



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


_UpperCamelCase  						=			16
_UpperCamelCase  						=			32
def 			_lowerCAmelCase(      UpperCAmelCase_      :	int					, UpperCAmelCase_      :	Tuple						) ->							Dict:






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

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

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

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

							def tokenize_function(UpperCAmelCase_      :	Optional[int]						):
														# max_length=None => use the model max length (it's actually the default)
														lowerCAmelCase__  =   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():
														lowerCAmelCase__  =   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
							lowerCAmelCase__  =   tokenized_datasets.rename_column("""label"""					, """labels"""						)

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

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

														return tokenizer.pad(
														    a__					, padding="""longest"""					, max_length=a__					, pad_to_multiple_of=a__					, return_tensors="""pt"""					, )

							# Instantiate dataloaders.
							lowerCAmelCase__  =   DataLoader(
							    tokenized_datasets["""train"""]					, shuffle=a__					, collate_fn=a__					, batch_size=a__						)
							lowerCAmelCase__  =   DataLoader(
							    tokenized_datasets["""validation"""]					, shuffle=a__					, collate_fn=a__					, batch_size=a__						)

							set_seed(a__						)

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							lowerCAmelCase__  =   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).
							lowerCAmelCase__  =   model.to(accelerator.device						)

							# Instantiate optimizer
							lowerCAmelCase__  =   AdamW(params=model.parameters()					, lr=a__						)

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

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

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

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

																					overall_step += 1

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

														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():
																												lowerCAmelCase__  =   model(**a__						)
																					lowerCAmelCase__  =   outputs.logits.argmax(dim=-1						)
																					lowerCAmelCase__  ,lowerCAmelCase__  =   accelerator.gather_for_metrics((predictions, batch["""labels"""])						)
																					metric.add_batch(
																					    predictions=a__					, references=a__					, )

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

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

							if args.with_tracking:
														accelerator.end_training()
def 			_lowerCAmelCase(      ) ->							int:
							lowerCAmelCase__  =   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."""					, )
							parser.add_argument("""--cpu"""					, action="""store_true"""					, help="""If passed, will train on the CPU."""						)
							parser.add_argument(
							    """--checkpointing_steps"""					, type=a__					, default=a__					, 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=a__					, default=a__					, 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=a__					, default="""."""					, help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""					, )
							parser.add_argument(
							    """--project_dir"""					, type=a__					, default="""logs"""					, help="""Location on where to store experiment tracking logs` and relevent project information"""					, )
							lowerCAmelCase__  =   parser.parse_args()
							lowerCAmelCase__  =   {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(a__					, a__						)


if __name__ == "__main__":
		main()






'''simple docstring'''

from itertools import compress, repeat
from math import ceil, sqrt



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






						if num <= 2:
												return []
						if num == 3:
												return [2]

						# Odd sieve for numbers in range [3, num - 1]
						_lowerCAmelCase					    =bytearray(B'\x01'   ) * ((num >> 1) - 1)

						for i in range(3      ,   int(sqrt(a__   )   ) + 1      ,   2   ):
												if sieve[(i >> 1) - 1]:
																		_lowerCAmelCase					    =i**2
																		_lowerCAmelCase					    =repeat(
																		    0      ,   ceil((num - i_squared) / (i << 1)   )   )

						return [2] + list(compress(range(3      ,   a__      ,   2   )      ,   a__   )   )


if __name__ == "__main__":
							import doctest

							doctest.testmod()





