




"""simple docstring"""


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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


A_      			=       16
A_      			=       32


def       UpperCAmelCase__ (snake_case__							:      Accelerator	,     snake_case__							:      int = 16					):
		"""simple docstring"""

		_snake_case							:	Union[str, Any]			=						AutoTokenizer.from_pretrained("""bert-base-cased"""					)
		_snake_case							:	Optional[Any]			=						load_dataset("""glue"""	,     """mrpc"""					)

		def tokenize_function(snake_case__							:      Optional[int]					):
				# max_length=None => use the model max length (it's actually the default)
				_snake_case							:	int			=						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							:	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
		_snake_case							:	Union[str, Any]			=						tokenized_datasets.rename_column("""label"""	,     """labels"""					)

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

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

		# Instantiate dataloaders.
		_snake_case							:	Any			=						DataLoader(
		    tokenized_datasets["""train"""]	,     shuffle=__UpperCamelCase	,     collate_fn=__UpperCamelCase	,     batch_size=__UpperCamelCase					)
		_snake_case							:	str			=						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_      			=       mocked_dataloaders  # noqa: F811


def       UpperCAmelCase__ (snake_case__							:      Tuple	,     snake_case__							:      Optional[int]					):
		"""simple docstring"""

		if os.environ.get("""TESTING_MOCKED_DATALOADERS"""	,     __UpperCamelCase					) == "1":
				_snake_case							:	Tuple			=						2
		# New Code #
		_snake_case							:	Optional[int]			=						int(args.gradient_accumulation_steps					)
		_snake_case							:	List[Any]			=						int(args.local_sgd_steps					)
		# Initialize accelerator
		_snake_case							:	Tuple			=						Accelerator(
		    cpu=args.cpu	,     mixed_precision=args.mixed_precision	,     gradient_accumulation_steps=__UpperCamelCase					)
		if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
				raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"""					)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		_snake_case							:	Tuple			=						config['''lr''']
		_snake_case							:	List[Any]			=						int(config["""num_epochs"""]					)
		_snake_case							:	int			=						int(config["""seed"""]					)
		_snake_case							:	List[Any]			=						int(config["""batch_size"""]					)

		_snake_case							:	List[Any]			=						evaluate.load("""glue"""	,     """mrpc"""					)

		set_seed(__UpperCamelCase					)
		_snake_case							:	List[Any]			=						get_dataloaders(__UpperCamelCase	,     __UpperCamelCase					)
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_snake_case							:	Dict			=						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[Any]			=						model.to(accelerator.device					)

		# Instantiate optimizer
		_snake_case							:	Dict			=						AdamW(params=model.parameters()	,     lr=__UpperCamelCase					)

		# Instantiate scheduler
		_snake_case							:	Optional[int]			=						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							:	Dict			=						accelerator.prepare(
		    __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase					)

		# Now we train the model
		for epoch in range(__UpperCamelCase					):
				model.train()
				with LocalSGD(
				    accelerator=__UpperCamelCase	,     model=__UpperCamelCase	,     local_sgd_steps=__UpperCamelCase	,     enabled=local_sgd_steps is not None					) as local_sgd:
						for step, batch in enumerate(__UpperCamelCase					):
								# 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(__UpperCamelCase					):
										_snake_case							:	Tuple			=						model(**__UpperCamelCase					)
										_snake_case							:	str			=						output.loss
										accelerator.backward(__UpperCamelCase					)
										optimizer.step()
										lr_scheduler.step()
										optimizer.zero_grad()
										# LocalSGD-specific line
										local_sgd.step()

				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							:	str			=						model(**__UpperCamelCase					)
						_snake_case							:	Dict			=						outputs.logits.argmax(dim=-1					)
						_snake_case							:	int			=						accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
						metric.add_batch(
						    predictions=__UpperCamelCase	,     references=__UpperCamelCase	,     )

				_snake_case							:	List[str]			=						metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(F"epoch {epoch}:"	,     __UpperCamelCase					)


def       UpperCAmelCase__ ():
		"""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."""	,     )
		# New Code #
		parser.add_argument(
		    """--gradient_accumulation_steps"""	,     type=__UpperCamelCase	,     default=1	,     help="""The number of minibatches to be ran before gradients are accumulated."""	,     )
		parser.add_argument(
		    """--local_sgd_steps"""	,     type=__UpperCamelCase	,     default=8	,     help="""Number of local SGD steps or None to disable local SGD"""					)
		parser.add_argument("""--cpu"""	,     action="""store_true"""	,     help="""If passed, will train on the CPU."""					)
		_snake_case							:	Optional[int]			=						parser.parse_args()
		_snake_case							:	Optional[Any]			=						{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(__UpperCamelCase	,     __UpperCamelCase					)


if __name__ == "__main__":
					main()








"""simple docstring"""


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      (IPNDMScheduler,)
 lowercase__				      =      (("num_inference_steps", 50),)


 def 				UpperCamelCase_     (       self:	Union[str, Any],   **a_:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						{"""num_train_timesteps""": 1_000}
   config.update(**a_	)
   return config


