




"""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	:      List[Any]   =							32

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase = 16						):




	"""simple docstring"""






	A_	:					int						 =		AutoTokenizer.from_pretrained('''bert-base-cased'''						)
	A_	:					Union[str, Any]						 =		load_dataset('''glue'''	,   '''mrpc'''						)

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

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

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

	# Instantiate dataloaders.
	A_	:					int						 =		DataLoader(
	    tokenized_datasets['''train''']	,   shuffle=_UpperCAmelCase	,   collate_fn=_UpperCAmelCase	,   batch_size=_UpperCAmelCase						)
	A_	:					int						 =		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

		_lowerCamelCase	:      str   =							mocked_dataloaders  # noqa: F811

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	,   _UpperCAmelCase						) == "1":
		A_	:					Union[str, Any]						 =		2
	# Initialize accelerator
	A_	:					int						 =		Accelerator(cpu=args.cpu	,   mixed_precision=args.mixed_precision						)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	A_	:					int						 =		config['''lr''']
	A_	:					Tuple						 =		int(config['''num_epochs''']						)
	A_	:					int						 =		int(config['''seed''']						)
	A_	:					int						 =		int(config['''batch_size''']						)

	A_	:					Any						 =		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(_UpperCAmelCase						)						)
	def inner_training_loop(_UpperCAmelCase						):
		# 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
		A_	:					Optional[Any]						 =		observed_batch_size // batch_size

		# And then set it in the Accelerator directly:
		A_	:					int						 =		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(_UpperCAmelCase						)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		A_	:					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).
		A_	:					int						 =		model.to(accelerator.device						)

		# Instantiate optimizer
		A_	:					int						 =		AdamW(params=model.parameters()	,   lr=_UpperCAmelCase						)
		A_ ,			A_	:					List[str]						 =		get_dataloaders(_UpperCAmelCase	,   _UpperCAmelCase						)

		# Instantiate scheduler
		A_	:					List[str]						 =		get_linear_schedule_with_warmup(
		    optimizer=_UpperCAmelCase	,   num_warmup_steps=100	,   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.
		A_ ,			A_ ,			A_ ,			A_ ,			A_	:					List[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						):
				# And perform gradient accumulation
				with accelerator.accumulate(_UpperCAmelCase						):
					# We could avoid this line since we set the accelerator with `device_placement=True`.
					batch.to(accelerator.device						)
					A_	:					int						 =		model(**_UpperCAmelCase						)
					A_	:					Any						 =		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():
					A_	:					List[str]						 =		model(**_UpperCAmelCase						)
				A_	:					int						 =		outputs.logits.argmax(dim=-1						)
				A_ ,			A_	:					Tuple						 =		accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
				metric.add_batch(
				    predictions=_UpperCAmelCase	,   references=_UpperCAmelCase	,   )

			A_	:					Union[str, Any]						 =		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 		lowercase_   (		):




	"""simple docstring"""






	A_	:					Dict						 =		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.'''						)
	A_	:					int						 =		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
	A_	:					List[str]						 =		{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
	training_function(_UpperCAmelCase	,   _UpperCAmelCase						)


if __name__ == "__main__":
		main()









"""simple docstring"""







from .stack import Stack
def _A		(		lowercase						):







    """simple docstring"""
    a											=Stack()
    a											={'''(''': ''')''', '''[''': ''']''', '''{''': '''}'''}
    for bracket in parentheses:
        if bracket in bracket_pairs:
            stack.push(lowercase						)
        elif bracket in (")", "]", "}"):
            if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                return False
    return stack.is_empty()


if __name__ == "__main__":
       from doctest import testmod

       testmod()

       lowerCamelCase_  :  List[str]     		=    ["""((()))""", """((())""", """(()))"""]
       print("""Balanced parentheses demonstration:\n""")
       for example in examples:
              lowerCamelCase_  :  str     		=    """""" if balanced_parentheses(example) else """not """
              print(F'{example} is {not_str}balanced')