code: 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
  #
  ########################################################################
  
  _SCREAMING_SNAKE_CASE   						=						16
  _SCREAMING_SNAKE_CASE   						=						32
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ = 16					)				->Union[str, Any]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     AutoTokenizer.from_pretrained("bert-base-cased"					)
    a__     		=     load_dataset("glue" ,				"mrpc"					)
  
    def tokenize_function(lowerCamelCase__					):
      # max_length=None => use the model max length (it's actually the default)
      a__     		=     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():
      a__     		=     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
    a__     		=     tokenized_datasets.rename_column("label" ,				"labels"					)
  
    def collate_fn(lowerCamelCase__					):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      a__     		=     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":
        a__     		=     16
      elif accelerator.mixed_precision != "no":
        a__     		=     8
      else:
        a__     		=     None
  
      return tokenizer.pad(
          __A ,				padding="longest" ,				max_length=__A ,				pad_to_multiple_of=__A ,				return_tensors="pt" ,				)
  
    # Instantiate dataloaders.
    a__     		=     DataLoader(
        tokenized_datasets["train"] ,				shuffle=__A ,				collate_fn=__A ,				batch_size=__A					)
    a__     		=     DataLoader(
        tokenized_datasets["validation"] ,				shuffle=__A ,				collate_fn=__A ,				batch_size=__A					)
  
    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
  
       _SCREAMING_SNAKE_CASE   						=						mocked_dataloaders  # noqa: F811
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->Dict:
  
    '''simple docstring'''
  
  
  
  
    if os.environ.get("TESTING_MOCKED_DATALOADERS" ,				__A					) == "1":
      a__     		=     2
    # Initialize accelerator
    a__     		=     Accelerator(cpu=args.cpu ,				mixed_precision=args.mixed_precision					)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    a__     		=     config["lr"]
    a__     		=     int(config["num_epochs"]					)
    a__     		=     int(config["seed"]					)
    a__     		=     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":
        a__     		=     args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
        a__     		=     int(args.checkpointing_steps					)
      else:
        raise ValueError(
            f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."					)
    else:
      a__     		=     None
  
    set_seed(__A					)
  
    a__		,a__     		=     get_dataloaders(__A ,				__A					)
    a__     		=     evaluate.load("glue" ,				"mrpc"					)
  
    # If the batch size is too big we use gradient accumulation
    a__     		=     1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      a__     		=     batch_size // MAX_GPU_BATCH_SIZE
      a__     		=     MAX_GPU_BATCH_SIZE
  
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    a__     		=     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).
    a__     		=     model.to(accelerator.device					)
  
    # Instantiate optimizer
    a__     		=     AdamW(params=model.parameters() ,				lr=__A					)
  
    # Instantiate scheduler
    a__     		=     get_linear_schedule_with_warmup(
        optimizer=__A ,				num_warmup_steps=1_00 ,				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.
    a__		,a__		,a__		,a__		,a__     		=     accelerator.prepare(
        __A ,				__A ,				__A ,				__A ,				__A					)
  
    # New Code #
    # We need to keep track of how many total steps we have iterated over
    a__     		=     0
    # We also need to keep track of the stating epoch so files are named properly
    a__     		=     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					)
        a__     		=     os.path.basename(args.resume_from_checkpoint					)
      else:
        # Get the most recent checkpoint
        a__     		=     [f.name for f in os.scandir(os.getcwd()					) if f.is_dir()]
        dirs.sort(key=os.path.getctime					)
        a__     		=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      a__     		=     os.path.splitext(__A					)[0]
  
      if "epoch" in training_difference:
        a__     		=     int(training_difference.replace("epoch_" ,				""					)					) + 1
        a__     		=     None
      else:
        a__     		=     int(training_difference.replace("step_" ,				""					)					)
        a__     		=     resume_step // len(__A					)
        resume_step -= starting_epoch * len(__A					)
  
         # Now we train the model
    for epoch in range(__A ,				__A					):
      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
        a__     		=     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
        a__     		=     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					)
        a__     		=     model(**__A					)
        a__     		=     outputs.loss
        a__     		=     loss / gradient_accumulation_steps
        accelerator.backward(__A					)
        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(__A ,				__A					):
          a__     		=     f"step_{overall_step}"
          if overall_step % checkpointing_steps == 0:
            if args.output_dir is not None:
              a__     		=     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` (the default).
        batch.to(accelerator.device					)
        with torch.no_grad():
          a__     		=     model(**__A					)
        a__     		=     outputs.logits.argmax(dim=-1					)
        a__		,a__     		=     accelerator.gather_for_metrics((predictions, batch["labels"])					)
        metric.add_batch(
            predictions=__A ,				references=__A ,				)
  
      a__     		=     metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f"epoch {epoch}:" ,				__A					)
  
      # 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":
        a__     		=     f"epoch_{epoch}"
        if args.output_dir is not None:
          a__     		=     os.path.join(args.output_dir ,				__A					)
        accelerator.save_state(__A					)
  def 				__lowercase	(						)				->Dict:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     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(
        "--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(
        "--resume_from_checkpoint" ,				type=__A ,				default=__A ,				help="If the training should continue from a checkpoint folder." ,				)
    a__     		=     parser.parse_args()
    a__     		=     {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(__A ,				__A					)
  
  
  if __name__ == "__main__":
       main()
  
  
style_context: import random
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				:
  
