




'''simple docstring'''




import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
							import numpy as np
							import torch

if is_vision_available():
							from PIL import Image




def     snake_case__     (			lowerCamelCase__					: Any      ,		lowerCamelCase__					: Tuple=False      ,		lowerCamelCase__					: List[str]=False      ,		lowerCamelCase__					: int=False      ) -> Optional[Any]:
					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					A_			:  int			=				[]
					for i in range(image_processor_tester.batch_size      ):
										if equal_resolution:
															A_			:  Optional[int]			=				image_processor_tester.max_resolution
										else:
															# To avoid getting image width/height 0
															A_			:  Dict			=				image_processor_tester.min_resolution
															if getattr(UpperCamelCase__      ,		'''size_divisor'''      ,		UpperCamelCase__      ):
																				# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
																				A_			:  List[Any]			=				max(image_processor_tester.size_divisor      ,		UpperCamelCase__      )
															A_			,A_			:  Optional[Any]			=				np.random.choice(np.arange(UpperCamelCase__      ,		image_processor_tester.max_resolution      )      ,		2      )
										image_inputs.append(
										    np.random.randint(2_5_5      ,		size=(image_processor_tester.num_channels, width, height)      ,		dtype=np.uinta      )      )

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

					if torchify:
										A_			:  List[Any]			=				[torch.from_numpy(UpperCamelCase__      ) for image in image_inputs]

					return image_inputs




def     snake_case__     (			lowerCamelCase__					: str      ,		lowerCamelCase__					: Optional[int]=1_0      ,		lowerCamelCase__					: Optional[int]=1_0      ,		lowerCamelCase__					: Optional[int]=False      ,		lowerCamelCase__					: Any=False      ) -> int:
					A_			:  Optional[Any]			=				[]
					for i in range(image_processor_tester.num_frames      ):
										video.append(np.random.randint(2_5_5      ,		size=(image_processor_tester.num_channels, width, height)      ,		dtype=np.uinta      )      )

					if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										A_			:  str			=				[Image.fromarray(np.moveaxis(UpperCamelCase__      ,		0      ,		-1      )      ) for frame in video]

					if torchify:
										A_			:  Union[str, Any]			=				[torch.from_numpy(UpperCamelCase__      ) for frame in video]

					return video




def     snake_case__     (			lowerCamelCase__					: Tuple      ,		lowerCamelCase__					: int=False      ,		lowerCamelCase__					: Optional[Any]=False      ,		lowerCamelCase__					: Dict=False      ) -> Optional[Any]:
					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					A_			:  Dict			=				[]
					for i in range(image_processor_tester.batch_size      ):
										if equal_resolution:
															A_			:  List[str]			=				image_processor_tester.max_resolution
										else:
															A_			,A_			:  Dict			=				np.random.choice(
															    np.arange(image_processor_tester.min_resolution      ,		image_processor_tester.max_resolution      )      ,		2      )
															A_			:  List[str]			=				prepare_video(
															    image_processor_tester=UpperCamelCase__      ,		width=UpperCamelCase__      ,		height=UpperCamelCase__      ,		numpify=UpperCamelCase__      ,		torchify=UpperCamelCase__      ,		)
										video_inputs.append(UpperCamelCase__      )

					return video_inputs






class      UpperCamelCase_  :







						"""simple docstring"""


						_lowerCAmelCase         =       None







						def    _a     (  self   :							Tuple     ):





											"""simple docstring"""







											A_			:  Optional[Any]			=				self.image_processing_class(**self.image_processor_dict     )
											A_			:  Dict			=				json.loads(image_processor.to_json_string()     )
											for key, value in self.image_processor_dict.items():
																self.assertEqual(obj[key]				,			lowerCAmelCase_     )







						def    _a     (  self   :							Tuple     ):





											"""simple docstring"""







											A_			:  str			=				self.image_processing_class(**self.image_processor_dict     )

											with tempfile.TemporaryDirectory() as tmpdirname:
																A_			:  Tuple			=				os.path.join(lowerCAmelCase_				,			'''image_processor.json'''     )
																image_processor_first.to_json_file(lowerCAmelCase_     )
																A_			:  List[str]			=				self.image_processing_class.from_json_file(lowerCAmelCase_     )

											self.assertEqual(image_processor_second.to_dict()				,			image_processor_first.to_dict()     )







						def    _a     (  self   :							List[Any]     ):





											"""simple docstring"""







