'''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
# and perform gradient accumulation
#
# 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
#
########################################################################


_A			:      Optional[Any]					      =				16
_A			:      Union[str, Any]					      =				32







def      UpperCamelCase_						(					snake_case_       :       Accelerator						,		snake_case_       :       int = 16      )					->     Optional[int]:






 '''simple docstring'''

 __lowerCAmelCase											=						AutoTokenizer.from_pretrained("""bert-base-cased"""      )
 __lowerCAmelCase											=						load_dataset("""glue"""						,		"""mrpc"""      )

 def tokenize_function(snake_case_       :       Dict      ):
  # max_length=None => use the model max length (it's actually the default)
  __lowerCAmelCase											=						tokenizer(examples["""sentence1"""]						,		examples["""sentence2"""]						,		truncation=snake_case_						,		max_length=snake_case_      )
  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(
      snake_case_						,		batched=snake_case_						,		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(snake_case_       :       Optional[Any]      ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  __lowerCAmelCase											=						1_28 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(
      snake_case_						,		padding="""longest"""						,		max_length=snake_case_						,		pad_to_multiple_of=snake_case_						,		return_tensors="""pt"""						,		)

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

 return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
 from accelerate.test_utils.training import mocked_dataloaders

 _A			:      str					      =				mocked_dataloaders  # noqa: F811







def      UpperCamelCase_						(					snake_case_       :       List[Any]						,		snake_case_       :       str      )					->     Dict:






 '''simple docstring'''

 if os.environ.get("""TESTING_MOCKED_DATALOADERS"""						,		snake_case_      ) == "1":
  __lowerCAmelCase											=						2
 # New Code #
 __lowerCAmelCase											=						int(args.gradient_accumulation_steps      )
 # Initialize accelerator
 __lowerCAmelCase											=						Accelerator(
     cpu=args.cpu						,		mixed_precision=args.mixed_precision						,		gradient_accumulation_steps=snake_case_      )
 if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
  raise NotImplementedError(
      """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""      )
 # 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"""      )

 set_seed(snake_case_      )
 __lowerCAmelCase							,       __lowerCAmelCase											=						get_dataloaders(snake_case_						,		snake_case_      )
 # 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=snake_case_      )

 # 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=snake_case_      )

 # Instantiate scheduler
 __lowerCAmelCase											=						get_linear_schedule_with_warmup(
     optimizer=snake_case_						,		num_warmup_steps=1_00						,		num_training_steps=(len(snake_case_      ) * num_epochs)						,		)

 # 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(
     snake_case_						,		snake_case_						,		snake_case_						,		snake_case_						,		snake_case_      )

 # Now we train the model
 for epoch in range(snake_case_      ):
  model.train()
  for step, batch in enumerate(snake_case_      ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device      )
   # New code #
   # We use the new `accumulate` context manager to perform gradient accumulation
   # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
   with accelerator.accumulate(snake_case_      ):
    __lowerCAmelCase											=						model(**snake_case_      )
    __lowerCAmelCase											=						output.loss
    accelerator.backward(snake_case_      )
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

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

  __lowerCAmelCase											=						metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(f"""epoch {epoch}:"""						,		snake_case_      )







def      UpperCamelCase_						(					)					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						argparse.ArgumentParser(description="""Simple example of training script."""      )
 parser.add_argument(
     """--mixed_precision"""						,		type=snake_case_						,		default=snake_case_						,		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."""						,		)
 # New Code #
 parser.add_argument(
     """--gradient_accumulation_steps"""						,		type=snake_case_						,		default=1						,		help="""The number of minibatches to be ran before gradients are accumulated."""						,		)
 parser.add_argument("""--cpu"""						,		action="""store_true"""						,		help="""If passed, will train on the CPU."""      )
 __lowerCAmelCase											=						parser.parse_args()
 __lowerCAmelCase											=						{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
 training_function(snake_case_						,		snake_case_      )


if __name__ == "__main__":
 main()







'''simple docstring'''

import numpy as np
import qiskit







def      UpperCamelCase_						(					snake_case_       :       int = 8						,		snake_case_       :       int | None = None      )					->     str:






 '''simple docstring'''

 __lowerCAmelCase											=						np.random.default_rng(seed=snake_case_      )

 # 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=snake_case_      )
 # The set of states Alice will prepare.
 __lowerCAmelCase											=						rng.integers(2						,		size=snake_case_      )
 # Measurement basis for Bob's qubits.
 __lowerCAmelCase											=						rng.integers(2						,		size=snake_case_      )

 # Quantum Circuit to simulate BB84
 __lowerCAmelCase											=						qiskit.QuantumCircuit(snake_case_						,		name="""BB84"""      )

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

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

 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(snake_case_						,		snake_case_						,		shots=1						,		seed_simulator=snake_case_      )
 # Returns the result of measurement.
 __lowerCAmelCase											=						job.result().get_counts(snake_case_      ).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(
             snake_case_						,		snake_case_						,		snake_case_      )
         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(snake_case_      ) >= key_len else gen_key.ljust(snake_case_						,		"""0"""      )
 return key


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

 testmod()






