




"""simple docstring"""







import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class       lowerCAmelCase__       (			UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_  ,			unittest.TestCase    ):


				"""simple docstring"""





				__UpperCAmelCase :     Any       =      StableDiffusionInpaintPipeline
				__UpperCAmelCase :     Optional[int]       =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				__UpperCAmelCase :     List[Any]       =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				__UpperCAmelCase :     List[str]       =      frozenset(
				    []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				__UpperCAmelCase :     Optional[Any]       =      frozenset([]    )



				def       snake_case    (     self					:	Optional[int]						):
						torch.manual_seed(0						)
						__lowercase				:		int												=				UNetaDConditionModel(
						    block_out_channels=(3_2, 6_4)    ,   layers_per_block=2    ,   sample_size=3_2    ,   in_channels=9    ,   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=UpperCamelCase__    ,   )
						__lowercase				:		int												=				PNDMScheduler(skip_prk_steps=UpperCamelCase__						)
						torch.manual_seed(0						)
						__lowercase				:		Optional[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						)
						__lowercase				:		Any												=				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    ,   )
						__lowercase				:		Dict												=				CLIPTextModel(UpperCamelCase__						)
						__lowercase				:		Optional[int]												=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

						__lowercase				:		List[str]												=				{
						    '''unet''': unet,
						    '''scheduler''': scheduler,
						    '''vae''': vae,
						    '''text_encoder''': text_encoder,
						    '''tokenizer''': tokenizer,
						    '''safety_checker''': None,
						    '''feature_extractor''': None,
						}
						return components



				def       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	Tuple    ,   lowercase__					:	Union[str, Any]=0						):
						__lowercase				:		Union[str, Any]												=				floats_tensor((1, 3, 3_2, 3_2)    ,   rng=random.Random(UpperCamelCase__						)						).to(UpperCamelCase__						)
						__lowercase				:		Optional[Any]												=				image.cpu().permute(0    ,   2    ,   3    ,   1						)[0]
						__lowercase				:		List[Any]												=				Image.fromarray(np.uinta(UpperCamelCase__						)						).convert("RGB"						).resize((6_4, 6_4)						)
						__lowercase				:		List[str]												=				Image.fromarray(np.uinta(image + 4						)						).convert("RGB"						).resize((6_4, 6_4)						)
						if str(UpperCamelCase__						).startswith("mps"						):
								__lowercase				:		List[Any]												=				torch.manual_seed(UpperCamelCase__						)
						else:
								__lowercase				:		List[Any]												=				torch.Generator(device=UpperCamelCase__						).manual_seed(UpperCamelCase__						)
						__lowercase				:		int												=				{
						    '''prompt''': '''A painting of a squirrel eating a burger''',
						    '''image''': init_image,
						    '''mask_image''': mask_image,
						    '''generator''': generator,
						    '''num_inference_steps''': 2,
						    '''guidance_scale''': 6.0,
						    '''output_type''': '''numpy''',
						}
						return inputs



				def       snake_case    (     self					:	List[Any]						):
						__lowercase				:		Union[str, Any]												=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						__lowercase				:		Any												=				self.get_dummy_components()
						__lowercase				:		int												=				StableDiffusionInpaintPipeline(**UpperCamelCase__						)
						__lowercase				:		Optional[Any]												=				sd_pipe.to(UpperCamelCase__						)
						sd_pipe.set_progress_bar_config(disable=UpperCamelCase__						)

						__lowercase				:		List[Any]												=				self.get_dummy_inputs(UpperCamelCase__						)
						__lowercase				:		Union[str, Any]												=				sd_pipe(**UpperCamelCase__						).images
						__lowercase				:		List[str]												=				image[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)
						__lowercase				:		List[str]												=				np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]						)

						assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2



				def       snake_case    (     self					:	List[Any]						):
						super().test_inference_batch_single_identical(expected_max_diff=3e-3						)





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


				"""simple docstring"""





