





'''simple docstring'''







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():
					from PIL import Image

					from transformers import ConvNextImageProcessor


class    __a (unittest.TestCase       ):

							def __init__(							self							:       Dict    ,       __magic_name__							:       List[str]    ,       __magic_name__							:       str=7    ,       __magic_name__							:       Optional[int]=3    ,       __magic_name__							:       Union[str, Any]=18    ,       __magic_name__							:       Dict=30    ,       __magic_name__							:       List[Any]=4_00    ,       __magic_name__							:       Dict=True    ,       __magic_name__							:       Union[str, Any]=None    ,       __magic_name__							:       Optional[Any]=0.8_7_5    ,       __magic_name__							:       List[Any]=True    ,       __magic_name__							:       Optional[int]=[0.5, 0.5, 0.5]    ,       __magic_name__							:       Optional[int]=[0.5, 0.5, 0.5]    ,       )   ->    Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					size if size is not None else {'''shortest_edge''': 20}
													UpperCAmelCase_     : Optional[Any]         =					parent
													UpperCAmelCase_     : Any         =					batch_size
													UpperCAmelCase_     : Optional[Any]         =					num_channels
													UpperCAmelCase_     : Optional[Any]         =					image_size
													UpperCAmelCase_     : Optional[int]         =					min_resolution
													UpperCAmelCase_     : Optional[Any]         =					max_resolution
													UpperCAmelCase_     : Optional[int]         =					do_resize
													UpperCAmelCase_     : Optional[Any]         =					size
													UpperCAmelCase_     : int         =					crop_pct
													UpperCAmelCase_     : Optional[int]         =					do_normalize
													UpperCAmelCase_     : Dict         =					image_mean
													UpperCAmelCase_     : int         =					image_std



							def       UpperCAmelCase__		(							self							:       Optional[int]				)   ->    int:







													"""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,
													    "crop_pct": self.crop_pct,
													}





@require_torch
@require_vision
class    __a (lowerCamelCase    ,						unittest.TestCase       ):
							__a   :				Dict       =    ConvNextImageProcessor if is_vision_available() else None

							def       UpperCAmelCase__		(							self							:       Optional[int]				)   ->    Dict:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					ConvNextImageProcessingTester(self				)

							@property
							def       UpperCAmelCase__		(							self							:       List[Any]				)   ->    Any:







													"""simple docstring"""
													return self.image_processor_tester.prepare_image_processor_dict()

							def       UpperCAmelCase__		(							self							:       List[Any]				)   ->    Tuple:







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					self.image_processing_class(**self.image_processor_dict				)
													self.assertTrue(hasattr(__magic_name__    ,       '''do_resize'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''size'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''crop_pct'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''do_normalize'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''image_mean'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''image_std'''				)				)

							def       UpperCAmelCase__		(							self							:       Dict				)   ->    List[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					self.image_processing_class.from_dict(self.image_processor_dict				)
													self.assertEqual(image_processor.size    ,       {'''shortest_edge''': 20}				)

													UpperCAmelCase_     : List[Any]         =					self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42				)
													self.assertEqual(image_processor.size    ,       {'''shortest_edge''': 42}				)

							def       UpperCAmelCase__		(							self							:       str				)   ->    Optional[int]:







													"""simple docstring"""
													pass

							def       UpperCAmelCase__		(							self							:       List[str]				)   ->    int:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : int         =					self.image_processing_class(**self.image_processor_dict				)
													# create random PIL images
													UpperCAmelCase_     : List[Any]         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       Image.Image				)

													# Test not batched input
													UpperCAmelCase_     : int         =					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['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : Tuple         =					image_processing(__magic_name__    ,       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['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

							def       UpperCAmelCase__		(							self							:       str				)   ->    Optional[int]:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : Optional[Any]         =					self.image_processing_class(**self.image_processor_dict				)
													# create random numpy tensors
													UpperCAmelCase_     : Tuple         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__    ,       numpify=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       np.ndarray				)

													# Test not batched input
													UpperCAmelCase_     : int         =					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['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : Union[str, Any]         =					image_processing(__magic_name__    ,       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['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )



							def       UpperCAmelCase__		(							self							:       str				)   ->    int:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : Union[str, Any]         =					self.image_processing_class(**self.image_processor_dict				)
													# create random PyTorch tensors
													UpperCAmelCase_     : List[Any]         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__    ,       torchify=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       torch.Tensor				)

													# Test not batched input
													UpperCAmelCase_     : List[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['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : str         =					image_processing(__magic_name__    ,       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['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )












'''simple docstring'''







import numpy as np
from numpy import floataa
from numpy.typing import NDArray





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     NDArray[floataa],     SCREAMING_SNAKE_CASE__ :     NDArray[floataa]				)	->		NDArray[floataa]:
						UpperCAmelCase_  ,    UpperCAmelCase_     : Any         =					np.shape(SCREAMING_SNAKE_CASE__				)

						UpperCAmelCase_     : NDArray[floataa]         =					np.zeros((rows, 1),     dtype=SCREAMING_SNAKE_CASE__				)
						for row in reversed(range(SCREAMING_SNAKE_CASE__				)				):
												UpperCAmelCase_     : Union[str, Any]         =					0
												for col in range(row + 1,     SCREAMING_SNAKE_CASE__				):
																		total += coefficients[row, col] * x[col]

												UpperCAmelCase_     : str         =					(vector[row] - total) / coefficients[row, row]

						return x





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     NDArray[floataa],     SCREAMING_SNAKE_CASE__ :     NDArray[floataa]				)	->		NDArray[floataa]:
						UpperCAmelCase_  ,    UpperCAmelCase_     : List[Any]         =					np.shape(SCREAMING_SNAKE_CASE__				)
						if rows != columns:
												return np.array((),     dtype=SCREAMING_SNAKE_CASE__				)

						# augmented matrix
						UpperCAmelCase_     : NDArray[floataa]         =					np.concatenate((coefficients, vector),     axis=1				)
						UpperCAmelCase_     : Optional[Any]         =					augmented_mat.astype('''float64'''				)

						# scale the matrix leaving it triangular
						for row in range(rows - 1				):
												UpperCAmelCase_     : Optional[int]         =					augmented_mat[row, row]
												for col in range(row + 1,     SCREAMING_SNAKE_CASE__				):
																		UpperCAmelCase_     : Optional[Any]         =					augmented_mat[col, row] / pivot
																		augmented_mat[col, :] -= factor * augmented_mat[row, :]

						UpperCAmelCase_     : Optional[Any]         =					retroactive_resolution(
						    augmented_mat[:, 0:columns],     augmented_mat[:, columns : columns + 1]				)

						return x


if __name__ == "__main__":
					import doctest

					doctest.testmod()





