
'''simple docstring'''

import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# 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
#
########################################################################

UpperCamelCase_            =    32


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Accelerator					, _lowerCamelCase  :    int = 16						)      ->  Dict:
  _lowerCAmelCase    :					Union[str, Any]							=   AutoTokenizer.from_pretrained("""bert-base-cased"""						)
  _lowerCAmelCase    :					str							=   load_dataset("""glue"""					, """mrpc"""						)

  def tokenize_function(_lowerCamelCase  :    str						):
    # max_length=None => use the model max length (it's actually the default)
    _lowerCAmelCase    :					List[str]							=   tokenizer(examples["""sentence1"""]					, examples["""sentence2"""]					, truncation=_lowerCamelCase					, max_length=_lowerCamelCase						)
    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    :					Union[str, Any]							=   datasets.map(
        _lowerCamelCase					, batched=_lowerCamelCase					, 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    :					List[Any]							=   tokenized_datasets.rename_column("""label"""					, """labels"""						)

  def collate_fn(_lowerCamelCase  :    str						):
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      _lowerCAmelCase    :					Optional[int]							=   16
    elif accelerator.mixed_precision != "no":
      _lowerCAmelCase    :					List[str]							=   8
    else:
      _lowerCAmelCase    :					Optional[int]							=   None

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

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

  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

     UpperCamelCase_            =    mocked_dataloaders  # noqa: F811


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Tuple					, _lowerCamelCase  :    str						)      ->  Optional[Any]:
  # For testing only
  if os.environ.get("""TESTING_MOCKED_DATALOADERS"""					, _lowerCamelCase						) == "1":
    _lowerCAmelCase    :					int							=   2
  # Initialize accelerator
  _lowerCAmelCase    :					Union[str, Any]							=   Accelerator(cpu=args.cpu					, mixed_precision=args.mixed_precision						)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _lowerCAmelCase    :					List[str]							=   config["""lr"""]
  _lowerCAmelCase    :					Dict							=   int(config["""num_epochs"""]						)
  _lowerCAmelCase    :					Optional[int]							=   int(config["""seed"""]						)
  _lowerCAmelCase    :					Tuple							=   int(config["""batch_size"""]						)

  _lowerCAmelCase    :					Union[str, Any]							=   evaluate.load("""glue"""					, """mrpc"""						)

  # New Code #
  # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
  # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
  # half each time. From this, we can calculate the number of gradient accumulation steps needed
  # and modify the Accelerator object as a result
  @find_executable_batch_size(starting_batch_size=int(_lowerCamelCase						)						)
  def inner_training_loop(_lowerCamelCase  :    Optional[int]						):
    # Since we need to modify the outside accelerator object, we need to bring it
    # to the local scope
    nonlocal accelerator

    # We can calculate the number of gradient accumulation steps based on the current
    # batch size vs the starting batch size
    _lowerCAmelCase    :					int							=   observed_batch_size // batch_size

    # And then set it in the Accelerator directly:
    _lowerCAmelCase    :					Union[str, Any]							=   num_gradient_accumulation_steps

    # Next we need to free all of the stored model references in the Accelerator each time
    accelerator.free_memory()

    # And set the seed so our results are reproducable each reset
    set_seed(_lowerCamelCase						)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowerCAmelCase    :					str							=   AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""					, return_dict=_lowerCamelCase						)

    # 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    :					int							=   model.to(accelerator.device						)

    # Instantiate optimizer
    _lowerCAmelCase    :					Optional[Any]							=   AdamW(params=model.parameters()					, lr=_lowerCamelCase						)
    _lowerCAmelCase			,   _lowerCAmelCase    :					List[str]							=   get_dataloaders(_lowerCamelCase					, _lowerCamelCase						)

    # Instantiate scheduler
    _lowerCAmelCase    :					List[str]							=   get_linear_schedule_with_warmup(
        optimizer=_lowerCamelCase					, num_warmup_steps=1_00					, num_training_steps=(len(_lowerCamelCase						) * 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    :					Union[str, Any]							=   accelerator.prepare(
        _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase						)

