



import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


__snake_case    :		Any  =1_6
__snake_case    :		Tuple  =3_2






def      lowerCAmelCase__		(   lowerCamelCase_		:		Accelerator	,lowerCamelCase_		:		int = 16	,lowerCamelCase_		:		str = "bert-base-cased"):





				'''simple docstring'''

				lowerCAmelCase__					:    Tuple												=					AutoTokenizer.from_pretrained(A__)
				lowerCAmelCase__					:    Tuple												=					load_dataset('''glue'''	,'''mrpc''')

				def tokenize_function(lowerCamelCase_		:		Dict):
								# max_length=None => use the model max length (it's actually the default)
								lowerCAmelCase__					:    Tuple												=					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
				lowerCAmelCase__					:    Any												=					datasets.map(
				    A__	,batched=A__	,remove_columns=['''idx''', '''sentence1''', '''sentence2''']	,load_from_cache_file=A__)

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				lowerCAmelCase__					:    Tuple												=					tokenized_datasets.rename_column('''label'''	,'''labels''')

				def collate_fn(lowerCamelCase_		:		List[str]):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(A__	,padding='''max_length'''	,max_length=128	,return_tensors='''pt''')
								return tokenizer.pad(A__	,padding='''longest'''	,return_tensors='''pt''')

				# Instantiate dataloaders.
				lowerCAmelCase__					:    Tuple												=					DataLoader(
				    tokenized_datasets['''train''']	,shuffle=A__	,collate_fn=A__	,batch_size=A__)
				lowerCAmelCase__					:    int												=					DataLoader(
				    tokenized_datasets['''validation''']	,shuffle=A__	,collate_fn=A__	,batch_size=A__)

				return train_dataloader, eval_dataloader






def      lowerCAmelCase__		(   lowerCamelCase_		:		List[str]	,lowerCamelCase_		:		Tuple	,lowerCamelCase_		:		int	,lowerCamelCase_		:		Union[str, Any]):





				'''simple docstring'''

				model.eval()
				lowerCAmelCase__					:    int												=					0
				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__					:    Any												=					model(**A__)
								lowerCAmelCase__					:    Tuple												=					outputs.logits.argmax(dim=-1)
								# It is slightly faster to call this once, than multiple times
								lowerCAmelCase__					:    Dict												=					accelerator.gather(
								    (predictions, batch['''labels''']))  # If we are in a multiprocess environment, the last batch has duplicates
								if accelerator.use_distributed:
												if step == len(A__) - 1:
																lowerCAmelCase__					:    List[str]												=					predictions[: len(eval_dataloader.dataset) - samples_seen]
																lowerCAmelCase__					:    Union[str, Any]												=					references[: len(eval_dataloader.dataset) - samples_seen]
												else:
																samples_seen += references.shape[0]
								metric.add_batch(
								    predictions=A__	,references=A__	,)

				lowerCAmelCase__					:    Optional[int]												=					metric.compute()
				return eval_metric["accuracy"]






def      lowerCAmelCase__		(   lowerCamelCase_		:		List[str]	,lowerCamelCase_		:		Tuple):





				'''simple docstring'''

				lowerCAmelCase__					:    List[str]												=					Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCAmelCase__					:    Union[str, Any]												=					config["""lr"""]
				lowerCAmelCase__					:    List[Any]												=					int(config['''num_epochs'''])
				lowerCAmelCase__					:    Tuple												=					int(config['''seed'''])
				lowerCAmelCase__					:    Union[str, Any]												=					int(config['''batch_size'''])
				lowerCAmelCase__					:    List[str]												=					args.model_name_or_path

				set_seed(A__)
				lowerCAmelCase__					:    List[Any]												=					get_dataloaders(A__	,A__	,A__)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowerCAmelCase__					:    int												=					AutoModelForSequenceClassification.from_pretrained(A__	,return_dict=A__)

				# Instantiate optimizer
				lowerCAmelCase__					:    str												=					(
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				lowerCAmelCase__					:    Union[str, Any]												=					optimizer_cls(params=model.parameters()	,lr=A__)

				if accelerator.state.deepspeed_plugin is not None:
								lowerCAmelCase__					:    Optional[Any]												=					accelerator.state.deepspeed_plugin.deepspeed_config[
								    """gradient_accumulation_steps"""
								]
				else:
								lowerCAmelCase__					:    Union[str, Any]												=					1
				lowerCAmelCase__					:    List[Any]												=					(len(A__) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								lowerCAmelCase__					:    List[str]												=					get_linear_schedule_with_warmup(
								    optimizer=A__	,num_warmup_steps=0	,num_training_steps=A__	,)
				else:
								lowerCAmelCase__					:    List[Any]												=					DummyScheduler(A__	,total_num_steps=A__	,warmup_num_steps=0)

				# 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__					:    Optional[int]												=					accelerator.prepare(
				    A__	,A__	,A__	,A__	,A__)

