code: 
  
  
  
  
  
  
  """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,
  # specifically showcasing the checkpointing capability,
  # 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
  #
  ########################################################################
  
  snake_case   :   Union[str, Any]    		=		1_6
  snake_case   :   Tuple    		=		3_2
  
  
  def        A	(							__snake_case:   Accelerator   ,    __snake_case:   int = 1_6     )						->		Any:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						AutoTokenizer.from_pretrained('bert-base-cased'     )
         __magic_name__										=						load_dataset('glue'   ,    'mrpc'     )
  
         def tokenize_function(__snake_case:   List[Any]     ):
                # max_length=None => use the model max length (it's actually the default)
                __magic_name__										=						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
         # starting with the main process first:
         with accelerator.main_process_first():
                __magic_name__										=						datasets.map(
                    lowercase_   ,    batched=lowercase_   ,    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__										=						tokenized_datasets.rename_column('label'   ,    'labels'     )
  
         def collate_fn(__snake_case:   Union[str, Any]     ):
                # On TPU it's best to pad everything to the same length or training will be very slow.
                __magic_name__										=						1_2_8 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__										=						1_6
                elif accelerator.mixed_precision != "no":
                       __magic_name__										=						8
                else:
                       __magic_name__										=						None
  
                return tokenizer.pad(
                    lowercase_   ,    padding='longest'   ,    max_length=lowercase_   ,    pad_to_multiple_of=lowercase_   ,    return_tensors='pt'   ,    )
  
         # Instantiate dataloaders.
         __magic_name__										=						DataLoader(
             tokenized_datasets['train']   ,    shuffle=lowercase_   ,    collate_fn=lowercase_   ,    batch_size=lowercase_     )
         __magic_name__										=						DataLoader(
             tokenized_datasets['validation']   ,    shuffle=lowercase_   ,    collate_fn=lowercase_   ,    batch_size=lowercase_     )
  
         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
  
        snake_case   :   Dict    		=		mocked_dataloaders  # noqa: F811
  
  
  def        A	(							__snake_case:   str   ,    __snake_case:   str     )						->		Union[str, Any]:
  
         """simple docstring"""
  
  
  
  
  
         if os.environ.get('TESTING_MOCKED_DATALOADERS'   ,    lowercase_     ) == "1":
                __magic_name__										=						2
         # Initialize accelerator
         __magic_name__										=						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__										=						config['lr']
         __magic_name__										=						int(config['num_epochs']     )
         __magic_name__										=						int(config['seed']     )
         __magic_name__										=						int(config['batch_size']     )
  
         # New Code #
         # Parse out whether we are saving every epoch or after a certain number of batches
         if hasattr(args.checkpointing_steps   ,    'isdigit'     ):
                if args.checkpointing_steps == "epoch":
                       __magic_name__										=						args.checkpointing_steps
                elif args.checkpointing_steps.isdigit():
                       __magic_name__										=						int(args.checkpointing_steps     )
                else:
                       raise ValueError(
                           F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""     )
         else:
                __magic_name__										=						None
  
         set_seed(lowercase_     )
  
         __magic_name__										=						get_dataloaders(lowercase_   ,    lowercase_     )
         __magic_name__										=						evaluate.load('glue'   ,    'mrpc'     )
  
         # If the batch size is too big we use gradient accumulation
         __magic_name__										=						1
         if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
                __magic_name__										=						batch_size // MAX_GPU_BATCH_SIZE
                __magic_name__										=						MAX_GPU_BATCH_SIZE
  
         # Instantiate the model (we build the model here so that the seed also control new weights initialization)
         __magic_name__										=						AutoModelForSequenceClassification.from_pretrained('bert-base-cased'   ,    return_dict=lowercase_     )
  
         # 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__										=						model.to(accelerator.device     )
  
         # Instantiate optimizer
         __magic_name__										=						AdamW(params=model.parameters()   ,    lr=lowercase_     )
  