  
  
  
      def __init__(					self  :   Optional[int]      ,      lowerCAmelCase  :   list[list[float]]      ,      lowerCAmelCase  :   list[int]      ,      lowerCAmelCase  :   float = 0.01      ,      lowerCAmelCase  :   int = 1000      ,      lowerCAmelCase  :   float = -1      ,      )	-> None:
        a__     		=     sample
        if len(self.sample   ) == 0:
          raise ValueError("Sample data can not be empty"   )
        a__     		=     target
        if len(self.target   ) == 0:
          raise ValueError("Target data can not be empty"   )
        if len(self.sample   ) != len(self.target   ):
          raise ValueError("Sample data and Target data do not have matching lengths"   )
        a__     		=     learning_rate
        a__     		=     epoch_number
        a__     		=     bias
        a__     		=     len(lowerCAmelCase   )
        a__     		=     len(sample[0]   )  # number of columns in dataset
        a__     		=     []
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> None:
        for sample in self.sample:
          sample.insert(0      ,      self.bias   )
  
        for _ in range(self.col_sample   ):
          self.weight.append(random.random()   )
  
        self.weight.insert(0      ,      self.bias   )
  
        a__     		=     0
  
        while True:
          a__     		=     False
          for i in range(self.number_sample   ):
            a__     		=     0
            for j in range(self.col_sample + 1   ):
              a__     		=     u + self.weight[j] * self.sample[i][j]
            a__     		=     self.sign(lowerCAmelCase   )
            if y != self.target[i]:
              for j in range(self.col_sample + 1   ):
                a__     		=     (
                    self.weight[j]
                    + self.learning_rate
                    * (self.target[i] - y)
                    * self.sample[i][j]
                )
              a__     		=     True
              # print('Epoch: \n',epoch_count)
          a__     		=     epoch_count + 1
          # if you want control the epoch or just by error
          if not has_misclassified:
            print(("\nEpoch:\n", epoch_count)   )
            print("------------------------\n"   )
            # if epoch_count > self.epoch_number or not error:
            break
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[str]      ,      lowerCAmelCase  :   list[float]   )	-> None:
        if len(self.sample   ) == 0:
          raise ValueError("Sample data can not be empty"   )
        sample.insert(0      ,      self.bias   )
        a__     		=     0
        for i in range(self.col_sample + 1   ):
          a__     		=     u + self.weight[i] * sample[i]
  
        a__     		=     self.sign(lowerCAmelCase   )
  
        if y == -1:
          print(("Sample: ", sample)   )
          print("classification: P1"   )
        else:
          print(("Sample: ", sample)   )
          print("classification: P2"   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   int      ,      lowerCAmelCase  :   float   )	-> int:
        return 1 if u >= 0 else -1
  
  _SCREAMING_SNAKE_CASE   						=    [
      [-0.6508, 0.1097, 4.0009],
      [-1.4492, 0.8896, 4.4005],
      [2.0850, 0.6876, 12.0710],
      [0.2626, 1.1476, 7.7985],
      [0.6418, 1.0234, 7.0427],
      [0.2569, 0.6730, 8.3265],
      [1.1155, 0.6043, 7.4446],
      [0.0914, 0.3399, 7.0677],
      [0.0121, 0.5256, 4.6316],
      [-0.0429, 0.4660, 5.4323],
      [0.4340, 0.6870, 8.2287],
      [0.2735, 1.0287, 7.1934],
      [0.4839, 0.4851, 7.4850],
      [0.4089, -0.1267, 5.5019],
      [1.4391, 0.1614, 8.5843],
      [-0.9115, -0.1973, 2.1962],
      [0.3654, 1.0475, 7.4858],
      [0.2144, 0.7515, 7.1699],
      [0.2013, 1.0014, 6.5489],
      [0.6483, 0.2183, 5.8991],
      [-0.1147, 0.2242, 7.2435],
      [-0.7970, 0.8795, 3.8762],
      [-1.0625, 0.6366, 2.4707],
      [0.5307, 0.1285, 5.6883],
      [-1.2200, 0.7777, 1.7252],
      [0.3957, 0.1076, 5.6623],
      [-0.1013, 0.5989, 7.1812],
      [2.4482, 0.9455, 11.2095],
      [2.0149, 0.6192, 10.9263],
      [0.2012, 0.2611, 5.4631],
  ]
  
  _SCREAMING_SNAKE_CASE   						=    [
      -1,
      -1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      -1,
      -1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      -1,
      1,
      -1,
      1,
  ]
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
  
       _SCREAMING_SNAKE_CASE   						=    Perceptron(
           sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
       )
       network.training()
       print("""Finished training perceptron""")
       print("""Enter values to predict or q to exit""")
       while True:
            _SCREAMING_SNAKE_CASE   						=    []
            for i in range(len(samples[0])):
                 _SCREAMING_SNAKE_CASE   						=    input("""value: """).strip()
                 if user_input == "q":
                      break
                 _SCREAMING_SNAKE_CASE   						=    float(user_input)
                 sample.insert(i, observation)
            network.sort(sample)
  
  