"""simple docstring"""




from __future__ import annotations

from typing import Any

class    a_   :




					def __init__(						self    :  Any	,    snake_case__    :  int	,    snake_case__    :  int	,    snake_case__    :  float = 0  ):
							lowerCAmelCase__ ,       lowerCAmelCase__   = row, column
							lowerCAmelCase__   = [[default_value for c in range(snake_case__  )] for r in range(snake_case__  )]




					def __str__(						self    :  List[str]  ):
							lowerCAmelCase__   = F"""Matrix consist of {self.row} rows and {self.column} columns\n"""

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

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

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




					def __repr__(						self    :  List[Any]  ):
							return str(self  )




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




					def __getitem__(						self    :  Optional[int]	,    snake_case__    :  tuple[int, int]  ):
							assert self.validate_indicies(snake_case__  )
							return self.array[loc[0]][loc[1]]




					def __setitem__(						self    :  Any	,    snake_case__    :  tuple[int, int]	,    snake_case__    :  float  ):
							assert self.validate_indicies(snake_case__  )
							lowerCAmelCase__   = value




					def __add__(						self    :  Union[str, Any]	,    snake_case__    :  Matrix  ):
							assert isinstance(snake_case__	,    snake_case__  )
							assert self.row == another.row and self.column == another.column

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




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




					def __sub__(						self    :  Optional[int]	,    snake_case__    :  Matrix  ):
							return self + (-another)




					def __mul__(						self    :  Any	,    snake_case__    :  int | float | Matrix  ):

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							lowerCAmelCase__   = Matrix(self.column	,    self.row  )
							for r in range(self.row  ):
									for c in range(self.column  ):
											lowerCAmelCase__   = self[r, c]
							return result




					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    snake_case__    :  Matrix	,    snake_case__    :  Matrix  ):
							assert isinstance(snake_case__	,    snake_case__  ) and isinstance(snake_case__	,    snake_case__  )
							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
							lowerCAmelCase__   = v.transpose()
							lowerCAmelCase__   = (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 				_UpperCAmelCase		(							):





					"""simple docstring"""




					lowerCAmelCase__   = Matrix(3      ,	3      ,	0  )
					for i in range(3  ):
							lowerCAmelCase__   = 1
					print(f"""a^(-1) is {ainv}"""  )
					# u, v
					lowerCAmelCase__   = Matrix(3      ,	1      ,	0  )
					lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__   = 1, 2, -3
					lowerCAmelCase__   = Matrix(3      ,	1      ,	0  )
					lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__   = 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 				_UpperCAmelCase		(							):





					"""simple docstring"""




					import doctest

					doctest.testmod()






			testa()





"""simple docstring"""




import unittest

import numpy as np
import requests

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

			from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
			__lowerCAmelCase     :				int    				=    False

if is_vision_available():
			from PIL import Image

			from transformers import PixaStructImageProcessor

class    a_   (  unittest.TestCase      ):




					def __init__(						self    :  Tuple	,    snake_case__    :  Any	,    snake_case__    :  Union[str, Any]=7	,    snake_case__    :  Optional[int]=3	,    snake_case__    :  int=18	,    snake_case__    :  Dict=30	,    snake_case__    :  Optional[Any]=400	,    snake_case__    :  Tuple=None	,    snake_case__    :  List[Any]=True	,    snake_case__    :  Any=True	,    snake_case__    :  Dict=None	,    ):
							lowerCAmelCase__   = size if size is not None else {"""height""": 20, """width""": 20}
							lowerCAmelCase__   = parent
							lowerCAmelCase__   = batch_size
							lowerCAmelCase__   = num_channels
							lowerCAmelCase__   = image_size
							lowerCAmelCase__   = min_resolution
							lowerCAmelCase__   = max_resolution
							lowerCAmelCase__   = size
							lowerCAmelCase__   = do_normalize
							lowerCAmelCase__   = do_convert_rgb
							lowerCAmelCase__   = [512, 1024, 2048, 4096]
							lowerCAmelCase__   = patch_size if patch_size is not None else {"""height""": 16, """width""": 16}




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
							lowerCAmelCase__   = Image.open(requests.get(snake_case__	,    stream=snake_case__  ).raw  ).convert("""RGB"""  )
							return raw_image

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,		reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."			,		)
@require_torch
@require_vision
class    a_   (  __UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						Optional[Any]      =     PixaStructImageProcessor if is_vision_available() else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple  ):
							lowerCAmelCase__   = PixaStructImageProcessingTester(self  )




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							return self.image_processor_tester.prepare_image_processor_dict()




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							self.assertTrue(hasattr(snake_case__	,    """do_normalize"""  )  )
							self.assertTrue(hasattr(snake_case__	,    """do_convert_rgb"""  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = self.image_processor_tester.prepare_dummy_image()

							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							lowerCAmelCase__   = 2048

							lowerCAmelCase__   = image_processor(snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  )
							self.assertTrue(torch.allclose(inputs.flattened_patches.mean()	,    torch.tensor(0.0606  )	,    atol=1E-3	,    rtol=1E-3  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowerCAmelCase__   = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=snake_case__  )
							for image in image_inputs:
									self.assertIsInstance(snake_case__	,    Image.Image  )

							# Test not batched input
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowerCAmelCase__   = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=snake_case__  )
							for image in image_inputs:
									self.assertIsInstance(snake_case__	,    Image.Image  )

							# Test not batched input
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							lowerCAmelCase__   = True

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									with self.assertRaises(snake_case__  ):
											lowerCAmelCase__   = image_processor(
											    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches

									lowerCAmelCase__   = """Hello"""

									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__	,    header_text=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__	,    header_text=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random numpy tensors
							lowerCAmelCase__   = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=snake_case__	,    numpify=snake_case__  )
							for image in image_inputs:
									self.assertIsInstance(snake_case__	,    np.ndarray  )

							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PyTorch tensors
							lowerCAmelCase__   = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=snake_case__	,    torchify=snake_case__  )
							for image in image_inputs:
									self.assertIsInstance(snake_case__	,    torch.Tensor  )

							# Test not batched input
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,		reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."			,		)
@require_torch
@require_vision
class    a_   (  __UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						List[Any]      =     PixaStructImageProcessor if is_vision_available() else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = PixaStructImageProcessingTester(self	,    num_channels=4  )
							lowerCAmelCase__   = 3




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							return self.image_processor_tester.prepare_image_processor_dict()




					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							self.assertTrue(hasattr(snake_case__	,    """do_normalize"""  )  )
							self.assertTrue(hasattr(snake_case__	,    """do_convert_rgb"""  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowerCAmelCase__   = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=snake_case__  )
							for image in image_inputs:
									self.assertIsInstance(snake_case__	,    Image.Image  )

							# Test not batched input
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * (self.image_processor_tester.num_channels - 1)
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




