"""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 the experiment tracking 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
#
########################################################################

_a   :	Optional[Any] 					=     16
_a   :	Tuple 					=     32





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

  def tokenize_function(_lowerCamelCase		: List[str]			):
    # max_length=None => use the model max length (it's actually the default)
    _lowerCAmelCase		: Any						    =  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		: List[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		: int						    =  tokenized_datasets.rename_column("""label"""    ,"""labels"""			)

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

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

  # Instantiate dataloaders.
  _lowerCAmelCase		: Tuple						    =  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

     _a   :	Optional[int] 					=     mocked_dataloaders  # noqa: F811





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int    ,_lowerCamelCase		: List[str]			) ->   Dict:







  if os.environ.get("""TESTING_MOCKED_DATALOADERS"""    ,_lowerCamelCase			) == "1":
    _lowerCAmelCase		: List[Any]						    =  2
  # Initialize Accelerator

  # New Code #
  # We pass in "all" to `log_with` to grab all available trackers in the environment
  # Note: If using a custom `Tracker` class, should be passed in here such as:
  # >>> log_with = ["all", MyCustomTrackerClassInstance()]
  if args.with_tracking:
    _lowerCAmelCase		: List[str]						    =  Accelerator(
        cpu=args.cpu    ,mixed_precision=args.mixed_precision    ,log_with="""all"""    ,project_dir=args.project_dir			)
  else:
    _lowerCAmelCase		: List[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		: Any						    =  config["""lr"""]
  _lowerCAmelCase		: Dict						    =  int(config["""num_epochs"""]			)
  _lowerCAmelCase		: List[Any]						    =  int(config["""seed"""]			)
  _lowerCAmelCase		: Dict						    =  int(config["""batch_size"""]			)
  set_seed(_lowerCamelCase			)

  _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[Any]						    =  get_dataloaders(_lowerCamelCase    ,_lowerCamelCase			)
  _lowerCAmelCase		: List[str]						    =  evaluate.load("""glue"""    ,"""mrpc"""			)

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

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _lowerCAmelCase		: List[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		: Any						    =  model.to(accelerator.device			)

  # Instantiate optimizer
  _lowerCAmelCase		: Tuple						    =  AdamW(params=model.parameters()    ,lr=_lowerCamelCase			)

  # Instantiate scheduler
  _lowerCAmelCase		: int						    =  get_linear_schedule_with_warmup(
      optimizer=_lowerCamelCase    ,num_warmup_steps=100    ,num_training_steps=(len(_lowerCamelCase			) * 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.
  _lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase		: Any						    =  accelerator.prepare(
      _lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase			)

  # New Code #
  # We need to initialize the trackers we use. Overall configurations can also be stored
  if args.with_tracking:
    _lowerCAmelCase		: str						    =  os.path.split(_lowerCamelCase			)[-1].split("""."""			)[0]
    accelerator.init_trackers(_lowerCamelCase    ,_lowerCamelCase			)

  # Now we train the model
  for epoch in range(_lowerCamelCase			):
    model.train()
    # New Code #
    # For our tracking example, we will log the total loss of each epoch
    if args.with_tracking:
      _lowerCAmelCase		: Any						    =  0
    for step, batch in enumerate(_lowerCamelCase			):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device			)
      _lowerCAmelCase		: Dict						    =  model(**_lowerCamelCase			)
      _lowerCAmelCase		: int						    =  outputs.loss
      # New Code #
      if args.with_tracking:
        total_loss += loss.detach().float()
      _lowerCAmelCase		: Tuple						    =  loss / gradient_accumulation_steps
      accelerator.backward(_lowerCamelCase			)
      if step % gradient_accumulation_steps == 0:
        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` (the default).
      batch.to(accelerator.device			)
      with torch.no_grad():
        _lowerCAmelCase		: Tuple						    =  model(**_lowerCamelCase			)
      _lowerCAmelCase		: Tuple						    =  outputs.logits.argmax(dim=-1			)
      _lowerCAmelCase    ,			_lowerCAmelCase		: Tuple						    =  accelerator.gather_for_metrics((predictions, batch["""labels"""])			)
      metric.add_batch(
          predictions=_lowerCamelCase    ,references=_lowerCamelCase    ,)

