






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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


__A          = 16
__A          = 32




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					if args.with_tracking:
										_A									=       Accelerator(
										    cpu=args.cpu    ,	mixed_precision=args.mixed_precision    ,	log_with='''all'''    ,	project_dir=args.project_dir       )
					else:
										_A									=       Accelerator(cpu=args.cpu    ,	mixed_precision=args.mixed_precision       )

					if hasattr(args.checkpointing_steps    ,	'''isdigit'''       ):
										if args.checkpointing_steps == "epoch":
															_A									=       args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															_A									=       int(args.checkpointing_steps       )
										else:
															raise ValueError(
															    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""       )
					else:
										_A									=       None
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					_A									=       config['''lr''']
					_A									=       int(config['''num_epochs''']       )
					_A									=       int(config['''seed''']       )
					_A									=       int(config['''batch_size''']       )

					# We need to initialize the trackers we use, and also store our configuration
					if args.with_tracking:
										_A									=       os.path.split(_lowercase       )[-1].split('''.'''       )[0]
										accelerator.init_trackers(_lowercase    ,	_lowercase       )

					_A									=       AutoTokenizer.from_pretrained('''bert-base-cased'''       )
					_A									=       load_dataset('''glue'''    ,	'''mrpc'''       )
					_A									=       evaluate.load('''glue'''    ,	'''mrpc'''       )

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

					# If the batch size is too big we use gradient accumulation
					_A									=       1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										_A									=       batch_size // MAX_GPU_BATCH_SIZE
										_A									=       MAX_GPU_BATCH_SIZE

					def collate_fn(_lowercase       ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										_A									=       1_28 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															_A									=       16
										elif accelerator.mixed_precision != "no":
															_A									=       8
										else:
															_A									=       None

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

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

					set_seed(_lowercase       )

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

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

					# Instantiate optimizer
					_A									=       AdamW(params=model.parameters()    ,	lr=_lowercase       )

					# Instantiate scheduler
					_A									=       get_linear_schedule_with_warmup(
					    optimizer=_lowercase    ,	num_warmup_steps=1_00    ,	num_training_steps=(len(_lowercase       ) * num_epochs) // gradient_accumulation_steps    ,	)

					# 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.
					_A							,_A							,_A							,_A							,_A									=       accelerator.prepare(
					    _lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase       )

					# We need to keep track of how many total steps we have iterated over
					_A									=       0
					# We also need to keep track of the stating epoch so files are named properly
					_A									=       0

					# Potentially load in the weights and states from a previous save
					if args.resume_from_checkpoint:
										if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
															accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""       )
															accelerator.load_state(args.resume_from_checkpoint       )
															_A									=       os.path.basename(args.resume_from_checkpoint       )
										else:
															# Get the most recent checkpoint
															_A									=       [f.name for f in os.scandir(os.getcwd()       ) if f.is_dir()]
															dirs.sort(key=os.path.getctime       )
															_A									=       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										_A									=       os.path.splitext(_lowercase       )[0]

										if "epoch" in training_difference:
															_A									=       int(training_difference.replace('''epoch_'''    ,	''''''       )       ) + 1
															_A									=       None
										else:
															_A									=       int(training_difference.replace('''step_'''    ,	''''''       )       )
															_A									=       resume_step // len(_lowercase       )
															resume_step -= starting_epoch * len(_lowercase       )

    # Now we train the model
					for epoch in range(_lowercase    ,	_lowercase       ):
										model.train()
										if args.with_tracking:
															_A									=       0
										if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
															# We need to skip steps until we reach the resumed step
															_A									=       accelerator.skip_first_batches(_lowercase    ,	_lowercase       )
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															_A									=       train_dataloader
										for step, batch in enumerate(_lowercase       ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device       )
															_A									=       model(**_lowercase       )
															_A									=       outputs.loss
															_A									=       loss / gradient_accumulation_steps
															# We keep track of the loss at each epoch
															if args.with_tracking:
																				total_loss += loss.detach().float()
															accelerator.backward(_lowercase       )
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1

															if isinstance(_lowercase    ,	_lowercase       ):
																				_A									=       f"""step_{overall_step}"""
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														_A									=       os.path.join(args.output_dir    ,	_lowercase       )
																									accelerator.save_state(_lowercase       )

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

										_A									=       metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(f"""epoch {epoch}:"""    ,	_lowercase       )
										if args.with_tracking:
															accelerator.log(
															    {
															        '''accuracy''': eval_metric['''accuracy'''],
															        '''f1''': eval_metric['''f1'''],
															        '''train_loss''': total_loss.item() / len(_lowercase       ),
															        '''epoch''': epoch,
															    }    ,	step=_lowercase    ,	)

										if checkpointing_steps == "epoch":
															_A									=       f"""epoch_{epoch}"""
															if args.output_dir is not None:
																				_A									=       os.path.join(args.output_dir    ,	_lowercase       )
															accelerator.save_state(_lowercase       )

					if args.with_tracking:
										accelerator.end_training()




def    __A    (   ):

					'''simple docstring'''







					_A									=       argparse.ArgumentParser(description='''Simple example of training script.'''       )
					parser.add_argument(
					    '''--mixed_precision'''    ,	type=_lowercase    ,	default=_lowercase    ,	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.'''    ,	)
					parser.add_argument('''--cpu'''    ,	action='''store_true'''    ,	help='''If passed, will train on the CPU.'''       )
					parser.add_argument(
					    '''--checkpointing_steps'''    ,	type=_lowercase    ,	default=_lowercase    ,	help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''    ,	)
					parser.add_argument(
					    '''--resume_from_checkpoint'''    ,	type=_lowercase    ,	default=_lowercase    ,	help='''If the training should continue from a checkpoint folder.'''    ,	)
					parser.add_argument(
					    '''--with_tracking'''    ,	action='''store_true'''    ,	help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''    ,	)
					parser.add_argument(
					    '''--output_dir'''    ,	type=_lowercase    ,	default='''.'''    ,	help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''    ,	)
					parser.add_argument(
					    '''--project_dir'''    ,	type=_lowercase    ,	default='''logs'''    ,	help='''Location on where to store experiment tracking logs` and relevent project information'''    ,	)
					_A									=       parser.parse_args()
					_A									=       {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(_lowercase    ,	_lowercase       )


if __name__ == "__main__":
				main()











from math import factorial




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					return factorial(_lowercase       ) / (factorial(_lowercase       ) * factorial(n - r       ))




def    __A    (   ):

					'''simple docstring'''







					_A									=       0

					for i in range(1    ,	1_01       ):
										for j in range(1    ,	i + 1       ):
															if combinations(_lowercase    ,	_lowercase       ) > 1e6:
																				total += 1
					return total


if __name__ == "__main__":
				print(solution())



