

"""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
# and perform 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
#
########################################################################


_A							=  16
_A							=  32




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

      def tokenize_function(lowerCAmelCase    ):
            # max_length=None => use the model max length (it's actually the default)
            UpperCAmelCase__			:							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():
            UpperCAmelCase__			:							str							=    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
      UpperCAmelCase__			:							Optional[Any]							=    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.
            UpperCAmelCase__			:							int							=    1_28 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  UpperCAmelCase__			:							List[Any]							=    16
            elif accelerator.mixed_precision != "no":
                  UpperCAmelCase__			:							List[Any]							=    8
            else:
                  UpperCAmelCase__			:							List[str]							=    None

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

      # Instantiate dataloaders.
      UpperCAmelCase__			:							Tuple							=    DataLoader(
          tokenized_datasets["""train"""]			,	shuffle=lowerCAmelCase			,	collate_fn=lowerCAmelCase			,	batch_size=lowerCAmelCase    )
      UpperCAmelCase__			:							Dict							=    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							=  mocked_dataloaders  # noqa: F811




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> Any:
      # For testing only
      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""			,	lowerCAmelCase    ) == "1":
            UpperCAmelCase__			:							Optional[Any]							=    2
      # New Code #
      UpperCAmelCase__			:							int							=    int(args.gradient_accumulation_steps    )
      # Initialize accelerator
      UpperCAmelCase__			:							List[str]							=    Accelerator(
          cpu=args.cpu			,	mixed_precision=args.mixed_precision			,	gradient_accumulation_steps=lowerCAmelCase    )
      if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
            raise NotImplementedError(
                """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""    )
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCAmelCase__			:							Optional[int]							=    config["""lr"""]
      UpperCAmelCase__			:							Optional[int]							=    int(config["""num_epochs"""]    )
      UpperCAmelCase__			:							Any							=    int(config["""seed"""]    )
      UpperCAmelCase__			:							str							=    int(config["""batch_size"""]    )

      UpperCAmelCase__			:							Dict							=    evaluate.load("""glue"""			,	"""mrpc"""    )

      set_seed(lowerCAmelCase    )
      UpperCAmelCase__					,		UpperCAmelCase__			:							Optional[int]							=    get_dataloaders(lowerCAmelCase			,	lowerCAmelCase    )
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      UpperCAmelCase__			:							Tuple							=    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).
      UpperCAmelCase__			:							Any							=    model.to(accelerator.device    )

      # Instantiate optimizer
      UpperCAmelCase__			:							Union[str, Any]							=    AdamW(params=model.parameters()			,	lr=lowerCAmelCase    )

      # Instantiate scheduler
      UpperCAmelCase__			:							Union[str, Any]							=    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.
      UpperCAmelCase__					,		UpperCAmelCase__					,		UpperCAmelCase__					,		UpperCAmelCase__					,		UpperCAmelCase__			:							Tuple							=    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    ):
                  # 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(lowerCAmelCase    ):
                        UpperCAmelCase__			:							Tuple							=    model(**lowerCAmelCase    )
                        UpperCAmelCase__			:							Union[str, Any]							=    output.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():
                        UpperCAmelCase__			:							Optional[int]							=    model(**lowerCAmelCase    )
                  UpperCAmelCase__			:							List[str]							=    outputs.logits.argmax(dim=-1    )
                  UpperCAmelCase__					,		UpperCAmelCase__			:							List[Any]							=    accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
                  metric.add_batch(
                      predictions=lowerCAmelCase			,	references=lowerCAmelCase			,	)

            UpperCAmelCase__			:							Dict							=    metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F"""epoch {epoch}:"""			,	lowerCAmelCase    )




def 					a__						(						)	-> Dict:
      UpperCAmelCase__			:							Union[str, 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."""			,	)
      # New Code #
      parser.add_argument(
          """--gradient_accumulation_steps"""			,	type=lowerCAmelCase			,	default=1			,	help="""The number of minibatches to be ran before gradients are accumulated."""			,	)
      parser.add_argument("""--cpu"""			,	action="""store_true"""			,	help="""If passed, will train on the CPU."""    )
      UpperCAmelCase__			:							Dict							=    parser.parse_args()
      UpperCAmelCase__			:							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

import math

class       lowerCamelCase	:


