


"""simple docstring"""







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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


__lowercase    =				16
__lowercase    =				32


def 					lowercase      (    A_  ,       A_ = 16	)->				Union[str, Any]:
  '''simple docstring'''







  a						:		Union[str, Any]											=      AutoTokenizer.from_pretrained("bert-base-cased"	)
  a						:		List[str]											=      load_dataset("glue"  ,       "mrpc"	)

  def tokenize_function(A_	):
    # max_length=None => use the model max length (it's actually the default)
    a						:		int											=      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						:		Any											=      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						:		Dict											=      tokenized_datasets.rename_column("label"  ,       "labels"	)

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

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

  # Instantiate dataloaders.
  a						:		List[Any]											=      DataLoader(
      tokenized_datasets["train"]  ,       shuffle=A_  ,       collate_fn=A_  ,       batch_size=A_	)
  a						:		Tuple											=      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

 __lowercase    =				mocked_dataloaders  # noqa: F811


def 					lowercase      (    A_  ,       A_	)->				str:
  '''simple docstring'''







  if os.environ.get("TESTING_MOCKED_DATALOADERS"  ,       A_	) == "1":
    a						:		str											=      2
  # Initialize accelerator
  a						:		str											=      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						:		List[Any]											=      int(config["num_epochs"]	)
  a						:		Optional[int]											=      int(config["seed"]	)
  a						:		Tuple											=      int(config["batch_size"]	)

  a						:		List[str]											=      evaluate.load("glue"  ,       "mrpc"	)

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

  set_seed(A_	)
  a							,		a						:		Union[str, Any]											=      get_dataloaders(A_  ,       A_	)
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  a						:		Any											=      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						:		Optional[Any]											=      model.to(accelerator.device	)

  # Instantiate optimizer
  a						:		Optional[Any]											=      AdamW(params=model.parameters()  ,       lr=A_	)

  # Instantiate scheduler
  a						:		Optional[Any]											=      get_linear_schedule_with_warmup(
      optimizer=A_  ,       num_warmup_steps=100  ,       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						:		Tuple											=      accelerator.prepare(
      A_  ,       A_  ,       A_  ,       A_  ,       A_	)

  # Now we train the model
  for epoch in range(A_	):
    model.train()
    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						:		Tuple											=      model(**A_	)
      a						:		Tuple											=      outputs.loss
      a						:		Any											=      loss / gradient_accumulation_steps
      accelerator.backward(A_	)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    a						:		List[str]											=      0
    for step, batch in enumerate(A_	):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device	)
      with torch.no_grad():
        a						:		Any											=      model(**A_	)
      a						:		Union[str, Any]											=      outputs.logits.argmax(dim=-1	)
      a							,		a						:		int											=      accelerator.gather((predictions, batch["labels"])	)
      # New Code #
      # First we check if it's a distributed system
      if accelerator.use_distributed:
        # Then see if we're on the last batch of our eval dataloader
        if step == len(A_	) - 1:
          # Last batch needs to be truncated on distributed systems as it contains additional samples
          a						:		List[Any]											=      predictions[: len(eval_dataloader.dataset	) - samples_seen]
          a						:		Optional[int]											=      references[: len(eval_dataloader.dataset	) - samples_seen]
        else:
          # Otherwise we add the number of samples seen
          samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
      metric.add_batch(
          predictions=A_  ,       references=A_  ,       )

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


def 					lowercase      (    )->				Union[str, Any]:
  '''simple docstring'''







  a						:		str											=      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."	)
  a						:		str											=      parser.parse_args()
  a						:		List[str]											=      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(A_  ,       A_	)


if __name__ == "__main__":
 main()










"""simple docstring"""







from __future__ import annotations

import queue






class 						_A  :