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



				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		Optional[Any]												=				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"						)
						__lowercase				:		int												=				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"						)
						__lowercase				:		int												=				load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
						    "/yellow_cat_sitting_on_a_park_bench.npy"						)

						__lowercase				:		Union[str, Any]												=				'''stabilityai/stable-diffusion-2-inpainting'''
						__lowercase				:		Any												=				StableDiffusionInpaintPipeline.from_pretrained(UpperCamelCase__    ,   safety_checker=UpperCamelCase__						)
						pipe.to(UpperCamelCase__						)
						pipe.set_progress_bar_config(disable=UpperCamelCase__						)
						pipe.enable_attention_slicing()

						__lowercase				:		Optional[Any]												=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

						__lowercase				:		int												=				torch.manual_seed(0						)
						__lowercase				:		Optional[int]												=				pipe(
						    prompt=UpperCamelCase__    ,   image=UpperCamelCase__    ,   mask_image=UpperCamelCase__    ,   generator=UpperCamelCase__    ,   output_type="np"    ,   )
						__lowercase				:		List[Any]												=				output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)
						assert np.abs(expected_image - image						).max() < 9e-3



				def       snake_case    (     self					:	List[Any]						):
						__lowercase				:		Any												=				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"						)
						__lowercase				:		List[Any]												=				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"						)
						__lowercase				:		List[str]												=				load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
						    "/yellow_cat_sitting_on_a_park_bench_fp16.npy"						)

						__lowercase				:		Optional[int]												=				'''stabilityai/stable-diffusion-2-inpainting'''
						__lowercase				:		Union[str, Any]												=				StableDiffusionInpaintPipeline.from_pretrained(
						    UpperCamelCase__    ,   torch_dtype=torch.floataa    ,   safety_checker=UpperCamelCase__    ,   )
						pipe.to(UpperCamelCase__						)
						pipe.set_progress_bar_config(disable=UpperCamelCase__						)
						pipe.enable_attention_slicing()

						__lowercase				:		Union[str, Any]												=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

						__lowercase				:		List[str]												=				torch.manual_seed(0						)
						__lowercase				:		Optional[int]												=				pipe(
						    prompt=UpperCamelCase__    ,   image=UpperCamelCase__    ,   mask_image=UpperCamelCase__    ,   generator=UpperCamelCase__    ,   output_type="np"    ,   )
						__lowercase				:		List[Any]												=				output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)
						assert np.abs(expected_image - image						).max() < 5e-1



				def       snake_case    (     self					:	Optional[int]						):
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						__lowercase				:		Union[str, Any]												=				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"						)
						__lowercase				:		Union[str, Any]												=				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"						)

						__lowercase				:		Optional[Any]												=				'''stabilityai/stable-diffusion-2-inpainting'''
						__lowercase				:		Tuple												=				PNDMScheduler.from_pretrained(UpperCamelCase__    ,   subfolder="scheduler"						)
						__lowercase				:		Tuple												=				StableDiffusionInpaintPipeline.from_pretrained(
						    UpperCamelCase__    ,   safety_checker=UpperCamelCase__    ,   scheduler=UpperCamelCase__    ,   torch_dtype=torch.floataa    ,   )
						pipe.to(UpperCamelCase__						)
						pipe.set_progress_bar_config(disable=UpperCamelCase__						)
						pipe.enable_attention_slicing(1						)
						pipe.enable_sequential_cpu_offload()

						__lowercase				:		Optional[int]												=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

						__lowercase				:		str												=				torch.manual_seed(0						)
						__lowercase				:		List[Any]												=				pipe(
						    prompt=UpperCamelCase__    ,   image=UpperCamelCase__    ,   mask_image=UpperCamelCase__    ,   generator=UpperCamelCase__    ,   num_inference_steps=2    ,   output_type="np"    ,   )

