





import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict






@pytest.mark.parametrize(
    '''split_dict'''       ,      [
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''       ,      num_bytes=1337       ,      num_examples=42       ,      dataset_name='''my_dataset'''       )}       ),
        SplitDict({'''train''': SplitInfo(name='''train'''       ,      num_bytes=1337       ,      num_examples=42       )}       ),
        SplitDict({'''train''': SplitInfo()}       ),
    ]       ,      )
def lowerCamelCase__    (							snake_case_	:  SplitDict       )      ->				Optional[Any]:
   __snake_case							       =   split_dict._to_yaml_list()
   assert len(snake_case_       ) == len(snake_case_       )
   __snake_case							       =   SplitDict._from_yaml_list(snake_case_       )
   for split_name, split_info in split_dict.items():
      # dataset_name field is deprecated, and is therefore not part of the YAML dump
      __snake_case							       =   None
      # the split name of split_dict takes over the name of the split info object
      __snake_case							       =   split_name
   assert split_dict == reloaded







@pytest.mark.parametrize(
    '''split_info'''       ,      [SplitInfo(), SplitInfo(dataset_name=snake_case_       ), SplitInfo(dataset_name='''my_dataset'''       )]       )
def lowerCamelCase__    (							snake_case_	:  int       )      ->				str:
   # For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
   # field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
   __snake_case							       =   asdict(SplitDict({'''train''': split_info}       )       )
   assert "dataset_name" in split_dict_asdict["train"]
   assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name







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
# and perform 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)
#
# 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
#
########################################################################


__UpperCamelCase					:       Any    					=      16
__UpperCamelCase					:       Union[str, Any]    					=      32





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase = 16    )      ->       List[str]:
	a										=							AutoTokenizer.from_pretrained("""bert-base-cased"""    )
	a										=							load_dataset("""glue"""							,			"""mrpc"""    )

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

	def collate_fn(__lowerCamelCase    ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		a										=							128 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			a										=							16
		elif accelerator.mixed_precision != "no":
			a										=							8
		else:
			a										=							None

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

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

		__UpperCamelCase					:       Any    					=      mocked_dataloaders  # noqa: F811





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       List[str]:
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,			__lowerCamelCase    ) == "1":
		a										=							2
	# New Code #
	a										=							int(args.gradient_accumulation_steps    )
	# Initialize accelerator
	a										=							Accelerator(
	    cpu=args.cpu							,			mixed_precision=args.mixed_precision							,			gradient_accumulation_steps=__lowerCamelCase    )
	if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
		raise NotImplementedError(
		    """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""    )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	a										=							config["""lr"""]
	a										=							int(config["""num_epochs"""]    )
	a										=							int(config["""seed"""]    )
	a										=							int(config["""batch_size"""]    )

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

	set_seed(__lowerCamelCase    )
	a				, a										=							get_dataloaders(__lowerCamelCase							,			__lowerCamelCase    )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	a										=							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).
	a										=							model.to(accelerator.device    )

	# Instantiate optimizer
	a										=							AdamW(params=model.parameters()							,			lr=__lowerCamelCase    )

	# Instantiate scheduler
	a										=							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.
	a				, a				, a				, a				, a										=							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    )
			# New code #
			# We use the new `accumulate` context manager to perform gradient accumulation
			# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
			with accelerator.accumulate(__lowerCamelCase    ):
				a										=							model(**__lowerCamelCase    )
				a										=							output.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():
				a										=							model(**__lowerCamelCase    )
			a										=							outputs.logits.argmax(dim=-1    )
			a				, a										=							accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
			metric.add_batch(
			    predictions=__lowerCamelCase							,			references=__lowerCamelCase							,			)

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





def 		__A		(	)      ->       int:
	a										=							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."""							,			)
	# New Code #
	parser.add_argument(
	    """--gradient_accumulation_steps"""							,			type=__lowerCamelCase							,			default=1							,			help="""The number of minibatches to be ran before gradients are accumulated."""							,			)
	parser.add_argument("""--cpu"""							,			action="""store_true"""							,			help="""If passed, will train on the CPU."""    )
	a										=							parser.parse_args()
	a										=							{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(__lowerCamelCase							,			__lowerCamelCase    )


if __name__ == "__main__":
		main()

