

'''simple docstring'''



import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


_lowercase    =		16
_lowercase    =		32


def A				(__lowerCamelCase				:Accelerator			,     __lowerCamelCase				:DatasetDict			,     __lowerCamelCase				:List[int]			,     __lowerCamelCase				:List[int]			,     __lowerCamelCase				:int = 16		):
						_lowerCAmelCase  						=			AutoTokenizer.from_pretrained("""bert-base-cased"""		)
						_lowerCAmelCase  						=			DatasetDict(
						    {
						        """train""": dataset["""train"""].select(UpperCamelCase__		),
						        """validation""": dataset["""train"""].select(UpperCamelCase__		),
						        """test""": dataset["""validation"""],
						    }		)

						def tokenize_function(__lowerCamelCase				:Union[str, Any]		):
												# max_length=None => use the model max length (it's actually the default)
												_lowerCAmelCase  						=			tokenizer(examples["""sentence1"""]			,     examples["""sentence2"""]			,     truncation=UpperCamelCase__			,     max_length=UpperCamelCase__		)
												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(
												    UpperCamelCase__			,     batched=UpperCamelCase__			,     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"""		)

						def collate_fn(__lowerCamelCase				:Tuple		):
												# 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(
												    UpperCamelCase__			,     padding="""longest"""			,     max_length=UpperCamelCase__			,     pad_to_multiple_of=UpperCamelCase__			,     return_tensors="""pt"""			,     )

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

						_lowerCAmelCase  						=			DataLoader(
						    tokenized_datasets["""test"""]			,     shuffle=UpperCamelCase__			,     collate_fn=UpperCamelCase__			,     batch_size=UpperCamelCase__		)

						return train_dataloader, eval_dataloader, test_dataloader


def A				(__lowerCamelCase				:Optional[Any]			,     __lowerCamelCase				:Tuple		):
						# New Code #
						_lowerCAmelCase  						=			[]
						# Download the dataset
						_lowerCAmelCase  						=			load_dataset("""glue"""			,     """mrpc"""		)
						# Create our splits
						_lowerCAmelCase  						=			StratifiedKFold(n_splits=int(args.num_folds		)		)
						# Initialize accelerator
						_lowerCAmelCase  						=			Accelerator(cpu=args.cpu			,     mixed_precision=args.mixed_precision		)
						# 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"""]		)

						_lowerCAmelCase  						=			evaluate.load("""glue"""			,     """mrpc"""		)

						# 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

						set_seed(UpperCamelCase__		)

						# New Code #
						# Create our folds:
						_lowerCAmelCase  						=			kfold.split(np.zeros(datasets["""train"""].num_rows		)			,     datasets["""train"""]["""label"""]		)
						_lowerCAmelCase  						=			[]
						# Iterate over them
						for i, (train_idxs, valid_idxs) in enumerate(UpperCamelCase__		):
												_lowerCAmelCase  						=			get_fold_dataloaders(
												    UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     )
												# 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=UpperCamelCase__		)

												# 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=UpperCamelCase__		)

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

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

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

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

												# New Code #
												# We also run predictions on the test set at the very end
												_lowerCAmelCase  						=			[]
												for step, batch in enumerate(UpperCamelCase__		):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device		)
																		with torch.no_grad():
																								_lowerCAmelCase  						=			model(**UpperCamelCase__		)
																		_lowerCAmelCase  						=			outputs.logits
																		_lowerCAmelCase  						=			accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
																		fold_predictions.append(predictions.cpu()		)
																		if i == 0:
																								# We need all of the test predictions
																								test_references.append(references.cpu()		)
        # Use accelerator.print to print only on the main process.
												test_predictions.append(torch.cat(UpperCamelCase__			,     dim=0		)		)
												# We now need to release all our memory and get rid of the current model, optimizer, etc
												accelerator.free_memory()
						# New Code #
						# Finally we check the accuracy of our folded results:
						_lowerCAmelCase  						=			torch.cat(UpperCamelCase__			,     dim=0		)
						_lowerCAmelCase  						=			torch.stack(UpperCamelCase__			,     dim=0		).sum(dim=0		).div(int(args.num_folds		)		).argmax(dim=-1		)
						_lowerCAmelCase  						=			metric.compute(predictions=UpperCamelCase__			,     references=UpperCamelCase__		)
						accelerator.print("""Average test metrics from all folds:"""			,     UpperCamelCase__		)


