




'''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
#
########################################################################


_snake_case   :   List[str]     							=	16
_snake_case   :   Optional[Any]     							=	32
def 	snake_case_					(UpperCamelCase :						Accelerator				,		UpperCamelCase :						int = 16   ):





							'''simple docstring'''


							_a			  =       AutoTokenizer.from_pretrained('''bert-base-cased'''   )
							_a			  =       load_dataset('''glue'''				,		'''mrpc'''   )

							def tokenize_function(UpperCamelCase :						List[Any]   ):
														# 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(UpperCamelCase :						List[str]   ):
														# 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

				_snake_case   :   Tuple     							=	mocked_dataloaders  # noqa: F811
def 	snake_case_					(UpperCamelCase :						Tuple				,		UpperCamelCase :						Dict   ):





							'''simple docstring'''


							if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,		_lowerCamelCase   ) == "1":
														_a			  =       2
							# Initialize accelerator
							_a			  =       Accelerator(cpu=args.cpu				,		mixed_precision=args.mixed_precision   )
							# 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'''   )

							# 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=_lowerCamelCase   )
							def inner_training_loop(UpperCamelCase :						Optional[Any]   ):
														# 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(_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   )
														_a			  =       get_dataloaders(_lowerCamelCase				,		_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			  =       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   )
																												_a			  =       model(**_lowerCamelCase   )
																												_a			  =       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():
																																			_a			  =       model(**_lowerCamelCase   )
																												_a			  =       outputs.logits.argmax(dim=-1   )
																												_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   )

    # 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 	snake_case_					():





							'''simple docstring'''


							_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.'''				,		)
							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()












'''simple docstring'''


import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
				import ctypes
				import msvcrt  # noqa

				class     A					(	ctypes.Structure					):
											# _fields is a specific attr expected by ctypes
											lowercase_           =       [('size', ctypes.c_int), ('visible', ctypes.c_byte)]
def 	snake_case_					():





							'''simple docstring'''


							if os.name == "nt":
														_a			  =       CursorInfo()
														_a			  =       ctypes.windll.kernelaa.GetStdHandle(-11   )
														ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCamelCase				,		ctypes.byref(UpperCamelCase   )   )
														_a			  =       False
														ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCamelCase				,		ctypes.byref(UpperCamelCase   )   )
							elif os.name == "posix":
														sys.stdout.write('''\033[?25l'''   )
														sys.stdout.flush()
def 	snake_case_					():





							'''simple docstring'''


							if os.name == "nt":
														_a			  =       CursorInfo()
														_a			  =       ctypes.windll.kernelaa.GetStdHandle(-11   )
														ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCamelCase				,		ctypes.byref(UpperCamelCase   )   )
														_a			  =       True
														ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCamelCase				,		ctypes.byref(UpperCamelCase   )   )
							elif os.name == "posix":
														sys.stdout.write('''\033[?25h'''   )
														sys.stdout.flush()

@contextmanager
def 	snake_case_					():





							'''simple docstring'''


							try:
														hide_cursor()
														yield
							finally:
														show_cursor()