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	Optional[int]=0,   **a_:	int	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						dict(self.forward_default_kwargs	)
   _snake_case							:	Optional[Any]			=						kwargs.pop("""num_inference_steps""",   a_	)
   _snake_case							:	Optional[Any]			=						self.dummy_sample
   _snake_case							:	Dict			=						0.1 * sample
   _snake_case							:	Any			=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

   for scheduler_class in self.scheduler_classes:
     _snake_case							:	int			=						self.get_scheduler_config(**a_	)
     _snake_case							:	Dict			=						scheduler_class(**a_	)
     scheduler.set_timesteps(a_	)
     # copy over dummy past residuals
     _snake_case							:	int			=						dummy_past_residuals[:]

     if time_step is None:
       _snake_case							:	Union[str, Any]			=						scheduler.timesteps[len(scheduler.timesteps	) // 2]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(a_	)
       _snake_case							:	Tuple			=						scheduler_class.from_pretrained(a_	)
       new_scheduler.set_timesteps(a_	)
       # copy over dummy past residuals
       _snake_case							:	Optional[Any]			=						dummy_past_residuals[:]

     _snake_case							:	List[Any]			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	str			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

     _snake_case							:	Optional[Any]			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	Optional[int]			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	str,   a_:	Any=0,   **a_:	Tuple	):




   '''simple docstring'''



   _snake_case							:	str			=						dict(self.forward_default_kwargs	)
   _snake_case							:	List[Any]			=						kwargs.pop("""num_inference_steps""",   a_	)
   _snake_case							:	Optional[int]			=						self.dummy_sample
   _snake_case							:	Tuple			=						0.1 * sample
   _snake_case							:	Optional[int]			=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

   for scheduler_class in self.scheduler_classes:
     _snake_case							:	Any			=						self.get_scheduler_config()
     _snake_case							:	Tuple			=						scheduler_class(**a_	)
     scheduler.set_timesteps(a_	)

     # copy over dummy past residuals (must be after setting timesteps)
     _snake_case							:	Union[str, Any]			=						dummy_past_residuals[:]

     if time_step is None:
       _snake_case							:	Tuple			=						scheduler.timesteps[len(scheduler.timesteps	) // 2]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(a_	)
       _snake_case							:	List[str]			=						scheduler_class.from_pretrained(a_	)
       # copy over dummy past residuals
       new_scheduler.set_timesteps(a_	)

       # copy over dummy past residual (must be after setting timesteps)
       _snake_case							:	List[str]			=						dummy_past_residuals[:]

     _snake_case							:	str			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	Any			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

     _snake_case							:	str			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	int			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"


 def 				UpperCamelCase_     (       self:	List[Any],   **a_:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						self.scheduler_classes[0]
   _snake_case							:	Any			=						self.get_scheduler_config(**a_	)
   _snake_case							:	List[Any]			=						scheduler_class(**a_	)

   _snake_case							:	Union[str, Any]			=						10
   _snake_case							:	Union[str, Any]			=						self.dummy_model()
   _snake_case							:	List[Any]			=						self.dummy_sample_deter
   scheduler.set_timesteps(a_	)

   for i, t in enumerate(scheduler.timesteps	):
     _snake_case							:	Optional[Any]			=						model(a_,   a_	)
     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_	).prev_sample

   for i, t in enumerate(scheduler.timesteps	):
     _snake_case							:	Union[str, Any]			=						model(a_,   a_	)
     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_	).prev_sample

   return sample


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	str			=						dict(self.forward_default_kwargs	)

   _snake_case							:	int			=						kwargs.pop("""num_inference_steps""",   a_	)

   for scheduler_class in self.scheduler_classes:
     _snake_case							:	Union[str, Any]			=						self.get_scheduler_config()
     _snake_case							:	Tuple			=						scheduler_class(**a_	)

     _snake_case							:	Dict			=						self.dummy_sample
     _snake_case							:	List[str]			=						0.1 * sample

     if num_inference_steps is not None and hasattr(a_,   """set_timesteps"""	):
       scheduler.set_timesteps(a_	)
     elif num_inference_steps is not None and not hasattr(a_,   """set_timesteps"""	):
       _snake_case							:	Dict			=						num_inference_steps

     # copy over dummy past residuals (must be done after set_timesteps)
     _snake_case							:	Union[str, Any]			=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
     _snake_case							:	List[str]			=						dummy_past_residuals[:]

     _snake_case							:	Optional[int]			=						scheduler.timesteps[5]
     _snake_case							:	Optional[Any]			=						scheduler.timesteps[6]

     _snake_case							:	str			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	List[str]			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     self.assertEqual(output_a.shape,   sample.shape	)
     self.assertEqual(output_a.shape,   output_a.shape	)

     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     self.assertEqual(output_a.shape,   sample.shape	)
     self.assertEqual(output_a.shape,   output_a.shape	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   for timesteps in [100, 1_000]:
     self.check_over_configs(num_train_timesteps=a_,   time_step=a_	)


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   for t, num_inference_steps in zip([1, 5, 10],   [10, 50, 100]	):
     self.check_over_forward(num_inference_steps=a_,   time_step=a_	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.full_loop()
   _snake_case							:	Optional[int]			=						torch.mean(torch.abs(a_	)	)

   assert abs(result_mean.item() - 2_540_529	) < 10


