






"""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       :      Tuple       =					16
_snake_case       :      List[Any]       =					32




def 						lowerCamelCase_				(  __A  :		List[Any]			,	__A  :		List[str]			):
					# Initialize accelerator
					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(__A  :		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(__A  :		Optional[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_				(  ):
					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"""



import math

import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute




def 						lowerCamelCase_				(  __A  :		int = 1			,	__A  :		int = 1			):
					if isinstance(__A			,	__A			) or isinstance(__A			,	__A			):
										raise TypeError('inputs must be integers.'			)
					if (bit_a < 0) or (bit_a < 0):
										raise ValueError('inputs must be positive.'			)
					if (math.floor(__A			) != bit_a) or (math.floor(__A			) != bit_a):
										raise ValueError('inputs must be exact integers.'			)
					if (bit_a > 1) or (bit_a > 1):
										raise ValueError('inputs must be less or equal to 1.'			)

					# build registers
					lowerCamelCase__				=QuantumRegister(2			,	'qr'			)
					lowerCamelCase__				=ClassicalRegister(2			,	'cr'			)

					lowerCamelCase__				=QuantumCircuit(__A			,	__A			)

					# entanglement the qubits
					quantum_circuit.h(1			)
					quantum_circuit.cx(1			,	0			)

					# send the information
					lowerCamelCase__				=str(__A			) + str(__A			)

					if c_information == "11":
										quantum_circuit.x(1			)
										quantum_circuit.z(1			)
					elif c_information == "10":
										quantum_circuit.z(1			)
					elif c_information == "01":
										quantum_circuit.x(1			)
					else:
										quantum_circuit.i(1			)

					# unentangled the circuit
					quantum_circuit.cx(1			,	0			)
					quantum_circuit.h(1			)

					# measure the circuit
					quantum_circuit.measure(__A			,	__A			)

					lowerCamelCase__				=Aer.get_backend('aer_simulator'			)
					lowerCamelCase__				=execute(__A			,	__A			,	shots=1000			)

					return job.result().get_counts(__A			)


if __name__ == "__main__":
						print(f"Counts for classical state send: {superdense_coding(1,1)}")