





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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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)
#
# 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__       :			List[str]			      =						1_6
SCREAMING_SNAKE_CASE__       :			List[Any]			      =						3_2






def       _A  (      lowerCamelCase							, lowerCamelCase = 16   ):
   a__	:						Optional[Any]      		=  AutoTokenizer.from_pretrained("bert-base-cased"   )
   a__	:						str      		=  load_dataset("glue"							, "mrpc"   )

   def tokenize_function(lowerCamelCase   ):
      # max_length=None => use the model max length (it's actually the default)
      a__	:						List[str]      		=  tokenizer(examples["sentence1"]							, examples["sentence2"]							, truncation=__UpperCamelCase							, max_length=__UpperCamelCase   )
      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__	:						str      		=  datasets.map(
          __UpperCamelCase							, batched=__UpperCamelCase							, 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__	:						List[str]      		=  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__	:						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__	:						List[str]      		=  16
      elif accelerator.mixed_precision != "no":
         a__	:						Optional[int]      		=  8
      else:
         a__	:						List[Any]      		=  None

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

   # Instantiate dataloaders.
   a__	:						Optional[int]      		=  DataLoader(
       tokenized_datasets["train"]							, shuffle=__UpperCamelCase							, collate_fn=__UpperCamelCase							, batch_size=__UpperCamelCase   )
   a__	:						Union[str, Any]      		=  DataLoader(
       tokenized_datasets["validation"]							, shuffle=__UpperCamelCase							, collate_fn=__UpperCamelCase							, batch_size=__UpperCamelCase   )

   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__       :			str			      =						mocked_dataloaders  # noqa: F811






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   # For testing only
   if os.environ.get("TESTING_MOCKED_DATALOADERS"							, __UpperCamelCase   ) == "1":
      a__	:						Dict      		=  2
   # New Code #
   a__	:						Tuple      		=  int(args.gradient_accumulation_steps   )
   a__	:						List[Any]      		=  int(args.local_sgd_steps   )
   # Initialize accelerator
   a__	:						int      		=  Accelerator(
       cpu=args.cpu							, mixed_precision=args.mixed_precision							, gradient_accumulation_steps=__UpperCamelCase   )
   if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
      raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"   )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   a__	:						Optional[Any]      		=  config["lr"]
   a__	:						List[str]      		=  int(config["num_epochs"]   )
   a__	:						str      		=  int(config["seed"]   )
   a__	:						Any      		=  int(config["batch_size"]   )

   a__	:						Optional[Any]      		=  evaluate.load("glue"							, "mrpc"   )

   set_seed(__UpperCamelCase   )
   a__		,     a__	:						List[Any]      		=  get_dataloaders(__UpperCamelCase							, __UpperCamelCase   )
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   a__	:						str      		=  AutoModelForSequenceClassification.from_pretrained("bert-base-cased"							, return_dict=__UpperCamelCase   )

   # 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__	:						List[Any]      		=  AdamW(params=model.parameters()							, lr=__UpperCamelCase   )

   # Instantiate scheduler
   a__	:						Any      		=  get_linear_schedule_with_warmup(
       optimizer=__UpperCamelCase							, num_warmup_steps=100							, num_training_steps=(len(__UpperCamelCase   ) * 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.
   a__		,     a__		,     a__		,     a__		,     a__	:						Dict      		=  accelerator.prepare(
       __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase   )

   # Now we train the model
   for epoch in range(__UpperCamelCase   ):
      model.train()
      with LocalSGD(
          accelerator=__UpperCamelCase							, model=__UpperCamelCase							, local_sgd_steps=__UpperCamelCase							, enabled=local_sgd_steps is not None   ) as local_sgd:
         for step, batch in enumerate(__UpperCamelCase   ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device   )
            # New code #
            # We use the new `accumulate` context manager to perform gradient accumulation
            # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
            with accelerator.accumulate(__UpperCamelCase   ):
               a__	:						Optional[int]      		=  model(**__UpperCamelCase   )
               a__	:						Tuple      		=  output.loss
               accelerator.backward(__UpperCamelCase   )
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()
               # LocalSGD-specific line
               local_sgd.step()

