



'''simple docstring'''
def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		Tuple			    =       len(UpperCAmelCase_							)
						_UpperCamelCase :		Optional[int]			    =       len(matrix[0]							)
						_UpperCamelCase :		List[Any]			    =       min(UpperCAmelCase_  ,  UpperCAmelCase_							)

						for row in range(UpperCAmelCase_							):
												# Check if diagonal element is not zero
												if matrix[row][row] != 0:
																		# Eliminate all the elements below the diagonal
																		for col in range(row + 1  ,  UpperCAmelCase_							):
																								_UpperCamelCase :		int			    =       matrix[col][row] / matrix[row][row]
																								for i in range(UpperCAmelCase_  ,  UpperCAmelCase_							):
																														matrix[col][i] -= multiplier * matrix[row][i]
												else:
																		# Find a non-zero diagonal element to swap rows
																		_UpperCamelCase :		Tuple			    =       True
																		for i in range(row + 1  ,  UpperCAmelCase_							):
																								if matrix[i][row] != 0:
																														_UpperCamelCase    ,      _UpperCamelCase :		Optional[Any]			    =       matrix[i], matrix[row]
																														_UpperCamelCase :		Optional[int]			    =       False
																														break
																		if reduce:
																								rank -= 1
																								for i in range(UpperCAmelCase_							):
																														_UpperCamelCase :		str			    =       matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
																		row -= 1

						return rank


if __name__ == "__main__":
					import doctest

					doctest.testmod()







'''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 MobileViTImageProcessor






class 							lowercase__     (							unittest.TestCase       ):





	def __init__(			self :    List[str]			,lowerCamelCase__ :    Optional[Any]			,lowerCamelCase__ :    Tuple=7			,lowerCamelCase__ :    Tuple=3			,lowerCamelCase__ :    Any=18			,lowerCamelCase__ :    Tuple=30			,lowerCamelCase__ :    Tuple=400			,lowerCamelCase__ :    int=True			,lowerCamelCase__ :    int=None			,lowerCamelCase__ :    str=True			,lowerCamelCase__ :    str=None			,lowerCamelCase__ :    Any=True			,):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       size if size is not None else {'shortest_edge': 20}
							_UpperCamelCase :		int			    =       crop_size if crop_size is not None else {'height': 18, 'width': 18}
							_UpperCamelCase :		List[str]			    =       parent
							_UpperCamelCase :		List[Any]			    =       batch_size
							_UpperCamelCase :		int			    =       num_channels
							_UpperCamelCase :		Optional[int]			    =       image_size
							_UpperCamelCase :		List[Any]			    =       min_resolution
							_UpperCamelCase :		List[str]			    =       max_resolution
							_UpperCamelCase :		int			    =       do_resize
							_UpperCamelCase :		Optional[int]			    =       size
							_UpperCamelCase :		str			    =       do_center_crop
							_UpperCamelCase :		List[Any]			    =       crop_size
							_UpperCamelCase :		Optional[Any]			    =       do_flip_channel_order





	def      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_flip_channel_order": self.do_flip_channel_order,
							}






@require_torch
@require_vision
class 							lowercase__     (							lowercase						, unittest.TestCase       ):
	lowercase__      =      MobileViTImageProcessor if is_vision_available() else None





	def      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Tuple			    =       MobileViTImageProcessingTester(self			)





	@property
	def      UpperCamelCase_  (			self :    List[Any]			):







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





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		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_center_crop'			)			)
							self.assertTrue(hasattr(lowerCamelCase__			,'center_crop'			)			)
							self.assertTrue(hasattr(lowerCamelCase__			,'do_flip_channel_order'			)			)





	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							_UpperCamelCase :		Tuple			    =       self.image_processing_class.from_dict(self.image_processor_dict			)
							self.assertEqual(image_processor.size			,{'shortest_edge': 20}			)
							self.assertEqual(image_processor.crop_size			,{'height': 18, 'width': 18}			)

							_UpperCamelCase :		Union[str, Any]			    =       self.image_processing_class.from_dict(self.image_processor_dict			,size=42			,crop_size=84			)
							self.assertEqual(image_processor.size			,{'shortest_edge': 42}			)
							self.assertEqual(image_processor.crop_size			,{'height': 84, 'width': 84}			)





	def      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							pass





	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase :		int			    =       self.image_processing_class(**self.image_processor_dict			)
							# create random PIL images
							_UpperCamelCase :		int			    =       prepare_image_inputs(self.image_processor_tester			,equal_resolution=lowerCamelCase__			)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__			,Image.Image			)

							# Test not batched input
							_UpperCamelCase :		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.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )			,)

							# Test batched
							_UpperCamelCase :		int			    =       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.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )			,)





	def      UpperCamelCase_  (			self :    Dict			):







							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase :		int			    =       self.image_processing_class(**self.image_processor_dict			)
							# create random numpy tensors
							_UpperCamelCase :		List[Any]			    =       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
							_UpperCamelCase :		Optional[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.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )			,)

							# Test batched
							_UpperCamelCase :		List[str]			    =       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.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )			,)





	def      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase :		Optional[Any]			    =       self.image_processing_class(**self.image_processor_dict			)
							# create random PyTorch tensors
							_UpperCamelCase :		str			    =       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
							_UpperCamelCase :		str			    =       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.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )			,)

							# Test batched
							_UpperCamelCase :		Optional[int]			    =       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.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )			,)