    # Now we train the model
    for epoch in range(_lowerCamelCase						):
      model.train()
      for step, batch in enumerate(_lowerCamelCase						):
        # And perform gradient accumulation
        with accelerator.accumulate(_lowerCamelCase						):
          # We could avoid this line since we set the accelerator with `device_placement=True`.
          batch.to(accelerator.device						)
          _lowerCAmelCase    :					Tuple							=   model(**_lowerCamelCase						)
          _lowerCAmelCase    :					List[Any]							=   outputs.loss
          accelerator.backward(_lowerCamelCase						)
          optimizer.step()
          lr_scheduler.step()
          optimizer.zero_grad()

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

      _lowerCAmelCase    :					Union[str, Any]							=   metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'epoch {epoch}:'					, _lowerCamelCase						)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()


def 		_UpperCAmelCase			(					)      ->  Dict:
  _lowerCAmelCase    :					Optional[int]							=   argparse.ArgumentParser(description="""Simple example of training script."""						)
  parser.add_argument(
      """--mixed_precision"""					, type=_lowerCamelCase					, default=_lowerCamelCase					, 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."""						)
  _lowerCAmelCase    :					Optional[Any]							=   parser.parse_args()
  # New Code #
  # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
  _lowerCAmelCase    :					List[str]							=   {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 2_56}
  training_function(_lowerCamelCase					, _lowerCamelCase						)


if __name__ == "__main__":
     main()




'''simple docstring'''

def 		_UpperCAmelCase			(					_lowerCamelCase  :    list[list[int]]					, _lowerCamelCase  :    list[list[int]]						)      ->  list[list[int]]:
  _lowerCAmelCase    :					int							=   []
  _lowerCAmelCase    :					Union[str, Any]							=   len(_lowerCamelCase						)
  for i in range(_lowerCamelCase						):
    _lowerCAmelCase    :					List[str]							=   []
    for j in range(_lowerCamelCase						):
      _lowerCAmelCase    :					Union[str, Any]							=   0
      for k in range(_lowerCamelCase						):
        _lowerCAmelCase    :					Any							=   val + matrix_a[i][k] * matrix_b[k][j]
      list_a.append(_lowerCamelCase						)
    matrix_c.append(_lowerCamelCase						)
  return matrix_c


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int						)      ->  list[list[int]]:
  return [[int(row == column						) for column in range(_lowerCamelCase						)] for row in range(_lowerCamelCase						)]


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int						)      ->  int:
  if n <= 1:
    return n
  _lowerCAmelCase    :					Tuple							=   identity(2						)
  _lowerCAmelCase    :					Union[str, Any]							=   [[1, 1], [1, 0]]
  _lowerCAmelCase    :					Optional[int]							=   n - 1
  while n > 0:
    if n % 2 == 1:
      _lowerCAmelCase    :					str							=   multiply(_lowerCamelCase					, _lowerCamelCase						)
    _lowerCAmelCase    :					Tuple							=   multiply(_lowerCamelCase					, _lowerCamelCase						)
    _lowerCAmelCase    :					Dict							=   int(n / 2						)
  return res_matrix[0][0]


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int						)      ->  int:
  if n <= 1:
    return n
  _lowerCAmelCase    :					Any							=   0
  _lowerCAmelCase    :					Dict							=   1
  for _ in range(2					, n + 1						):
    _lowerCAmelCase			,   _lowerCAmelCase    :					int							=   fiba, fiba + fiba
  return fiba


def 		_UpperCAmelCase			(					)      ->  None:
  for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
    _lowerCAmelCase    :					Union[str, Any]							=   int("""""".join(c for c in ordinal if c in """0123456789"""						)						)  # 1000th --> 1000
    print(
        f'{ordinal} fibonacci number using matrix exponentiation is '
        f'{nth_fibonacci_matrix(_lowerCamelCase						)} and using bruteforce is '
        f'{nth_fibonacci_bruteforce(_lowerCamelCase						)}\n'						)
  # from timeit import timeit
  # print(timeit("nth_fibonacci_matrix(1000000)",
  #              "from main import nth_fibonacci_matrix", number=5))
  # print(timeit("nth_fibonacci_bruteforce(1000000)",
  #              "from main import nth_fibonacci_bruteforce", number=5))
  # 2.3342058970001744
  # 57.256506615000035


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()