						__lowercase				:		Optional[int]												=				torch.cuda.max_memory_allocated()
						# make sure that less than 2.65 GB is allocated
						assert mem_bytes < 2.6_5 * 1_0**9






"""simple docstring"""







from __future__ import annotations

import math

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->list:






		"""simple docstring"""


		if len(_lowerCamelCase				) != 2 or len(a[0]				) != 2 or len(_lowerCamelCase				) != 2 or len(b[0]				) != 2:
				raise Exception("Matrices are not 2x2"				)
		__lowercase				:		Tuple												=				[
		    [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
		    [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
		]
		return new_matrix

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Dict:






		"""simple docstring"""


		return [
		    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]				)				)]
		    for row in range(len(_lowerCamelCase				)				)
		]

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Tuple:






		"""simple docstring"""


		return [
		    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]				)				)]
		    for row in range(len(_lowerCamelCase				)				)
		]

def 						snake_case__       (					_lowerCamelCase				)			->tuple[list, list, list, list]:






		"""simple docstring"""


		if len(_lowerCamelCase				) % 2 != 0 or len(a[0]				) % 2 != 0:
				raise Exception("Odd matrices are not supported!"				)

		__lowercase				:		Tuple												=				len(_lowerCamelCase				)
		__lowercase				:		Any												=				matrix_length // 2

		__lowercase				:		Dict												=				[[a[i][j] for j in range(_lowerCamelCase,  _lowerCamelCase				)] for i in range(_lowerCamelCase				)]
		__lowercase				:		List[str]												=				[
		    [a[i][j] for j in range(_lowerCamelCase,  _lowerCamelCase				)] for i in range(_lowerCamelCase,  _lowerCamelCase				)
		]

		__lowercase				:		Union[str, Any]												=				[[a[i][j] for j in range(_lowerCamelCase				)] for i in range(_lowerCamelCase				)]
		__lowercase				:		List[str]												=				[[a[i][j] for j in range(_lowerCamelCase				)] for i in range(_lowerCamelCase,  _lowerCamelCase				)]

		return top_left, top_right, bot_left, bot_right

def 						snake_case__       (					_lowerCamelCase				)			->tuple[int, int]:






		"""simple docstring"""


		return len(_lowerCamelCase				), len(matrix[0]				)

def 						snake_case__       (					_lowerCamelCase				)			->None:






		"""simple docstring"""


		print("\n".join(str(_lowerCamelCase				) for line in matrix				)				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->list:






		"""simple docstring"""


		if matrix_dimensions(_lowerCamelCase				) == (2, 2):
				return default_matrix_multiplication(_lowerCamelCase,  _lowerCamelCase				)

		__lowercase						,__lowercase						,__lowercase						,__lowercase				:		Optional[Any]												=				split_matrix(_lowerCamelCase				)
		__lowercase						,__lowercase						,__lowercase						,__lowercase				:		Any												=				split_matrix(_lowerCamelCase				)

		__lowercase				:		str												=				actual_strassen(_lowerCamelCase,  matrix_subtraction(_lowerCamelCase,  _lowerCamelCase				)				)
		__lowercase				:		Dict												=				actual_strassen(matrix_addition(_lowerCamelCase,  _lowerCamelCase				),  _lowerCamelCase				)
		__lowercase				:		Union[str, Any]												=				actual_strassen(matrix_addition(_lowerCamelCase,  _lowerCamelCase				),  _lowerCamelCase				)
		__lowercase				:		Any												=				actual_strassen(_lowerCamelCase,  matrix_subtraction(_lowerCamelCase,  _lowerCamelCase				)				)
		__lowercase				:		List[Any]												=				actual_strassen(matrix_addition(_lowerCamelCase,  _lowerCamelCase				),  matrix_addition(_lowerCamelCase,  _lowerCamelCase				)				)
		__lowercase				:		Optional[int]												=				actual_strassen(matrix_subtraction(_lowerCamelCase,  _lowerCamelCase				),  matrix_addition(_lowerCamelCase,  _lowerCamelCase				)				)
		__lowercase				:		Optional[Any]												=				actual_strassen(matrix_subtraction(_lowerCamelCase,  _lowerCamelCase				),  matrix_addition(_lowerCamelCase,  _lowerCamelCase				)				)

