



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 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 def __init__(    self					:					Dict			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					str=7			,						__lowerCAmelCase					:					Optional[int]=3			,						__lowerCAmelCase					:					Dict=18			,						__lowerCAmelCase					:					int=30			,						__lowerCAmelCase					:					Any=4_00			,						__lowerCAmelCase					:					Dict=True			,						__lowerCAmelCase					:					Dict=None			,						__lowerCAmelCase					:					Optional[int]=True			,						__lowerCAmelCase					:					Tuple=None			,						__lowerCAmelCase					:					List[Any]=True			,						)   ->	Optional[Any]:
    _A      =       size if size is not None else {'''shortest_edge''': 20}
    _A      =       crop_size if crop_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_center_crop
    _A      =       crop_size
    _A      =       do_flip_channel_order

 def     snake_case_      (    self					:					Any      )   ->	Optional[Any]:
    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 	lowerCamelCase__	(       _A							,  unittest.TestCase):



 """simple docstring"""

 a__					: Tuple =  MobileViTImageProcessor if is_vision_available() else None

 def     snake_case_      (    self					:					Optional[int]      )   ->	Tuple:
    _A      =       MobileViTImageProcessingTester(self      )

 @property
 def     snake_case_      (    self					:					List[Any]      )   ->	str:
    return self.image_processor_tester.prepare_image_processor_dict()

 def     snake_case_      (    self					:					Dict      )   ->	Optional[Any]:
    _A      =       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     snake_case_      (    self					:					int      )   ->	Optional[int]:
    _A      =       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}      )

    _A      =       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     snake_case_      (    self					:					Optional[int]      )   ->	Union[str, Any]:
    pass

 def     snake_case_      (    self					:					str      )   ->	Any:
    # Initialize image_processing
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    # create random PIL images
    _A      =       prepare_image_inputs(self.image_processor_tester			,						equal_resolution=__lowerCAmelCase      )
    for image in image_inputs:
       self.assertIsInstance(__lowerCAmelCase			,						Image.Image      )

    # Test not batched input
    _A      =       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
    _A      =       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     snake_case_      (    self					:					Union[str, Any]      )   ->	List[str]:
    # Initialize image_processing
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    # create random numpy tensors
    _A      =       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
    _A      =       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
    _A      =       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     snake_case_      (    self					:					Optional[int]      )   ->	str:
    # Initialize image_processing
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    # create random PyTorch tensors
    _A      =       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
    _A      =       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
    _A      =       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'''],
        )			,						)





from __future__ import annotations





class     lowerCamelCase      :







	def __init__(     self      :List[Any]   ,     lowercase      :list[list[int]]			)		->      Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= TypeError(
				    '''Matrices must be formed from a list of zero or more lists containing at '''
				    '''least one and the same number of values, each of which must be of type '''
				    '''int or float.'''			)
				if len(lowercase			) != 0:
							SCREAMING_SNAKE_CASE     						= len(rows[0]			)
							if cols == 0:
										raise error
							for row in rows:
										if len(lowercase			) != cols:
													raise error
										for value in row:
													if not isinstance(lowercase   ,     (int, float)			):
																raise error
							SCREAMING_SNAKE_CASE     						= rows
				else:
							SCREAMING_SNAKE_CASE     						= []







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







				"""simple docstring"""






				return [[row[i] for row in self.rows] for i in range(len(self.rows[0]			)			)]







	@property
	def  snake_case__     (     self      :Any			)		->      int:







				"""simple docstring"""






				return len(self.rows			)







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







				"""simple docstring"""






				return len(self.rows[0]			)







	@property
	def  snake_case__     (     self      :Dict			)		->      tuple[int, int]:







				"""simple docstring"""






				return (self.num_rows, self.num_columns)







	@property
	def  snake_case__     (     self      :Any			)		->      bool:







				"""simple docstring"""






				return self.order[0] == self.order[1]







	def  snake_case__     (     self      :Tuple			)		->      Matrix:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [
				    [0 if column_num != row_num else 1 for column_num in range(self.num_rows			)]
				    for row_num in range(self.num_rows			)
				]
				return Matrix(lowercase			)







	def  snake_case__     (     self      :Union[str, Any]			)		->      int:







				"""simple docstring"""






				if not self.is_square:
							return 0
				if self.order == (0, 0):
							return 1
				if self.order == (1, 1):
							return int(self.rows[0][0]			)
				if self.order == (2, 2):
							return int(
							    (self.rows[0][0] * self.rows[1][1])
							    - (self.rows[0][1] * self.rows[1][0])			)
				else:
							return sum(
							    self.rows[0][column] * self.cofactors().rows[0][column]
							    for column in range(self.num_columns			)			)







	def  snake_case__     (     self      :int			)		->      bool:







				"""simple docstring"""






				return bool(self.determinant()			)







	def  snake_case__     (     self      :List[str]   ,     lowercase      :int   ,     lowercase      :int			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [
				    [
				        self.rows[other_row][other_column]
				        for other_column in range(self.num_columns			)
				        if other_column != column
				    ]
				    for other_row in range(self.num_rows			)
				    if other_row != row
				]
				return Matrix(lowercase			).determinant()







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :int   ,     lowercase      :int			)		->      int:







				"""simple docstring"""






				if (row + column) % 2 == 0:
							return self.get_minor(lowercase   ,     lowercase			)
				return -1 * self.get_minor(lowercase   ,     lowercase			)







	def  snake_case__     (     self      :List[Any]			)		->      Matrix:







				"""simple docstring"""






				return Matrix(
				    [
				        [self.get_minor(lowercase   ,     lowercase			) for column in range(self.num_columns			)]
				        for row in range(self.num_rows			)
				    ]			)







	def  snake_case__     (     self      :Dict			)		->      Matrix:







