'''simple docstring'''

import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# 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[int]    	=   32


def __a			(						__lowerCamelCase      :    Accelerator			,		__lowerCamelCase      :    int = 16					)						->				Union[str, Any]:
      '''simple docstring'''


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

      def tokenize_function(__lowerCamelCase      :    List[str]					):
            # 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					):
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  lowercase_     			=   16
            elif accelerator.mixed_precision != "no":
                  lowercase_     			=   8
            else:
                  lowercase_     			=   None

            return tokenizer.pad(
                __lowerCamelCase			,		padding="longest"			,		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 __a			(						__lowerCamelCase      :    Optional[Any]			,		__lowerCamelCase      :    int					)						->				List[str]:
      '''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"					)

      # New Code #
      # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
      # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
      # half each time. From this, we can calculate the number of gradient accumulation steps needed
      # and modify the Accelerator object as a result
      @find_executable_batch_size(starting_batch_size=int(__lowerCamelCase					)					)
      def inner_training_loop(__lowerCamelCase      :    str					):
            # Since we need to modify the outside accelerator object, we need to bring it
            # to the local scope
            nonlocal accelerator

            # We can calculate the number of gradient accumulation steps based on the current
            # batch size vs the starting batch size
            lowercase_     			=   observed_batch_size // batch_size

            # And then set it in the Accelerator directly:
            lowercase_     			=   num_gradient_accumulation_steps

            # Next we need to free all of the stored model references in the Accelerator each time
            accelerator.free_memory()

            # And set the seed so our results are reproducable each reset
            set_seed(__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					)
            lowercase_			,				lowercase_     			=   get_dataloaders(__lowerCamelCase			,		__lowerCamelCase					)

            # Instantiate scheduler
            lowercase_     			=   get_linear_schedule_with_warmup(
                optimizer=__lowerCamelCase			,		num_warmup_steps=100			,		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.
            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					):
                        # And perform gradient accumulation
                        with accelerator.accumulate(__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
                              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():
                              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 #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
      inner_training_loop()


def __a			(						)						->				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."					)
      lowercase_     			=   parser.parse_args()
      # New Code #
      # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
      lowercase_     			=   {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
      training_function(__lowerCamelCase			,		__lowerCamelCase					)


if __name__ == "__main__":
       main()







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase_			:			Tuple    	=   {"configuration_vilt": ["VILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViltConfig"]}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_			:			Optional[int]    	=   ["ViltFeatureExtractor"]
       lowerCAmelCase_			:			str    	=   ["ViltImageProcessor"]
       lowerCAmelCase_			:			Any    	=   ["ViltProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_			:			Any    	=   [
           "VILT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ViltForImageAndTextRetrieval",
           "ViltForImagesAndTextClassification",
           "ViltForTokenClassification",
           "ViltForMaskedLM",
           "ViltForQuestionAnswering",
           "ViltLayer",
           "ViltModel",
           "ViltPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_vilt import ViltFeatureExtractor
              from .image_processing_vilt import ViltImageProcessor
              from .processing_vilt import ViltProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vilt import (
                  VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViltForImageAndTextRetrieval,
                  ViltForImagesAndTextClassification,
                  ViltForMaskedLM,
                  ViltForQuestionAnswering,
                  ViltForTokenClassification,
                  ViltLayer,
                  ViltModel,
                  ViltPreTrainedModel,
              )


else:
       import sys

       lowerCAmelCase_			:			str    	=   _LazyModule(__name__, globals()["__file__"], _import_structure)






