from __future__ import annotations

import math
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					if len(__lowerCamelCase						) != 2 or len(a[0]						) != 2 or len(__lowerCamelCase						) != 2 or len(b[0]						) != 2:
										raise Exception('Matrices are not 2x2'						)
					__a		 =       [
					    [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 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					return [
					    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]						)						)]
					    for row in range(len(__lowerCamelCase						)						)
					]
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					return [
					    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]						)						)]
					    for row in range(len(__lowerCamelCase						)						)
					]
def 							lowerCAmelCase(     __lowerCamelCase						):
					if len(__lowerCamelCase						) % 2 != 0 or len(a[0]						) % 2 != 0:
										raise Exception('Odd matrices are not supported!'						)

					__a		 =       len(__lowerCamelCase						)
					__a		 =       matrix_length // 2

					__a		 =       [[a[i][j] for j in range(__lowerCamelCase				,    __lowerCamelCase						)] for i in range(__lowerCamelCase						)]
					__a		 =       [
					    [a[i][j] for j in range(__lowerCamelCase				,    __lowerCamelCase						)] for i in range(__lowerCamelCase				,    __lowerCamelCase						)
					]

					__a		 =       [[a[i][j] for j in range(__lowerCamelCase						)] for i in range(__lowerCamelCase						)]
					__a		 =       [[a[i][j] for j in range(__lowerCamelCase						)] for i in range(__lowerCamelCase				,    __lowerCamelCase						)]

					return top_left, top_right, bot_left, bot_right
def 							lowerCAmelCase(     __lowerCamelCase						):
					return len(__lowerCamelCase						), len(matrix[0]						)
def 							lowerCAmelCase(     __lowerCamelCase						):
					print('\n'.join(str(__lowerCamelCase						) for line in matrix						)						)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					if matrix_dimensions(__lowerCamelCase						) == (2, 2):
										return default_matrix_multiplication(__lowerCamelCase				,    __lowerCamelCase						)

					__a ,				__a ,				__a ,				__a		 =       split_matrix(__lowerCamelCase						)
					__a ,				__a ,				__a ,				__a		 =       split_matrix(__lowerCamelCase						)

					__a		 =       actual_strassen(__lowerCamelCase				,    matrix_subtraction(__lowerCamelCase				,    __lowerCamelCase						)						)
					__a		 =       actual_strassen(matrix_addition(__lowerCamelCase				,    __lowerCamelCase						)				,    __lowerCamelCase						)
					__a		 =       actual_strassen(matrix_addition(__lowerCamelCase				,    __lowerCamelCase						)				,    __lowerCamelCase						)
					__a		 =       actual_strassen(__lowerCamelCase				,    matrix_subtraction(__lowerCamelCase				,    __lowerCamelCase						)						)
					__a		 =       actual_strassen(matrix_addition(__lowerCamelCase				,    __lowerCamelCase						)				,    matrix_addition(__lowerCamelCase				,    __lowerCamelCase						)						)
					__a		 =       actual_strassen(matrix_subtraction(__lowerCamelCase				,    __lowerCamelCase						)				,    matrix_addition(__lowerCamelCase				,    __lowerCamelCase						)						)
					__a		 =       actual_strassen(matrix_subtraction(__lowerCamelCase				,    __lowerCamelCase						)				,    matrix_addition(__lowerCamelCase				,    __lowerCamelCase						)						)

					__a		 =       matrix_addition(matrix_subtraction(matrix_addition(__lowerCamelCase				,    __lowerCamelCase						)				,    __lowerCamelCase						)				,    __lowerCamelCase						)
					__a		 =       matrix_addition(__lowerCamelCase				,    __lowerCamelCase						)
					__a		 =       matrix_addition(__lowerCamelCase				,    __lowerCamelCase						)
					__a		 =       matrix_subtraction(matrix_subtraction(matrix_addition(__lowerCamelCase				,    __lowerCamelCase						)				,    __lowerCamelCase						)				,    __lowerCamelCase						)