    """simple docstring"""




    def __init__( self    :   List[str]    ,			__UpperCAmelCase    :   Union[str, Any]):
      a						:		Optional[Any]											=      data
      a						:		Optional[int]											=      None
      a						:		Union[str, Any]											=      None






def 					lowercase      (    )->				TreeNode:
  '''simple docstring'''







  print("\n********Press N to stop entering at any point of time********\n"	)
  a						:		int											=      input("Enter the value of the root node: "	).strip().lower()
  a						:		queue.Queue											=      queue.Queue()
  a						:		Tuple											=      TreeNode(int(A_	)	)
  q.put(A_	)
  while not q.empty():
    a						:		Union[str, Any]											=      q.get()
    a						:		Optional[int]											=      F'''Enter the left node of {node_found.data}: '''
    a						:		Union[str, Any]											=      input(A_	).strip().lower() or "n"
    if check == "n":
      return tree_node
    a						:		List[str]											=      TreeNode(int(A_	)	)
    a						:		Any											=      left_node
    q.put(A_	)
    a						:		Dict											=      F'''Enter the right node of {node_found.data}: '''
    a						:		str											=      input(A_	).strip().lower() or "n"
    if check == "n":
      return tree_node
    a						:		Any											=      TreeNode(int(A_	)	)
    a						:		str											=      right_node
    q.put(A_	)
  raise


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  print(node.data  ,       end=","	)
  pre_order(node.left	)
  pre_order(node.right	)


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  in_order(node.left	)
  print(node.data  ,       end=","	)
  in_order(node.right	)


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  post_order(node.left	)
  post_order(node.right	)
  print(node.data  ,       end=","	)


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		queue.Queue											=      queue.Queue()
  q.put(A_	)
  while not q.empty():
    a						:		str											=      q.get()
    print(node_dequeued.data  ,       end=","	)
    if node_dequeued.left:
      q.put(node_dequeued.left	)
    if node_dequeued.right:
      q.put(node_dequeued.right	)


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		queue.Queue											=      queue.Queue()
  q.put(A_	)
  while not q.empty():
    a						:		Any											=      []
    while not q.empty():
      a						:		Any											=      q.get()
      print(node_dequeued.data  ,       end=","	)
      if node_dequeued.left:
        list_.append(node_dequeued.left	)
      if node_dequeued.right:
        list_.append(node_dequeued.right	)
    print()
    for node in list_:
      q.put(A_	)


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		list[TreeNode]											=      []
  a						:		int											=      node
  while n or stack:
    while n:  # start from root node, find its left child
      print(n.data  ,       end=","	)
      stack.append(A_	)
      a						:		Tuple											=      n.left
    # end of while means current node doesn't have left child
    a						:		Optional[Any]											=      stack.pop()
    # start to traverse its right child
    a						:		str											=      n.right


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		list[TreeNode]											=      []
  a						:		Union[str, Any]											=      node
  while n or stack:
    while n:
      stack.append(A_	)
      a						:		int											=      n.left
    a						:		str											=      stack.pop()
    print(n.data  ,       end=","	)
    a						:		Optional[int]											=      n.right


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a							,		a						:		Tuple											=      [], []
  a						:		Tuple											=      node
  stacka.append(A_	)
  while stacka:  # to find the reversed order of post order, store it in stack2
    a						:		Optional[Any]											=      stacka.pop()
    if n.left:
      stacka.append(n.left	)
    if n.right:
      stacka.append(n.right	)
    stacka.append(A_	)
  while stacka:  # pop up from stack2 will be the post order
    print(stacka.pop().data  ,       end=","	)


def 					lowercase      (    A_ = ""  ,       A_=50  ,       A_="*"	)->				str:
  '''simple docstring'''







  if not s:
    return "\n" + width * char
  a							,		a						:		Dict											=      divmod(width - len(A_	) - 2  ,       2	)
  return F'''{left * char} {s} {(left + extra) * char}'''


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(prompt("""Binary Tree Traversals"""))

 __lowercase    =				build_tree()
 print(prompt("""Pre Order Traversal"""))
 pre_order(node)
 print(prompt() + """\n""")

 print(prompt("""In Order Traversal"""))
 in_order(node)
 print(prompt() + """\n""")

 print(prompt("""Post Order Traversal"""))
 post_order(node)
 print(prompt() + """\n""")

 print(prompt("""Level Order Traversal"""))
 level_order(node)
 print(prompt() + """\n""")

 print(prompt("""Actual Level Order Traversal"""))
 level_order_actual(node)
 print("""*""" * 50 + """\n""")

 print(prompt("""Pre Order Traversal - Iteration Version"""))
 pre_order_iter(node)
 print(prompt() + """\n""")

 print(prompt("""In Order Traversal - Iteration Version"""))
 in_order_iter(node)
 print(prompt() + """\n""")

 print(prompt("""Post Order Traversal - Iteration Version"""))
 post_order_iter(node)
 print(prompt())






