




import unittest

import numpy as np

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


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import BlipImageProcessor




class 		UpperCamelCase__     (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__=False					,  UpperCamelCase__=[0.48145466, 0.4578275, 0.40821073]					,  UpperCamelCase__=[0.26862954, 0.26130258, 0.27577711]					,  UpperCamelCase__=True					,  )					->				Optional[Any]:
           lowerCamelCase       :				Optional[int]      			= size if size is not None else {"height": 20, "width": 20}
           lowerCamelCase       :				Tuple      			= parent
           lowerCamelCase       :				int      			= batch_size
           lowerCamelCase       :				int      			= num_channels
           lowerCamelCase       :				str      			= image_size
           lowerCamelCase       :				Optional[int]      			= min_resolution
           lowerCamelCase       :				Union[str, Any]      			= max_resolution
           lowerCamelCase       :				Union[str, Any]      			= do_resize
           lowerCamelCase       :				str      			= size
           lowerCamelCase       :				Optional[int]      			= do_normalize
           lowerCamelCase       :				Dict      			= image_mean
           lowerCamelCase       :				Optional[int]      			= image_std
           lowerCamelCase       :				str      			= do_pad
           lowerCamelCase       :				List[Any]      			= do_convert_rgb







    def    _lowercase					(   self					)					->				str:
           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_convert_rgb": self.do_convert_rgb,
               "do_pad": self.do_pad,
           }







    def    _lowercase					(   self					,  UpperCamelCase__=False					,  UpperCamelCase__=False					,  UpperCamelCase__=False					)					->				List[Any]:
           assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

           if equal_resolution:
                  lowerCamelCase       :				int      			= []
                  for i in range(self.batch_size					):
                         image_inputs.append(
                             np.random.randint(
                                 255					,  size=(self.num_channels, self.max_resolution, self.max_resolution)					,  dtype=np.uinta					)					)
           else:
                  lowerCamelCase       :				str      			= []
                  for i in range(self.batch_size					):
                         lowerCamelCase			,				lowerCamelCase       :				Any      			= np.random.choice(np.arange(self.min_resolution					,  self.max_resolution					)					,  2					)
                         image_inputs.append(np.random.randint(255					,  size=(self.num_channels, width, height)					,  dtype=np.uinta					)					)

           if not numpify and not torchify:
                  # PIL expects the channel dimension as last dimension
                  lowerCamelCase       :				int      			= [Image.fromarray(np.moveaxis(UpperCamelCase__					,  0					,  -1					)					) for x in image_inputs]

           if torchify:
                  lowerCamelCase       :				Tuple      			= [torch.from_numpy(UpperCamelCase__					) for x in image_inputs]

           return image_inputs




@require_torch
@require_vision
class 		UpperCamelCase__     (lowerCAmelCase__   ,    unittest.TestCase					):

    '''simple docstring'''



    lowerCamelCase_				:     Union[str, Any]     					=				BlipImageProcessor if is_vision_available() else None







    def    _lowercase					(   self					)					->				List[Any]:
           lowerCamelCase       :				Dict      			= BlipImageProcessingTester(self					)







    @property
    def    _lowercase					(   self					)					->				int:
           return self.image_processor_tester.prepare_image_processor_dict()







    def    _lowercase					(   self					)					->				List[str]:
           lowerCamelCase       :				List[str]      			= self.image_processing_class(**self.image_processor_dict					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "do_resize"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "size"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "do_normalize"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "image_mean"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "image_std"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "do_convert_rgb"					)					)







    def    _lowercase					(   self					)					->				List[Any]:
           pass







    def    _lowercase					(   self					)					->				Any:
           # Initialize image_processor
           lowerCamelCase       :				Optional[int]      			= self.image_processing_class(**self.image_processor_dict					)
           # create random PIL images
           lowerCamelCase       :				Tuple      			= self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__					)
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase__					,  Image.Image					)

           # Test not batched input
           lowerCamelCase       :				str      			= image_processor(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
           lowerCamelCase       :				int      			= image_processor(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.size["height"],
                   self.image_processor_tester.size["width"],
               )					,  )







    def    _lowercase					(   self					)					->				Dict:
           # Initialize image_processor
           lowerCamelCase       :				Optional[int]      			= self.image_processing_class(**self.image_processor_dict					)
           # create random numpy tensors
           lowerCamelCase       :				List[Any]      			= self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__					,  numpify=UpperCamelCase__					)
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase__					,  np.ndarray					)

           # Test not batched input
           lowerCamelCase       :				Tuple      			= image_processor(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
           lowerCamelCase       :				List[Any]      			= image_processor(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.size["height"],
                   self.image_processor_tester.size["width"],
               )					,  )







    def    _lowercase					(   self					)					->				Dict:
           # Initialize image_processor
           lowerCamelCase       :				Tuple      			= self.image_processing_class(**self.image_processor_dict					)
           # create random PyTorch tensors
           lowerCamelCase       :				Union[str, Any]      			= self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__					,  torchify=UpperCamelCase__					)
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase__					,  torch.Tensor					)

           # Test not batched input
           lowerCamelCase       :				List[Any]      			= image_processor(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
           lowerCamelCase       :				List[str]      			= image_processor(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.size["height"],
                   self.image_processor_tester.size["width"],
               )					,  )




@require_torch
@require_vision
class 		UpperCamelCase__     (lowerCAmelCase__   ,    unittest.TestCase					):

    '''simple docstring'''



    lowerCamelCase_				:     List[Any]     					=				BlipImageProcessor if is_vision_available() else None







    def    _lowercase					(   self					)					->				Dict:
           lowerCamelCase       :				Dict      			= BlipImageProcessingTester(self					,  num_channels=4					)
           lowerCamelCase       :				str      			= 3







    @property
    def    _lowercase					(   self					)					->				Optional[Any]:
           return self.image_processor_tester.prepare_image_processor_dict()







    def    _lowercase					(   self					)					->				List[str]:
           lowerCamelCase       :				Union[str, Any]      			= self.image_processing_class(**self.image_processor_dict					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "do_resize"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "size"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "do_normalize"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "image_mean"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "image_std"					)					)
           self.assertTrue(hasattr(UpperCamelCase__					,  "do_convert_rgb"					)					)







    def    _lowercase					(   self					)					->				List[Any]:
           pass







    def    _lowercase					(   self					)					->				Optional[int]:
           # Initialize image_processor
           lowerCamelCase       :				Tuple      			= self.image_processing_class(**self.image_processor_dict					)
           # create random PIL images
           lowerCamelCase       :				List[Any]      			= self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__					)
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase__					,  Image.Image					)

           # Test not batched input
           lowerCamelCase       :				Tuple      			= image_processor(image_inputs[0]					,  return_tensors="pt"					).pixel_values
           self.assertEqual(
               encoded_images.shape					,  (
                   1,
                   self.expected_encoded_image_num_channels,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )					,  )

           # Test batched
           lowerCamelCase       :				str      			= image_processor(UpperCamelCase__					,  return_tensors="pt"					).pixel_values
           self.assertEqual(
               encoded_images.shape					,  (
                   self.image_processor_tester.batch_size,
                   self.expected_encoded_image_num_channels,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )					,  )












import numpy as np

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			str:
       lowerCamelCase			,				lowerCamelCase       :				Dict      			= a.shape
       lowerCamelCase       :				Union[str, Any]      			= min(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Union[str, Any]      			= np.eye(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[Any]      			= a.copy()

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

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

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

       return q, r


if __name__ == "__main__":
       import doctest

       doctest.testmod()






