






from __future__ import annotations

from typing import Any


class 							__UpperCamelCase     :







	def __init__(	self   :				Optional[Any]							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				float = 0      )		->			None:
		"""simple docstring"""







		__lowercase							,    __lowercase   							=	row, column
		__lowercase   							=	[[default_value for c in range(_lowerCAmelCase      )] for r in range(_lowerCAmelCase      )]







	def __str__(	self   :				List[Any]      )		->			str:
		"""simple docstring"""







		__lowercase   							=	F'Matrix consist of {self.row} rows and {self.column} columns\n'

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

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

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







	def __repr__(	self   :				int      )		->			str:
		"""simple docstring"""







		return str(self      )







	def 			_a (	self   :				Dict							,					_lowerCAmelCase   :				tuple[int, int]      )		->			bool:
		"""simple docstring"""







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







	def __getitem__(	self   :				List[str]							,					_lowerCAmelCase   :				tuple[int, int]      )		->			Any:
		"""simple docstring"""







		assert self.validate_indicies(_lowerCAmelCase      )
		return self.array[loc[0]][loc[1]]







	def __setitem__(	self   :				List[Any]							,					_lowerCAmelCase   :				tuple[int, int]							,					_lowerCAmelCase   :				float      )		->			None:
		"""simple docstring"""







		assert self.validate_indicies(_lowerCAmelCase      )
		__lowercase   							=	value







	def __add__(	self   :				Any							,					_lowerCAmelCase   :				Matrix      )		->			Matrix:
		"""simple docstring"""







		assert isinstance(_lowerCAmelCase							,					_lowerCAmelCase      )
		assert self.row == another.row and self.column == another.column

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







	def __neg__(	self   :				str      )		->			Matrix:
		"""simple docstring"""







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







	def __sub__(	self   :				Tuple							,					_lowerCAmelCase   :				Matrix      )		->			Matrix:
		"""simple docstring"""







		return self + (-another)







	def __mul__(	self   :				Dict							,					_lowerCAmelCase   :				int | float | Matrix      )		->			Matrix:
		"""simple docstring"""







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







	def 			_a (	self   :				Any      )		->			Matrix:
		"""simple docstring"""







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







	def 			_a (	self   :				Dict							,					_lowerCAmelCase   :				Matrix							,					_lowerCAmelCase   :				Matrix      )		->			Any:
		"""simple docstring"""







		assert isinstance(_lowerCAmelCase							,					_lowerCAmelCase      ) and isinstance(_lowerCAmelCase							,					_lowerCAmelCase      )
		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
		__lowercase   							=	v.transpose()
		__lowercase   							=	(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 					snake_case	(  ):




							'''simple docstring'''







							__lowercase   							=	Matrix(3					,	3					,	0       )
							for i in range(3       ):
								__lowercase   							=	1
							print(F'a^(-1) is {ainv}'       )
							# u, v
							__lowercase   							=	Matrix(3					,	1					,	0       )
							__lowercase							,    __lowercase							,    __lowercase   							=	1, 2, -3
							__lowercase   							=	Matrix(3					,	1					,	0       )
							__lowercase							,    __lowercase							,    __lowercase   							=	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 					snake_case	(  ):




							'''simple docstring'''







							import doctest

							doctest.testmod()




						testa()




import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor

class    _lowercase				(					unittest.TestCase			):

   def __init__(				self	:    List[str]			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Dict[str, int] = None			,						lowerCamelCase__	:    int = 3_2			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Union[int, float] = 1 / 2_5_5			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073]			,						lowerCamelCase__	:    Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711]			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    List[str]=7			,						lowerCamelCase__	:    int=3_0			,						lowerCamelCase__	:    List[Any]=4_0_0			,						lowerCamelCase__	:    int=3			,						) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		parent
         A_							       =		do_resize
         A_							       =		size if size is not None else {'''shortest_edge''': 2_8_8}
         A_							       =		size_divisor
         A_							       =		do_rescale
         A_							       =		rescale_factor
         A_							       =		do_normalize
         A_							       =		do_center_crop
         A_							       =		image_mean
         A_							       =		image_std
         A_							       =		do_pad
         A_							       =		batch_size
         A_							       =		num_channels
         A_							       =		min_resolution
         A_							       =		max_resolution

   def     UpperCamelCase					(				self	:    str       ) ->			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,
             "size_divisor": self.size_divisor,
         }





   def     UpperCamelCase					(				self	:    List[str]			,						lowerCamelCase__	:    Optional[Any]			,						lowerCamelCase__	:    int=False       ) ->			Dict:
         """simple docstring"""





         if not batched:
               A_							       =		self.size['''shortest_edge''']
               A_							       =		image_inputs[0]
               if isinstance(lowerCamelCase__			,						Image.Image       ):
                     A_       ,A_							       =		image.size
               else:
                     A_       ,A_							       =		image.shape[1], image.shape[2]
               A_							       =		size / min(lowerCamelCase__			,						lowerCamelCase__       )
               if h < w:
                     A_       ,A_							       =		size, scale * w
               else:
                     A_       ,A_							       =		scale * h, size

               A_							       =		int((1_3_3_3 / 8_0_0) * size       )
               if max(lowerCamelCase__			,						lowerCamelCase__       ) > max_size:
                     A_							       =		max_size / max(lowerCamelCase__			,						lowerCamelCase__       )
                     A_							       =		newh * scale
                     A_							       =		neww * scale

               A_       ,A_							       =		int(newh + 0.5       ), int(neww + 0.5       )
               A_       ,A_							       =		(
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

         else:
               A_							       =		[]
               for image in image_inputs:
                     A_       ,A_							       =		self.get_expected_values([image]       )
                     expected_values.append((expected_height, expected_width)       )
               A_							       =		max(lowerCamelCase__			,						key=lambda lowerCamelCase__       : item[0]       )[0]
               A_							       =		max(lowerCamelCase__			,						key=lambda lowerCamelCase__       : item[1]       )[1]

         return expected_height, expected_width



@require_torch
@require_vision
class    _lowercase				(					__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       List[str]  					=    BridgeTowerImageProcessor if is_vision_available() else None

   def     UpperCamelCase					(				self	:    int       ) ->			Tuple:
         """simple docstring"""





         A_							       =		BridgeTowerImageProcessingTester(self       )

   @property
   def     UpperCamelCase					(				self	:    str       ) ->			Optional[int]:
         """simple docstring"""





         return self.image_processor_tester.prepare_image_processor_dict()

   def     UpperCamelCase					(				self	:    Tuple       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''image_mean'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''image_std'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''do_normalize'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''do_resize'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''size'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''size_divisor'''       )       )

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			str:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			Union[str, Any]:
         """simple docstring"""





         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

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

         # Test batched
         A_							       =		image_processing(lowerCamelCase__			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,						)

   def     UpperCamelCase					(				self	:    str       ) ->			Optional[int]:
         """simple docstring"""





         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

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

         # Test batched
         A_							       =		image_processing(lowerCamelCase__			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,						)





   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			int:
         """simple docstring"""





         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

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

         # Test batched
         A_							       =		image_processing(lowerCamelCase__			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,						)





