



"""simple docstring"""
from __future__ import annotations

from typing import Any
class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(					self       ,	A       ,	A       ,	A = 0		)							->					None:
         _UpperCAmelCase							,				_UpperCAmelCase							:  Tuple											=    row, column
         _UpperCAmelCase							:  Tuple											=    [[default_value for c in range(A		)] for r in range(A		)]





     def __str__(					self		)							->					str:
         _UpperCAmelCase							:  Optional[int]											=    f'Matrix consist of {self.row} rows and {self.column} columns\n'

         # Make string identifier
         _UpperCAmelCase							:  List[str]											=    0
         for row_vector in self.array:
             for obj in row_vector:
                 _UpperCAmelCase							:  int											=    max(A       ,	len(str(A		)		)		)
         _UpperCAmelCase							:  int											=    f'%{max_element_length}s'

         # Make string and return
         def single_line(A		) -> str:
             nonlocal string_format_identifier
             _UpperCAmelCase							:  Optional[int]											=    '''['''
             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		)							->					str:
         return str(self		)





     def  __lowerCAmelCase	(					self       ,	A		)							->					bool:
         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		)							->					Any:
         assert self.validate_indicies(A		)
         return self.array[loc[0]][loc[1]]





     def __setitem__(					self       ,	A       ,	A		)							->					None:
         assert self.validate_indicies(A		)
         _UpperCAmelCase							:  List[str]											=    value





     def __add__(					self       ,	A		)							->					Matrix:
         assert isinstance(A       ,	A		)
         assert self.row == another.row and self.column == another.column

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





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





     def __sub__(					self       ,	A		)							->					Matrix:
         return self + (-another)





     def __mul__(					self       ,	A		)							->					Matrix:

         if isinstance(A       ,	(int, float)		):  # Scalar multiplication
             _UpperCAmelCase							:  str											=    Matrix(self.row       ,	self.column		)
             for r in range(self.row		):
                 for c in range(self.column		):
                     _UpperCAmelCase							:  Optional[Any]											=    self[r, c] * another
             return result
         elif isinstance(A       ,	A		):  # Matrix multiplication
             assert self.column == another.row
             _UpperCAmelCase							:  Dict											=    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:
             _UpperCAmelCase							:  List[str]											=    f'Unsupported type given for another ({type(A		)})'
             raise TypeError(A		)





     def  __lowerCAmelCase	(					self		)							->					Matrix:
         _UpperCAmelCase							:  str											=    Matrix(self.column       ,	self.row		)
         for r in range(self.row		):
             for c in range(self.column		):
                 _UpperCAmelCase							:  Tuple											=    self[r, c]
         return result





     def  __lowerCAmelCase	(					self       ,	A       ,	A		)							->					Any:
         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
         _UpperCAmelCase							:  Any											=    v.transpose()
         _UpperCAmelCase							:  Optional[Any]											=    (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  lowerCamelCase_						():
      # a^(-1)
      _UpperCAmelCase							:  List[Any]											=    Matrix(3     ,       3     ,       0							)
      for i in range(3							):
          _UpperCAmelCase							:  List[str]											=    1
      print(F'a^(-1) is {ainv}'							)
      # u, v
      _UpperCAmelCase							:  int											=    Matrix(3     ,       1     ,       0							)
      _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  Any											=    1, 2, -3
      _UpperCAmelCase							:  str											=    Matrix(3     ,       1     ,       0							)
      _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  Optional[Any]											=    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(UpperCamelCase__     ,       UpperCamelCase__							)}'							)





  def  lowerCamelCase_						():
      import doctest

      doctest.testmod()


  testa()







"""simple docstring"""
import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class 							_UpperCAmelCase							( a		,a		,unittest.TestCase      ):





     '''simple docstring'''



     a__									=IFImgaImgSuperResolutionPipeline
     a__									=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
     a__									=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''original_image'''}      )
     a__									=PipelineTesterMixin.required_optional_params - {'''latents'''}





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         return self._get_superresolution_dummy_components()





     def  __lowerCAmelCase	(					self       ,	A       ,	A=0		)							->					Union[str, Any]:
         if str(A		).startswith('''mps'''		):
             _UpperCAmelCase							:  Any											=    torch.manual_seed(A		)
         else:
             _UpperCAmelCase							:  int											=    torch.Generator(device=A		).manual_seed(A		)

         _UpperCAmelCase							:  str											=    floats_tensor((1, 3, 3_2, 3_2)       ,	rng=random.Random(A		)		).to(A		)
         _UpperCAmelCase							:  Dict											=    floats_tensor((1, 3, 1_6, 1_6)       ,	rng=random.Random(A		)		).to(A		)

         _UpperCAmelCase							:  List[Any]											=    {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': image,
             '''original_image''': original_image,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }

         return inputs





     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available()       ,	reason='''XFormers attention is only available with CUDA and `xformers` installed'''       ,	)
     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         self._test_save_load_optional_components()





     @unittest.skipIf(torch_device != '''cuda'''       ,	reason='''float16 requires CUDA'''		)
     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
         super().test_save_load_floataa(expected_max_diff=1E-1		)





     def  __lowerCAmelCase	(					self		)							->					int:
         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2		)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         self._test_save_load_local()





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         self._test_inference_batch_single_identical(
             expected_max_diff=1E-2       ,	)


