




"""simple docstring"""







import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler




@dataclass
class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				__UpperCAmelCase :     int       =      0
				__UpperCAmelCase :     bool       =      False
				__UpperCAmelCase :     float       =      3.0





class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				def       snake_case    (     self					:	Optional[Any]						):
						# If no defaults are changed, `to_kwargs` returns an empty dict.
						self.assertDictEqual(MockClass().to_kwargs()    ,   {}						)
						self.assertDictEqual(MockClass(a=2						).to_kwargs()    ,   {"a": 2}						)
						self.assertDictEqual(MockClass(a=2    ,   b=lowercase__						).to_kwargs()    ,   {"a": 2, "b": True}						)
						self.assertDictEqual(MockClass(a=2    ,   c=2.2_5						).to_kwargs()    ,   {"a": 2, "c": 2.2_5}						)



				@require_cuda
				def       snake_case    (     self					:	Optional[int]						):
						# If no defaults are changed, `to_kwargs` returns an empty dict.
						__lowercase				:		Dict												=				GradScalerKwargs(init_scale=1_0_2_4    ,   growth_factor=2						)
						AcceleratorState._reset_state()
						__lowercase				:		Any												=				Accelerator(mixed_precision="fp16"    ,   kwargs_handlers=[scaler_handler]						)
						print(accelerator.use_fpaa						)
						__lowercase				:		Optional[Any]												=				accelerator.scaler

						# Check the kwargs have been applied
						self.assertEqual(scaler._init_scale    ,   1_0_2_4.0						)
						self.assertEqual(scaler._growth_factor    ,   2.0						)

						# Check the other values are at the default
						self.assertEqual(scaler._backoff_factor    ,   0.5						)
						self.assertEqual(scaler._growth_interval    ,   2_0_0_0						)
						self.assertEqual(scaler._enabled    ,   lowercase__						)



				@require_multi_gpu
				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		List[Any]												=				["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__						)]
						execute_subprocess_async(lowercase__    ,   env=os.environ.copy()						)


if __name__ == "__main__":
						__A   :  str					=      DistributedDataParallelKwargs(bucket_cap_mb=1_5, find_unused_parameters=True)
						__A   :  Union[str, Any]					=      Accelerator(kwargs_handlers=[ddp_scaler])
						__A   :  str					=      torch.nn.Linear(1_0_0, 2_0_0)
						__A   :  List[Any]					=      accelerator.prepare(model)

						# Check the values changed in kwargs
						__A   :  Optional[Any]					=      ''
						__A   :  Optional[int]					=      model.bucket_bytes_cap // (1_0_2_4 * 1_0_2_4)
						if observed_bucket_cap_map != 1_5:
												error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
						if model.find_unused_parameters is not True:
												error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

						# Check the values of the defaults
						if model.dim != 0:
												error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
						if model.broadcast_buffers is not True:
												error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
						if model.gradient_as_bucket_view is not False:
												error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

						# Raise error at the end to make sure we don't stop at the first failure.
						if len(error_msg) > 0:
												raise ValueError(error_msg)






"""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))
