


'''simple docstring'''

import argparse

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)
#
# 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
#
########################################################################


lowerCAmelCase				:str        =      1_6
lowerCAmelCase				:List[str]        =      3_2





def lowerCamelCase      (      lowerCAmelCase    :     Accelerator		,		lowerCAmelCase    :     int = 16		):





	"""simple docstring"""







	__magic_name__	:   Optional[int]					     =					AutoTokenizer.from_pretrained('bert-base-cased'		)
	__magic_name__	:   Optional[int]					     =					load_dataset('glue'		,		'mrpc'		)

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

	def collate_fn(lowerCAmelCase    :     Dict		):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		__magic_name__	:   int					     =					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":
			__magic_name__	:   List[Any]					     =					16
		elif accelerator.mixed_precision != "no":
			__magic_name__	:   Optional[Any]					     =					8
		else:
			__magic_name__	:   str					     =					None

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

	# Instantiate dataloaders.
	__magic_name__	:   List[Any]					     =					DataLoader(
	    tokenized_datasets['train']		,		shuffle=lowerCAmelCase		,		collate_fn=lowerCAmelCase		,		batch_size=lowerCAmelCase		,		drop_last=lowerCAmelCase		)
	__magic_name__	:   Any					     =					DataLoader(
	    tokenized_datasets['validation']		,		shuffle=lowerCAmelCase		,		collate_fn=lowerCAmelCase		,		batch_size=lowerCAmelCase		,		drop_last=(accelerator.mixed_precision == 'fp8')		,		)

	return train_dataloader, eval_dataloader





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	__magic_name__	:   Union[str, Any]					     =					Accelerator(cpu=args.cpu		,		mixed_precision=args.mixed_precision		)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	__magic_name__	:   Union[str, Any]					     =					config['lr']
	__magic_name__	:   str					     =					int(config['num_epochs']		)
	__magic_name__	:   Optional[Any]					     =					int(config['seed']		)
	__magic_name__	:   Optional[Any]					     =					int(config['batch_size']		)

	__magic_name__	:   Union[str, Any]					     =					evaluate.load('glue'		,		'mrpc'		)

	# If the batch size is too big we use gradient accumulation
	__magic_name__	:   Optional[int]					     =					1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		__magic_name__	:   Optional[Any]					     =					batch_size // MAX_GPU_BATCH_SIZE
		__magic_name__	:   Union[str, Any]					     =					MAX_GPU_BATCH_SIZE

	set_seed(lowerCAmelCase		)
	__magic_name__     ,    __magic_name__	:   List[Any]					     =					get_dataloaders(lowerCAmelCase		,		lowerCAmelCase		)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	__magic_name__	:   Tuple					     =					AutoModelForSequenceClassification.from_pretrained('bert-base-cased'		,		return_dict=lowerCAmelCase		)

	# 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__	:   Union[str, Any]					     =					model.to(accelerator.device		)
	# Instantiate optimizer
	__magic_name__	:   List[str]					     =					AdamW(params=model.parameters()		,		lr=lowerCAmelCase		)

	# Instantiate scheduler
	__magic_name__	:   str					     =					get_linear_schedule_with_warmup(
	    optimizer=lowerCAmelCase		,		num_warmup_steps=100		,		num_training_steps=(len(lowerCAmelCase		) * 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__	:   Optional[Any]					     =					accelerator.prepare(
	    lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)

	# Now we train the model
	for epoch in range(lowerCAmelCase		):
		model.train()
		for step, batch in enumerate(lowerCAmelCase		):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device		)
			__magic_name__	:   Union[str, Any]					     =					model(**lowerCAmelCase		)
			__magic_name__	:   Union[str, Any]					     =					outputs.loss
			__magic_name__	:   List[Any]					     =					loss / gradient_accumulation_steps
			accelerator.backward(lowerCAmelCase		)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

		__magic_name__	:   str					     =					metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f'epoch {epoch}:'		,		lowerCAmelCase		)





def lowerCamelCase      (      ):





	"""simple docstring"""







	__magic_name__	:   Optional[Any]					     =					argparse.ArgumentParser(description='Simple example of training script.'		)
	parser.add_argument(
	    '--mixed_precision'		,		type=lowerCAmelCase		,		default=lowerCAmelCase		,		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.'		)
	__magic_name__	:   Dict					     =					parser.parse_args()
	__magic_name__	:   Union[str, Any]					     =					{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
	training_function(lowerCAmelCase		,		lowerCAmelCase		)


if __name__ == "__main__":
							main()



'''simple docstring'''

import socket





def lowerCamelCase      (      ):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					socket.socket(socket.AF_INET		,		socket.SOCK_STREAM		)
	__magic_name__	:   Union[str, Any]					     =					socket.gethostname()
	__magic_name__	:   int					     =					1_2312

	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:
			__magic_name__	:   Optional[int]					     =					sock.recv(1024		)
			if not data:
				break
			out_file.write(lowerCAmelCase		)

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


if __name__ == "__main__":
							main()