


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 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      def __init__( self       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : List[Any]=7  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=3  ,							SCREAMING_SNAKE_CASE__       : List[Any]=1_8  ,							SCREAMING_SNAKE_CASE__       : Tuple=3_0  ,							SCREAMING_SNAKE_CASE__       : Any=4_0_0  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=True  ,							SCREAMING_SNAKE_CASE__       : Tuple=None  ,							SCREAMING_SNAKE_CASE__       : List[Any]=True  ,							SCREAMING_SNAKE_CASE__       : Tuple=None  ,							SCREAMING_SNAKE_CASE__       : Any=True  ,							SCREAMING_SNAKE_CASE__       : int=[0.5, 0.5, 0.5]  ,							SCREAMING_SNAKE_CASE__       : int=[0.5, 0.5, 0.5]  ,							SCREAMING_SNAKE_CASE__       : Tuple=False  ,							)	->    Optional[int]:
             a_      :	Any							=						size if size is not None else {'height': 2_0, 'width': 2_0}
             a_      :	int							=						crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
             a_      :	str							=						parent
             a_      :	Tuple							=						batch_size
             a_      :	List[str]							=						num_channels
             a_      :	int							=						image_size
             a_      :	Tuple							=						min_resolution
             a_      :	List[Any]							=						max_resolution
             a_      :	Optional[Any]							=						do_resize
             a_      :	List[Any]							=						size
             a_      :	List[str]							=						do_center_crop
             a_      :	Any							=						crop_size
             a_      :	str							=						do_normalize
             a_      :	Union[str, Any]							=						image_mean
             a_      :	List[str]							=						image_std
             a_      :	Tuple							=						do_reduce_labels

      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Optional[Any]:
             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  SCREAMING_SNAKE_CASE_		(    )			->	Optional[Any]:

       """simple docstring"""

       a_      :	Any							=						load_dataset('hf-internal-testing/fixtures_ade20k'						,					split='test'       )

       a_      :	Any							=						Image.open(dataset[0]['file']       )
       a_      :	Dict							=						Image.open(dataset[1]['file']       )

       return image, map







def  SCREAMING_SNAKE_CASE_		(    )			->	List[str]:

       """simple docstring"""

       a_      :	List[str]							=						load_dataset('hf-internal-testing/fixtures_ade20k'						,					split='test'       )

       a_      :	Tuple							=						Image.open(ds[0]['file']       )
       a_      :	str							=						Image.open(ds[1]['file']       )
       a_      :	List[Any]							=						Image.open(ds[2]['file']       )
       a_      :	int							=						Image.open(ds[3]['file']       )

       return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					unittest.TestCase    ):
      snake_case__       :  Union[str, Any]       =		BeitImageProcessor if is_vision_available() else None
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Any:
             a_      :	List[str]							=						BeitImageProcessingTester(self  )
      @property
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    int:
             return self.image_processor_tester.prepare_image_processor_dict()
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Dict:
             a_      :	Optional[Any]							=						self.image_processing_class(**self.image_processor_dict  )
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,							'do_resize'  )  )
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,							'size'  )  )
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,							'do_center_crop'  )  )
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,							'center_crop'  )  )
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,							'do_normalize'  )  )
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,							'image_mean'  )  )
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,							'image_std'  )  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Dict:
             a_      :	Dict							=						self.image_processing_class.from_dict(self.image_processor_dict  )
             self.assertEqual(image_processor.size  ,							{'height': 2_0, 'width': 2_0}  )
             self.assertEqual(image_processor.crop_size  ,							{'height': 1_8, 'width': 1_8}  )
             self.assertEqual(image_processor.do_reduce_labels  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Dict							=						self.image_processing_class.from_dict(
                 self.image_processor_dict  ,							size=4_2  ,							crop_size=8_4  ,							reduce_labels=SCREAMING_SNAKE_CASE__  )
             self.assertEqual(image_processor.size  ,							{'height': 4_2, 'width': 4_2}  )
             self.assertEqual(image_processor.crop_size  ,							{'height': 8_4, 'width': 8_4}  )
             self.assertEqual(image_processor.do_reduce_labels  ,							SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Tuple:
             pass
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Optional[int]:
             # Initialize image_processing
             a_      :	Tuple							=						self.image_processing_class(**self.image_processor_dict  )
             # create random PIL images
             a_      :	Optional[int]							=						prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=SCREAMING_SNAKE_CASE__  )
             for image in image_inputs:
                    self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,							Image.Image  )

             # Test not batched input
             a_      :	Any							=						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_      :	Tuple							=						image_processing(SCREAMING_SNAKE_CASE__  ,							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  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Tuple:
             # Initialize image_processing
             a_      :	Optional[Any]							=						self.image_processing_class(**self.image_processor_dict  )
             # create random numpy tensors
             a_      :	str							=						prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=SCREAMING_SNAKE_CASE__  ,							numpify=SCREAMING_SNAKE_CASE__  )
             for image in image_inputs:
                    self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,							np.ndarray  )

             # Test not batched input
             a_      :	str							=						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_      :	Optional[int]							=						image_processing(SCREAMING_SNAKE_CASE__  ,							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  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Tuple:
             # Initialize image_processing
             a_      :	List[str]							=						self.image_processing_class(**self.image_processor_dict  )
             # create random PyTorch tensors
             a_      :	int							=						prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=SCREAMING_SNAKE_CASE__  ,							torchify=SCREAMING_SNAKE_CASE__  )
             for image in image_inputs:
                    self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,							torch.Tensor  )

             # Test not batched input
             a_      :	Dict							=						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_      :	List[str]							=						image_processing(SCREAMING_SNAKE_CASE__  ,							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  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Optional[Any]:
             # Initialize image_processing
             a_      :	int							=						self.image_processing_class(**self.image_processor_dict  )
             # create random PyTorch tensors
             a_      :	Union[str, Any]							=						prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=SCREAMING_SNAKE_CASE__  ,							torchify=SCREAMING_SNAKE_CASE__  )
             a_      :	Dict							=						[]
             for image in image_inputs:
                    self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,							torch.Tensor  )
                    maps.append(torch.zeros(image.shape[-2:]  ).long()  )

             # Test not batched input
             a_      :	Tuple							=						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() <= 2_5_5  )

             # Test batched
             a_      :	Optional[Any]							=						image_processing(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							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() <= 2_5_5  )

             # Test not batched input (PIL images)
             a_      :	Any							=						prepare_semantic_single_inputs()

             a_      :	str							=						image_processing(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							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() <= 2_5_5  )

             # Test batched input (PIL images)
             a_      :	Dict							=						prepare_semantic_batch_inputs()

             a_      :	Optional[Any]							=						image_processing(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							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() <= 2_5_5  )

      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Optional[int]:
             # Initialize image_processing
             a_      :	Optional[int]							=						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_      :	List[Any]							=						prepare_semantic_single_inputs()
             a_      :	str							=						image_processing(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
             self.assertTrue(encoding['labels'].min().item() >= 0  )
             self.assertTrue(encoding['labels'].max().item() <= 1_5_0  )

             a_      :	Tuple							=						True
             a_      :	Any							=						image_processing(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
             self.assertTrue(encoding['labels'].min().item() >= 0  )
             self.assertTrue(encoding['labels'].max().item() <= 2_5_5  )






import unittest

import numpy as np







def  SCREAMING_SNAKE_CASE_		(    __A				:    np.ndarray						,					__A				:    np.ndarray						,					__A				:    np.ndarray						,					__A				:    np.ndarray | None = None						,					)			->	np.ndarray:

       """simple docstring"""

       a_      :	List[str]							=						np.shape(__A       )
       a_      :	Any							=						np.shape(__A       )
       a_      :	List[Any]							=						np.shape(__A       )

       if shape_a[0] != shape_b[0]:
              a_      :	List[Any]							=						(
                  'Expected the same number of rows for A and B. '
                  F"""Instead found A of size {shape_a} and B of size {shape_b}"""
              )
              raise ValueError(__A       )

       if shape_b[1] != shape_c[1]:
              a_      :	int							=						(
                  'Expected the same number of columns for B and C. '
                  F"""Instead found B of size {shape_b} and C of size {shape_c}"""
              )
              raise ValueError(__A       )

       a_      :	Any							=						pseudo_inv
       if a_inv is None:
              try:
                     a_      :	List[str]							=						np.linalg.inv(__A       )
              except np.linalg.LinAlgError:
                     raise ValueError(
                         'Input matrix A is not invertible. Cannot compute Schur complement.'       )

       return mat_c - mat_b.T @ a_inv @ mat_b
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    None:
             a_      :	str							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Any							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	List[str]							=						np.array([[2, 1], [6, 3]]  )

             a_      :	int							=						schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[Any]							=						np.block([[a, b], [b.T, c]]  )

             a_      :	str							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )
             a_      :	str							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )

             self.assertAlmostEqual(SCREAMING_SNAKE_CASE__  ,							det_a * det_s  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    None:
             a_      :	Optional[int]							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Dict							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	str							=						np.array([[2, 1], [6, 3]]  )

             with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
                    schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    None:
             a_      :	Tuple							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Dict							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	List[str]							=						np.array([[2, 1, 3], [6, 3, 5]]  )

             with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
                    schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   unittest.main()


