


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 SegformerImageProcessor







class 	_snake_case						(   unittest.TestCase							):




 def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase=7      ,						_lowerCamelCase=3      ,						_lowerCamelCase=30      ,						_lowerCamelCase=400      ,						_lowerCamelCase=True      ,						_lowerCamelCase=None      ,						_lowerCamelCase=True      ,						_lowerCamelCase=[0.5, 0.5, 0.5]      ,						_lowerCamelCase=[0.5, 0.5, 0.5]      ,						_lowerCamelCase=False      ,						):
       a		:str											= size if size is not None else {'''height''': 30, '''width''': 30}
       a		:int											= parent
       a		:Dict											= batch_size
       a		:Union[str, Any]											= num_channels
       a		:Any											= min_resolution
       a		:Union[str, Any]											= max_resolution
       a		:Optional[int]											= do_resize
       a		:Dict											= size
       a		:List[Any]											= do_normalize
       a		:str											= image_mean
       a		:Any											= image_std
       a		:Any											= do_reduce_labels




 def  SCREAMING_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_reduce_labels": self.do_reduce_labels,
       }







def        __lowerCamelCase		(				):




      """simple docstring"""





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

      a		:int											= Image.open(dataset[0]['''file''']		)
      a		:Any											= Image.open(dataset[1]['''file''']		)

      return image, map
def        __lowerCamelCase		(				):




      """simple docstring"""





      a		:Dict											= load_dataset('''hf-internal-testing/fixtures_ade20k'''				,						split='''test'''		)

      a		:Union[str, Any]											= Image.open(dataset[0]['''file''']		)
      a		:Optional[int]											= Image.open(dataset[1]['''file''']		)
      a		:str											= Image.open(dataset[2]['''file''']		)
      a		:List[Any]											= Image.open(dataset[3]['''file''']		)

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







@require_torch
@require_vision
class 	_snake_case						(   _snake_case		,					unittest.TestCase							):
 SCREAMING_SNAKE_CASE__   = SegformerImageProcessor if is_vision_available() else None




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= SegformerImageProcessingTester(self					)




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




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:List[Any]											= self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(_lowerCamelCase      ,						'''do_resize'''					)					)
       self.assertTrue(hasattr(_lowerCamelCase      ,						'''size'''					)					)
       self.assertTrue(hasattr(_lowerCamelCase      ,						'''do_normalize'''					)					)
       self.assertTrue(hasattr(_lowerCamelCase      ,						'''image_mean'''					)					)
       self.assertTrue(hasattr(_lowerCamelCase      ,						'''image_std'''					)					)
       self.assertTrue(hasattr(_lowerCamelCase      ,						'''do_reduce_labels'''					)					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[int]											= self.image_processing_class.from_dict(self.image_processor_dict					)
       self.assertEqual(image_processor.size      ,						{'''height''': 30, '''width''': 30}					)
       self.assertEqual(image_processor.do_reduce_labels      ,						_lowerCamelCase					)

       a		:Union[str, Any]											= self.image_processing_class.from_dict(self.image_processor_dict      ,						size=42      ,						reduce_labels=_lowerCamelCase					)
       self.assertEqual(image_processor.size      ,						{'''height''': 42, '''width''': 42}					)
       self.assertEqual(image_processor.do_reduce_labels      ,						_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       pass




 def  SCREAMING_SNAKE_CASE__       (	self					):
       # Initialize image_processing
       a		:Optional[int]											= self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       a		:List[Any]											= prepare_image_inputs(self.image_processor_tester      ,						equal_resolution=_lowerCamelCase					)
       for image in image_inputs:
             self.assertIsInstance(_lowerCamelCase      ,						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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )      ,						)

       # Test batched
       a		:List[Any]											= image_processing(_lowerCamelCase      ,						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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )      ,						)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       # Initialize image_processing
       a		:int											= self.image_processing_class(**self.image_processor_dict					)
       # create random numpy tensors
       a		:Tuple											= prepare_image_inputs(self.image_processor_tester      ,						equal_resolution=_lowerCamelCase      ,						numpify=_lowerCamelCase					)
       for image in image_inputs:
             self.assertIsInstance(_lowerCamelCase      ,						np.ndarray					)

       # 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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )      ,						)

       # Test batched
       a		:Optional[Any]											= image_processing(_lowerCamelCase      ,						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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )      ,						)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       # Initialize image_processing
       a		:List[str]											= self.image_processing_class(**self.image_processor_dict					)
       # create random PyTorch tensors
       a		:List[Any]											= prepare_image_inputs(self.image_processor_tester      ,						equal_resolution=_lowerCamelCase      ,						torchify=_lowerCamelCase					)
       for image in image_inputs:
             self.assertIsInstance(_lowerCamelCase      ,						torch.Tensor					)

