
import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin



def      __UpperCAmelCase    (  snake_case_							:		Tuple=2				,					snake_case_							:		str=True				,					snake_case_							:		Tuple=False				):



	'''simple docstring'''



	UpperCAmelCase:					str 						=     Accelerator(step_scheduler_with_optimizer=snake_case_				,					split_batches=snake_case_				)
	UpperCAmelCase:					Union[str, Any] 						=     torch.nn.Linear(2				,					4				)
	UpperCAmelCase:					List[Any] 						=     torch.optim.AdamW(model.parameters()				,					lr=1.0				)
	UpperCAmelCase:					Optional[int] 						=     torch.optim.lr_scheduler.OneCycleLR(snake_case_				,					max_lr=0.01				,					steps_per_epoch=2				,					epochs=1				)
	UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					str 						=     accelerator.prepare(snake_case_				,					snake_case_				,					snake_case_				)

	# Optimizer has stepped
	scheduler.step()
	if step_scheduler_with_optimizer or (num_processes == 1):
		assert (
		    scheduler.scheduler.last_epoch == num_processes
		), F'Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'
	else:
		assert (
		    scheduler.scheduler.last_epoch != num_processes
		), F'Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'



def      __UpperCAmelCase    (  snake_case_							:		int=2				,					snake_case_							:		List[str]=True				,					snake_case_							:		List[Any]=False				):



	'''simple docstring'''



	UpperCAmelCase:					Any 						=     Accelerator(step_scheduler_with_optimizer=snake_case_				,					split_batches=snake_case_				)
	UpperCAmelCase:					List[Any] 						=     torch.nn.Linear(2				,					4				)
	UpperCAmelCase:					List[str] 						=     torch.optim.AdamW(model.parameters()				,					lr=1.0				)
	UpperCAmelCase:					Any 						=     torch.optim.lr_scheduler.LambdaLR(snake_case_				,					lr_lambda=lambda snake_case_				: 1 - n / 1_0				)
	UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					Any 						=     accelerator.prepare(snake_case_				,					snake_case_				,					snake_case_				)

	# Optimizer has stepped
	UpperCAmelCase:					str 						=     False
	scheduler.step()
	UpperCAmelCase:					List[Any] 						=     1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 1_0
	assert (
	    scheduler.get_last_lr()[0] == expected_lr
	), F'Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'

	# Optimizer has not stepped
	UpperCAmelCase:					List[str] 						=     True
	scheduler.step()
	if not step_scheduler_with_optimizer:
		UpperCAmelCase:					List[str] 						=     1 - 2 / 1_0
	assert (
	    scheduler.get_last_lr()[0] == expected_lr
	), F'Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'



def      __UpperCAmelCase    (  snake_case_							:		List[str] = 2				):



	'''simple docstring'''



	from transformers import get_linear_schedule_with_warmup

	UpperCAmelCase:					Tuple 						=     [1, 2, 4]
	for num_steps in steps:
		UpperCAmelCase:					str 						=     GradientAccumulationPlugin(num_steps=snake_case_				,					adjust_scheduler=num_steps > 1				)
		UpperCAmelCase:					int 						=     Accelerator(gradient_accumulation_plugin=snake_case_				)
		UpperCAmelCase:					Dict 						=     torch.nn.Linear(2				,					4				)
		UpperCAmelCase:					str 						=     torch.optim.AdamW(model.parameters()				,					lr=10.0				)
		UpperCAmelCase:					Dict 						=     get_linear_schedule_with_warmup(optimizer=snake_case_				,					num_warmup_steps=0				,					num_training_steps=2_0				)

		UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					Optional[Any] 						=     accelerator.prepare(snake_case_				,					snake_case_				,					snake_case_				)

		for i in range(1_0 * num_steps				):
			with accelerator.accumulate(snake_case_				):
				optimizer.step()
				scheduler.step()

			if i == (1_0 * num_steps - 2):
				assert (
				    scheduler.get_last_lr()[0] != 0
				), F'Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'
		assert (
		    scheduler.get_last_lr()[0] == 0
		), F'Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'
		GradientState._reset_state()





@require_cpu
class 		__lowerCamelCase     (   unittest.TestCase		):



							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								debug_launcher(partial(__lowercase	,				num_processes=1							)	,				num_processes=1							)
								debug_launcher(partial(__lowercase	,				num_processes=1	,				split_batches=__lowercase							)	,				num_processes=1							)



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								debug_launcher(partial(__lowercase	,				num_processes=1							)	,				num_processes=1							)
								debug_launcher(partial(__lowercase	,				num_processes=1	,				split_batches=__lowercase							)	,				num_processes=1							)