				"""simple docstring"""






				return Matrix(
				    [
				        [
				            self.minors().rows[row][column]
				            if (row + column) % 2 == 0
				            else self.minors().rows[row][column] * -1
				            for column in range(self.minors().num_columns			)
				        ]
				        for row in range(self.minors().num_rows			)
				    ]			)







	def  snake_case__     (     self      :str			)		->      Matrix:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [
				    [self.cofactors().rows[column][row] for column in range(self.num_columns			)]
				    for row in range(self.num_rows			)
				]
				return Matrix(lowercase			)







	def  snake_case__     (     self      :Any			)		->      Matrix:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.determinant()
				if not determinant:
							raise TypeError('''Only matrices with a non-zero determinant have an inverse'''			)
				return self.adjugate() * (1 / determinant)







	def __repr__(     self      :Union[str, Any]			)		->      str:







				"""simple docstring"""






				return str(self.rows			)







	def __str__(     self      :List[str]			)		->      str:







				"""simple docstring"""






				if self.num_rows == 0:
							return "[]"
				if self.num_rows == 1:
							return "[[" + ". ".join(str(self.rows[0]			)			) + "]]"
				return (
				    "["
				    + "\n ".join(
				        [
				            '''[''' + '''. '''.join([str(lowercase			) for value in row]			) + '''.]'''
				            for row in self.rows
				        ]			)
				    + "]"
				)







	def  snake_case__     (     self      :int   ,     lowercase      :list[int]   ,     lowercase      :int | None = None			)		->      None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= TypeError('''Row must be a list containing all ints and/or floats'''			)
				if not isinstance(lowercase   ,     lowercase			):
							raise type_error
				for value in row:
							if not isinstance(lowercase   ,     (int, float)			):
										raise type_error
				if len(lowercase			) != self.num_columns:
							raise ValueError(
							    '''Row must be equal in length to the other rows in the matrix'''			)
				if position is None:
							self.rows.append(lowercase			)
				else:
							SCREAMING_SNAKE_CASE     						= self.rows[0:position] + [row] + self.rows[position:]







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :list[int]   ,     lowercase      :int | None = None			)		->      None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= TypeError(
				    '''Column must be a list containing all ints and/or floats'''			)
				if not isinstance(lowercase   ,     lowercase			):
							raise type_error
				for value in column:
							if not isinstance(lowercase   ,     (int, float)			):
										raise type_error
				if len(lowercase			) != self.num_rows:
							raise ValueError(
							    '''Column must be equal in length to the other columns in the matrix'''			)
				if position is None:
							SCREAMING_SNAKE_CASE     						= [self.rows[i] + [column[i]] for i in range(self.num_rows			)]
				else:
							SCREAMING_SNAKE_CASE     						= [
							    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
							    for i in range(self.num_rows			)
							]







	def __eq__(     self      :Union[str, Any]   ,     lowercase      :object			)		->      bool:







				"""simple docstring"""






				if not isinstance(lowercase   ,     lowercase			):
							return NotImplemented
				return self.rows == other.rows







	def __ne__(     self      :int   ,     lowercase      :object			)		->      bool:







				"""simple docstring"""






				return not self == other







	def __neg__(     self      :int			)		->      Matrix:







				"""simple docstring"""






				return self * -1







	def __add__(     self      :Optional[Any]   ,     lowercase      :Matrix			)		->      Matrix:







				"""simple docstring"""






				if self.order != other.order:
							raise ValueError('''Addition requires matrices of the same order'''			)
				return Matrix(
				    [
				        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns			)]
				        for i in range(self.num_rows			)
				    ]			)







	def __sub__(     self      :Optional[Any]   ,     lowercase      :Matrix			)		->      Matrix:







				"""simple docstring"""






				if self.order != other.order:
							raise ValueError('''Subtraction requires matrices of the same order'''			)
				return Matrix(
				    [
				        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns			)]
				        for i in range(self.num_rows			)
				    ]			)







	def __mul__(     self      :Optional[int]   ,     lowercase      :Matrix | int | float			)		->      Matrix:







				"""simple docstring"""






				if isinstance(lowercase   ,     (int, float)			):
							return Matrix(
							    [[int(element * other			) for element in row] for row in self.rows]			)
				elif isinstance(lowercase   ,     lowercase			):
							if self.num_columns != other.num_rows:
										raise ValueError(
										    '''The number of columns in the first matrix must '''
										    '''be equal to the number of rows in the second'''			)
							return Matrix(
							    [
							        [Matrix.dot_product(lowercase   ,     lowercase			) for column in other.columns()]
							        for row in self.rows
							    ]			)
				else:
							raise TypeError(
							    '''A Matrix can only be multiplied by an int, float, or another matrix'''			)







	def __pow__(     self      :str   ,     lowercase      :int			)		->      Matrix:







				"""simple docstring"""






				if not isinstance(lowercase   ,     lowercase			):
							raise TypeError('''A Matrix can only be raised to the power of an int'''			)
				if not self.is_square:
							raise ValueError('''Only square matrices can be raised to a power'''			)
				if other == 0:
							return self.identity()
				if other < 0:
							if self.is_invertable():
										return self.inverse() ** (-other)
							raise ValueError(
							    '''Only invertable matrices can be raised to a negative power'''			)
				SCREAMING_SNAKE_CASE     						= self
				for _ in range(other - 1			):
							result *= self
				return result







	@classmethod
	def  snake_case__     (     cls      :List[Any]   ,     lowercase      :list[int]   ,     lowercase      :list[int]			)		->      int:







				"""simple docstring"""






				return sum(row[i] * column[i] for i in range(len(lowercase			)			)			)


if __name__ == "__main__":
							import doctest

							doctest.testmod()