  '''simple docstring'''







  def __init__(self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    size
        # approximate the overall size of segment tree with given value
        UpperCAmelCase__			:							Tuple							=    [0 for i in range(0							,       4 * size	)]
        # create array to store lazy update
        UpperCAmelCase__			:							int							=    [0 for i in range(0							,       4 * size	)]
        UpperCAmelCase__			:							Tuple							=    [0 for i in range(0							,       4 * size	)]  # flag for lazy update



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        return idx * 2



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        return idx * 2 + 1



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if left_element == right_element:
              UpperCAmelCase__			:							Union[str, Any]							=    a[left_element - 1]
        else:
              UpperCAmelCase__			:							List[str]							=    (left_element + right_element) // 2
              self.build(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
              self.build(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase	)
              UpperCAmelCase__			:							int							=    max(
                  self.segment_tree[self.left(_lowerCamelCase	)]							,       self.segment_tree[self.right(_lowerCamelCase	)]	)



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if self.flag[idx] is True:
              UpperCAmelCase__			:							Union[str, Any]							=    self.lazy[idx]
              UpperCAmelCase__			:							Dict							=    False
              if left_element != right_element:
                    UpperCAmelCase__			:							Optional[Any]							=    self.lazy[idx]
                    UpperCAmelCase__			:							Optional[int]							=    self.lazy[idx]
                    UpperCAmelCase__			:							Tuple							=    True
                    UpperCAmelCase__			:							str							=    True

        if right_element < a or left_element > b:
              return True
        if left_element >= a and right_element <= b:
              UpperCAmelCase__			:							Union[str, Any]							=    val
              if left_element != right_element:
                    UpperCAmelCase__			:							Union[str, Any]							=    val
                    UpperCAmelCase__			:							List[Any]							=    val
                    UpperCAmelCase__			:							Union[str, Any]							=    True
                    UpperCAmelCase__			:							List[Any]							=    True
              return True
        UpperCAmelCase__			:							Tuple							=    (left_element + right_element) // 2
        self.update(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        self.update(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        UpperCAmelCase__			:							Dict							=    max(
            self.segment_tree[self.left(_lowerCamelCase	)]							,       self.segment_tree[self.right(_lowerCamelCase	)]	)
        return True



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if self.flag[idx] is True:
              UpperCAmelCase__			:							List[Any]							=    self.lazy[idx]
              UpperCAmelCase__			:							Optional[Any]							=    False
              if left_element != right_element:
                    UpperCAmelCase__			:							Tuple							=    self.lazy[idx]
                    UpperCAmelCase__			:							Union[str, Any]							=    self.lazy[idx]
                    UpperCAmelCase__			:							List[str]							=    True
                    UpperCAmelCase__			:							str							=    True
        if right_element < a or left_element > b:
              return -math.inf
        if left_element >= a and right_element <= b:
              return self.segment_tree[idx]
        UpperCAmelCase__			:							Union[str, Any]							=    (left_element + right_element) // 2
        UpperCAmelCase__			:							Tuple							=    self.query(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        UpperCAmelCase__			:							List[Any]							=    self.query(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        return max(_lowerCamelCase							,       _lowerCamelCase	)




  def __str__(self	):


        """simple docstring"""

        return str([self.query(1							,       1							,       self.size							,       _lowerCamelCase							,       _lowerCamelCase	) for i in range(1							,       self.size + 1	)]	)


if __name__ == "__main__":
    _A							=  [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
    _A							=  15
    _A							=  SegmentTree(size)
    segt.build(1, 1, size, A)
    print(segt.query(1, 1, size, 4, 6))
    print(segt.query(1, 1, size, 7, 11))
    print(segt.query(1, 1, size, 7, 12))
    segt.update(1, 1, size, 1, 3, 1_11)
    print(segt.query(1, 1, size, 1, 15))
    segt.update(1, 1, size, 7, 8, 2_35)
    print(segt)

