
from __future__ import annotations

from typing import Any


class   __lowerCAmelCase		:



      """simple docstring"""







      def __init__(					self						:				Dict ,				_snake_case						:				Optional[int] ,				_snake_case						:				str ,				_snake_case						:				Optional[int] = 0 ):
          __lowercase						:	Tuple					=				row, column
          __lowercase						:	Tuple					=				[[default_value for c in range(_snake_case )] for r in range(_snake_case )]




      def __str__(					self						:				Optional[int] ):
          __lowercase						:	Optional[int]					=				F'Matrix consist of {self.row} rows and {self.column} columns\n'

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

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




      def 						snake_case_	(					self						:				Optional[int] ,				_snake_case						:				Union[str, Any] ):
          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						:				Dict ,				_snake_case						:				str ):
          assert self.validate_indicies(_snake_case )
          return self.array[loc[0]][loc[1]]




      def __setitem__(					self						:				Dict ,				_snake_case						:				Optional[int] ,				_snake_case						:				Any ):
          assert self.validate_indicies(_snake_case )
          __lowercase						:	List[str]					=				value




      def __add__(					self						:				Tuple ,				_snake_case						:				List[Any] ):
          assert isinstance(_snake_case ,				_snake_case )
          assert self.row == another.row and self.column == another.column

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




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




      def __sub__(					self						:				Optional[int] ,				_snake_case						:				Optional[Any] ):
          return self + (-another)




      def __mul__(					self						:				List[Any] ,				_snake_case						:				Optional[Any] ):

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




      def 						snake_case_	(					self						:				Optional[int] ):
          __lowercase						:	str					=				Matrix(self.column ,				self.row )
          for r in range(self.row ):
              for c in range(self.column ):
                  __lowercase						:	Tuple					=				self[r, c]
          return result




      def 						snake_case_	(					self						:				Union[str, Any] ,				_snake_case						:				str ,				_snake_case						:				Dict ):
          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
          __lowercase						:	Any					=				v.transpose()
          __lowercase						:	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 			UpperCAmelCase_     (  )       ->  Union[str, Any]:
     # a^(-1)
     __lowercase						:	List[Any]					=				Matrix(3		,    3		,    0		)
     for i in range(3		):
         __lowercase						:	List[str]					=				1
     print(F'a^(-1) is {ainv}'		)
     # u, v
     __lowercase						:	int					=				Matrix(3		,    1		,    0		)
     __lowercase						:	Any					=				1, 2, -3
     __lowercase						:	str					=				Matrix(3		,    1		,    0		)
     __lowercase						:	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 			UpperCAmelCase_     (  )       ->  str:
     import doctest

     doctest.testmod()





 testa()







"""simple docstring"""
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


_lowerCAmelCase    :Any     =					False
@skip_mps
class 							_UpperCAmelCase							( a		,a		,a		,unittest.TestCase      ):





     '''simple docstring'''



     a__									=StableDiffusionAttendAndExcitePipeline
     a__									=False
     a__									=TEXT_TO_IMAGE_PARAMS
     a__									=TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''}      )
     a__									=TEXT_TO_IMAGE_IMAGE_PARAMS
     a__									=TEXT_TO_IMAGE_IMAGE_PARAMS





     @classmethod
     def  __lowerCAmelCase	(					cls		)							->					List[str]:
         super().setUpClass()
         torch.use_deterministic_algorithms(A		)





     @classmethod
     def  __lowerCAmelCase	(					cls		)							->					Union[str, Any]:
         super().tearDownClass()
         torch.use_deterministic_algorithms(A		)