							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								debug_launcher(partial(__lowercase	,				num_processes=1	,				step_scheduler_with_optimizer=__lowercase							)	,				num_processes=1							)



							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								debug_launcher(partial(__lowercase	,				num_processes=1	,				step_scheduler_with_optimizer=__lowercase							)	,				num_processes=1							)



							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								AcceleratorState._reset_state(__lowercase							)
								debug_launcher(__lowercase							)
								debug_launcher(partial(__lowercase	,				num_processes=1	,				split_batches=__lowercase							)	,				num_processes=1							)



							def     A__ (     self							)						->     Union[str, Any]:






								"""simple docstring"""



								AcceleratorState._reset_state(__lowercase							)
								debug_launcher(__lowercase							)
								debug_launcher(partial(__lowercase	,				num_processes=1	,				split_batches=__lowercase							)	,				num_processes=1							)



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								AcceleratorState._reset_state(__lowercase							)
								debug_launcher(partial(__lowercase	,				step_scheduler_with_optimizer=__lowercase							)							)



							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								AcceleratorState._reset_state(__lowercase							)
								debug_launcher(partial(__lowercase	,				step_scheduler_with_optimizer=__lowercase							)							)




							@require_huggingface_suite
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								AcceleratorState._reset_state(__lowercase							)
								debug_launcher(partial(__lowercase	,				num_processes=1							)							)
								debug_launcher(__lowercase							)




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

_UpperCAmelCase					:			List[Any]				     =					3_2

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase = 1_6):
						__lowerCAmelCase											=					AutoTokenizer.from_pretrained('''bert-base-cased''')
						__lowerCAmelCase											=					load_dataset('''glue''',					'''mrpc''')

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

						def collate_fn(lowerCamelCase):
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		__lowerCAmelCase											=					1_6
												elif accelerator.mixed_precision != "no":
																		__lowerCAmelCase											=					8
												else:
																		__lowerCAmelCase											=					None

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

						# Instantiate dataloaders.
						__lowerCAmelCase											=					DataLoader(
						    tokenized_datasets['''train'''],					shuffle=lowerCamelCase,					collate_fn=lowerCamelCase,					batch_size=lowerCamelCase)
						__lowerCAmelCase											=					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					:			Optional[int]				     =					mocked_dataloaders  # noqa: F811

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase):
						# For testing only
						if os.environ.get('''TESTING_MOCKED_DATALOADERS''',					lowerCamelCase) == "1":
												__lowerCAmelCase											=					2
						# Initialize accelerator
						__lowerCAmelCase											=					Accelerator(cpu=args.cpu,					mixed_precision=args.mixed_precision)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__lowerCAmelCase											=					config['''lr''']
						__lowerCAmelCase											=					int(config['''num_epochs'''])
						__lowerCAmelCase											=					int(config['''seed'''])
						__lowerCAmelCase											=					int(config['''batch_size'''])

						__lowerCAmelCase											=					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):
												# 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
												__lowerCAmelCase											=					observed_batch_size // batch_size

												# And then set it in the Accelerator directly:
												__lowerCAmelCase											=					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)
												__lowerCAmelCase											=					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).
												__lowerCAmelCase											=					model.to(accelerator.device)

												# Instantiate optimizer
												__lowerCAmelCase											=					AdamW(params=model.parameters(),					lr=lowerCamelCase)
												__lowerCAmelCase							,       __lowerCAmelCase											=					get_dataloaders(lowerCamelCase,					lowerCamelCase)

												# Instantiate scheduler
												__lowerCAmelCase											=					get_linear_schedule_with_warmup(
												    optimizer=lowerCamelCase,					num_warmup_steps=1_0_0,					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.
												__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=					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)
																														__lowerCAmelCase											=					model(**lowerCamelCase)
																														__lowerCAmelCase											=					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():
																														__lowerCAmelCase											=					model(**lowerCamelCase)
																								__lowerCAmelCase											=					outputs.logits.argmax(dim=-1)
																								__lowerCAmelCase							,       __lowerCAmelCase											=					accelerator.gather_for_metrics((predictions, batch['''labels''']))
																								metric.add_batch(
																								    predictions=lowerCamelCase,					references=lowerCamelCase,					)

																		__lowerCAmelCase											=					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 				__magic_name__(    ):
						__lowerCAmelCase											=					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.''')
						__lowerCAmelCase											=					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
						__lowerCAmelCase											=					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 2_5_6}
						training_function(lowerCamelCase,					lowerCamelCase)


if __name__ == "__main__":
	main()