         # Instantiate scheduler
         __magic_name__										=						get_linear_schedule_with_warmup(
             optimizer=lowercase_   ,    num_warmup_steps=1_0_0   ,    num_training_steps=(len(lowercase_     ) * 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__										=						accelerator.prepare(
             lowercase_   ,    lowercase_   ,    lowercase_   ,    lowercase_   ,    lowercase_     )
  
         # New Code #
         # We need to keep track of how many total steps we have iterated over
         __magic_name__										=						0
         # We also need to keep track of the stating epoch so files are named properly
         __magic_name__										=						0
  
         # We need to load the checkpoint back in before training here with `load_state`
         # The total number of epochs is adjusted based on where the state is being loaded from,
         # as we assume continuation of the same training script
         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     )
                       __magic_name__										=						os.path.basename(args.resume_from_checkpoint     )
                else:
                       # Get the most recent checkpoint
                       __magic_name__										=						[f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
                       dirs.sort(key=os.path.getctime     )
                       __magic_name__										=						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
                # Extract `epoch_{i}` or `step_{i}`
                __magic_name__										=						os.path.splitext(lowercase_     )[0]
  
                if "epoch" in training_difference:
                       __magic_name__										=						int(training_difference.replace('epoch_'   ,    ''     )     ) + 1
                       __magic_name__										=						None
                else:
                       __magic_name__										=						int(training_difference.replace('step_'   ,    ''     )     )
                       __magic_name__										=						resume_step // len(lowercase_     )
                       resume_step -= starting_epoch * len(lowercase_     )
  
      # Now we train the model
         for epoch in range(lowercase_   ,    lowercase_     ):
                model.train()
                # New Code #
                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
                       __magic_name__										=						accelerator.skip_first_batches(lowercase_   ,    lowercase_     )
                       overall_step += resume_step
                else:
                       # After the first iteration though, we need to go back to the original dataloader
                       __magic_name__										=						train_dataloader
                for step, batch in enumerate(lowercase_     ):
                       # We could avoid this line since we set the accelerator with `device_placement=True`.
                       batch.to(accelerator.device     )
                       __magic_name__										=						model(**lowercase_     )
                       __magic_name__										=						outputs.loss
                       __magic_name__										=						loss / gradient_accumulation_steps
                       accelerator.backward(lowercase_     )
                       if step % gradient_accumulation_steps == 0:
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()
                       # New Code #
                       overall_step += 1
  
                       # New Code #
                       # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                       # These are saved to folders named `step_{overall_step}`
                       # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                       # If mixed precision was used, will also save a "scalar.bin" file
                       if isinstance(lowercase_   ,    lowercase_     ):
                              __magic_name__										=						F"""step_{overall_step}"""
                              if overall_step % checkpointing_steps == 0:
                                     if args.output_dir is not None:
                                            __magic_name__										=						os.path.join(args.output_dir   ,    lowercase_     )
                                     accelerator.save_state(lowercase_     )
  
                model.eval()
                for step, batch in enumerate(lowercase_     ):
                       # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
                       batch.to(accelerator.device     )
                       with torch.no_grad():
                              __magic_name__										=						model(**lowercase_     )
                       __magic_name__										=						outputs.logits.argmax(dim=-1     )
                       __magic_name__										=						accelerator.gather_for_metrics((predictions, batch['labels'])     )
                       metric.add_batch(
                           predictions=lowercase_   ,    references=lowercase_   ,    )
  
                __magic_name__										=						metric.compute()
                # Use accelerator.print to print only on the main process.
                accelerator.print(F"""epoch {epoch}:"""   ,    lowercase_     )
  
