


'''simple docstring'''



from __future__ import annotations

from typing import Any






class 	UpperCamelCase__:




				def __init__(		self      :			Any				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			float = 0				)->					None:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     row, column
										UpperCAmelCase					    =     [[default_value for c in range(lowerCAmelCase				)] for r in range(lowerCAmelCase				)]




				def __str__(		self      :			int				)->					str:

										"""simple docstring"""




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

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

										# Make string and return
										def single_line(lowerCAmelCase      :			list[float]				) -> str:
																nonlocal string_format_identifier
																UpperCAmelCase					    =     '''['''
																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      :			Tuple				)->					str:

										"""simple docstring"""




										return str(self				)




				def 					a__(		self      :			str				,   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      :			int				,   lowerCAmelCase      :			tuple[int, int]				)->					Any:

										"""simple docstring"""




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




				def __setitem__(		self      :			List[str]				,   lowerCAmelCase      :			tuple[int, int]				,   lowerCAmelCase      :			float				)->					None:

										"""simple docstring"""




										assert self.validate_indicies(lowerCAmelCase				)
										UpperCAmelCase					    =     value




				def __add__(		self      :			int				,   lowerCAmelCase      :			Matrix				)->					Matrix:

										"""simple docstring"""




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

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




				def __neg__(		self      :			Dict				)->					Matrix:

										"""simple docstring"""




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




				def __sub__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			Matrix				)->					Matrix:

										"""simple docstring"""




										return self + (-another)




				def __mul__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			int | float | Matrix				)->					Matrix:

										"""simple docstring"""




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




				def 					a__(		self      :			Optional[Any]				)->					Matrix:

										"""simple docstring"""




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





				def 					a__(		self      :			Tuple				,   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
										UpperCAmelCase					    =     v.transpose()
										UpperCAmelCase					    =     (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__				(	):




							'''simple docstring'''






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


	def 				lowerCamelCase__				(	):




							'''simple docstring'''






							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,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, 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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 	UpperCamelCase__(       lowerCAmelCase						,    lowerCAmelCase						,    unittest.TestCase	):
				__magic_name__    :				List[str]  		=	StableDiffusionSAGPipeline
				__magic_name__    :				str  		=	TEXT_TO_IMAGE_PARAMS
				__magic_name__    :				Any  		=	TEXT_TO_IMAGE_BATCH_PARAMS
				__magic_name__    :				Optional[int]  		=	TEXT_TO_IMAGE_IMAGE_PARAMS
				__magic_name__    :				List[Any]  		=	TEXT_TO_IMAGE_IMAGE_PARAMS
				__magic_name__    :				str  		=	False




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

										"""simple docstring"""




										torch.manual_seed(0				)
										UpperCAmelCase					    =     UNetaDConditionModel(
										    block_out_channels=(32, 64)				,   layers_per_block=2				,   sample_size=32				,   in_channels=4				,   out_channels=4				,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,   cross_attention_dim=32				,   )
										UpperCAmelCase					    =     DDIMScheduler(
										    beta_start=0.00085				,   beta_end=0.012				,   beta_schedule='''scaled_linear'''				,   clip_sample=lowerCAmelCase				,   set_alpha_to_one=lowerCAmelCase				,   )
										torch.manual_seed(0				)
										UpperCAmelCase					    =     AutoencoderKL(
										    block_out_channels=[32, 64]				,   in_channels=3				,   out_channels=3				,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,   latent_channels=4				,   )
										torch.manual_seed(0				)
										UpperCAmelCase					    =     CLIPTextConfig(
										    bos_token_id=0				,   eos_token_id=2				,   hidden_size=32				,   intermediate_size=37				,   layer_norm_eps=1E-05				,   num_attention_heads=4				,   num_hidden_layers=5				,   pad_token_id=1				,   vocab_size=1000				,   )
										UpperCAmelCase					    =     CLIPTextModel(lowerCAmelCase				)
										UpperCAmelCase					    =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

										UpperCAmelCase					    =     {
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''safety_checker''': None,
										    '''feature_extractor''': None,
										}
										return components




				def 					a__(		self      :			Optional[Any]				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Tuple=0				)->					str:

										"""simple docstring"""




										if str(lowerCAmelCase				).startswith('''mps'''				):
																UpperCAmelCase					    =     torch.manual_seed(lowerCAmelCase				)
										else:
																UpperCAmelCase					    =     torch.Generator(device=lowerCAmelCase				).manual_seed(lowerCAmelCase				)
										UpperCAmelCase					    =     {
										    '''prompt''': '''.''',
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 1.0,
										    '''sag_scale''': 1.0,
										    '''output_type''': '''numpy''',
										}
										return inputs





				def 					a__(		self      :			Any				)->					List[str]:

										"""simple docstring"""




										super().test_inference_batch_single_identical(expected_max_diff=3E-3				)







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




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

										"""simple docstring"""




										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




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

										"""simple docstring"""




										UpperCAmelCase					    =     StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''				)
										UpperCAmelCase					    =     sag_pipe.to(lowerCAmelCase				)
										sag_pipe.set_progress_bar_config(disable=lowerCAmelCase				)

										UpperCAmelCase					    =     '''.'''
										UpperCAmelCase					    =     torch.manual_seed(0				)
										UpperCAmelCase					    =     sag_pipe(
										    [prompt]				,   generator=lowerCAmelCase				,   guidance_scale=7.5				,   sag_scale=1.0				,   num_inference_steps=20				,   output_type='''np'''				)

										UpperCAmelCase					    =     output.images

										UpperCAmelCase					    =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 512, 512, 3)
										UpperCAmelCase					    =     np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2




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

										"""simple docstring"""




										UpperCAmelCase					    =     StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
										UpperCAmelCase					    =     sag_pipe.to(lowerCAmelCase				)
										sag_pipe.set_progress_bar_config(disable=lowerCAmelCase				)

										UpperCAmelCase					    =     '''.'''
										UpperCAmelCase					    =     torch.manual_seed(0				)
										UpperCAmelCase					    =     sag_pipe(
										    [prompt]				,   generator=lowerCAmelCase				,   guidance_scale=7.5				,   sag_scale=1.0				,   num_inference_steps=20				,   output_type='''np'''				)

										UpperCAmelCase					    =     output.images

										UpperCAmelCase					    =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 512, 512, 3)
										UpperCAmelCase					    =     np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2





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

										"""simple docstring"""




										UpperCAmelCase					    =     StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
										UpperCAmelCase					    =     sag_pipe.to(lowerCAmelCase				)
										sag_pipe.set_progress_bar_config(disable=lowerCAmelCase				)

										UpperCAmelCase					    =     '''.'''
										UpperCAmelCase					    =     torch.manual_seed(0				)
										UpperCAmelCase					    =     sag_pipe(
										    [prompt]				,   width=768				,   height=512				,   generator=lowerCAmelCase				,   guidance_scale=7.5				,   sag_scale=1.0				,   num_inference_steps=20				,   output_type='''np'''				,   )

										UpperCAmelCase					    =     output.images

										assert image.shape == (1, 512, 768, 3)