					# construct the new matrix from our 4 quadrants
					__a		 =       []
					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 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					if matrix_dimensions(__lowerCamelCase						)[1] != matrix_dimensions(__lowerCamelCase						)[0]:
										__a		 =       (
										    'Unable to multiply these matrices, please check the dimensions.\n'
										    f'''Matrix A: {matrixa}\n'''
										    f'''Matrix B: {matrixa}'''
										)
										raise Exception(__lowerCamelCase						)
					__a		 =       matrix_dimensions(__lowerCamelCase						)
					__a		 =       matrix_dimensions(__lowerCamelCase						)

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

					__a		 =       max(*__lowerCamelCase				,    *__lowerCamelCase						)
					__a		 =       int(math.pow(2				,    math.ceil(math.loga(__lowerCamelCase						)						)						)						)
					__a		 =       matrixa
					__a		 =       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						)

					__a		 =       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__":
	lowerCamelCase_    :		Union[str, Any]							=	[
	    [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],
	]
	lowerCamelCase_    :		List[Any]							=	[[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
	print(strassen(matrixa, matrixa))





import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np


class 						a__							:



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								torch.manual_seed(0   )
								__a		 =       TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'   )

								torch.manual_seed(0   )
								__a		 =       AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'   )

								torch.manual_seed(0   )
								__a		 =       UNetaDConditionModel(
								    sample_size=3_2				,						layers_per_block=1				,						block_out_channels=[3_2, 6_4]				,						down_block_types=[
								        'ResnetDownsampleBlock2D',
								        'SimpleCrossAttnDownBlock2D',
								    ]				,						mid_block_type='UNetMidBlock2DSimpleCrossAttn'				,						up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']				,						in_channels=3				,						out_channels=6				,						cross_attention_dim=3_2				,						encoder_hid_dim=3_2				,						attention_head_dim=8				,						addition_embed_type='text'				,						addition_embed_type_num_heads=2				,						cross_attention_norm='group_norm'				,						resnet_time_scale_shift='scale_shift'				,						act_fn='gelu'				,						)
								unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

								torch.manual_seed(0   )
								__a		 =       DDPMScheduler(
								    num_train_timesteps=1_0_0_0				,						beta_schedule='squaredcos_cap_v2'				,						beta_start=0.0_001				,						beta_end=0.02				,						thresholding=UpperCAmelCase				,						dynamic_thresholding_ratio=0.95				,						sample_max_value=1.0				,						prediction_type='epsilon'				,						variance_type='learned_range'				,						)

								torch.manual_seed(0   )
								__a		 =       IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								torch.manual_seed(0   )
								__a		 =       TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'   )

								torch.manual_seed(0   )
								__a		 =       AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'   )

								torch.manual_seed(0   )
								__a		 =       UNetaDConditionModel(
								    sample_size=3_2				,						layers_per_block=[1, 2]				,						block_out_channels=[3_2, 6_4]				,						down_block_types=[
								        'ResnetDownsampleBlock2D',
								        'SimpleCrossAttnDownBlock2D',
								    ]				,						mid_block_type='UNetMidBlock2DSimpleCrossAttn'				,						up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']				,						in_channels=6				,						out_channels=6				,						cross_attention_dim=3_2				,						encoder_hid_dim=3_2				,						attention_head_dim=8				,						addition_embed_type='text'				,						addition_embed_type_num_heads=2				,						cross_attention_norm='group_norm'				,						resnet_time_scale_shift='scale_shift'				,						act_fn='gelu'				,						class_embed_type='timestep'				,						mid_block_scale_factor=1.414				,						time_embedding_act_fn='gelu'				,						time_embedding_dim=3_2				,						)
								unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

								torch.manual_seed(0   )
								__a		 =       DDPMScheduler(
								    num_train_timesteps=1_0_0_0				,						beta_schedule='squaredcos_cap_v2'				,						beta_start=0.0_001				,						beta_end=0.02				,						thresholding=UpperCAmelCase				,						dynamic_thresholding_ratio=0.95				,						sample_max_value=1.0				,						prediction_type='epsilon'				,						variance_type='learned_range'				,						)

								torch.manual_seed(0   )
								__a		 =       DDPMScheduler(
								    num_train_timesteps=1_0_0_0				,						beta_schedule='squaredcos_cap_v2'				,						beta_start=0.0_001				,						beta_end=0.02				,						)

								torch.manual_seed(0   )
								__a		 =       IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "image_noising_scheduler": image_noising_scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}