       # Test not batched input
       a		:Optional[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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )      ,						)

       # Test batched
       a		:Optional[Any]											= image_processing(_lowerCamelCase      ,						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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )      ,						)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       # Initialize image_processing
       a		:Union[str, Any]											= self.image_processing_class(**self.image_processor_dict					)
       # create random PyTorch tensors
       a		:Optional[int]											= prepare_image_inputs(self.image_processor_tester      ,						equal_resolution=_lowerCamelCase      ,						torchify=_lowerCamelCase					)
       a		:int											= []
       for image in image_inputs:
             self.assertIsInstance(_lowerCamelCase      ,						torch.Tensor					)
             maps.append(torch.zeros(image.shape[-2:]					).long()					)

       # Test not batched input
       a		:Any											= 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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )      ,						)
       self.assertEqual(
           encoding['''labels'''].shape      ,						(
               1,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.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		:Optional[Any]											= image_processing(_lowerCamelCase      ,						_lowerCamelCase      ,						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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )      ,						)
       self.assertEqual(
           encoding['''labels'''].shape      ,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.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		:List[Any]											= prepare_semantic_single_inputs()

       a		:List[Any]											= image_processing(_lowerCamelCase      ,						_lowerCamelCase      ,						return_tensors='''pt'''					)
       self.assertEqual(
           encoding['''pixel_values'''].shape      ,						(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )      ,						)
       self.assertEqual(
           encoding['''labels'''].shape      ,						(
               1,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.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		:List[Any]											= prepare_semantic_batch_inputs()

       a		:List[str]											= image_processing(_lowerCamelCase      ,						_lowerCamelCase      ,						return_tensors='''pt'''					)
       self.assertEqual(
           encoding['''pixel_values'''].shape      ,						(
               2,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )      ,						)
       self.assertEqual(
           encoding['''labels'''].shape      ,						(
               2,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )      ,						)
       self.assertEqual(encoding['''labels'''].dtype      ,						torch.long					)
       self.assertTrue(encoding['''labels'''].min().item() >= 0					)
       self.assertTrue(encoding['''labels'''].max().item() <= 255					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       # Initialize image_processing
       a		:Tuple											= 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		:Any											= prepare_semantic_single_inputs()
       a		:Optional[Any]											= image_processing(_lowerCamelCase      ,						_lowerCamelCase      ,						return_tensors='''pt'''					)
       self.assertTrue(encoding['''labels'''].min().item() >= 0					)
       self.assertTrue(encoding['''labels'''].max().item() <= 150					)

       a		:Tuple											= True
       a		:Optional[Any]											= image_processing(_lowerCamelCase      ,						_lowerCamelCase      ,						return_tensors='''pt'''					)
       self.assertTrue(encoding['''labels'''].min().item() >= 0					)
       self.assertTrue(encoding['''labels'''].max().item() <= 255					)









import numpy as np
def        __lowerCamelCase		(				UpperCAmelCase_		:	Optional[Any]		):




      """simple docstring"""





      a							,       a		:Union[str, Any]											= a.shape
      a		:Any											= min(UpperCAmelCase_				,						UpperCAmelCase_		)
      a		:Tuple											= np.eye(UpperCAmelCase_		)
      a		:Any											= a.copy()

      for k in range(t - 1		):
            # select a column of modified matrix A':
            a		:List[str]											= r[k:, [k]]
            # construct first basis vector
            a		:List[str]											= np.zeros_like(UpperCAmelCase_		)
            a		:List[Any]											= 1.0
            # determine scaling factor
            a		:Union[str, Any]											= np.linalg.norm(UpperCAmelCase_		)
            # construct vector v for Householder reflection
            a		:List[str]											= x + np.sign(x[0]		) * alpha * ea
            v /= np.linalg.norm(UpperCAmelCase_		)

            # construct the Householder matrix
            a		:Union[str, Any]											= np.eye(m - k		) - 2.0 * v @ v.T
            # pad with ones and zeros as necessary
            a		:Tuple											= np.block([[np.eye(UpperCAmelCase_		), np.zeros((k, m - k)		)], [np.zeros((m - k, k)		), q_k]]		)

            a		:List[Any]											= q @ q_k.T
            a		:int											= q_k @ r

      return q, r


if __name__ == "__main__":
     import doctest

     doctest.testmod()





