






'''simple docstring'''




import random
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, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   import PIL

   from transformers import FlavaImageProcessor
   from transformers.image_utils import PILImageResampling
   from transformers.models.flava.image_processing_flava import (
       FLAVA_CODEBOOK_MEAN,
       FLAVA_CODEBOOK_STD,
       FLAVA_IMAGE_MEAN,
       FLAVA_IMAGE_STD,
   )
else:
   lowerCamelCase_							      =      lowerCamelCase_							      =      lowerCamelCase_							      =      lowerCamelCase_							      =      None

class      _UpperCAmelCase    (  unittest.TestCase		):




   """simple docstring"""

   def __init__(	self				:			Union[str, Any]   ,   __UpperCAmelCase				:			List[Any]   ,   __UpperCAmelCase				:			List[Any]=7   ,   __UpperCAmelCase				:			List[str]=3   ,   __UpperCAmelCase				:			int=30   ,   __UpperCAmelCase				:			Optional[int]=400   ,   __UpperCAmelCase				:			Optional[Any]=True   ,   __UpperCAmelCase				:			Optional[int]=None   ,   __UpperCAmelCase				:			Union[str, Any]=True   ,   __UpperCAmelCase				:			Union[str, Any]=None   ,   __UpperCAmelCase				:			str=None   ,   __UpperCAmelCase				:			Optional[Any]=True   ,   __UpperCAmelCase				:			Union[str, Any]=1 / 255   ,   __UpperCAmelCase				:			Dict=True   ,   __UpperCAmelCase				:			Dict=FLAVA_IMAGE_MEAN   ,   __UpperCAmelCase				:			Optional[int]=FLAVA_IMAGE_STD   ,   __UpperCAmelCase				:			Tuple=14   ,   __UpperCAmelCase				:			int=75   ,   __UpperCAmelCase				:			str=None   ,   __UpperCAmelCase				:			str=16   ,   __UpperCAmelCase				:			str=0.3   ,   __UpperCAmelCase				:			List[str]=None   ,   __UpperCAmelCase				:			Tuple=True   ,   __UpperCAmelCase				:			Optional[Any]=None   ,   __UpperCAmelCase				:			Union[str, Any]=None   ,   __UpperCAmelCase				:			Optional[Any]=True   ,   __UpperCAmelCase				:			List[str]=None   ,   __UpperCAmelCase				:			Any=True   ,   __UpperCAmelCase				:			Any=True   ,   __UpperCAmelCase				:			Optional[Any]=FLAVA_CODEBOOK_MEAN   ,   __UpperCAmelCase				:			Any=FLAVA_CODEBOOK_STD   ,   ):





         '''simple docstring'''





         _A      =			size if size is not None else {"height": 224, "width": 224}
         _A      =			crop_size if crop_size is not None else {"height": 224, "width": 224}
         _A      =			codebook_size if codebook_size is not None else {"height": 112, "width": 112}
         _A      =			codebook_crop_size if codebook_crop_size is not None else {"height": 112, "width": 112}

         _A      =			parent
         _A      =			batch_size
         _A      =			num_channels
         _A      =			do_resize
         _A      =			do_rescale
         _A      =			rescale_factor
         _A      =			min_resolution
         _A      =			max_resolution
         _A      =			size
         _A      =			resample if resample is not None else PILImageResampling.BICUBIC
         _A      =			do_normalize
         _A      =			image_mean
         _A      =			image_std
         _A      =			do_center_crop
         _A      =			crop_size

         _A      =			input_size_patches
         _A      =			total_mask_patches
         _A      =			mask_group_max_patches
         _A      =			mask_group_min_patches
         _A      =			mask_group_min_aspect_ratio
         _A      =			mask_group_max_aspect_ratio

         _A      =			codebook_do_resize
         _A      =			codebook_size
         _A      =			codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
         _A      =			codebook_do_center_crop
         _A      =			codebook_crop_size
         _A      =			codebook_do_map_pixels
         _A      =			codebook_do_normalize
         _A      =			codebook_image_mean
         _A      =			codebook_image_std

   def 		lowerCAmelCase				(	self				:			Tuple	):





         '''simple docstring'''





