):
     UpperCamelCase__		    =				YolosImageProcessor if is_vision_available() else None




     def 		snake_case_		(				self):
         A__		 =					YolosImageProcessingTester(self)




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




     def 		snake_case_		(				self):
         A__		 =					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 		snake_case_		(				self):
         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 ,						a__)

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




     def 		snake_case_		(				self):
         pass




     def 		snake_case_		(				self):
         # Initialize image_processing
         A__		 =					self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         A__		 =					prepare_image_inputs(self.image_processor_tester ,						equal_resolution=a__)
         for image in image_inputs:
             self.assertIsInstance(a__ ,						Image.Image)

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

         A__ ,	A__		 =					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
         A__ ,	A__		 =					self.image_processor_tester.get_expected_values(a__ ,						batched=a__)

         A__		 =					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 		snake_case_		(				self):
         # Initialize image_processing
         A__		 =					self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         A__		 =					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
         A__		 =					image_processing(image_inputs[0] ,						return_tensors='''pt''').pixel_values

         A__ ,	A__		 =					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
         A__		 =					image_processing(a__ ,						return_tensors='''pt''').pixel_values

         A__ ,	A__		 =					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 		snake_case_		(				self):
         # Initialize image_processing
         A__		 =					self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         A__		 =					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
         A__		 =					image_processing(image_inputs[0] ,						return_tensors='''pt''').pixel_values

         A__ ,	A__		 =					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
         A__		 =					image_processing(a__ ,						return_tensors='''pt''').pixel_values

         A__ ,	A__		 =					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 		snake_case_		(				self):
         # Initialize image_processings
         A__		 =					self.image_processing_class(**self.image_processor_dict)
         A__		 =					self.image_processing_class(do_resize=a__ ,						do_normalize=a__ ,						do_rescale=a__)
         # create random PyTorch tensors
         A__		 =					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
         A__		 =					image_processing_a.pad(a__ ,						return_tensors='''pt''')
         A__		 =					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 		snake_case_		(				self):
         # prepare image and target
         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=a__ ,						annotations=a__ ,						return_tensors='''pt''')

         # verify pixel values
         A__		 =					torch.Size([1, 3, 8_0_0, 1_0_6_6])
         self.assertEqual(encoding['''pixel_values'''].shape ,						a__)

         A__		 =					torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1])
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] ,						a__ ,						atol=1e-4))

         # verify area
         A__		 =					torch.tensor([5_8_8_7.9_6_0_0, 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'''] ,						a__))
         # verify boxes
         A__		 =					torch.Size([6, 4])
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,						a__)
         A__		 =					torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5])
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,						a__ ,						atol=1e-3))
         # verify image_id
         A__		 =					torch.tensor([3_9_7_6_9])
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,						a__))
         # verify is_crowd
         A__		 =					torch.tensor([0, 0, 0, 0, 0, 0])
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,						a__))
         # 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'''] ,						a__))
         # verify orig_size
         A__		 =					torch.tensor([4_8_0, 6_4_0])
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,						a__))
         # verify size
         A__		 =					torch.tensor([8_0_0, 1_0_6_6])
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,						a__))




     @slow
     def 		snake_case_		(				self):
         # prepare image, target and masks_path
         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=a__ ,						annotations=a__ ,						masks_path=a__ ,						return_tensors='''pt''')

         # verify pixel values
         A__		 =					torch.Size([1, 3, 8_0_0, 1_0_6_6])
         self.assertEqual(encoding['''pixel_values'''].shape ,						a__)

         A__		 =					torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1])
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] ,						a__ ,						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, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7])
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,						a__))
         # verify boxes
         A__		 =					torch.Size([6, 4])
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,						a__)
         A__		 =					torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5])
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,						a__ ,						atol=1e-3))
         # verify image_id
         A__		 =					torch.tensor([3_9_7_6_9])
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,						a__))
         # verify is_crowd
         A__		 =					torch.tensor([0, 0, 0, 0, 0, 0])
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,						a__))
         # 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'''] ,						a__))
         # verify masks
         A__		 =					8_2_2_8_7_3
         self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() ,						a__)
         # verify orig_size
         A__		 =					torch.tensor([4_8_0, 6_4_0])
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,						a__))
         # verify size
         A__		 =					torch.tensor([8_0_0, 1_0_6_6])
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,						a__))





import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image
else:



   class     _UpperCAmelCase			:




        @staticmethod
        def 		snake_case_		(				*a__ ,						**a__):
            pass



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Image	)->  str:
    A__		 =					hashlib.mda(image.tobytes()	)
    return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     _UpperCAmelCase			(       unittest.TestCase     ):
     UpperCamelCase__		    =				MODEL_FOR_DEPTH_ESTIMATION_MAPPING




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):
         A__		 =					DepthEstimationPipeline(model=a__ ,						image_processor=a__)
         return depth_estimator, [
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]




     def 		snake_case_		(				self ,						a__ ,						a__):
         A__		 =					depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
         self.assertEqual({'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)} ,						a__)
         import datasets

         A__		 =					datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' ,						'''image''' ,						split='''test''')
         A__		 =					depth_estimator(
             [
                 Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''),
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                 # RGBA
                 dataset[0]['''file'''],
                 # LA
                 dataset[1]['''file'''],
                 # L
                 dataset[2]['''file'''],
             ])
         self.assertEqual(
             [
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                 {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
             ] ,						a__ ,						)




     @require_tf
     @unittest.skip('''Depth estimation is not implemented in TF''')
     def 		snake_case_		(				self):
         pass




     @slow
     @require_torch
     def 		snake_case_		(				self):
         A__		 =					'''Intel/dpt-large'''
         A__		 =					pipeline('''depth-estimation''' ,						model=a__)
         A__		 =					depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''')
         A__		 =					hashimage(outputs['''depth'''])

         # This seems flaky.
         # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
         self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()) ,						2_9.3_0_4)
         self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()) ,						2.6_6_2)




     @require_torch
     def 		snake_case_		(				self):
         # This is highly irregular to have no small tests.
         self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''')
