"""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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


_a  :  int=				16
_a  :  Tuple=				32
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Accelerator    ,							UpperCAmelCase_					:       int = 16						)							->    List[Any]:

	'''simple docstring'''





	__snake_case    :							List[Any]   	=				AutoTokenizer.from_pretrained('bert-base-cased'						)
	__snake_case    :							Optional[Any]   	=				load_dataset('glue'    ,							'mrpc'						)

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

	def collate_fn(UpperCAmelCase_					:       List[Any]						):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		__snake_case    :							List[str]   	=				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":
			__snake_case    :							Tuple   	=				16
		elif accelerator.mixed_precision != "no":
			__snake_case    :							Dict   	=				8
		else:
			__snake_case    :							str   	=				None

		return tokenizer.pad(
		    UpperCAmelCase_    ,							padding='longest'    ,							max_length=UpperCAmelCase_    ,							pad_to_multiple_of=UpperCAmelCase_    ,							return_tensors='pt'    ,							)

	# Instantiate dataloaders.
	__snake_case    :							int   	=				DataLoader(
	    tokenized_datasets['train']    ,							shuffle=UpperCAmelCase_    ,							collate_fn=UpperCAmelCase_    ,							batch_size=UpperCAmelCase_						)
	__snake_case    :							Tuple   	=				DataLoader(
	    tokenized_datasets['validation']    ,							shuffle=UpperCAmelCase_    ,							collate_fn=UpperCAmelCase_    ,							batch_size=UpperCAmelCase_						)

	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

	_a  :  int=				mocked_dataloaders  # noqa: F811
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[int]    ,							UpperCAmelCase_					:       Any						)							->    Union[str, Any]:

	'''simple docstring'''





	if os.environ.get('TESTING_MOCKED_DATALOADERS'    ,							UpperCAmelCase_						) == "1":
		__snake_case    :							Any   	=				2
	# Initialize accelerator
	__snake_case    :							Dict   	=				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    :							str   	=				config['lr']
	__snake_case    :							int   	=				int(config['num_epochs']						)
	__snake_case    :							Any   	=				int(config['seed']						)
	__snake_case    :							List[str]   	=				int(config['batch_size']						)

	__snake_case    :							Optional[Any]   	=				evaluate.load('glue'    ,							'mrpc'						)

	# New Code #
	# We now can define an inner training loop function. It should take a batch size as the only parameter,
	# and build the dataloaders in there.
	# It also gets our decorator
	@find_executable_batch_size(starting_batch_size=UpperCAmelCase_						)
	def inner_training_loop(UpperCAmelCase_					:       Dict						):
		# And now just move everything below under this function
		# We need to bring in the Accelerator object from earlier
		nonlocal accelerator
		# And reset all of its attributes that could hold onto any memory:
		accelerator.free_memory()

		# Then we can declare the model, optimizer, and everything else:
		set_seed(UpperCAmelCase_						)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__snake_case    :							Union[str, Any]   	=				AutoModelForSequenceClassification.from_pretrained('bert-base-cased'    ,							return_dict=UpperCAmelCase_						)

		# 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    :							List[str]   	=				model.to(accelerator.device						)

		# Instantiate optimizer
		__snake_case    :							List[Any]   	=				AdamW(params=model.parameters()    ,							lr=UpperCAmelCase_						)
		__snake_case	,       __snake_case    :							Union[str, Any]   	=				get_dataloaders(UpperCAmelCase_    ,							UpperCAmelCase_						)

		# Instantiate scheduler
		__snake_case    :							Any   	=				get_linear_schedule_with_warmup(
		    optimizer=UpperCAmelCase_    ,							num_warmup_steps=1_00    ,							num_training_steps=(len(UpperCAmelCase_						) * 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    :							Any   	=				accelerator.prepare(
		    UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_						)

		# Now we train the model
		for epoch in range(UpperCAmelCase_						):
			model.train()
			for step, batch in enumerate(UpperCAmelCase_						):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device						)
				__snake_case    :							Optional[Any]   	=				model(**UpperCAmelCase_						)
				__snake_case    :							int   	=				outputs.loss
				accelerator.backward(UpperCAmelCase_						)
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			model.eval()
			for step, batch in enumerate(UpperCAmelCase_						):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device						)
				with torch.no_grad():
					__snake_case    :							Optional[Any]   	=				model(**UpperCAmelCase_						)
				__snake_case    :							Optional[int]   	=				outputs.logits.argmax(dim=-1						)
				__snake_case	,       __snake_case    :							List[Any]   	=				accelerator.gather_for_metrics((predictions, batch['labels'])						)
				metric.add_batch(
				    predictions=UpperCAmelCase_    ,							references=UpperCAmelCase_    ,							)

			__snake_case    :							Tuple   	=				metric.compute()
			# Use accelerator.print to print only on the main process.
			accelerator.print(F"epoch {epoch}:"    ,							UpperCAmelCase_						)

    # 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 						__UpperCAmelCase       (					)							->    Optional[int]:

	'''simple docstring'''





	__snake_case    :							List[Any]   	=				argparse.ArgumentParser(description='Simple example of training script.'						)
	parser.add_argument(
	    '--mixed_precision'    ,							type=UpperCAmelCase_    ,							default=UpperCAmelCase_    ,							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    :							Optional[int]   	=				parser.parse_args()
	__snake_case    :							Dict   	=				{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
	training_function(UpperCAmelCase_    ,							UpperCAmelCase_						)


if __name__ == "__main__":
	main()






"""simple docstring"""







from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_a  :  Tuple=				HfArgumentParser(InitializationArguments)
_a  :  str=				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_a  :  int=				AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_a  :  Optional[Any]=				{
    "vocab_size": len(tokenizer),
    "scale_attn_by_inverse_layer_idx": True,
    "reorder_and_upcast_attn": True,
}

# Load model config (GPT-2 large in this case)
_a  :  Optional[int]=				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_a  :  str=				AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)





