


import math
from decimal import Decimal, getcontext
def 							UpperCamelCase_  (				__snake_case			:						int = 4_00_00_00     )->      int:




      '''simple docstring'''

      try:
            __magic_name__ 		=			int(__snake_case     )
      except (TypeError, ValueError):
            raise TypeError('Parameter n must be int or castable to int.'     )
      if n <= 0:
            raise ValueError('Parameter n must be greater than or equal to one.'     )
      __magic_name__ 		=			1_00
      __magic_name__ 		=			(Decimal(5     ) ** Decimal(0.5     ) + 1) / Decimal(2     )

      __magic_name__ 		=			(math.floor(math.log(n * (phi + 2)	,			__snake_case     ) - 1     ) // 3) * 3 + 2
      __magic_name__ 		=			Decimal(round(phi ** Decimal(index + 1     )     )     ) / (phi + 2)
      __magic_name__ 		=			num // 2
      return int(__snake_case     )


if __name__ == "__main__":
     print(f'''{solution() = }''')





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
#
########################################################################


UpperCamelCase :				List[Any]						    =       16
UpperCamelCase :				Dict						    =       32
def 							UpperCamelCase_  (				__snake_case			:						Any	,			__snake_case			:						Tuple     )->      List[Any]:




						'''simple docstring'''
						# Initialize accelerator
						if args.with_tracking:
												__magic_name__ 		=			Accelerator(
												    cpu=args.cpu	,			mixed_precision=args.mixed_precision	,			log_with='all'	,			project_dir=args.project_dir     )
						else:
												__magic_name__ 		=			Accelerator(cpu=args.cpu	,			mixed_precision=args.mixed_precision     )

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

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

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

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

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

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

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

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

						set_seed(__snake_case     )

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

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

						# Instantiate scheduler
						__magic_name__ 		=			get_linear_schedule_with_warmup(
						    optimizer=__snake_case	,			num_warmup_steps=1_00	,			num_training_steps=(len(__snake_case     ) * 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.
						__magic_name__		,			__magic_name__		,			__magic_name__		,			__magic_name__		,			__magic_name__ 		=			accelerator.prepare(
						    __snake_case	,			__snake_case	,			__snake_case	,			__snake_case	,			__snake_case     )

						# We need to keep track of how many total steps we have iterated over
						__magic_name__ 		=			0
						# We also need to keep track of the stating epoch so files are named properly
						__magic_name__ 		=			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     )
																		__magic_name__ 		=			os.path.basename(args.resume_from_checkpoint     )
												else:
																		# Get the most recent checkpoint
																		__magic_name__ 		=			[f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
																		dirs.sort(key=os.path.getctime     )
																		__magic_name__ 		=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												__magic_name__ 		=			os.path.splitext(__snake_case     )[0]

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

    # Now we train the model
						for epoch in range(__snake_case	,			__snake_case     ):
												model.train()
												if args.with_tracking:
																		__magic_name__ 		=			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
																		__magic_name__ 		=			accelerator.skip_first_batches(__snake_case	,			__snake_case     )
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		__magic_name__ 		=			train_dataloader
												for step, batch in enumerate(__snake_case     ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device     )
																		__magic_name__ 		=			model(**__snake_case     )
																		__magic_name__ 		=			outputs.loss
																		__magic_name__ 		=			loss / gradient_accumulation_steps
																		# We keep track of the loss at each epoch
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		accelerator.backward(__snake_case     )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

																		if isinstance(__snake_case	,			__snake_case     ):
																								__magic_name__ 		=			F'''step_{overall_step}'''
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				__magic_name__ 		=			os.path.join(args.output_dir	,			__snake_case     )
																														accelerator.save_state(__snake_case     )

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

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

												if checkpointing_steps == "epoch":
																		__magic_name__ 		=			F'''epoch_{epoch}'''
																		if args.output_dir is not None:
																								__magic_name__ 		=			os.path.join(args.output_dir	,			__snake_case     )
																		accelerator.save_state(__snake_case     )

						if args.with_tracking:
												accelerator.end_training()
def 							UpperCamelCase_  (				)->      Dict:




						'''simple docstring'''
						__magic_name__ 		=			argparse.ArgumentParser(description='Simple example of training script.'     )
						parser.add_argument(
						    '--mixed_precision'	,			type=__snake_case	,			default=__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.'	,			)
						parser.add_argument('--cpu'	,			action='store_true'	,			help='If passed, will train on the CPU.'     )
						parser.add_argument(
						    '--checkpointing_steps'	,			type=__snake_case	,			default=__snake_case	,			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=__snake_case	,			default=__snake_case	,			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=__snake_case	,			default='.'	,			help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'	,			)
						parser.add_argument(
						    '--project_dir'	,			type=__snake_case	,			default='logs'	,			help='Location on where to store experiment tracking logs` and relevent project information'	,			)
						__magic_name__ 		=			parser.parse_args()
						__magic_name__ 		=			{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(__snake_case	,			__snake_case     )


if __name__ == "__main__":
					main()