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

    # New Code #
    # To actually log, we call `Accelerator.log`
    # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
    if args.with_tracking:
      accelerator.log(
          {
              """accuracy""": eval_metric["""accuracy"""],
              """f1""": eval_metric["""f1"""],
              """train_loss""": total_loss.item() / len(_lowerCamelCase			),
              """epoch""": epoch,
          }    ,step=_lowerCamelCase    ,)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
  if args.with_tracking:
    accelerator.end_training()





def 							SCREAMING_SNAKE_CASE  (    ) ->   List[Any]:
  _lowerCAmelCase		: Optional[Any]						    =  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."""			)
  parser.add_argument(
      """--with_tracking"""    ,action="""store_true"""    ,help="""Whether to load in all available experiment trackers from the environment and use them for logging."""    ,)
  parser.add_argument(
      """--project_dir"""    ,type=_lowerCamelCase    ,default="""logs"""    ,help="""Location on where to store experiment tracking logs` and relevent project information"""    ,)
  _lowerCAmelCase		: Optional[Any]						    =  parser.parse_args()
  _lowerCAmelCase		: Optional[Any]						    =  {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(_lowerCamelCase    ,_lowerCamelCase			)


if __name__ == "__main__":
     main()





"""simple docstring"""




from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class 		__A							:
     _UpperCamelCase							:    int
     _UpperCamelCase							:    Node | None	      =	None
     _UpperCamelCase							:    Node | None	      =	None







def 							SCREAMING_SNAKE_CASE  (    ) ->   Node | None:
  _lowerCAmelCase		: Tuple						    =  Node(1			)
  _lowerCAmelCase		: int						    =  Node(2			)
  _lowerCAmelCase		: int						    =  Node(3			)
  _lowerCAmelCase		: Any						    =  Node(4			)
  _lowerCAmelCase		: Dict						    =  Node(5			)
  return tree





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   list[int]:
  return [root.data, *preorder(root.left			), *preorder(root.right			)] if root else []





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   list[int]:
  return postorder(root.left			) + postorder(root.right			) + [root.data] if root else []





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   list[int]:
  return [*inorder(root.left			), root.data, *inorder(root.right			)] if root else []





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   int:
  return (max(height(root.left			)    ,height(root.right			)			) + 1) if root else 0





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   Sequence[Node | None]:
  _lowerCAmelCase		: list[Any]						    =  []

  if root is None:
    return output

  _lowerCAmelCase		: Union[str, Any]						    =  deque([root]			)

  while process_queue:
    _lowerCAmelCase		: Optional[Any]						    =  process_queue.popleft()
    output.append(node.data			)

    if node.left:
      process_queue.append(node.left			)
    if node.right:
      process_queue.append(node.right			)
  return output





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) ->   Sequence[Node | None]:
  _lowerCAmelCase		: list[Any]						    =  []

  def populate_output(_lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) -> None:
    if not root:
      return
    if level == 1:
      output.append(root.data			)
    elif level > 1:
      populate_output(root.left    ,level - 1			)
      populate_output(root.right    ,level - 1			)

  populate_output(_lowerCamelCase    ,_lowerCamelCase			)
  return output





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) ->   Sequence[Node | None]:
  _lowerCAmelCase		: list[Any]						    =  []

  def populate_output(_lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) -> None:
    if root is None:
      return
    if level == 1:
      output.append(root.data			)
    elif level > 1:
      populate_output(root.right    ,level - 1			)
      populate_output(root.left    ,level - 1			)

  populate_output(_lowerCamelCase    ,_lowerCamelCase			)
  return output





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   Sequence[Node | None] | list[Any]:
  if root is None:
    return []

  _lowerCAmelCase		: list[Sequence[Node | None]]						    =  []

  _lowerCAmelCase		: Tuple						    =  0
  _lowerCAmelCase		: Dict						    =  height(_lowerCamelCase			)

  for h in range(1    ,height_tree + 1			):
    if not flag:
      output.append(get_nodes_from_left_to_right(_lowerCamelCase    ,_lowerCamelCase			)			)
      _lowerCAmelCase		: Any						    =  1
    else:
      output.append(get_nodes_from_right_to_left(_lowerCamelCase    ,_lowerCamelCase			)			)
      _lowerCAmelCase		: Optional[int]						    =  0

  return output





def 							SCREAMING_SNAKE_CASE  (    ) ->   None:  # Main function for testing.
  _lowerCAmelCase		: int						    =  make_tree()
  print(f"In-order Traversal: {inorder(_lowerCamelCase			)}"			)
  print(f"Pre-order Traversal: {preorder(_lowerCamelCase			)}"			)
  print(f"Post-order Traversal: {postorder(_lowerCamelCase			)}"    ,"""\n"""			)

  print(f"Height of Tree: {height(_lowerCamelCase			)}"    ,"""\n"""			)

  print("""Complete Level Order Traversal: """			)
  print(level_order(_lowerCamelCase			)    ,"""\n"""			)

  print("""Level-wise order Traversal: """			)

  for level in range(1    ,height(_lowerCamelCase			) + 1			):
    print(f"Level {level}:"    ,get_nodes_from_left_to_right(_lowerCamelCase    ,level=_lowerCamelCase			)			)

  print("""\nZigZag order Traversal: """			)
  print(zigzag(_lowerCamelCase			)			)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