     def  __lowerCAmelCase	(					self		)							->					Tuple:
         torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[int]											=    UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)       ,	layers_per_block=1       ,	sample_size=3_2       ,	in_channels=4       ,	out_channels=4       ,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,	up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,	cross_attention_dim=3_2       ,	attention_head_dim=(2, 4)       ,	use_linear_projection=A       ,	)
         _UpperCAmelCase							:  List[Any]											=    DDIMScheduler(
             beta_start=0.00_085       ,	beta_end=0.012       ,	beta_schedule='''scaled_linear'''       ,	clip_sample=A       ,	set_alpha_to_one=A       ,	)
         torch.manual_seed(0		)
         _UpperCAmelCase							:  int											=    AutoencoderKL(
             block_out_channels=[3_2, 6_4]       ,	in_channels=3       ,	out_channels=3       ,	down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,	up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,	latent_channels=4       ,	sample_size=1_2_8       ,	)
         torch.manual_seed(0		)
         _UpperCAmelCase							:  int											=    CLIPTextConfig(
             bos_token_id=0       ,	eos_token_id=2       ,	hidden_size=3_2       ,	intermediate_size=3_7       ,	layer_norm_eps=1E-05       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=1_0_0_0       ,	hidden_act='''gelu'''       ,	projection_dim=5_1_2       ,	)
         _UpperCAmelCase							:  List[str]											=    CLIPTextModel(A		)
         _UpperCAmelCase							:  str											=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

         _UpperCAmelCase							:  Union[str, Any]											=    {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }

         return components





     def  __lowerCAmelCase	(					self       ,	A       ,	A=0		)							->					List[Any]:
         if str(A		).startswith('''mps'''		):
             _UpperCAmelCase							:  Optional[int]											=    torch.manual_seed(A		)
         else:
             _UpperCAmelCase							:  Union[str, Any]											=    torch.Generator(device=A		).manual_seed(A		)
         _UpperCAmelCase							:  List[str]											=    {
             '''prompt''': '''a cat and a frog''',
             '''token_indices''': [2, 5],
             '''generator''': generator,
             '''num_inference_steps''': 1,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
             '''max_iter_to_alter''': 2,
             '''thresholds''': {0: 0.7},
         }
         return inputs





     def  __lowerCAmelCase	(					self		)							->					int:
         _UpperCAmelCase							:  List[str]											=    '''cpu'''

         _UpperCAmelCase							:  Tuple											=    self.get_dummy_components()
         _UpperCAmelCase							:  int											=    self.pipeline_class(**A		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  Dict											=    self.get_dummy_inputs(A		)
         _UpperCAmelCase							:  Union[str, Any]											=    pipe(**A		).images
         _UpperCAmelCase							:  Tuple											=    image[0, -3:, -3:, -1]

         self.assertEqual(image.shape       ,	(1, 6_4, 6_4, 3)		)
         _UpperCAmelCase							:  int											=    np.array(
             [0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496]		)
         _UpperCAmelCase							:  Tuple											=    np.abs(image_slice.flatten() - expected_slice		).max()
         self.assertLessEqual(A       ,	1E-3		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         super().test_cpu_offload_forward_pass(expected_max_diff=5E-4		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
         self._test_inference_batch_consistent(batch_sizes=[1, 2]		)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         self._test_inference_batch_single_identical(batch_size=2       ,	expected_max_diff=7E-4		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4		)





     def  __lowerCAmelCase	(					self		)							->					str:
         super().test_save_load_local(expected_max_difference=5E-4		)





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         super().test_save_load_optional_components(expected_max_difference=4E-4		)



@require_torch_gpu
@slow
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     @classmethod
     def  __lowerCAmelCase	(					cls		)							->					Union[str, Any]:
         super().setUpClass()
         torch.use_deterministic_algorithms(A		)





     @classmethod
     def  __lowerCAmelCase	(					cls		)							->					Optional[int]:
         super().tearDownClass()
         torch.use_deterministic_algorithms(A		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Any											=    torch.manual_seed(5_1		)

         _UpperCAmelCase							:  Optional[Any]											=    StableDiffusionAttendAndExcitePipeline.from_pretrained(
             '''CompVis/stable-diffusion-v1-4'''       ,	safety_checker=A       ,	torch_dtype=torch.floataa		)
         pipe.to('''cuda'''		)

         _UpperCAmelCase							:  Optional[int]											=    '''a painting of an elephant with glasses'''
         _UpperCAmelCase							:  int											=    [5, 7]

         _UpperCAmelCase							:  Dict											=    pipe(
             prompt=A       ,	token_indices=A       ,	guidance_scale=7.5       ,	generator=A       ,	num_inference_steps=5       ,	max_iter_to_alter=5       ,	output_type='''numpy'''       ,	).images[0]

         _UpperCAmelCase							:  List[Any]											=    load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'''		)
         assert np.abs((expected_image - image).max()		) < 5E-1


