


import socket




def     __SCREAMING_SNAKE_CASE							(       )		->     List[Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							Any	    =	socket.socket(socket.AF_INET      ,       socket.SOCK_STREAM      )
						__UpperCAmelCase			:							int	    =	socket.gethostname()
						__UpperCAmelCase			:							Optional[int]	    =	12312

						sock.connect((host, port)      )
						sock.send(b'''Hello server!'''      )

						with open('''Received_file'''      ,       '''wb'''      ) as out_file:
												print('''File opened'''      )
												print('''Receiving data...'''      )
												while True:
																		__UpperCAmelCase			:							List[Any]	    =	sock.recv(1024      )
																		if not data:
																								break
																		out_file.write(lowercase_      )

						print('''Successfully received the file'''      )
						sock.close()
						print('''Connection closed'''      )


if __name__ == "__main__":
				main()





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


lowerCAmelCase       		=    16
lowerCAmelCase       		=    32




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_ = 16      ,       lowercase_ = "bert-base-cased"      )		->     Union[str, Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							List[str]	    =	AutoTokenizer.from_pretrained(lowercase_      )
						__UpperCAmelCase			:							int	    =	load_dataset('''glue'''      ,       '''mrpc'''      )

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

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

						def collate_fn(lowercase_      ):
												# 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(lowercase_      ,       padding='''max_length'''      ,       max_length=128      ,       return_tensors='''pt'''      )
												return tokenizer.pad(lowercase_      ,       padding='''longest'''      ,       return_tensors='''pt'''      )

						# Instantiate dataloaders.
						__UpperCAmelCase			:							Optional[Any]	    =	DataLoader(
						    tokenized_datasets['''train''']      ,       shuffle=lowercase_      ,       collate_fn=lowercase_      ,       batch_size=lowercase_      )
						__UpperCAmelCase			:							Any	    =	DataLoader(
						    tokenized_datasets['''validation''']      ,       shuffle=lowercase_      ,       collate_fn=lowercase_      ,       batch_size=lowercase_      )

						return train_dataloader, eval_dataloader




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )		->     str:




						'''simple docstring'''







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

						__UpperCAmelCase			:							Union[str, Any]	    =	metric.compute()
						return eval_metric["accuracy"]




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     Dict:




						'''simple docstring'''







						__UpperCAmelCase			:							List[Any]	    =	Accelerator()

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

						set_seed(lowercase_      )
						__UpperCAmelCase    ,      __UpperCAmelCase			:							Tuple	    =	get_dataloaders(lowercase_      ,       lowercase_      ,       lowercase_      )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__UpperCAmelCase			:							List[str]	    =	AutoModelForSequenceClassification.from_pretrained(lowercase_      ,       return_dict=lowercase_      )

						# Instantiate optimizer
						__UpperCAmelCase			:							str	    =	(
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						__UpperCAmelCase			:							int	    =	optimizer_cls(params=model.parameters()      ,       lr=lowercase_      )

						if accelerator.state.deepspeed_plugin is not None:
												__UpperCAmelCase			:							int	    =	accelerator.state.deepspeed_plugin.deepspeed_config[
												    '''gradient_accumulation_steps'''
												]
						else:
												__UpperCAmelCase			:							List[Any]	    =	1
						__UpperCAmelCase			:							str	    =	(len(lowercase_      ) * num_epochs) // gradient_accumulation_steps

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

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

						if args.partial_train_epoch is not None:
												__UpperCAmelCase			:							Any	    =	args.partial_train_epoch

						if args.resume_from_checkpoint:
												accelerator.load_state(args.resume_from_checkpoint      )
												__UpperCAmelCase			:							List[Any]	    =	args.resume_from_checkpoint.split('''epoch_'''      )[1]
												__UpperCAmelCase			:							Union[str, Any]	    =	''''''
												for char in epoch_string:
																		if char.isdigit():
																								state_epoch_num += char
																		else:
																								break
												__UpperCAmelCase			:							Tuple	    =	int(lowercase_      ) + 1
												__UpperCAmelCase			:							Optional[int]	    =	evaluation_loop(lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )
												accelerator.print('''resumed checkpoint performance:'''      ,       lowercase_      )
												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:
																		__UpperCAmelCase			:							str	    =	json.load(lowercase_      )
																		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
						__UpperCAmelCase			:							Tuple	    =	{}
						for epoch in range(lowercase_      ,       lowercase_      ):
												model.train()
												for step, batch in enumerate(lowercase_      ):
																		__UpperCAmelCase			:							Dict	    =	model(**lowercase_      )
																		__UpperCAmelCase			:							Union[str, Any]	    =	outputs.loss
																		__UpperCAmelCase			:							Tuple	    =	loss / gradient_accumulation_steps
																		accelerator.backward(lowercase_      )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

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

												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(lowercase_      ,       lowercase_      )




def     __SCREAMING_SNAKE_CASE							(       )		->     List[Any]:




						'''simple docstring'''







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

						training_function(lowercase_      ,       lowercase_      )


if __name__ == "__main__":
				main()

