"""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
#
########################################################################

__lowerCAmelCase    :       Any					     =1_6
__lowerCAmelCase    :       Optional[Any]					     =3_2


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Accelerator					,		lowerCAmelCase__     :int = 1_6		) ->    Any:
      '''simple docstring'''


      lowercase														=	AutoTokenizer.from_pretrained("""bert-base-cased"""		)
      lowercase														=	load_dataset("""glue"""					,		"""mrpc"""		)

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

      def collate_fn(lowerCAmelCase__     :str		):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            lowercase														=	1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  lowercase														=	1_6
            elif accelerator.mixed_precision != "no":
                  lowercase														=	8
            else:
                  lowercase														=	None

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

      # Instantiate dataloaders.
      lowercase														=	DataLoader(
          tokenized_datasets["""train"""]					,		shuffle=lowerCAmelCase__					,		collate_fn=lowerCAmelCase__					,		batch_size=lowerCAmelCase__		)
      lowercase														=	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

       __lowerCAmelCase    :       Any					     =mocked_dataloaders  # noqa: F811


def 			UpperCAmelCase__       (				lowerCAmelCase__     :List[Any]					,		lowerCAmelCase__     :Dict		) ->    List[Any]:
      '''simple docstring'''


      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""					,		lowerCAmelCase__		) == "1":
            lowercase														=	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:
            lowercase														=	Accelerator(
                cpu=args.cpu					,		mixed_precision=args.mixed_precision					,		log_with="""all"""					,		project_dir=args.project_dir		)
      else:
            lowercase														=	Accelerator(cpu=args.cpu					,		mixed_precision=args.mixed_precision		)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowercase														=	config["""lr"""]
      lowercase														=	int(config["""num_epochs"""]		)
      lowercase														=	int(config["""seed"""]		)
      lowercase														=	int(config["""batch_size"""]		)
      set_seed(lowerCAmelCase__		)

      lowercase							,							lowercase														=	get_dataloaders(lowerCAmelCase__					,		lowerCAmelCase__		)
      lowercase														=	evaluate.load("""glue"""					,		"""mrpc"""		)

      # If the batch size is too big we use gradient accumulation
      lowercase														=	1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            lowercase														=	batch_size // MAX_GPU_BATCH_SIZE
            lowercase														=	MAX_GPU_BATCH_SIZE

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowercase														=	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).
      lowercase														=	model.to(accelerator.device		)

      # Instantiate optimizer
      lowercase														=	AdamW(params=model.parameters()					,		lr=lowerCAmelCase__		)

      # Instantiate scheduler
      lowercase														=	get_linear_schedule_with_warmup(
          optimizer=lowerCAmelCase__					,		num_warmup_steps=1_0_0					,		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.
      lowercase							,							lowercase							,							lowercase							,							lowercase							,							lowercase														=	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:
            lowercase														=	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:
                  lowercase														=	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		)
                  lowercase														=	model(**lowerCAmelCase__		)
                  lowercase														=	outputs.loss
                  # New Code #
                  if args.with_tracking:
                        total_loss += loss.detach().float()
                  lowercase														=	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():
                        lowercase														=	model(**lowerCAmelCase__		)
                  lowercase														=	outputs.logits.argmax(dim=-1		)
                  lowercase							,							lowercase														=	accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
                  metric.add_batch(
                      predictions=lowerCAmelCase__					,		references=lowerCAmelCase__					,		)

            lowercase														=	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 			UpperCAmelCase__       (				) ->    List[Any]:
      '''simple docstring'''


      lowercase														=	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"""					,		)
      lowercase														=	parser.parse_args()
      lowercase														=	{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
      training_function(lowerCAmelCase__					,		lowerCAmelCase__		)


if __name__ == "__main__":
       main()




"""simple docstring"""






from __future__ import annotations


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list[int]		) ->    int:
      '''simple docstring'''


      if not nums:
            return 0
      lowercase														=	nums[0]
      lowercase														=	0
      for num in nums[1:]:
            lowercase							,							lowercase														=	(
                max_excluding + num,
                max(lowerCAmelCase__					,		lowerCAmelCase__		),
            )
      return max(lowerCAmelCase__					,		lowerCAmelCase__		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()



