


from __future__ import annotations

from typing import Any


class     __UpperCAmelCase							:


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A  ,       __A = 0    ):
        __a	,    __a					=  row, column
        __a					=  [[default_value for c in range(__A    )] for r in range(__A    )]


     def __str__(	self    ):
        __a					=  f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

        # Make string identifier
        __a					=  0
        for row_vector in self.array:
           for obj in row_vector:
              __a					=  max(__A  ,       len(str(__A    )    )    )
        __a					=  f'''%{max_element_length}s'''

        # Make string and return
        def single_line(__A    ) -> str:
           nonlocal string_format_identifier
           __a					=  """["""
           line += ", ".join(string_format_identifier % (obj,) for obj in row_vector    )
           line += "]"
           return line

        s += "\n".join(single_line(__A    ) for row_vector in self.array    )
        return s


     def __repr__(	self    ):
        return str(self    )


     def 	snake_case_  (	self  ,       __A    ):
        if not (isinstance(__A  ,       (list, tuple)    ) and len(__A    ) == 2):
           return False
        elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
           return False
        else:
           return True


     def __getitem__(	self  ,       __A    ):
        assert self.validate_indicies(__A    )
        return self.array[loc[0]][loc[1]]


     def __setitem__(	self  ,       __A  ,       __A    ):
        assert self.validate_indicies(__A    )
        __a					=  value


     def __add__(	self  ,       __A    ):
        assert isinstance(__A  ,       __A    )
        assert self.row == another.row and self.column == another.column

        # Add
        __a					=  Matrix(self.row  ,       self.column    )
        for r in range(self.row    ):
           for c in range(self.column    ):
              __a					=  self[r, c] + another[r, c]
        return result


     def __neg__(	self    ):
        __a					=  Matrix(self.row  ,       self.column    )
        for r in range(self.row    ):
           for c in range(self.column    ):
              __a					=  -self[r, c]
        return result


     def __sub__(	self  ,       __A    ):
        return self + (-another)


     def __mul__(	self  ,       __A    ):

        if isinstance(__A  ,       (int, float)    ):  # Scalar multiplication
           __a					=  Matrix(self.row  ,       self.column    )
           for r in range(self.row    ):
              for c in range(self.column    ):
                 __a					=  self[r, c] * another
           return result
        elif isinstance(__A  ,       __A    ):  # Matrix multiplication
           assert self.column == another.row
           __a					=  Matrix(self.row  ,       another.column    )
           for r in range(self.row    ):
              for c in range(another.column    ):
                 for i in range(self.column    ):
                    result[r, c] += self[r, i] * another[i, c]
           return result
        else:
           __a					=  f'''Unsupported type given for another ({type(__A    )})'''
           raise TypeError(__A    )


     def 	snake_case_  (	self    ):
        __a					=  Matrix(self.column  ,       self.row    )
        for r in range(self.row    ):
           for c in range(self.column    ):
              __a					=  self[r, c]
        return result


     def 	snake_case_  (	self  ,       __A  ,       __A    ):
        assert isinstance(__A  ,       __A    ) and isinstance(__A  ,       __A    )
        assert self.row == self.column == u.row == v.row  # u, v should be column vector
        assert u.column == v.column == 1  # u, v should be column vector

        # Calculate
        __a					=  v.transpose()
        __a					=  (v_t * self * u)[0, 0] + 1
        if numerator_factor == 0:
           return None  # It's not invertable
        return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":




  def  a   ():
     # a^(-1)
     __a					=  Matrix(3		,				3		,				0   )
     for i in range(3   ):
        __a					=  1
     print(f'''a^(-1) is {ainv}'''   )
     # u, v
     __a					=  Matrix(3		,				1		,				0   )
     __a	,    __a	,    __a					=  1, 2, -3
     __a					=  Matrix(3		,				1		,				0   )
     __a	,    __a	,    __a					=  4, -2, 5
     print(f'''u is {u}'''   )
     print(f'''v is {v}'''   )
     print(f'''uv^T is {u * v.transpose()}'''   )
     # Sherman Morrison
     print(f'''(a + uv^T)^(-1) is {ainv.sherman_morrison(lowerCAmelCase__		,				lowerCAmelCase__   )}'''   )




  def  a   ():
     import doctest

     doctest.testmod()

  testa()







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 ViTImageProcessor


class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A=13  ,       __A=3  ,       __A=224  ,       __A=30  ,       __A=400  ,       __A=True  ,       __A=None  ,       __A=True  ,       __A=[0.5, 0.5, 0.5]  ,       __A=[0.5, 0.5, 0.5]  ,       ):
        __a					=  size if size is not None else {"""height""": 18, """width""": 18}
        __a					=  parent
        __a					=  batch_size
        __a					=  num_channels
        __a					=  image_size
        __a					=  min_resolution
        __a					=  max_resolution
        __a					=  do_resize
        __a					=  size
        __a					=  do_normalize
        __a					=  image_mean
        __a					=  image_std


     def 	snake_case_  (	self    ):
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
        }







@require_torch
@require_vision
class     __UpperCAmelCase							(       __A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      ViTImageProcessor if is_vision_available() else None


     def 	snake_case_  (	self    ):
        __a					=  EfficientFormerImageProcessorTester(self    )


     @property
     def 	snake_case_  (	self    ):
        return self.image_proc_tester.prepare_image_processor_dict()


     def 	snake_case_  (	self    ):
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        self.assertTrue(hasattr(__A  ,       """image_mean"""    )    )
        self.assertTrue(hasattr(__A  ,       """image_std"""    )    )
        self.assertTrue(hasattr(__A  ,       """do_normalize"""    )    )
        self.assertTrue(hasattr(__A  ,       """do_resize"""    )    )
        self.assertTrue(hasattr(__A  ,       """size"""    )    )


     def 	snake_case_  (	self    ):
        pass


     def 	snake_case_  (	self    ):
        # Initialize image_processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        __a					=  prepare_image_inputs(self.image_proc_tester  ,       equal_resolution=__A    )
        for image in image_inputs:
           self.assertIsInstance(__A  ,       Image.Image    )

        # Test not batched input
        __a					=  image_processor(image_inputs[0]  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )

        # Test batched
        __a					=  image_processor(__A  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )


     def 	snake_case_  (	self    ):
        # Initialize image_processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random numpy tensors
        __a					=  prepare_image_inputs(self.image_proc_tester  ,       equal_resolution=__A  ,       numpify=__A    )
        for image in image_inputs:
           self.assertIsInstance(__A  ,       np.ndarray    )

        # Test not batched input
        __a					=  image_processor(image_inputs[0]  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )

        # Test batched
        __a					=  image_processor(__A  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )


     def 	snake_case_  (	self    ):
        # Initialize image_processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random PyTorch tensors
        __a					=  prepare_image_inputs(self.image_proc_tester  ,       equal_resolution=__A  ,       torchify=__A    )
        for image in image_inputs:
           self.assertIsInstance(__A  ,       torch.Tensor    )

        # Test not batched input
        __a					=  image_processor(image_inputs[0]  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )

        # Test batched
        __a					=  image_processor(__A  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )



