


'''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 DetaImageProcessor






class 			UpperCamelCase__(  unittest.TestCase			):




				def __init__(						self     :					Any ,       lowerCAmelCase     :					Union[str, Any] ,       lowerCAmelCase     :					Any=7 ,       lowerCAmelCase     :					List[Any]=3 ,       lowerCAmelCase     :					Optional[int]=30 ,       lowerCAmelCase     :					Dict=400 ,       lowerCAmelCase     :					Optional[Any]=True ,       lowerCAmelCase     :					int=None ,       lowerCAmelCase     :					int=True ,       lowerCAmelCase     :					List[Any]=[0.5, 0.5, 0.5] ,       lowerCAmelCase     :					Optional[int]=[0.5, 0.5, 0.5] ,       lowerCAmelCase     :					int=True ,       lowerCAmelCase     :					List[Any]=1 / 255 ,       lowerCAmelCase     :					Union[str, Any]=True ,       )->	Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase								=      size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
										UpperCAmelCase								=      parent
										UpperCAmelCase								=      batch_size
										UpperCAmelCase								=      num_channels
										UpperCAmelCase								=      min_resolution
										UpperCAmelCase								=      max_resolution
										UpperCAmelCase								=      do_resize
										UpperCAmelCase								=      size
										UpperCAmelCase								=      do_normalize
										UpperCAmelCase								=      image_mean
										UpperCAmelCase								=      image_std
										UpperCAmelCase								=      do_rescale
										UpperCAmelCase								=      rescale_factor
										UpperCAmelCase								=      do_pad




				def 	a__(						self     :					List[str] )->	str:

										"""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 	a__(						self     :					Dict ,       lowerCAmelCase     :					str ,       lowerCAmelCase     :					int=False )->	List[Any]:

										"""simple docstring"""




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

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

										return expected_height, expected_width







@require_torch
@require_vision
class 			UpperCamelCase__(  lowerCAmelCase		,							unittest.TestCase			):
				__magic_name__							: Tuple    						=			DetaImageProcessor if is_vision_available() else None




				def 	a__(						self     :					Optional[Any] )->	Any:

										"""simple docstring"""




										UpperCAmelCase								=      DetaImageProcessingTester(self )




				@property
				def 	a__(						self     :					Tuple )->	List[Any]:

										"""simple docstring"""




										return self.image_processor_tester.prepare_image_processor_dict()




				def 	a__(						self     :					str )->	Optional[int]:

										"""simple docstring"""




										UpperCAmelCase								=      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_ ,       '''do_rescale''' ) )
										self.assertTrue(hasattr(a_ ,       '''do_pad''' ) )
										self.assertTrue(hasattr(a_ ,       '''size''' ) )




				def 	a__(						self     :					str )->	str:

										"""simple docstring"""




										UpperCAmelCase								=      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_ )




				def 	a__(						self     :					Tuple )->	str:

										"""simple docstring"""




										pass




				def 	a__(						self     :					List[Any] )->	List[str]:

										"""simple docstring"""




										UpperCAmelCase								=      self.image_processing_class(**self.image_processor_dict )
										# create random PIL images
										UpperCAmelCase								=      prepare_image_inputs(self.image_processor_tester ,       equal_resolution=a_ )
										for image in image_inputs:
																self.assertIsInstance(a_ ,       Image.Image )

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

										UpperCAmelCase							,							UpperCAmelCase								=      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
										UpperCAmelCase							,							UpperCAmelCase								=      self.image_processor_tester.get_expected_values(a_ ,       batched=a_ )

										UpperCAmelCase								=      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 	a__(						self     :					Union[str, Any] )->	Any:

										"""simple docstring"""




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

										UpperCAmelCase							,							UpperCAmelCase								=      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
										UpperCAmelCase								=      image_processing(a_ ,       return_tensors='''pt''' ).pixel_values

										UpperCAmelCase							,							UpperCAmelCase								=      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 	a__(						self     :					str )->	Optional[Any]:

										"""simple docstring"""




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

										UpperCAmelCase							,							UpperCAmelCase								=      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
										UpperCAmelCase								=      image_processing(a_ ,       return_tensors='''pt''' ).pixel_values

										UpperCAmelCase							,							UpperCAmelCase								=      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,
										    ) ,       )




				@slow
				def 	a__(						self     :					Union[str, Any] )->	str:

										"""simple docstring"""




										UpperCAmelCase								=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
										with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' ,       '''r''' ) as f:
																UpperCAmelCase								=      json.loads(f.read() )

										UpperCAmelCase								=      {'''image_id''': 39769, '''annotations''': target}

										# encode them
										UpperCAmelCase								=      DetaImageProcessor()
										UpperCAmelCase								=      image_processing(images=a_ ,       annotations=a_ ,       return_tensors='''pt''' )

										# verify pixel values
										UpperCAmelCase								=      torch.Size([1, 3, 800, 1066] )
										self.assertEqual(encoding['''pixel_values'''].shape ,       a_ )

										UpperCAmelCase								=      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
										UpperCAmelCase								=      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
										UpperCAmelCase								=      torch.Size([6, 4] )
										self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,       a_ )
										UpperCAmelCase								=      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
										UpperCAmelCase								=      torch.tensor([39769] )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,       a_ ) )
										# verify is_crowd
										UpperCAmelCase								=      torch.tensor([0, 0, 0, 0, 0, 0] )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,       a_ ) )
										# verify class_labels
										UpperCAmelCase								=      torch.tensor([75, 75, 63, 65, 17, 17] )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,       a_ ) )
										# verify orig_size
										UpperCAmelCase								=      torch.tensor([480, 640] )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,       a_ ) )
										# verify size
										UpperCAmelCase								=      torch.tensor([800, 1066] )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,       a_ ) )





				@slow
				def 	a__(						self     :					str )->	List[Any]:

										"""simple docstring"""




										UpperCAmelCase								=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
										with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' ,       '''r''' ) as f:
																UpperCAmelCase								=      json.loads(f.read() )

										UpperCAmelCase								=      {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

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

										# encode them
										UpperCAmelCase								=      DetaImageProcessor(format='''coco_panoptic''' )
										UpperCAmelCase								=      image_processing(images=a_ ,       annotations=a_ ,       masks_path=a_ ,       return_tensors='''pt''' )

										# verify pixel values
										UpperCAmelCase								=      torch.Size([1, 3, 800, 1066] )
										self.assertEqual(encoding['''pixel_values'''].shape ,       a_ )

										UpperCAmelCase								=      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
										UpperCAmelCase								=      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
										UpperCAmelCase								=      torch.Size([6, 4] )
										self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,       a_ )
										UpperCAmelCase								=      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
										UpperCAmelCase								=      torch.tensor([39769] )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,       a_ ) )
										# verify is_crowd
										UpperCAmelCase								=      torch.tensor([0, 0, 0, 0, 0, 0] )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,       a_ ) )
										# verify class_labels
										UpperCAmelCase								=      torch.tensor([17, 17, 63, 75, 75, 93] )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,       a_ ) )
										# verify masks
										UpperCAmelCase								=      822873
										self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() ,       a_ )
										# verify orig_size
										UpperCAmelCase								=      torch.tensor([480, 640] )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,       a_ ) )
										# verify size
										UpperCAmelCase								=      torch.tensor([800, 1066] )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,       a_ ) )








'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


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



class 							lowercase_						(		a			):

  '''simple docstring'''





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


    """simple docstring"""
    warnings.warn(
        'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
        ' use DonutImageProcessor instead.' ,			a_ ,			)
    super().__init__(*a_ ,			**a_  )


