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()


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)

