


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

SCREAMING_SNAKE_CASE__       			=				3_2




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Optional[Any]  ,			__lowerCamelCase:		str = 16  ):







	'''simple docstring'''



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

	def tokenize_function(__lowerCamelCase:		List[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:		str  ):
		# 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

							SCREAMING_SNAKE_CASE__       			=				mocked_dataloaders  # noqa: F811




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Dict  ,			__lowerCamelCase:		Dict  ):







	'''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:		Optional[int]  ):
		# 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 SCREAMING_SNAKE_CASE_  (				):







	'''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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__  :	Optional[Any]            =      {
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	List[Any]            =      ['MarianTokenizer']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	List[str]            =      [
          'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MarianForCausalLM',
          'MarianModel',
          'MarianMTModel',
          'MarianPreTrainedModel',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	Optional[Any]            =      ['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	Any            =      ['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
      from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_marian import MarianTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_marian import (
                MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
                MarianForCausalLM,
                MarianModel,
                MarianMTModel,
                MarianPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
      import sys

      lowercase__  :	Any            =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




