
'''simple docstring'''
import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 how to perform Cross Validation,
# 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
#
########################################################################


A__   :		Optional[int] 		=16
A__   :		int 		=32







def        A_     (						__SCREAMING_SNAKE_CASE						:  Accelerator			,     __SCREAMING_SNAKE_CASE						:  DatasetDict			,     __SCREAMING_SNAKE_CASE						:  List[int]			,     __SCREAMING_SNAKE_CASE						:  List[int]			,     __SCREAMING_SNAKE_CASE						:  int = 16 )				->       Dict:





  """simple docstring"""

  __A		:     Any												=		AutoTokenizer.from_pretrained("""bert-base-cased""" )
  __A		:     int												=		DatasetDict(
      {
          """train""": dataset["""train"""].select(__SCREAMING_SNAKE_CASE ),
          """validation""": dataset["""train"""].select(__SCREAMING_SNAKE_CASE ),
          """test""": dataset["""validation"""],
      } )

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

  def collate_fn(__SCREAMING_SNAKE_CASE						:  List[str] ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    __A		:     Optional[Any]												=		128 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		:     int												=		16
    elif accelerator.mixed_precision != "no":
      __A		:     Any												=		8
    else:
      __A		:     List[str]												=		None

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

  # Instantiate dataloaders.
  __A		:     Tuple												=		DataLoader(
      tokenized_datasets["""train"""]			,     shuffle=__SCREAMING_SNAKE_CASE			,     collate_fn=__SCREAMING_SNAKE_CASE			,     batch_size=__SCREAMING_SNAKE_CASE )
  __A		:     Union[str, Any]												=		DataLoader(
      tokenized_datasets["""validation"""]			,     shuffle=__SCREAMING_SNAKE_CASE			,     collate_fn=__SCREAMING_SNAKE_CASE			,     batch_size=__SCREAMING_SNAKE_CASE )

  __A		:     Union[str, Any]												=		DataLoader(
      tokenized_datasets["""test"""]			,     shuffle=__SCREAMING_SNAKE_CASE			,     collate_fn=__SCREAMING_SNAKE_CASE			,     batch_size=__SCREAMING_SNAKE_CASE )

  return train_dataloader, eval_dataloader, test_dataloader







def        A_     (						__SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  Tuple )				->       str:





  """simple docstring"""

  __A		:     Optional[Any]												=		[]
  # Download the dataset
  __A		:     int												=		load_dataset("""glue"""			,     """mrpc""" )
  # Create our splits
  __A		:     Any												=		StratifiedKFold(n_splits=int(args.num_folds ) )
  # Initialize accelerator
  __A		:     Optional[Any]												=		Accelerator(cpu=args.cpu			,     mixed_precision=args.mixed_precision )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  __A		:     str												=		config["""lr"""]
  __A		:     int												=		int(config["""num_epochs"""] )
  __A		:     Tuple												=		int(config["""seed"""] )
  __A		:     Any												=		int(config["""batch_size"""] )

  __A		:     Any												=		evaluate.load("""glue"""			,     """mrpc""" )

  # If the batch size is too big we use gradient accumulation
  __A		:     Optional[Any]												=		1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    __A		:     Tuple												=		batch_size // MAX_GPU_BATCH_SIZE
    __A		:     List[Any]												=		MAX_GPU_BATCH_SIZE

  set_seed(__SCREAMING_SNAKE_CASE )

  # New Code #
  # Create our folds:
  __A		:     Dict												=		kfold.split(np.zeros(datasets["""train"""].num_rows )			,     datasets["""train"""]["""label"""] )
  __A		:     Tuple												=		[]
  # Iterate over them
  for i, (train_idxs, valid_idxs) in enumerate(__SCREAMING_SNAKE_CASE ):
    __A							,					__A							,					__A		:     Optional[int]												=		get_fold_dataloaders(
        __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __A		:     List[Any]												=		AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""			,     return_dict=__SCREAMING_SNAKE_CASE )

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

    # Instantiate optimizer
    __A		:     int												=		AdamW(params=model.parameters()			,     lr=__SCREAMING_SNAKE_CASE )

    # Instantiate scheduler
    __A		:     str												=		get_linear_schedule_with_warmup(
        optimizer=__SCREAMING_SNAKE_CASE			,     num_warmup_steps=100			,     num_training_steps=(len(__SCREAMING_SNAKE_CASE ) * 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		:     List[Any]												=		accelerator.prepare(
        __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

    # Now we train the model
    for epoch in range(__SCREAMING_SNAKE_CASE ):
      model.train()
      for step, batch in enumerate(__SCREAMING_SNAKE_CASE ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device )
        __A		:     Optional[int]												=		model(**__SCREAMING_SNAKE_CASE )
        __A		:     List[Any]												=		outputs.loss
        __A		:     List[Any]												=		loss / gradient_accumulation_steps
        accelerator.backward(__SCREAMING_SNAKE_CASE )
        if step % gradient_accumulation_steps == 0:
          optimizer.step()
          lr_scheduler.step()
          optimizer.zero_grad()

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

      __A		:     Optional[Any]												=		metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"epoch {epoch}:"			,     __SCREAMING_SNAKE_CASE )

