


"""simple docstring"""






import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
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 BeitImageProcessor


class     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=7    ,				__UpperCAmelCase=3    ,				__UpperCAmelCase=18    ,				__UpperCAmelCase=30    ,				__UpperCAmelCase=400    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=[0.5, 0.5, 0.5]    ,				__UpperCAmelCase=[0.5, 0.5, 0.5]    ,				__UpperCAmelCase=False    ,				)       ->							Optional[int]:
          _a    	=				size if size is not None else {'''height''': 20, '''width''': 20}
          _a    	=				crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
          _a    	=				parent
          _a    	=				batch_size
          _a    	=				num_channels
          _a    	=				image_size
          _a    	=				min_resolution
          _a    	=				max_resolution
          _a    	=				do_resize
          _a    	=				size
          _a    	=				do_center_crop
          _a    	=				crop_size
          _a    	=				do_normalize
          _a    	=				image_mean
          _a    	=				image_std
          _a    	=				do_reduce_labels





     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_reduce_labels": self.do_reduce_labels,
          }







def       A_   (   ):






     """simple docstring"""

     _a    	=				load_dataset('''hf-internal-testing/fixtures_ade20k''',     split='''test'''       )

     _a    	=				Image.open(dataset[0]['''file''']       )
     _a    	=				Image.open(dataset[1]['''file''']       )

     return image, map
def       A_   (   ):






     """simple docstring"""

     _a    	=				load_dataset('''hf-internal-testing/fixtures_ade20k''',     split='''test'''       )

     _a    	=				Image.open(ds[0]['''file''']       )
     _a    	=				Image.open(ds[1]['''file''']       )
     _a    	=				Image.open(ds[2]['''file''']       )
     _a    	=				Image.open(ds[3]['''file''']       )

     return [imagea, imagea], [mapa, mapa]


@require_torch
@require_vision
class     __lowerCamelCase  (					a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	int    =		BeitImageProcessor if is_vision_available() else None


     def 							_UpperCAmelCase      (      self							)       ->							str:
          _a    	=				BeitImageProcessingTester(self							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          return self.image_processor_tester.prepare_image_processor_dict()


     def 							_UpperCAmelCase      (      self							)       ->							int:
          _a    	=				self.image_processing_class(**self.image_processor_dict							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''do_resize'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''size'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''do_center_crop'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''center_crop'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''do_normalize'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''image_mean'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''image_std'''							)							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				self.image_processing_class.from_dict(self.image_processor_dict							)
          self.assertEqual(image_processor.size    ,				{'''height''': 20, '''width''': 20}							)
          self.assertEqual(image_processor.crop_size    ,				{'''height''': 18, '''width''': 18}							)
          self.assertEqual(image_processor.do_reduce_labels    ,				__UpperCAmelCase							)

          _a    	=				self.image_processing_class.from_dict(
              self.image_processor_dict    ,				size=42    ,				crop_size=84    ,				reduce_labels=__UpperCAmelCase							)
          self.assertEqual(image_processor.size    ,				{'''height''': 42, '''width''': 42}							)
          self.assertEqual(image_processor.crop_size    ,				{'''height''': 84, '''width''': 84}							)
          self.assertEqual(image_processor.do_reduce_labels    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          pass


     def 							_UpperCAmelCase      (      self							)       ->							str:
          # 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=__UpperCAmelCase							)
          for image in image_inputs:
               self.assertIsInstance(__UpperCAmelCase    ,				Image.Image							)

          # Test not batched input
          _a    	=				image_processing(image_inputs[0]    ,				return_tensors='''pt'''							).pixel_values
          self.assertEqual(
              encoded_images.shape    ,				(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )    ,				)

          # Test batched
          _a    	=				image_processing(__UpperCAmelCase    ,				return_tensors='''pt'''							).pixel_values
          self.assertEqual(
              encoded_images.shape    ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )    ,				)


     def 							_UpperCAmelCase      (      self							)       ->							int:
          # 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=__UpperCAmelCase    ,				numpify=__UpperCAmelCase							)
          for image in image_inputs:
               self.assertIsInstance(__UpperCAmelCase    ,				np.ndarray							)

          # Test not batched input
          _a    	=				image_processing(image_inputs[0]    ,				return_tensors='''pt'''							).pixel_values
          self.assertEqual(
              encoded_images.shape    ,				(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )    ,				)

          # Test batched
          _a    	=				image_processing(__UpperCAmelCase    ,				return_tensors='''pt'''							).pixel_values
          self.assertEqual(
              encoded_images.shape    ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )    ,				)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          # 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=__UpperCAmelCase    ,				torchify=__UpperCAmelCase							)
          for image in image_inputs:
               self.assertIsInstance(__UpperCAmelCase    ,				torch.Tensor							)