         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "resample": self.resample,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
             "input_size_patches": self.input_size_patches,
             "total_mask_patches": self.total_mask_patches,
             "mask_group_max_patches": self.mask_group_max_patches,
             "mask_group_min_patches": self.mask_group_min_patches,
             "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
             "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
             "codebook_do_resize": self.codebook_do_resize,
             "codebook_size": self.codebook_size,
             "codebook_resample": self.codebook_resample,
             "codebook_do_center_crop": self.codebook_do_center_crop,
             "codebook_crop_size": self.codebook_crop_size,
             "codebook_do_map_pixels": self.codebook_do_map_pixels,
             "codebook_do_normalize": self.codebook_do_normalize,
             "codebook_image_mean": self.codebook_image_mean,
             "codebook_image_std": self.codebook_image_std,
         }

   def 		lowerCAmelCase				(	self				:			List[str]	):





         '''simple docstring'''





         return (self.size["height"], self.size["width"])

   def 		lowerCAmelCase				(	self				:			str	):





         '''simple docstring'''





         return (
             (self.input_size_patches, self.input_size_patches)
             if not isinstance(self.input_size_patches   ,   __UpperCAmelCase	)
             else self.input_size_patches
         )

   def 		lowerCAmelCase				(	self				:			List[Any]	):





         '''simple docstring'''





         return (self.codebook_size["height"], self.codebook_size["width"])





@require_torch
@require_vision
class      _UpperCAmelCase    (  _a							,					unittest.TestCase		):




   """simple docstring"""

   snake_case					       =		FlavaImageProcessor if is_vision_available() else None
   snake_case					       =		None

   def 		lowerCAmelCase				(	self				:			Optional[int]	):





         '''simple docstring'''





         _A      =			FlavaImageProcessingTester(self	)

   @property
   def 		lowerCAmelCase				(	self				:			int	):





         '''simple docstring'''





         return self.image_processor_tester.prepare_image_processor_dict()

   def 		lowerCAmelCase				(	self				:			List[str]	):





         '''simple docstring'''





         _A      =			self.image_processing_class(**self.image_processor_dict	)
         self.assertTrue(hasattr(__UpperCAmelCase   ,   "image_mean"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase   ,   "image_std"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase   ,   "do_normalize"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase   ,   "do_resize"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase   ,   "resample"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase   ,   "crop_size"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase   ,   "do_center_crop"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase   ,   "do_rescale"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase   ,   "rescale_factor"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase   ,   "masking_generator"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase   ,   "codebook_do_resize"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase   ,   "codebook_size"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase   ,   "codebook_resample"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase   ,   "codebook_do_center_crop"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase   ,   "codebook_crop_size"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase   ,   "codebook_do_map_pixels"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase   ,   "codebook_do_normalize"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase   ,   "codebook_image_mean"	)	)
         self.assertTrue(hasattr(__UpperCAmelCase   ,   "codebook_image_std"	)	)

   def 		lowerCAmelCase				(	self				:			Any	):





         '''simple docstring'''





         _A      =			self.image_processing_class.from_dict(self.image_processor_dict	)
         self.assertEqual(image_processor.size   ,   {"height": 224, "width": 224}	)
         self.assertEqual(image_processor.crop_size   ,   {"height": 224, "width": 224}	)
         self.assertEqual(image_processor.codebook_size   ,   {"height": 112, "width": 112}	)
         self.assertEqual(image_processor.codebook_crop_size   ,   {"height": 112, "width": 112}	)

         _A      =			self.image_processing_class.from_dict(
             self.image_processor_dict   ,   size=42   ,   crop_size=84   ,   codebook_size=33   ,   codebook_crop_size=66	)
         self.assertEqual(image_processor.size   ,   {"height": 42, "width": 42}	)
         self.assertEqual(image_processor.crop_size   ,   {"height": 84, "width": 84}	)
         self.assertEqual(image_processor.codebook_size   ,   {"height": 33, "width": 33}	)
         self.assertEqual(image_processor.codebook_crop_size   ,   {"height": 66, "width": 66}	)

   def 		lowerCAmelCase				(	self				:			Tuple	):





         '''simple docstring'''





         pass

   def 		lowerCAmelCase				(	self				:			List[str]	):





         '''simple docstring'''





         _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   ,   PIL.Image.Image	)

