




'''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
#
# 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
#
########################################################################


_snake_case      :      List[Any]   							=		16
_snake_case      :      int   							=		32
def 		snake_case_    (UpperCamelCase       :  List[Any] ,						UpperCamelCase       :  Tuple      ):





							'''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(UpperCamelCase      )[-1].split('''.'''      )[0]
														accelerator.init_trackers(UpperCamelCase ,						UpperCamelCase      )

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

							def tokenize_function(UpperCamelCase       :  Union[str, Any]      ):
														# max_length=None => use the model max length (it's actually the default)
														_a	     =				tokenizer(examples['''sentence1'''] ,						examples['''sentence2'''] ,						truncation=UpperCamelCase ,						max_length=UpperCamelCase      )
														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(
														    UpperCamelCase ,						batched=UpperCamelCase ,						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(UpperCamelCase       :  Optional[int]      ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														_a	     =				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":
																					_a	     =				16
														elif accelerator.mixed_precision != "no":
																					_a	     =				8
														else:
																					_a	     =				None

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

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

							set_seed(UpperCamelCase      )

							# 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=UpperCamelCase      )

							# 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=UpperCamelCase      )

							# Instantiate scheduler
							_a	     =				get_linear_schedule_with_warmup(
							    optimizer=UpperCamelCase ,						num_warmup_steps=100 ,						num_training_steps=(len(UpperCamelCase      ) * 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(
							    UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      )

							# 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(UpperCamelCase      )[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(UpperCamelCase      )
																					resume_step -= starting_epoch * len(UpperCamelCase      )

    # Now we train the model
							for epoch in range(UpperCamelCase ,						UpperCamelCase      ):
														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(UpperCamelCase ,						UpperCamelCase      )
																					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(UpperCamelCase      ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device      )
																					_a	     =				model(**UpperCamelCase      )
																					_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(UpperCamelCase      )
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

																					overall_step += 1

																					if isinstance(UpperCamelCase ,						UpperCamelCase      ):
																												_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 ,						UpperCamelCase      )
																																			accelerator.save_state(UpperCamelCase      )

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

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

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

							if args.with_tracking:
														accelerator.end_training()
def 		snake_case_    ():





							'''simple docstring'''


							_a	     =				argparse.ArgumentParser(description='''Simple example of training script.'''      )
							parser.add_argument(
							    '''--mixed_precision''' ,						type=UpperCamelCase ,						default=UpperCamelCase ,						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=UpperCamelCase ,						default=UpperCamelCase ,						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=UpperCamelCase ,						default=UpperCamelCase ,						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=UpperCamelCase ,						default='''.''' ,						help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' ,						)
							parser.add_argument(
							    '''--project_dir''' ,						type=UpperCamelCase ,						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(UpperCamelCase ,						UpperCamelCase      )


if __name__ == "__main__":
				main()







'''simple docstring'''


from __future__ import annotations
def 		snake_case_    (UpperCamelCase       :  int      ):





							'''simple docstring'''


							_a	     =				[0] * max_one

							for i in range(0 ,						UpperCamelCase      ):
														_a	     =				i

							for i in range(2 ,						UpperCamelCase      ):
														if totients[i] == i:
																					for j in range(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      ):
																												totients[j] -= totients[j] // i

							return totients
def 		snake_case_    (UpperCamelCase       :  int ,						UpperCamelCase       :  int      ):





							'''simple docstring'''


							return sorted(str(UpperCamelCase      )      ) == sorted(str(UpperCamelCase      )      )
def 		snake_case_    (UpperCamelCase       :  int = 1000_0000      ):





							'''simple docstring'''


							_a	     =				1  # i
							_a	     =				0  # φ(i)
							_a	     =				get_totients(max_n + 1      )

							for i in range(2 ,						max_n + 1      ):
														_a	     =				totients[i]

														if i * min_denominator < min_numerator * t and has_same_digits(UpperCamelCase ,						UpperCamelCase      ):
																					_a	     =				i
																					_a	     =				t

							return min_numerator


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