          # Test not batched input
          _a    	=				image_processing(image_inputs[0]    ,				return_tensors='''pt'''							).pixel_values
          self.assertEqual(
              encoded_images.shape    ,				(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )    ,				)

          # Test batched
          _a    	=				image_processing(__UpperCAmelCase    ,				return_tensors='''pt'''							).pixel_values
          self.assertEqual(
              encoded_images.shape    ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )    ,				)


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          # 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=__UpperCAmelCase    ,				torchify=__UpperCAmelCase							)
          _a    	=				[]
          for image in image_inputs:
               self.assertIsInstance(__UpperCAmelCase    ,				torch.Tensor							)
               maps.append(torch.zeros(image.shape[-2:]							).long()							)

          # Test not batched input
          _a    	=				image_processing(image_inputs[0]    ,				maps[0]    ,				return_tensors='''pt'''							)
          self.assertEqual(
              encoding['''pixel_values'''].shape    ,				(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )    ,				)
          self.assertEqual(
              encoding['''labels'''].shape    ,				(
                  1,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )    ,				)
          self.assertEqual(encoding['''labels'''].dtype    ,				torch.long							)
          self.assertTrue(encoding['''labels'''].min().item() >= 0							)
          self.assertTrue(encoding['''labels'''].max().item() <= 255							)

          # Test batched
          _a    	=				image_processing(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_tensors='''pt'''							)
          self.assertEqual(
              encoding['''pixel_values'''].shape    ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )    ,				)
          self.assertEqual(
              encoding['''labels'''].shape    ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )    ,				)
          self.assertEqual(encoding['''labels'''].dtype    ,				torch.long							)
          self.assertTrue(encoding['''labels'''].min().item() >= 0							)
          self.assertTrue(encoding['''labels'''].max().item() <= 255							)

          # Test not batched input (PIL images)
          _a	,      _a    	=				prepare_semantic_single_inputs()

          _a    	=				image_processing(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_tensors='''pt'''							)
          self.assertEqual(
              encoding['''pixel_values'''].shape    ,				(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )    ,				)
          self.assertEqual(
              encoding['''labels'''].shape    ,				(
                  1,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )    ,				)
          self.assertEqual(encoding['''labels'''].dtype    ,				torch.long							)
          self.assertTrue(encoding['''labels'''].min().item() >= 0							)
          self.assertTrue(encoding['''labels'''].max().item() <= 255							)

          # Test batched input (PIL images)
          _a	,      _a    	=				prepare_semantic_batch_inputs()

          _a    	=				image_processing(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_tensors='''pt'''							)
          self.assertEqual(
              encoding['''pixel_values'''].shape    ,				(
                  2,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )    ,				)
          self.assertEqual(
              encoding['''labels'''].shape    ,				(
                  2,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )    ,				)
          self.assertEqual(encoding['''labels'''].dtype    ,				torch.long							)
          self.assertTrue(encoding['''labels'''].min().item() >= 0							)
          self.assertTrue(encoding['''labels'''].max().item() <= 255							)





     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          # Initialize image_processing
          _a    	=				self.image_processing_class(**self.image_processor_dict							)

          # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
          _a	,      _a    	=				prepare_semantic_single_inputs()
          _a    	=				image_processing(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_tensors='''pt'''							)
          self.assertTrue(encoding['''labels'''].min().item() >= 0							)
          self.assertTrue(encoding['''labels'''].max().item() <= 150							)

          _a    	=				True
          _a    	=				image_processing(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_tensors='''pt'''							)
          self.assertTrue(encoding['''labels'''].min().item() >= 0							)
          self.assertTrue(encoding['''labels'''].max().item() <= 255							)



"""simple docstring"""






def       A_   (   _lowerCAmelCase     :					list,     _lowerCAmelCase     :					list,     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     _a    	=				len(_lowerCAmelCase       )
     _a    	=				[[0] * n for i in range(_lowerCAmelCase       )]
     for i in range(_lowerCAmelCase       ):
          _a    	=				y_points[i]

     for i in range(2,     _lowerCAmelCase       ):
          for j in range(_lowerCAmelCase,     _lowerCAmelCase       ):
               _a    	=				(
                   (xa - x_points[j - i + 1]) * q[j][i - 1]
                   - (xa - x_points[j]) * q[j - 1][i - 1]
               ) / (x_points[j] - x_points[j - i + 1])

     return [q[n - 1][n - 1], q]


if __name__ == "__main__":
   import doctest

   doctest.testmod()