		__lowercase				:		str												=				matrix_addition(matrix_subtraction(matrix_addition(_lowerCamelCase,  _lowerCamelCase				),  _lowerCamelCase				),  _lowerCamelCase				)
		__lowercase				:		Dict												=				matrix_addition(_lowerCamelCase,  _lowerCamelCase				)
		__lowercase				:		int												=				matrix_addition(_lowerCamelCase,  _lowerCamelCase				)
		__lowercase				:		Union[str, Any]												=				matrix_subtraction(matrix_subtraction(matrix_addition(_lowerCamelCase,  _lowerCamelCase				),  _lowerCamelCase				),  _lowerCamelCase				)

		# construct the new matrix from our 4 quadrants
		__lowercase				:		Optional[int]												=				[]
		for i in range(len(_lowerCamelCase				)				):
				new_matrix.append(top_left[i] + top_right[i]				)
		for i in range(len(_lowerCamelCase				)				):
				new_matrix.append(bot_left[i] + bot_right[i]				)
		return new_matrix

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->list:






		"""simple docstring"""


		if matrix_dimensions(_lowerCamelCase				)[1] != matrix_dimensions(_lowerCamelCase				)[0]:
				__lowercase				:		int												=				(
				    "Unable to multiply these matrices, please check the dimensions.\n"
				    F'Matrix A: {matrixa}\n'
				    F'Matrix B: {matrixa}'
				)
				raise Exception(_lowerCamelCase				)
		__lowercase				:		List[Any]												=				matrix_dimensions(_lowerCamelCase				)
		__lowercase				:		Tuple												=				matrix_dimensions(_lowerCamelCase				)

		if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
				return [matrixa, matrixa]

		__lowercase				:		int												=				max(*_lowerCamelCase,  *_lowerCamelCase				)
		__lowercase				:		Any												=				int(math.pow(2,  math.ceil(math.loga(_lowerCamelCase				)				)				)				)
		__lowercase				:		Dict												=				matrixa
		__lowercase				:		str												=				matrixa

		# Adding zeros to the matrices so that the arrays dimensions are the same and also
		# power of 2
		for i in range(0,  _lowerCamelCase				):
				if i < dimensiona[0]:
						for _ in range(dimensiona[1],  _lowerCamelCase				):
								new_matrixa[i].append(0				)
				else:
						new_matrixa.append([0] * maxim				)
				if i < dimensiona[0]:
						for _ in range(dimensiona[1],  _lowerCamelCase				):
								new_matrixa[i].append(0				)
				else:
						new_matrixa.append([0] * maxim				)

		__lowercase				:		Dict												=				actual_strassen(_lowerCamelCase,  _lowerCamelCase				)

		# Removing the additional zeros
		for i in range(0,  _lowerCamelCase				):
				if i < dimensiona[0]:
						for _ in range(dimensiona[1],  _lowerCamelCase				):
								final_matrix[i].pop()
				else:
						final_matrix.pop()
		return final_matrix


if __name__ == "__main__":
						__A   :  int					=      [
						    [2, 3, 4, 5],
						    [6, 4, 3, 1],
						    [2, 3, 6, 7],
						    [3, 1, 2, 4],
						    [2, 3, 4, 5],
						    [6, 4, 3, 1],
						    [2, 3, 6, 7],
						    [3, 1, 2, 4],
						    [2, 3, 4, 5],
						    [6, 2, 3, 1],
						]
						__A   :  Union[str, Any]					=      [[0, 2, 1, 1], [1_6, 2, 3, 3], [2, 2, 7, 7], [1_3, 1_1, 2_2, 4]]
						print(strassen(matrixa, matrixa))
