
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
#
########################################################################


lowerCamelCase__     :		Union[str, Any]        = 1_6
lowerCamelCase__     :		List[Any]        = 3_2

def 		UpperCamelCase						(   lowercase_			,			lowercase_ = 16 )		->						List[str]:


  '''simple docstring'''






  lowercase__	: Tuple     		=   AutoTokenizer.from_pretrained("""bert-base-cased""" )
  lowercase__	: Optional[int]     		=   load_dataset("""glue"""			,			"""mrpc""" )

  def tokenize_function(lowercase_ ):
    # max_length=None => use the model max length (it's actually the default)
    lowercase__	: Optional[Any]     		=   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():
    lowercase__	: List[str]     		=   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
  lowercase__	: Union[str, Any]     		=   tokenized_datasets.rename_column("""label"""			,			"""labels""" )

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

    return tokenizer.pad(
        lowercase_			,			padding="""longest"""			,			max_length=lowercase_			,			pad_to_multiple_of=lowercase_			,			return_tensors="""pt"""			,			)

  # Instantiate dataloaders.
  lowercase__	: int     		=   DataLoader(
      tokenized_datasets["""train"""]			,			shuffle=lowercase_			,			collate_fn=lowercase_			,			batch_size=lowercase_ )
  lowercase__	: Optional[int]     		=   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

     lowerCamelCase__     :		str        = mocked_dataloaders  # noqa: F811

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






  if os.environ.get("""TESTING_MOCKED_DATALOADERS"""			,			lowercase_ ) == "1":
    lowercase__	: List[str]     		=   2
  # New Code #
  lowercase__	: List[str]     		=   int(args.gradient_accumulation_steps )
  # Initialize accelerator
  lowercase__	: Optional[int]     		=   Accelerator(
      cpu=args.cpu			,			mixed_precision=args.mixed_precision			,			gradient_accumulation_steps=lowercase_ )
  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
  lowercase__	: Dict     		=   config["""lr"""]
  lowercase__	: Dict     		=   int(config["""num_epochs"""] )
  lowercase__	: int     		=   int(config["""seed"""] )
  lowercase__	: int     		=   int(config["""batch_size"""] )

  lowercase__	: str     		=   evaluate.load("""glue"""			,			"""mrpc""" )

  set_seed(lowercase_ )
  lowercase__		,     lowercase__	: Dict     		=   get_dataloaders(lowercase_			,			lowercase_ )
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowercase__	: int     		=   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).
  lowercase__	: str     		=   model.to(accelerator.device )

  # Instantiate optimizer
  lowercase__	: Any     		=   AdamW(params=model.parameters()			,			lr=lowercase_ )

  # Instantiate scheduler
  lowercase__	: Optional[int]     		=   get_linear_schedule_with_warmup(
      optimizer=lowercase_			,			num_warmup_steps=1_00			,			num_training_steps=(len(lowercase_ ) * 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.
  lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__	: str     		=   accelerator.prepare(
      lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )

  # Now we train the model
  for epoch in range(lowercase_ ):
    model.train()
    for step, batch in enumerate(lowercase_ ):
      # 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(lowercase_ ):
        lowercase__	: List[str]     		=   model(**lowercase_ )
        lowercase__	: Any     		=   output.loss
        accelerator.backward(lowercase_ )
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

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

    lowercase__	: Optional[int]     		=   metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F'epoch {epoch}:'			,			lowercase_ )

def 		UpperCamelCase						(   )		->						str:


  '''simple docstring'''






  lowercase__	: int     		=   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."""			,			)
  # New Code #
  parser.add_argument(
      """--gradient_accumulation_steps"""			,			type=lowercase_			,			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.""" )
  lowercase__	: str     		=   parser.parse_args()
  lowercase__	: List[Any]     		=   {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(lowercase_			,			lowercase_ )


if __name__ == "__main__":
     main()













import qiskit





def  __magic_name__  (				lowercase				,       lowercase				)				-> qiskit.result.counts.Counts:

							"""simple docstring"""



							lowercase_	:   Dict  				=     qiskit.Aer.get_backend("""aer_simulator"""				)

							# Create a Quantum Circuit acting on the q register
							lowercase_	:   str  				=     qiskit.QuantumCircuit(lowercase				,       lowercase				)

							# Map the quantum measurement to the classical bits
							circuit.measure([0]				,       [0]				)

							# Execute the circuit on the simulator
							lowercase_	:   List[Any]  				=     qiskit.execute(lowercase				,       lowercase				,       shots=1000				)

							# Return the histogram data of the results of the experiment.
							return job.result().get_counts(lowercase				)


if __name__ == "__main__":
							print(F'''Total count for various states are: {single_qubit_measure(1, 1)}''')