			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       self.get_dummy_components()
								__a		 =       self.pipeline_class(**UpperCAmelCase   )
								pipe.to(UpperCAmelCase   )
								pipe.set_progress_bar_config(disable=UpperCAmelCase   )

								__a		 =       self.get_dummy_inputs(UpperCAmelCase   )

								__a		 =       inputs['prompt']
								__a		 =       inputs['generator']
								__a		 =       inputs['num_inference_steps']
								__a		 =       inputs['output_type']

								if "image" in inputs:
													__a		 =       inputs['image']
								else:
													__a		 =       None

								if "mask_image" in inputs:
													__a		 =       inputs['mask_image']
								else:
													__a		 =       None

								if "original_image" in inputs:
													__a		 =       inputs['original_image']
								else:
													__a		 =       None

								__a ,				__a		 =       pipe.encode_prompt(UpperCAmelCase   )

								# inputs with prompt converted to embeddings
								__a		 =       {
								    'prompt_embeds': prompt_embeds,
								    'negative_prompt_embeds': negative_prompt_embeds,
								    'generator': generator,
								    'num_inference_steps': num_inference_steps,
								    'output_type': output_type,
								}

								if image is not None:
													__a		 =       image

								if mask_image is not None:
													__a		 =       mask_image

								if original_image is not None:
													__a		 =       original_image

								# set all optional components to None
								for optional_component in pipe._optional_components:
													setattr(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )

								__a		 =       pipe(**UpperCAmelCase   )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(UpperCAmelCase   )
													__a		 =       self.pipeline_class.from_pretrained(UpperCAmelCase   )
													pipe_loaded.to(UpperCAmelCase   )
													pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase   )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

								for optional_component in pipe._optional_components:
													self.assertTrue(
													    getattr(UpperCAmelCase				,						UpperCAmelCase   ) is None				,						f'''`{optional_component}` did not stay set to None after loading.'''				,						)

								__a		 =       self.get_dummy_inputs(UpperCAmelCase   )

								__a		 =       inputs['generator']
								__a		 =       inputs['num_inference_steps']
								__a		 =       inputs['output_type']

								# inputs with prompt converted to embeddings
								__a		 =       {
								    'prompt_embeds': prompt_embeds,
								    'negative_prompt_embeds': negative_prompt_embeds,
								    'generator': generator,
								    'num_inference_steps': num_inference_steps,
								    'output_type': output_type,
								}

								if image is not None:
													__a		 =       image

								if mask_image is not None:
													__a		 =       mask_image

								if original_image is not None:
													__a		 =       original_image

								__a		 =       pipe_loaded(**UpperCAmelCase   )[0]

								__a		 =       np.abs(to_np(UpperCAmelCase   ) - to_np(UpperCAmelCase   )   ).max()
								self.assertLess(UpperCAmelCase				,						1e-4   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a		 =       self.get_dummy_components()
								__a		 =       self.pipeline_class(**UpperCAmelCase   )
								pipe.to(UpperCAmelCase   )
								pipe.set_progress_bar_config(disable=UpperCAmelCase   )

								__a		 =       self.get_dummy_inputs(UpperCAmelCase   )
								__a		 =       pipe(**UpperCAmelCase   )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(UpperCAmelCase   )
													__a		 =       self.pipeline_class.from_pretrained(UpperCAmelCase   )
													pipe_loaded.to(UpperCAmelCase   )
													pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase   )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

								__a		 =       self.get_dummy_inputs(UpperCAmelCase   )
								__a		 =       pipe_loaded(**UpperCAmelCase   )[0]

								__a		 =       np.abs(to_np(UpperCAmelCase   ) - to_np(UpperCAmelCase   )   ).max()
								self.assertLess(UpperCAmelCase				,						1e-4   )




