
'''simple docstring'''



import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_lowercase    =		16
_lowercase    =		32


def A				(__lowerCamelCase				:Accelerator			,     __lowerCamelCase				:int = 16		):
						_lowerCAmelCase  						=			AutoTokenizer.from_pretrained("""bert-base-cased"""		)
						_lowerCAmelCase  						=			load_dataset("""glue"""			,     """mrpc"""		)

						def tokenize_function(__lowerCamelCase				:List[Any]		):
												# max_length=None => use the model max length (it's actually the default)
												_lowerCAmelCase  						=			tokenizer(examples["""sentence1"""]			,     examples["""sentence2"""]			,     truncation=__lowerCamelCase			,     max_length=__lowerCamelCase		)
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												_lowerCAmelCase  						=			datasets.map(
												    __lowerCamelCase			,     batched=__lowerCamelCase			,     remove_columns=["""idx""", """sentence1""", """sentence2"""]			,     )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						_lowerCAmelCase  						=			tokenized_datasets.rename_column("""label"""			,     """labels"""		)

						def collate_fn(__lowerCamelCase				:Dict		):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												_lowerCAmelCase  						=			128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		_lowerCAmelCase  						=			16
												elif accelerator.mixed_precision != "no":
																		_lowerCAmelCase  						=			8
												else:
																		_lowerCAmelCase  						=			None

												return tokenizer.pad(
												    __lowerCamelCase			,     padding="""longest"""			,     max_length=__lowerCamelCase			,     pad_to_multiple_of=__lowerCamelCase			,     return_tensors="""pt"""			,     )

						# Instantiate dataloaders.
						_lowerCAmelCase  						=			DataLoader(
						    tokenized_datasets["""train"""]			,     shuffle=__lowerCamelCase			,     collate_fn=__lowerCamelCase			,     batch_size=__lowerCamelCase		)
						_lowerCAmelCase  						=			DataLoader(
						    tokenized_datasets["""validation"""]			,     shuffle=__lowerCamelCase			,     collate_fn=__lowerCamelCase			,     batch_size=__lowerCamelCase		)

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
						from accelerate.test_utils.training import mocked_dataloaders

						_lowercase    =		mocked_dataloaders  # noqa: F811


def A				(__lowerCamelCase				:int			,     __lowerCamelCase				:int		):
						# For testing only
						if os.environ.get("""TESTING_MOCKED_DATALOADERS"""			,     __lowerCamelCase		) == "1":
												_lowerCAmelCase  						=			2
						# New Code #
						_lowerCAmelCase  						=			int(args.gradient_accumulation_steps		)
						_lowerCAmelCase  						=			int(args.local_sgd_steps		)
						# Initialize accelerator
						_lowerCAmelCase  						=			Accelerator(
						    cpu=args.cpu			,     mixed_precision=args.mixed_precision			,     gradient_accumulation_steps=__lowerCamelCase		)
						if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
												raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"""		)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						_lowerCAmelCase  						=			config['lr']
						_lowerCAmelCase  						=			int(config["""num_epochs"""]		)
						_lowerCAmelCase  						=			int(config["""seed"""]		)
						_lowerCAmelCase  						=			int(config["""batch_size"""]		)

						_lowerCAmelCase  						=			evaluate.load("""glue"""			,     """mrpc"""		)

						set_seed(__lowerCamelCase		)
						_lowerCAmelCase  						=			get_dataloaders(__lowerCamelCase			,     __lowerCamelCase		)
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						_lowerCAmelCase  						=			AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""			,     return_dict=__lowerCamelCase		)

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						_lowerCAmelCase  						=			model.to(accelerator.device		)

						# Instantiate optimizer
						_lowerCAmelCase  						=			AdamW(params=model.parameters()			,     lr=__lowerCamelCase		)

						# Instantiate scheduler
						_lowerCAmelCase  						=			get_linear_schedule_with_warmup(
						    optimizer=__lowerCamelCase			,     num_warmup_steps=100			,     num_training_steps=(len(__lowerCamelCase		) * num_epochs)			,     )

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						_lowerCAmelCase  						=			accelerator.prepare(
						    __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)

