





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


SCREAMING_SNAKE_CASE__					=   16
SCREAMING_SNAKE_CASE__					=   32


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Accelerator							,						SCREAMING_SNAKE_CASE     :						int = 16			):







      '''simple docstring'''




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

      def tokenize_function(SCREAMING_SNAKE_CASE     :						Any			):
            # max_length=None => use the model max length (it's actually the default)
            lowerCAmelCase  						=       tokenizer(examples["""sentence1"""]							,						examples["""sentence2"""]							,						truncation=SCREAMING_SNAKE_CASE							,						max_length=SCREAMING_SNAKE_CASE			)
            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  						=       datasets.map(
                SCREAMING_SNAKE_CASE							,						batched=SCREAMING_SNAKE_CASE							,						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  						=       tokenized_datasets.rename_column("""label"""							,						"""labels"""			)

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

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

      # Instantiate dataloaders.
      lowerCAmelCase  						=       DataLoader(
          tokenized_datasets["""train"""]							,						shuffle=SCREAMING_SNAKE_CASE							,						collate_fn=SCREAMING_SNAKE_CASE							,						batch_size=SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       DataLoader(
          tokenized_datasets["""validation"""]							,						shuffle=SCREAMING_SNAKE_CASE							,						collate_fn=SCREAMING_SNAKE_CASE							,						batch_size=SCREAMING_SNAKE_CASE			)

      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__					=   mocked_dataloaders  # noqa: F811


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Optional[int]							,						SCREAMING_SNAKE_CASE     :						Dict			):







      '''simple docstring'''




      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,						SCREAMING_SNAKE_CASE			) == "1":
            lowerCAmelCase  						=       2
      # Initialize accelerator
      lowerCAmelCase  						=       Accelerator(cpu=args.cpu							,						mixed_precision=args.mixed_precision			)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowerCAmelCase  						=       config["""lr"""]
      lowerCAmelCase  						=       int(config["""num_epochs"""]			)
      lowerCAmelCase  						=       int(config["""seed"""]			)
      lowerCAmelCase  						=       int(config["""batch_size"""]			)

      lowerCAmelCase  						=       evaluate.load("""glue"""							,						"""mrpc"""			)

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

      set_seed(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase						,		lowerCAmelCase  						=       get_dataloaders(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowerCAmelCase  						=       AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""							,						return_dict=SCREAMING_SNAKE_CASE			)

      # 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  						=       model.to(accelerator.device			)

      # Instantiate optimizer
      lowerCAmelCase  						=       AdamW(params=model.parameters()							,						lr=SCREAMING_SNAKE_CASE			)

      # Instantiate scheduler
      lowerCAmelCase  						=       get_linear_schedule_with_warmup(
          optimizer=SCREAMING_SNAKE_CASE							,						num_warmup_steps=1_00							,						num_training_steps=(len(SCREAMING_SNAKE_CASE			) * 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  						=       accelerator.prepare(
          SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)

      # Now we train the model
      for epoch in range(SCREAMING_SNAKE_CASE			):
            model.train()
            for step, batch in enumerate(SCREAMING_SNAKE_CASE			):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device			)
                  lowerCAmelCase  						=       model(**SCREAMING_SNAKE_CASE			)
                  lowerCAmelCase  						=       outputs.loss
                  lowerCAmelCase  						=       loss / gradient_accumulation_steps
                  accelerator.backward(SCREAMING_SNAKE_CASE			)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            lowerCAmelCase  						=       0
            for step, batch in enumerate(SCREAMING_SNAKE_CASE			):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device			)
                  with torch.no_grad():
                        lowerCAmelCase  						=       model(**SCREAMING_SNAKE_CASE			)
                  lowerCAmelCase  						=       outputs.logits.argmax(dim=-1			)
                  lowerCAmelCase						,		lowerCAmelCase  						=       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(SCREAMING_SNAKE_CASE			) - 1:
                              # Last batch needs to be truncated on distributed systems as it contains additional samples
                              lowerCAmelCase  						=       predictions[: len(eval_dataloader.dataset			) - samples_seen]
                              lowerCAmelCase  						=       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=SCREAMING_SNAKE_CASE							,						references=SCREAMING_SNAKE_CASE							,						)

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


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       argparse.ArgumentParser(description="""Simple example of training script."""			)
      parser.add_argument(
          """--mixed_precision"""							,						type=SCREAMING_SNAKE_CASE							,						default=SCREAMING_SNAKE_CASE							,						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."""			)
      lowerCAmelCase  						=       parser.parse_args()
      lowerCAmelCase  						=       {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
       main()













"""simple docstring"""





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__					=   {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__					=   [
           "FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FocalNetForImageClassification",
           "FocalNetForMaskedImageModeling",
           "FocalNetBackbone",
           "FocalNetModel",
           "FocalNetPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_focalnet import (
                  FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FocalNetBackbone,
                  FocalNetForImageClassification,
                  FocalNetForMaskedImageModeling,
                  FocalNetModel,
                  FocalNetPreTrainedModel,
              )

else:
       import sys

       SCREAMING_SNAKE_CASE__					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