         # Test not batched input
         _A      =			image_processing(image_inputs[0]   ,   return_tensors="pt"	)

         # Test no bool masked pos
         self.assertFalse("bool_masked_pos" in encoded_images	)

         _A  ,			_A      =			self.image_processor_tester.get_expected_image_size()

         self.assertEqual(
             encoded_images.pixel_values.shape   ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,   )

         # Test batched
         _A      =			image_processing(__UpperCAmelCase   ,   return_tensors="pt"	)
         _A  ,			_A      =			self.image_processor_tester.get_expected_image_size()

         # Test no bool masked pos
         self.assertFalse("bool_masked_pos" in encoded_images	)

         self.assertEqual(
             encoded_images.pixel_values.shape   ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )   ,   )

   def 		lowerCAmelCase				(	self				:			Dict   ,   __UpperCAmelCase				:			Tuple   ,   __UpperCAmelCase				:			int	):





         '''simple docstring'''





         _A      =			self.image_processing_class(**self.image_processor_dict	)
         # create random tensors
         _A      =			prepare_image_inputs(self.image_processor_tester   ,   equal_resolution=__UpperCAmelCase   ,   **__UpperCAmelCase	)
         for image in image_inputs:
               self.assertIsInstance(__UpperCAmelCase   ,   __UpperCAmelCase	)

         # Test not batched input
         _A      =			image_processing(image_inputs[0]   ,   return_tensors="pt"	)

         _A  ,			_A      =			self.image_processor_tester.get_expected_image_size()
         self.assertEqual(
             encoded_images.pixel_values.shape   ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,   )

         _A      =			image_processing(__UpperCAmelCase   ,   return_image_mask=__UpperCAmelCase   ,   return_tensors="pt"	)

         _A  ,			_A      =			self.image_processor_tester.get_expected_image_size()
         self.assertEqual(
             encoded_images.pixel_values.shape   ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )   ,   )

         _A  ,			_A      =			self.image_processor_tester.get_expected_mask_size()
         self.assertEqual(
             encoded_images.bool_masked_pos.shape   ,   (
                 self.image_processor_tester.batch_size,
                 expected_height,
                 expected_width,
             )   ,   )

         # Test batched
         _A      =			image_processing(__UpperCAmelCase   ,   return_tensors="pt"	).pixel_values

         _A  ,			_A      =			self.image_processor_tester.get_expected_image_size()
         self.assertEqual(
             encoded_images.shape   ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )   ,   )

         # Test masking
         _A      =			image_processing(__UpperCAmelCase   ,   return_image_mask=__UpperCAmelCase   ,   return_tensors="pt"	)

         _A  ,			_A      =			self.image_processor_tester.get_expected_image_size()
         self.assertEqual(
             encoded_images.pixel_values.shape   ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )   ,   )

         _A  ,			_A      =			self.image_processor_tester.get_expected_mask_size()
         self.assertEqual(
             encoded_images.bool_masked_pos.shape   ,   (
                 self.image_processor_tester.batch_size,
                 expected_height,
                 expected_width,
             )   ,   )

   def 		lowerCAmelCase				(	self				:			Union[str, Any]	):





         '''simple docstring'''





         self._test_call_framework(np.ndarray   ,   prepare_kwargs={"numpify": True}	)

   def 		lowerCAmelCase				(	self				:			Dict	):





         '''simple docstring'''





         self._test_call_framework(torch.Tensor   ,   prepare_kwargs={"torchify": True}	)

   def 		lowerCAmelCase				(	self				:			Dict	):





         '''simple docstring'''





         random.seed(1234	)
         _A      =			self.image_processing_class(**self.image_processor_dict	)
         _A      =			prepare_image_inputs(self.image_processor_tester   ,   equal_resolution=__UpperCAmelCase   ,   torchify=__UpperCAmelCase	)

         # Test not batched input
         _A      =			image_processing(image_inputs[0]   ,   return_image_mask=__UpperCAmelCase   ,   return_tensors="pt"	)
         self.assertEqual(encoded_images.bool_masked_pos.sum().item()   ,   75	)

   def 		lowerCAmelCase				(	self				:			Tuple	):





         '''simple docstring'''





         _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   ,   PIL.Image.Image	)