											A_			:  Any			=				self.image_processing_class(**self.image_processor_dict     )

											with tempfile.TemporaryDirectory() as tmpdirname:
																A_			:  Dict			=				image_processor_first.save_pretrained(lowerCAmelCase_     )[0]
																check_json_file_has_correct_format(lowerCAmelCase_     )
																A_			:  Dict			=				self.image_processing_class.from_pretrained(lowerCAmelCase_     )

											self.assertEqual(image_processor_second.to_dict()				,			image_processor_first.to_dict()     )







						def    _a     (  self   :							Any     ):





											"""simple docstring"""







											A_			:  str			=				self.image_processing_class()
											self.assertIsNotNone(lowerCAmelCase_     )







						@require_torch
						@require_vision
						def    _a     (  self   :							Union[str, Any]     ):





											"""simple docstring"""







											if self.test_cast_dtype is not None:
																# Initialize image_processor
																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=lowerCAmelCase_				,			torchify=lowerCAmelCase_     )

																A_			:  str			=				image_processor(lowerCAmelCase_				,			return_tensors='''pt'''     )
																# for layoutLM compatiblity
																self.assertEqual(encoding.pixel_values.device				,			torch.device('''cpu'''     )     )
																self.assertEqual(encoding.pixel_values.dtype				,			torch.floataa     )

																A_			:  Any			=				image_processor(lowerCAmelCase_				,			return_tensors='''pt'''     ).to(torch.floataa     )
																self.assertEqual(encoding.pixel_values.device				,			torch.device('''cpu'''     )     )
																self.assertEqual(encoding.pixel_values.dtype				,			torch.floataa     )

																A_			:  List[str]			=				image_processor(lowerCAmelCase_				,			return_tensors='''pt'''     ).to('''cpu'''				,			torch.bfloataa     )
																self.assertEqual(encoding.pixel_values.device				,			torch.device('''cpu'''     )     )
																self.assertEqual(encoding.pixel_values.dtype				,			torch.bfloataa     )

																with self.assertRaises(lowerCAmelCase_     ):
																					A_			:  Optional[Any]			=				image_processor(lowerCAmelCase_				,			return_tensors='''pt'''     ).to(torch.bfloataa				,			'''cpu'''     )

																# Try with text + image feature
																A_			:  List[Any]			=				image_processor(lowerCAmelCase_				,			return_tensors='''pt'''     )
																encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]]     )}     )
																A_			:  List[Any]			=				encoding.to(torch.floataa     )

																self.assertEqual(encoding.pixel_values.device				,			torch.device('''cpu'''     )     )
																self.assertEqual(encoding.pixel_values.dtype				,			torch.floataa     )
																self.assertEqual(encoding.input_ids.dtype				,			torch.long     )










from typing import Any

import numpy as np





def     lowerCamelCase__  (  UpperCamelCase__      :							np.ndarray    )      ->			bool:



					'''simple docstring'''




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

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

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

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





def     lowerCamelCase__  (  UpperCamelCase__      :							int    )      ->			Any:



					'''simple docstring'''




					_snake_case						=					np.random.randn(UpperCamelCase__							,     UpperCamelCase__    )
					_snake_case						=					np.dot(UpperCamelCase__							,     random_matrix.T    )
					assert _is_matrix_spd(UpperCamelCase__    )
					return spd_matrix





def     lowerCamelCase__  (  UpperCamelCase__      :							np.ndarray							,     UpperCamelCase__      :							np.ndarray							,     UpperCamelCase__      :							int = 1_000							,     UpperCamelCase__      :							float = 1e-8							,     )      ->			Any:



					'''simple docstring'''




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

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

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

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

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

										# The main algorithm.

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

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

										# Update variables.
										_snake_case						=					np.copy(UpperCamelCase__    )
										_snake_case						=					np.copy(UpperCamelCase__    )
										_snake_case						=					np.copy(UpperCamelCase__    )

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

					return x





def     lowerCamelCase__  (  )      ->			None:



					'''simple docstring'''




					_snake_case						=					3
					_snake_case						=					_create_spd_matrix(UpperCamelCase__    )
					_snake_case						=					np.random.randn(UpperCamelCase__							,     1    )
					_snake_case						=					np.dot(UpperCamelCase__							,     UpperCamelCase__    )

					# Numpy solution.
					_snake_case						=					np.linalg.solve(UpperCamelCase__							,     UpperCamelCase__    )

					# Our implementation.
					_snake_case						=					conjugate_gradient(UpperCamelCase__							,     UpperCamelCase__    )

					# 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()