                # New Code #
                # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                # These are saved to folders named `epoch_{epoch}`
                # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                # If mixed precision was used, will also save a "scalar.bin" file
                if checkpointing_steps == "epoch":
                       __magic_name__										=						F"""epoch_{epoch}"""
                       if args.output_dir is not None:
                              __magic_name__										=						os.path.join(args.output_dir   ,    lowercase_     )
                       accelerator.save_state(lowercase_     )
  
  
  def        A	(							)						->		List[Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						argparse.ArgumentParser(description='Simple example of training script.'     )
         parser.add_argument(
             '--mixed_precision'   ,    type=lowercase_   ,    default=lowercase_   ,    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=lowercase_   ,    default=lowercase_   ,    help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'   ,    )
         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.'   ,    )
         __magic_name__										=						parser.parse_args()
         __magic_name__										=						{'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
         training_function(lowercase_   ,    lowercase_     )
  
  
  if __name__ == "__main__":
        main()
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  import numpy as np
  import qiskit
  
  
  def        A	(							__snake_case:   str   ,    __snake_case:   int     )						->		qiskit.QuantumCircuit:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						qiskit.QuantumCircuit(num_qubits + 1     )
  
         # First, let's deal with the case in which oracle is balanced
         if case == "balanced":
                # First generate a random number that tells us which CNOTs to
                # wrap in X-gates:
                __magic_name__										=						np.random.randint(1   ,    2**num_qubits     )
                # Next, format 'b' as a binary string of length 'n', padded with zeros:
                __magic_name__										=						format(__snake_case   ,    F"""0{num_qubits}b"""     )
                # Next, we place the first X-gates. Each digit in our binary string
                # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
                # we apply an X-gate to that qubit:
                for index, bit in enumerate(__snake_case     ):
                       if bit == "1":
                              oracle_qc.x(__snake_case     )
          # Do the controlled-NOT gates for each qubit, using the output qubit
          # as the target:
                for index in range(__snake_case     ):
                       oracle_qc.cx(__snake_case   ,    __snake_case     )
                # Next, place the final X-gates
                for index, bit in enumerate(__snake_case     ):
                       if bit == "1":
                              oracle_qc.x(__snake_case     )
  
      # Case in which oracle is constant
         if case == "constant":
                # First decide what the fixed output of the oracle will be
                # (either always 0 or always 1)
                __magic_name__										=						np.random.randint(2     )
                if output == 1:
                       oracle_qc.x(__snake_case     )
  
         __magic_name__										=						oracle_qc.to_gate()
         __magic_name__										=						'Oracle'  # To show when we display the circuit
         return oracle_gate
  
  
  def        A	(							__snake_case:   qiskit.QuantumCircuit   ,    __snake_case:   int     )						->		qiskit.QuantumCircuit:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						qiskit.QuantumCircuit(num_qubits + 1   ,    __snake_case     )
         # Set up the output qubit:
         dj_circuit.x(__snake_case     )
         dj_circuit.h(__snake_case     )
         # And set up the input register:
         for qubit in range(__snake_case     ):
                dj_circuit.h(__snake_case     )
         # Let's append the oracle gate to our circuit:
         dj_circuit.append(__snake_case   ,    range(num_qubits + 1     )     )
         # Finally, perform the H-gates again and measure:
         for qubit in range(__snake_case     ):
                dj_circuit.h(__snake_case     )
  
         for i in range(__snake_case     ):
                dj_circuit.measure(__snake_case   ,    __snake_case     )
  
         return dj_circuit
  
  
  def        A	(							__snake_case:   str   ,    __snake_case:   int     )						->		qiskit.result.counts.Counts:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						qiskit.Aer.get_backend('aer_simulator'     )
  
         __magic_name__										=						dj_oracle(__snake_case   ,    __snake_case     )
         __magic_name__										=						dj_algorithm(__snake_case   ,    __snake_case     )
  
         # Execute the circuit on the simulator
         __magic_name__										=						qiskit.execute(__snake_case   ,    __snake_case   ,    shots=1_0_0_0     )
  
         # Return the histogram data of the results of the experiment.
         return job.result().get_counts(__snake_case     )
  
  
  if __name__ == "__main__":
   print(f"""Deutsch Jozsa - Constant Oracle: {deutsch_jozsa('constant', 3)}""")
   print(f"""Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa('balanced', 3)}""")