    # New Code #
    # We also run predictions on the test set at the very end
    __A		:     Union[str, Any]												=		[]
    for step, batch in enumerate(__SCREAMING_SNAKE_CASE ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device )
      with torch.no_grad():
        __A		:     Optional[int]												=		model(**__SCREAMING_SNAKE_CASE )
      __A		:     List[Any]												=		outputs.logits
      __A							,					__A		:     Dict												=		accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
      fold_predictions.append(predictions.cpu() )
      if i == 0:
        # We need all of the test predictions
        test_references.append(references.cpu() )
        # Use accelerator.print to print only on the main process.
    test_predictions.append(torch.cat(__SCREAMING_SNAKE_CASE			,     dim=0 ) )
    # We now need to release all our memory and get rid of the current model, optimizer, etc
    accelerator.free_memory()
  # New Code #
  # Finally we check the accuracy of our folded results:
  __A		:     Dict												=		torch.cat(__SCREAMING_SNAKE_CASE			,     dim=0 )
  __A		:     Optional[Any]												=		torch.stack(__SCREAMING_SNAKE_CASE			,     dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
  __A		:     Any												=		metric.compute(predictions=__SCREAMING_SNAKE_CASE			,     references=__SCREAMING_SNAKE_CASE )
  accelerator.print("""Average test metrics from all folds:"""			,     __SCREAMING_SNAKE_CASE )







def        A_     (						)				->       Optional[Any]:





  """simple docstring"""

  __A		:     List[Any]												=		argparse.ArgumentParser(description="""Simple example of training script.""" )
  parser.add_argument(
      """--mixed_precision"""			,     type=__SCREAMING_SNAKE_CASE			,     default=__SCREAMING_SNAKE_CASE			,     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.""" )
  # New Code #
  parser.add_argument("""--num_folds"""			,     type=__SCREAMING_SNAKE_CASE			,     default=3			,     help="""The number of splits to perform across the dataset""" )
  __A		:     str												=		parser.parse_args()
  __A		:     str												=		{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
     main()





'''simple docstring'''
def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       None:





  """simple docstring"""

  __A		:     Tuple												=		generate_pascal_triangle(__SCREAMING_SNAKE_CASE )
  for row_idx in range(__SCREAMING_SNAKE_CASE ):
    # Print left spaces
    for _ in range(num_rows - row_idx - 1 ):
      print(end=""" """ )
    # Print row values
    for col_idx in range(row_idx + 1 ):
      if col_idx != row_idx:
        print(triangle[row_idx][col_idx]			,     end=""" """ )
      else:
        print(triangle[row_idx][col_idx]			,     end="""""" )
    print()







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       list[list[int]]:





  """simple docstring"""

  if not isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
    raise TypeError("""The input value of 'num_rows' should be 'int'""" )

  if num_rows == 0:
    return []
  elif num_rows < 0:
    raise ValueError(
        """The input value of 'num_rows' should be greater than or equal to 0""" )

  __A		:     list[list[int]]												=		[]
  for current_row_idx in range(__SCREAMING_SNAKE_CASE ):
    __A		:     Tuple												=		populate_current_row(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
    triangle.append(__SCREAMING_SNAKE_CASE )
  return triangle







def        A_     (						__SCREAMING_SNAKE_CASE						:  list[list[int]]			,     __SCREAMING_SNAKE_CASE						:  int )				->       list[int]:





  """simple docstring"""

  __A		:     Union[str, Any]												=		[-1] * (current_row_idx + 1)
  # first and last elements of current row are equal to 1
  __A							,					__A		:     Tuple												=		1, 1
  for current_col_idx in range(1			,     __SCREAMING_SNAKE_CASE ):
    calculate_current_element(
        __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
  return current_row







def        A_     (						__SCREAMING_SNAKE_CASE						:  list[list[int]]			,     __SCREAMING_SNAKE_CASE						:  list[int]			,     __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  int			,     )				->       None:





  """simple docstring"""

  __A		:     str												=		triangle[current_row_idx - 1][current_col_idx - 1]
  __A		:     Union[str, Any]												=		triangle[current_row_idx - 1][current_col_idx]
  __A		:     Any												=		above_to_left_elt + above_to_right_elt







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       list[list[int]]:





  """simple docstring"""

  if not isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
    raise TypeError("""The input value of 'num_rows' should be 'int'""" )

  if num_rows == 0:
    return []
  elif num_rows < 0:
    raise ValueError(
        """The input value of 'num_rows' should be greater than or equal to 0""" )

  __A		:     list[list[int]]												=		[[1]]

  for row_index in range(1			,     __SCREAMING_SNAKE_CASE ):
    __A		:     Optional[Any]												=		[0] + result[-1] + [0]
    __A		:     Optional[Any]												=		row_index + 1
    # Calculate the number of distinct elements in a row
    __A		:     int												=		sum(divmod(__SCREAMING_SNAKE_CASE			,     2 ) )
    __A		:     str												=		[
        temp_row[i - 1] + temp_row[i] for i in range(1			,     distinct_elements + 1 )
    ]
    __A		:     Tuple												=		row_first_half[: (row_index + 1) // 2]
    row_second_half.reverse()
    __A		:     Any												=		row_first_half + row_second_half
    result.append(__SCREAMING_SNAKE_CASE )

  return result







def        A_     (						)				->       None:





  """simple docstring"""

  from collections.abc import Callable
  from timeit import timeit

  def benchmark_a_function(__SCREAMING_SNAKE_CASE						:  Callable			,     __SCREAMING_SNAKE_CASE						:  int ) -> None:
    __A		:     List[Any]												=		F"{func.__name__}({value})"
    __A		:     Dict												=		timeit(F"__main__.{call}"			,     setup="""import __main__""" )
    # print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
    print(F"{call:38} -- {timing:.4f} seconds" )

  for value in range(15 ):  # (1, 7, 14):
    for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
      benchmark_a_function(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
    print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()



