




'''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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform 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 						lowerCamelCase__   (				a    ,	a = 16							):
						__snake_case						 =   AutoTokenizer.from_pretrained('bert-base-cased'							)
						__snake_case						 =   load_dataset('glue'    ,	'mrpc'							)

						def tokenize_function(a							):
												# max_length=None => use the model max length (it's actually the default)
												__snake_case						 =   tokenizer(examples['sentence1']    ,	examples['sentence2']    ,	truncation=a    ,	max_length=a							)
												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():
												__snake_case						 =   datasets.map(
												    a    ,	batched=a    ,	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
						__snake_case						 =   tokenized_datasets.rename_column('label'    ,	'labels'							)

						def collate_fn(a							):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												__snake_case						 =   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":
																		__snake_case						 =   16
												elif accelerator.mixed_precision != "no":
																		__snake_case						 =   8
												else:
																		__snake_case						 =   None

												return tokenizer.pad(
												    a    ,	padding='longest'    ,	max_length=a    ,	pad_to_multiple_of=a    ,	return_tensors='pt'    ,	)

						# Instantiate dataloaders.
						__snake_case						 =   DataLoader(
						    tokenized_datasets['train']    ,	shuffle=a    ,	collate_fn=a    ,	batch_size=a							)
						__snake_case						 =   DataLoader(
						    tokenized_datasets['validation']    ,	shuffle=a    ,	collate_fn=a    ,	batch_size=a							)

						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 						lowerCamelCase__   (				a    ,	a							):
						# For testing only
						if os.environ.get('TESTING_MOCKED_DATALOADERS'    ,	a							) == "1":
												__snake_case						 =   2
						# New Code #
						__snake_case						 =   int(args.gradient_accumulation_steps							)
						# Initialize accelerator
						__snake_case						 =   Accelerator(
						    cpu=args.cpu    ,	mixed_precision=args.mixed_precision    ,	gradient_accumulation_steps=a							)
						if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
												raise NotImplementedError(
												    'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'							)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__snake_case						 =   config['lr']
						__snake_case						 =   int(config['num_epochs']							)
						__snake_case						 =   int(config['seed']							)
						__snake_case						 =   int(config['batch_size']							)

						__snake_case						 =   evaluate.load('glue'    ,	'mrpc'							)

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

						# 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).
						__snake_case						 =   model.to(accelerator.device							)

						# Instantiate optimizer
						__snake_case						 =   AdamW(params=model.parameters()    ,	lr=a							)

						# Instantiate scheduler
						__snake_case						 =   get_linear_schedule_with_warmup(
						    optimizer=a    ,	num_warmup_steps=100    ,	num_training_steps=(len(a							) * 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.
						__snake_case ,       __snake_case ,       __snake_case ,       __snake_case ,       __snake_case						 =   accelerator.prepare(
						    a    ,	a    ,	a    ,	a    ,	a							)

						# Now we train the model
						for epoch in range(a							):
												model.train()
												for step, batch in enumerate(a							):
																		# 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(a							):
																								__snake_case						 =   model(**a							)
																								__snake_case						 =   output.loss
																								accelerator.backward(a							)
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

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

												__snake_case						 =   metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'epoch {epoch}:'    ,	a							)
def 						lowerCamelCase__   (				):
						__snake_case						 =   argparse.ArgumentParser(description='Simple example of training script.'							)
						parser.add_argument(
						    '--mixed_precision'    ,	type=a    ,	default=a    ,	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=a    ,	default=1    ,	help='The number of minibatches to be ran before gradients are accumulated.'    ,	)
						parser.add_argument('--cpu'    ,	action='store_true'    ,	help='If passed, will train on the CPU.'							)
						__snake_case						 =   parser.parse_args()
						__snake_case						 =   {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(a    ,	a							)


if __name__ == "__main__":
	main()







'''simple docstring'''


from collections import defaultdict
def 						lowerCamelCase__   (				a    ,	a							):
						__snake_case						 =   first_str.lower().strip()
						__snake_case						 =   second_str.lower().strip()

						# Remove whitespace
						__snake_case						 =   first_str.replace(' '    ,	''							)
						__snake_case						 =   second_str.replace(' '    ,	''							)

						# Strings of different lengths are not anagrams
						if len(a							) != len(a							):
												return False

						# Default values for count should be 0
						__snake_case						 =   defaultdict(a							)

						# For each character in input strings,
						# increment count in the corresponding
						for i in range(len(a							)							):
												count[first_str[i]] += 1
												count[second_str[i]] -= 1

						return all(_count == 0 for _count in count.values()							)


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	_lowercase  =			input("""Enter the first string """).strip()
	_lowercase  =			input("""Enter the second string """).strip()

	_lowercase  =			check_anagrams(input_a, input_b)
	print(f'''{input_a} and {input_b} are {'' if status else 'not '}anagrams.''')