def A				():
						_lowerCAmelCase  						=			argparse.ArgumentParser(description="""Simple example of training script."""		)
						parser.add_argument(
						    """--mixed_precision"""			,     type=UpperCamelCase__			,     default=UpperCamelCase__			,     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."""		)
						# New Code #
						parser.add_argument("""--num_folds"""			,     type=UpperCamelCase__			,     default=3			,     help="""The number of splits to perform across the dataset"""		)
						_lowerCAmelCase  						=			parser.parse_args()
						_lowerCAmelCase  						=			{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(UpperCamelCase__			,     UpperCamelCase__		)


if __name__ == "__main__":
						main()


'''simple docstring'''



import numpy as np
import qiskit


def A				(__lowerCamelCase				:int = 8			,     __lowerCamelCase				:int | None = None		):
						_lowerCAmelCase  						=			np.random.default_rng(seed=__lowerCamelCase		)

						# Roughly 25% of the qubits will contribute to the key.
						# So we take more than we need.
						_lowerCAmelCase  						=			6 * key_len
						# Measurement basis for Alice's qubits.
						_lowerCAmelCase  						=			rng.integers(2			,     size=__lowerCamelCase		)
						# The set of states Alice will prepare.
						_lowerCAmelCase  						=			rng.integers(2			,     size=__lowerCamelCase		)
						# Measurement basis for Bob's qubits.
						_lowerCAmelCase  						=			rng.integers(2			,     size=__lowerCamelCase		)

						# Quantum Circuit to simulate BB84
						_lowerCAmelCase  						=			qiskit.QuantumCircuit(__lowerCamelCase			,     name="""BB84"""		)

						# Alice prepares her qubits according to rules above.
						for index, _ in enumerate(__lowerCamelCase		):
												if alice_state[index] == 1:
																		bbaa_circ.x(__lowerCamelCase		)
												if alice_basis[index] == 1:
																		bbaa_circ.h(__lowerCamelCase		)
						bbaa_circ.barrier()

						# Bob measures the received qubits according to rules above.
						for index, _ in enumerate(__lowerCamelCase		):
												if bob_basis[index] == 1:
																		bbaa_circ.h(__lowerCamelCase		)

						bbaa_circ.barrier()
						bbaa_circ.measure_all()

						# Simulate the quantum circuit.
						_lowerCAmelCase  						=			qiskit.Aer.get_backend("""aer_simulator"""		)
						# We only need to run one shot because the key is unique.
						# Multiple shots will produce the same key.
						_lowerCAmelCase  						=			qiskit.execute(__lowerCamelCase			,     __lowerCamelCase			,     shots=1			,     seed_simulator=__lowerCamelCase		)
						# Returns the result of measurement.
						_lowerCAmelCase  						=			job.result().get_counts(__lowerCamelCase		).most_frequent()

						# Extracting the generated key from the simulation results.
						# Only keep measurement results where Alice and Bob chose the same basis.
						_lowerCAmelCase  						=			"""""".join(
						    [
						        result_bit
						        for alice_basis_bit, bob_basis_bit, result_bit in zip(
						            __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)
						        if alice_basis_bit == bob_basis_bit
						    ]		)

						# Get final key. Pad with 0 if too short, otherwise truncate.
						_lowerCAmelCase  						=			gen_key[:key_len] if len(__lowerCamelCase		) >= key_len else gen_key.ljust(__lowerCamelCase			,     """0"""		)
						return key


if __name__ == "__main__":
						print(F"""The generated key is : {bbaa(8, seed=0)}""")
						from doctest import testmod

						testmod()