						# Now we train the model
						for epoch in range(__lowerCamelCase		):
												model.train()
												with LocalSGD(
												    accelerator=__lowerCamelCase			,     model=__lowerCamelCase			,     local_sgd_steps=__lowerCamelCase			,     enabled=local_sgd_steps is not None		) as local_sgd:
																		for step, batch in enumerate(__lowerCamelCase		):
																								# We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device		)
																								# New code #
																								# We use the new `accumulate` context manager to perform gradient accumulation
																								# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																								with accelerator.accumulate(__lowerCamelCase		):
																														_lowerCAmelCase  						=			model(**__lowerCamelCase		)
																														_lowerCAmelCase  						=			output.loss
																														accelerator.backward(__lowerCamelCase		)
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()
																														# LocalSGD-specific line
																														local_sgd.step()

												model.eval()
												for step, batch in enumerate(__lowerCamelCase		):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device		)
																		with torch.no_grad():
																								_lowerCAmelCase  						=			model(**__lowerCamelCase		)
																		_lowerCAmelCase  						=			outputs.logits.argmax(dim=-1		)
																		_lowerCAmelCase  						=			accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
																		metric.add_batch(
																		    predictions=__lowerCamelCase			,     references=__lowerCamelCase			,     )

												_lowerCAmelCase  						=			metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'epoch {epoch}:'			,     __lowerCamelCase		)


def A				():
						_lowerCAmelCase  						=			argparse.ArgumentParser(description="""Simple example of training script."""		)
						parser.add_argument(
						    """--mixed_precision"""			,     type=__lowerCamelCase			,     default=__lowerCamelCase			,     choices=["""no""", """fp16""", """bf16""", """fp8"""]			,     help="""Whether to use mixed precision. Choose"""
						    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
						    """and an Nvidia Ampere GPU."""			,     )
						# New Code #
						parser.add_argument(
						    """--gradient_accumulation_steps"""			,     type=__lowerCamelCase			,     default=1			,     help="""The number of minibatches to be ran before gradients are accumulated."""			,     )
						parser.add_argument(
						    """--local_sgd_steps"""			,     type=__lowerCamelCase			,     default=8			,     help="""Number of local SGD steps or None to disable local SGD"""		)
						parser.add_argument("""--cpu"""			,     action="""store_true"""			,     help="""If passed, will train on the CPU."""		)
						_lowerCAmelCase  						=			parser.parse_args()
						_lowerCAmelCase  						=			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(__lowerCamelCase			,     __lowerCamelCase		)


if __name__ == "__main__":
						main()


'''simple docstring'''



from __future__ import annotations

import math


def A				(__lowerCamelCase				:list			,     __lowerCamelCase				:list		):
						if len(__lowerCamelCase		) != 2 or len(a[0]		) != 2 or len(__lowerCamelCase		) != 2 or len(b[0]		) != 2:
												raise Exception("""Matrices are not 2x2"""		)
						_lowerCAmelCase  						=			[
						    [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 A				(__lowerCamelCase				:list			,     __lowerCamelCase				:list		):
						return [
						    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]		)		)]
						    for row in range(len(__lowerCamelCase		)		)
						]


def A				(__lowerCamelCase				:list			,     __lowerCamelCase				:list		):
						return [
						    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]		)		)]
						    for row in range(len(__lowerCamelCase		)		)
						]


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

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

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

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

						return top_left, top_right, bot_left, bot_right


def A				(__lowerCamelCase				:list		):
						return len(__lowerCamelCase		), len(matrix[0]		)


def A				(__lowerCamelCase				:list		):
						print("""\n""".join(str(__lowerCamelCase		) for line in matrix		)		)


def A				(__lowerCamelCase				:list			,     __lowerCamelCase				:list		):
						if matrix_dimensions(__lowerCamelCase		) == (2, 2):
												return default_matrix_multiplication(__lowerCamelCase			,     __lowerCamelCase		)

						_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			split_matrix(__lowerCamelCase		)
						_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			split_matrix(__lowerCamelCase		)

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

						_lowerCAmelCase  						=			matrix_addition(matrix_subtraction(matrix_addition(__lowerCamelCase			,     __lowerCamelCase		)			,     __lowerCamelCase		)			,     __lowerCamelCase		)
						_lowerCAmelCase  						=			matrix_addition(__lowerCamelCase			,     __lowerCamelCase		)
						_lowerCAmelCase  						=			matrix_addition(__lowerCamelCase			,     __lowerCamelCase		)
						_lowerCAmelCase  						=			matrix_subtraction(matrix_subtraction(matrix_addition(__lowerCamelCase			,     __lowerCamelCase		)			,     __lowerCamelCase		)			,     __lowerCamelCase		)

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

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

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

						_lowerCAmelCase  						=			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__":
						_lowercase    =		[
						    [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],
						]
						_lowercase    =		[[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
						print(strassen(matrixa, matrixa))
