




"""simple docstring"""







from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				def __init__(     self					:	int    ,   lowercase__					:	Any    ,   lowercase__					:	List[str]    ,   lowercase__					:	Optional[Any]    ,   lowercase__					:	str = None    ,   ):
						super().__init__()
						self.register_modules(transformer=__a    ,   vae=__a    ,   scheduler=__a						)

						# create a imagenet -> id dictionary for easier use
						__lowercase				:		Any												=				{}
						if idalabel is not None:
								for key, value in idalabel.items():
										for label in value.split(","						):
												__lowercase				:		Optional[Any]												=				int(__a						)
								__lowercase				:		Tuple												=				dict(sorted(self.labels.items()						)						)



				def       snake_case    (     self					:	int    ,   lowercase__					:	int						):

						if not isinstance(__a    ,   __a						):
								__lowercase				:		List[str]												=				list(__a						)

						for l in label:
								if l not in self.labels:
										raise ValueError(
										    f'{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'						)

						return [self.labels[l] for l in label]



				@torch.no_grad()
				def __call__(     self					:	Any    ,   lowercase__					:	List[Any]    ,   lowercase__					:	int = 4.0    ,   lowercase__					:	Union[str, Any] = None    ,   lowercase__					:	Dict = 5_0    ,   lowercase__					:	Union[str, Any] = "pil"    ,   lowercase__					:	List[Any] = True    ,   ):
						__lowercase				:		Union[str, Any]												=				len(__a						)
						__lowercase				:		List[Any]												=				self.transformer.config.sample_size
						__lowercase				:		str												=				self.transformer.config.in_channels

						__lowercase				:		Any												=				randn_tensor(
						    shape=(batch_size, latent_channels, latent_size, latent_size)    ,   generator=__a    ,   device=self.device    ,   dtype=self.transformer.dtype    ,   )
						__lowercase				:		Optional[Any]												=				torch.cat([latents] * 2						) if guidance_scale > 1 else latents

						__lowercase				:		Any												=				torch.tensor(__a    ,   device=self.device						).reshape(-1						)
						__lowercase				:		int												=				torch.tensor([1_0_0_0] * batch_size    ,   device=self.device						)
						__lowercase				:		Any												=				torch.cat([class_labels, class_null]    ,   0						) if guidance_scale > 1 else class_labels

						# set step values
						self.scheduler.set_timesteps(__a						)

						for t in self.progress_bar(self.scheduler.timesteps						):
								if guidance_scale > 1:
										__lowercase				:		Union[str, Any]												=				latent_model_input[: len(__a						) // 2]
										__lowercase				:		List[Any]												=				torch.cat([half, half]    ,   dim=0						)
								__lowercase				:		Dict												=				self.scheduler.scale_model_input(__a    ,   __a						)

								__lowercase				:		Union[str, Any]												=				t
								if not torch.is_tensor(__a						):
										# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
										# This would be a good case for the `match` statement (Python 3.10+)
										__lowercase				:		List[str]												=				latent_model_input.device.type == "mps"
										if isinstance(__a    ,   __a						):
												__lowercase				:		Dict												=				torch.floataa if is_mps else torch.floataa
										else:
												__lowercase				:		Optional[Any]												=				torch.intaa if is_mps else torch.intaa
										__lowercase				:		Union[str, Any]												=				torch.tensor([timesteps]    ,   dtype=__a    ,   device=latent_model_input.device						)
								elif len(timesteps.shape						) == 0:
										__lowercase				:		int												=				timesteps[None].to(latent_model_input.device						)
								# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
								__lowercase				:		List[str]												=				timesteps.expand(latent_model_input.shape[0]						)
								# predict noise model_output
								__lowercase				:		Dict												=				self.transformer(
								    __a    ,   timestep=__a    ,   class_labels=__a						).sample

								# perform guidance
								if guidance_scale > 1:
										__lowercase						,__lowercase				:		str												=				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
										__lowercase						,__lowercase				:		Optional[Any]												=				torch.split(__a    ,   len(__a						) // 2    ,   dim=0						)

										__lowercase				:		Optional[int]												=				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
										__lowercase				:		str												=				torch.cat([half_eps, half_eps]    ,   dim=0						)

										__lowercase				:		Any												=				torch.cat([eps, rest]    ,   dim=1						)

								# learned sigma
								if self.transformer.config.out_channels // 2 == latent_channels:
										__lowercase						,__lowercase				:		Dict												=				torch.split(__a    ,   __a    ,   dim=1						)
								else:
										__lowercase				:		str												=				noise_pred

								# compute previous image: x_t -> x_t-1
								__lowercase				:		str												=				self.scheduler.step(__a    ,   __a    ,   __a						).prev_sample

						if guidance_scale > 1:
								__lowercase						,__lowercase				:		Union[str, Any]												=				latent_model_input.chunk(2    ,   dim=0						)
						else:
								__lowercase				:		Optional[int]												=				latent_model_input

						__lowercase				:		Any												=				1 / self.vae.config.scaling_factor * latents
						__lowercase				:		List[str]												=				self.vae.decode(__a						).sample

						__lowercase				:		List[str]												=				(samples / 2 + 0.5).clamp(0    ,   1						)

						# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
						__lowercase				:		Union[str, Any]												=				samples.cpu().permute(0    ,   2    ,   3    ,   1						).float().numpy()

						if output_type == "pil":
								__lowercase				:		str												=				self.numpy_to_pil(__a						)

						if not return_dict:
								return (samples,)

						return ImagePipelineOutput(images=__a						)






"""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))
