
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
#
########################################################################

__magic_name__           =					32





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase = 16							):
    snake_case__  =			AutoTokenizer.from_pretrained("bert-base-cased"							)
    snake_case__  =			load_dataset("glue"      ,						"mrpc"							)

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

    def collate_fn(__lowerCAmelCase							):
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            snake_case__  =			16
        elif accelerator.mixed_precision != "no":
            snake_case__  =			8
        else:
            snake_case__  =			None

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

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

     __magic_name__           =					mocked_dataloaders  # noqa: F811





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

    snake_case__  =			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							):
        # 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
        snake_case__  =			observed_batch_size // batch_size

        # And then set it in the Accelerator directly:
        snake_case__  =			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)
        snake_case__  =			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).
        snake_case__  =			model.to(accelerator.device							)

        # Instantiate optimizer
        snake_case__  =			AdamW(params=model.parameters()      ,						lr=__lowerCAmelCase							)
        snake_case__  ,   snake_case__  =			get_dataloaders(__lowerCAmelCase      ,						__lowerCAmelCase							)

        # Instantiate scheduler
        snake_case__  =			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.
        snake_case__  ,   snake_case__  ,   snake_case__  ,   snake_case__  ,   snake_case__  =			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							)
                    snake_case__  =			model(**__lowerCAmelCase							)
                    snake_case__  =			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():
                    snake_case__  =			model(**__lowerCAmelCase							)
                snake_case__  =			outputs.logits.argmax(dim=-1							)
                snake_case__  ,   snake_case__  =			accelerator.gather_for_metrics((predictions, batch["labels"])							)
                metric.add_batch(
                    predictions=__lowerCAmelCase      ,						references=__lowerCAmelCase      ,						)

            snake_case__  =			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 		SCREAMING_SNAKE_CASE__      ( ):
    snake_case__  =			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."							)
    snake_case__  =			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
    snake_case__  =			{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
    training_function(__lowerCAmelCase      ,						__lowerCAmelCase							)


if __name__ == "__main__":
     main()





import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image




class    _SCREAMING_SNAKE_CASE					( unittest.TestCase    ,   __UpperCamelCase	):
      def A_     (     self	):
          snake_case__  =			load_tool("image-captioning"	)
          self.tool.setup()
          snake_case__  =			load_tool("image-captioning"						,		remote=lowerCamelCase	)
      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.tool(lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)
      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.remote_tool(lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)
      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.tool(image=lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)



      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.remote_tool(image=lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)