				# We need to keep track of how many total steps we have iterated over
				lowerCAmelCase__					:    str												=					0
				# We also need to keep track of the stating epoch so files are named properly
				lowerCAmelCase__					:    List[str]												=					0
				lowerCAmelCase__					:    Any												=					evaluate.load('''glue'''	,'''mrpc''')
				lowerCAmelCase__					:    List[Any]												=					num_epochs

				if args.partial_train_epoch is not None:
								lowerCAmelCase__					:    Dict												=					args.partial_train_epoch

				if args.resume_from_checkpoint:
								accelerator.load_state(args.resume_from_checkpoint)
								lowerCAmelCase__					:    List[str]												=					args.resume_from_checkpoint.split('''epoch_''')[1]
								lowerCAmelCase__					:    List[str]												=					""""""
								for char in epoch_string:
												if char.isdigit():
																state_epoch_num += char
												else:
																break
								lowerCAmelCase__					:    Optional[int]												=					int(A__) + 1
								lowerCAmelCase__					:    List[Any]												=					evaluation_loop(A__	,A__	,A__	,A__)
								accelerator.print('''resumed checkpoint performance:'''	,A__)
								accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''	,lr_scheduler.get_lr()[0])
								accelerator.print('''resumed optimizers\'s lr:'''	,optimizer.param_groups[0]['''lr'''])
								with open(os.path.join(args.output_dir	,f"""state_{starting_epoch-1}.json""")	,'''r''') as f:
												lowerCAmelCase__					:    Any												=					json.load(A__)
												assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["lr"] == lr_scheduler.get_lr()[0]
												), "Scheduler learning rate mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
												), "Optimizer learning rate mismatch, loading from checkpoint failed"
												assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
												return

								# Now we train the model
				lowerCAmelCase__					:    int												=					{}
				for epoch in range(A__	,A__):
								model.train()
								for step, batch in enumerate(A__):
												lowerCAmelCase__					:    Optional[int]												=					model(**A__)
												lowerCAmelCase__					:    Dict												=					outputs.loss
												lowerCAmelCase__					:    Union[str, Any]												=					loss / gradient_accumulation_steps
												accelerator.backward(A__)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1
								lowerCAmelCase__					:    str												=					f"""epoch_{epoch}"""
								lowerCAmelCase__					:    Union[str, Any]												=					os.path.join(args.output_dir	,A__)
								accelerator.save_state(A__)
								lowerCAmelCase__					:    Any												=					evaluation_loop(A__	,A__	,A__	,A__)
								lowerCAmelCase__					:    Any												=					accuracy
								lowerCAmelCase__					:    Optional[Any]												=					lr_scheduler.get_lr()[0]
								lowerCAmelCase__					:    Tuple												=					optimizer.param_groups[0]["""lr"""]
								lowerCAmelCase__					:    Any												=					epoch
								lowerCAmelCase__					:    Union[str, Any]												=					overall_step
								accelerator.print(f"""epoch {epoch}:"""	,A__)

								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												with open(os.path.join(args.output_dir	,f"""state_{epoch}.json""")	,'''w''') as f:
																json.dump(A__	,A__)






def      lowerCAmelCase__		(   ):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[Any]												=					argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''')
				parser.add_argument(
				    '''--model_name_or_path'''	,type=A__	,default='''bert-base-cased'''	,help='''Path to pretrained model or model identifier from huggingface.co/models.'''	,required=A__	,)
				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(
				    '''--resume_from_checkpoint'''	,type=A__	,default=A__	,help='''If the training should continue from a checkpoint folder.'''	,)
				parser.add_argument(
				    '''--partial_train_epoch'''	,type=A__	,default=A__	,help='''If passed, the training will stop after this number of epochs.'''	,)
				parser.add_argument(
				    '''--num_epochs'''	,type=A__	,default=2	,help='''Number of train epochs.'''	,)
				lowerCAmelCase__					:    List[Any]												=					parser.parse_args()
				lowerCAmelCase__					:    Optional[int]												=					{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

				training_function(A__	,A__)


if __name__ == "__main__":
							main()






def      lowerCAmelCase__		(   lowerCamelCase_		:		int = 1000000):





				'''simple docstring'''

				lowerCAmelCase__					:    int												=					set(range(3	,lowerCamelCase_	,2))
				primes.add(2)
				for p in range(3	,lowerCamelCase_	,2):
								if p not in primes:
												continue
								primes.difference_update(set(range(p * p	,lowerCamelCase_	,lowerCamelCase_)))

				lowerCAmelCase__					:    int												=					[float(lowerCamelCase_) for n in range(limit + 1)]

				for p in primes:
								for n in range(lowerCamelCase_	,limit + 1	,lowerCamelCase_):
												phi[n] *= 1 - 1 / p

				return int(sum(phi[2:]))


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