      model.eval()
      for step, batch in enumerate(__UpperCamelCase   ):
         # 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(**__UpperCamelCase   )
         a__	:						List[Any]      		=  outputs.logits.argmax(dim=-1   )
         a__		,     a__	:						Optional[Any]      		=  accelerator.gather_for_metrics((predictions, batch["labels"])   )
         metric.add_batch(
             predictions=__UpperCamelCase							, references=__UpperCamelCase							, )

      a__	:						Tuple      		=  metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:"""							, __UpperCamelCase   )






def       _A  (      ):
   a__	:						Optional[int]      		=  argparse.ArgumentParser(description="Simple example of training script."   )
   parser.add_argument(
       "--mixed_precision"							, type=__UpperCamelCase							, default=__UpperCamelCase							, 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."							, )
   # New Code #
   parser.add_argument(
       "--gradient_accumulation_steps"							, type=__UpperCamelCase							, default=1							, help="The number of minibatches to be ran before gradients are accumulated."							, )
   parser.add_argument(
       "--local_sgd_steps"							, type=__UpperCamelCase							, default=8							, help="Number of local SGD steps or None to disable local SGD"   )
   parser.add_argument("--cpu"							, action="store_true"							, help="If passed, will train on the CPU."   )
   a__	:						List[Any]      		=  parser.parse_args()
   a__	:						Dict      		=  {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(__UpperCamelCase							, __UpperCamelCase   )


if __name__ == "__main__":
  main()








from __future__ import annotations

from random import random







class   __lowerCAmelCase			:





   def __init__( self					,			snake_case = None      )				->  Any:
      """simple docstring"""





      a__	:						Optional[int]      		=  value
      a__	:						Tuple      		=  random()
      a__	:						Node | None      		=  None
      a__	:						Node | None      		=  None





   def __repr__( self      )				->  str:
      """simple docstring"""





      from pprint import pformat

      if self.left is None and self.right is None:
         return F"""'{self.value}: {self.prior:.5}'"""
      else:
         return pformat(
             {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}					,			indent=1      )





   def __str__( self      )				->  str:
      """simple docstring"""





      a__	:						List[Any]      		=  str(self.value      ) + " "
      a__	:						List[str]      		=  str(self.left or ""      )
      a__	:						Tuple      		=  str(self.right or ""      )
      return value + left + right







def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if root is None:  # None tree is split into 2 Nones
      return None, None
   elif root.value is None:
      return None, None
   else:
      if value < root.value:
         a__		,     a__	:						Dict      		=  split(root.left							, lowerCamelCase   )
         return left, root
      else:
         a__		,     a__	:						int      		=  split(root.right							, lowerCamelCase   )
         return root, right






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if (not left) or (not right):  # If one node is None, return the other
      return left or right
   elif left.prior < right.prior:
      a__	:						List[Any]      		=  merge(left.right							, lowerCamelCase   )
      return left
   else:
      a__	:						int      		=  merge(lowerCamelCase							, right.left   )
      return right






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Any      		=  Node(lowerCamelCase   )
   a__		,     a__	:						List[str]      		=  split(lowerCamelCase							, lowerCamelCase   )
   return merge(merge(lowerCamelCase							, lowerCamelCase   )							, lowerCamelCase   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__		,     a__	:						Optional[int]      		=  split(lowerCamelCase							, value - 1   )
   a__		,     a__	:						Tuple      		=  split(lowerCamelCase							, lowerCamelCase   )
   return merge(lowerCamelCase							, lowerCamelCase   )






def       _A  (      lowerCamelCase   ):
   if not root:  # None
      return
   else:
      inorder(root.left   )
      print(root.value							, end=","   )
      inorder(root.right   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   for arg in args.split():
      if arg[0] == "+":
         a__	:						int      		=  insert(lowerCamelCase							, int(arg[1:]   )   )

      elif arg[0] == "-":
         a__	:						Union[str, Any]      		=  erase(lowerCamelCase							, int(arg[1:]   )   )

      else:
         print("Unknown command"   )

   return root






def       _A  (      ):
   a__	:						List[str]      		=  None
   print(
       "enter numbers to create a tree, + value to add value into treap, "
       "- value to erase all nodes with value. 'q' to quit. "   )

   a__	:						int      		=  input()
   while args != "q":
      a__	:						Union[str, Any]      		=  interact_treap(lowerCamelCase							, lowerCamelCase   )
      print(lowerCamelCase   )
      a__	:						Optional[Any]      		=  input()

   print("good by!"   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

