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


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


def     UpperCAmelCase__ (       lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :List[str] = 1_6	)				->					Tuple:
      '''simple docstring'''


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

      def tokenize_function(lowerCAmelCase__  :Optional[Any]	):
            # 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__  :Any	):
            # 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					: List[str]        =mocked_dataloaders  # noqa: F811


def     UpperCAmelCase__ (       lowerCAmelCase__  :Optional[Any]      ,	lowerCAmelCase__  :str	)				->					Tuple:
      '''simple docstring'''


      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,	lowerCAmelCase__	) == "1":
            lowercase														=			2
      # Initialize accelerator
      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"""]	)

      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

      set_seed(lowerCAmelCase__	)
      lowercase							,							lowercase														=			get_dataloaders(lowerCAmelCase__      ,	lowerCAmelCase__	)
      # 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__	)

      # 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	)
                  lowercase														=			model(**lowerCAmelCase__	)
                  lowercase														=			outputs.loss
                  lowercase														=			loss / gradient_accumulation_steps
                  accelerator.backward(lowerCAmelCase__	)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            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	)
                  with torch.no_grad():
                        lowercase														=			model(**lowerCAmelCase__	)
                  lowercase														=			outputs.logits.argmax(dim=-1	)
                  lowercase							,							lowercase														=			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(lowerCAmelCase__	) - 1:
                              # Last batch needs to be truncated on distributed systems as it contains additional samples
                              lowercase														=			predictions[: len(eval_dataloader.dataset	) - samples_seen]
                              lowercase														=			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=lowerCAmelCase__      ,	references=lowerCAmelCase__      ,	)

            lowercase														=			metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f'epoch {epoch}:'      ,	lowerCAmelCase__	)


def     UpperCAmelCase__ (       )				->					Tuple:
      '''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."""	)
      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"""






import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


__lowerCAmelCase					: List[str]        =logging.get_logger(__name__)





class 				_A		( lowerCAmelCase			):

      def __init__(		self	, *__lowerCAmelCase	, **__lowerCAmelCase		):







            """simple docstring"""



            warnings.warn(
                """The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
                """ use YolosImageProcessor instead."""	, __lowerCAmelCase	, )
            super().__init__(*__lowerCAmelCase	, **__lowerCAmelCase		)