         # Test not batched input
         _A      =			image_processing(image_inputs[0]   ,   return_codebook_pixels=__UpperCAmelCase   ,   return_tensors="pt"	)
         _A  ,			_A      =			self.image_processor_tester.get_expected_codebook_image_size()
         self.assertEqual(
             encoded_images.codebook_pixel_values.shape   ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,   )

         # Test batched
         _A      =			image_processing(__UpperCAmelCase   ,   return_codebook_pixels=__UpperCAmelCase   ,   return_tensors="pt"	)
         _A  ,			_A      =			self.image_processor_tester.get_expected_codebook_image_size()
         self.assertEqual(
             encoded_images.codebook_pixel_values.shape   ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )   ,   )













'''simple docstring'''




from typing import Any

import numpy as np

def       __lowercase		(    __lowercase ) ->		bool:

      '''simple docstring'''


      assert np.shape(__lowercase )[0] == np.shape(__lowercase )[1]

      # If matrix not symmetric, exit right away.
      if np.allclose(__lowercase     ,						matrix.T ) is False:
            return False

      # Get eigenvalues and eignevectors for a symmetric matrix.
      _A  ,			_A      =			np.linalg.eigh(__lowercase )

      # Check sign of all eigenvalues.
      # np.all returns a value of type np.bool_
      return bool(np.all(eigen_values > 0 ) )

def       __lowercase		(    __lowercase ) ->		Any:

      '''simple docstring'''


      _A      =			np.random.randn(__lowercase     ,						__lowercase )
      _A      =			np.dot(__lowercase     ,						random_matrix.T )
      assert _is_matrix_spd(__lowercase )
      return spd_matrix

def       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase = 1000     ,						__lowercase = 1e-8     ,						) ->		Any:

      '''simple docstring'''


      assert np.shape(__lowercase )[0] == np.shape(__lowercase )[1]
      assert np.shape(__lowercase )[0] == np.shape(__lowercase )[0]
      assert _is_matrix_spd(__lowercase )

      # Initialize solution guess, residual, search direction.
      _A      =			np.zeros((np.shape(__lowercase )[0], 1) )
      _A      =			np.copy(__lowercase )
      _A      =			np.copy(__lowercase )

      # Set initial errors in solution guess and residual.
      _A      =			1e9
      _A      =			1e9
      _A      =			1e9

      # Set iteration counter to threshold number of iterations.
      _A      =			0

      while error > tol:
            # Save this value so we only calculate the matrix-vector product once.
            _A      =			np.dot(__lowercase     ,						__lowercase )

            # The main algorithm.

            # Update search direction magnitude.
            _A      =			np.dot(ra.T     ,						__lowercase ) / np.dot(pa.T     ,						__lowercase )
            # Update solution guess.
            _A      =			xa + alpha * pa
            # Calculate new residual.
            _A      =			ra - alpha * w
            # Calculate new Krylov subspace scale.
            _A      =			np.dot(r.T     ,						__lowercase ) / np.dot(ra.T     ,						__lowercase )
            # Calculate new A conjuage search direction.
            _A      =			r + beta * pa

            # Calculate errors.
            _A      =			np.linalg.norm(r - ra )
            _A      =			np.linalg.norm(x - xa )
            _A      =			np.maximum(__lowercase     ,						__lowercase )

            # Update variables.
            _A      =			np.copy(__lowercase )
            _A      =			np.copy(__lowercase )
            _A      =			np.copy(__lowercase )

            # Update number of iterations.
            iterations += 1
            if iterations > max_iterations:
                  break

      return x

def       __lowercase		(    ) ->		None:

      '''simple docstring'''


      _A      =			3
      _A      =			_create_spd_matrix(__lowercase )
      _A      =			np.random.randn(__lowercase     ,						1 )
      _A      =			np.dot(__lowercase     ,						__lowercase )

      # Numpy solution.
      _A      =			np.linalg.solve(__lowercase     ,						__lowercase )

      # Our implementation.
      _A      =			conjugate_gradient(__lowercase     ,						__lowercase )

      # Ensure both solutions are close to x_true (and therefore one another).
      assert np.linalg.norm(x_numpy - x_true ) <= 1e-6
      assert np.linalg.norm(x_conjugate_gradient - x_true ) <= 1e-6


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    test_conjugate_gradient()





