



"""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__					:						List[str]												=			16
lowercase__					:						Any												=			32




def __lowercase    (							_a	,    _a = 16					):
				snake_case_				:  Dict       =			AutoTokenizer.from_pretrained('''bert-base-cased'''					)
				snake_case_				:  str       =			load_dataset('''glue'''	,    '''mrpc'''					)

				def tokenize_function(_a					):
								# max_length=None => use the model max length (it's actually the default)
								snake_case_				:  List[str]       =			tokenizer(examples['''sentence1''']	,    examples['''sentence2''']	,    truncation=SCREAMING_SNAKE_CASE_	,    max_length=SCREAMING_SNAKE_CASE_					)
								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_				:  List[Any]       =			datasets.map(
								    SCREAMING_SNAKE_CASE_	,    batched=SCREAMING_SNAKE_CASE_	,    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_				:  Dict       =			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_				:  Dict       =			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_				:  Any       =			16
								elif accelerator.mixed_precision != "no":
												snake_case_				:  Dict       =			8
								else:
												snake_case_				:  List[Any]       =			None

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

				# Instantiate dataloaders.
				snake_case_				:  List[Any]       =			DataLoader(
				    tokenized_datasets['''train''']	,    shuffle=SCREAMING_SNAKE_CASE_	,    collate_fn=SCREAMING_SNAKE_CASE_	,    batch_size=SCREAMING_SNAKE_CASE_					)
				snake_case_				:  Union[str, Any]       =			DataLoader(
				    tokenized_datasets['''validation''']	,    shuffle=SCREAMING_SNAKE_CASE_	,    collate_fn=SCREAMING_SNAKE_CASE_	,    batch_size=SCREAMING_SNAKE_CASE_					)

				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__					:						List[str]												=			mocked_dataloaders  # noqa: F811




def __lowercase    (							_a	,    _a					):

				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	,    SCREAMING_SNAKE_CASE_					) == "1":
								snake_case_				:  Dict       =			2
				# New Code #
				snake_case_				:  Optional[Any]       =			int(args.gradient_accumulation_steps					)
				snake_case_				:  str       =			int(args.local_sgd_steps					)
				# Initialize accelerator
				snake_case_				:  Optional[Any]       =			Accelerator(
				    cpu=args.cpu	,    mixed_precision=args.mixed_precision	,    gradient_accumulation_steps=SCREAMING_SNAKE_CASE_					)
				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
				snake_case_				:  Optional[int]       =			config["lr"]
				snake_case_				:  Optional[int]       =			int(config['''num_epochs''']					)
				snake_case_				:  Optional[int]       =			int(config['''seed''']					)
				snake_case_				:  Dict       =			int(config['''batch_size''']					)

				snake_case_				:  Tuple       =			evaluate.load('''glue'''	,    '''mrpc'''					)

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

				# 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_				:  Any       =			model.to(accelerator.device					)

				# Instantiate optimizer
				snake_case_				:  Tuple       =			AdamW(params=model.parameters()	,    lr=SCREAMING_SNAKE_CASE_					)

				# Instantiate scheduler
				snake_case_				:  Any       =			get_linear_schedule_with_warmup(
				    optimizer=SCREAMING_SNAKE_CASE_	,    num_warmup_steps=100	,    num_training_steps=(len(SCREAMING_SNAKE_CASE_					) * 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_				:  int       =			accelerator.prepare(
				    SCREAMING_SNAKE_CASE_	,    SCREAMING_SNAKE_CASE_	,    SCREAMING_SNAKE_CASE_	,    SCREAMING_SNAKE_CASE_	,    SCREAMING_SNAKE_CASE_					)

				# Now we train the model
				for epoch in range(SCREAMING_SNAKE_CASE_					):
								model.train()
								with LocalSGD(
								    accelerator=SCREAMING_SNAKE_CASE_	,    model=SCREAMING_SNAKE_CASE_	,    local_sgd_steps=SCREAMING_SNAKE_CASE_	,    enabled=local_sgd_steps is not None					) as local_sgd:
												for step, batch in enumerate(SCREAMING_SNAKE_CASE_					):
																# 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(SCREAMING_SNAKE_CASE_					):
																				snake_case_				:  int       =			model(**SCREAMING_SNAKE_CASE_					)
																				snake_case_				:  Dict       =			output.loss
																				accelerator.backward(SCREAMING_SNAKE_CASE_					)
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# LocalSGD-specific line
																				local_sgd.step()

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

								snake_case_				:  str       =			metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f"epoch {epoch}:"	,    SCREAMING_SNAKE_CASE_					)




def __lowercase    (							):
				snake_case_				:  Tuple       =			argparse.ArgumentParser(description='''Simple example of training script.'''					)
				parser.add_argument(
				    '''--mixed_precision'''	,    type=SCREAMING_SNAKE_CASE_	,    default=SCREAMING_SNAKE_CASE_	,    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=SCREAMING_SNAKE_CASE_	,    default=1	,    help='''The number of minibatches to be ran before gradients are accumulated.'''	,    )
				parser.add_argument(
				    '''--local_sgd_steps'''	,    type=SCREAMING_SNAKE_CASE_	,    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.'''					)
				snake_case_				:  Tuple       =			parser.parse_args()
				snake_case_				:  Any       =			{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
				training_function(SCREAMING_SNAKE_CASE_	,    SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
					main()










"""simple docstring"""

def __lowercase    (							_a	,    _a					):
    snake_case_				:  str       =			[0 for i in range(r + 1					)]
    # nc0 = 1
    snake_case_				:  int       =			1
    for i in range(1	,    n + 1					):
        # to compute current row from previous row.
        snake_case_				:  Any       =			min(_a	,    _a					)
        while j > 0:
            c[j] += c[j - 1]
            j -= 1
    return c[r]






print(binomial_coefficient(n=10, r=5))





