

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


UpperCamelCase_							=					16
UpperCamelCase_							=					32






def 							_lowerCAmelCase (      __magic_name__	:    Accelerator      ,   __magic_name__	:    int = 16	)							->     Optional[int]:
						lowercase   :       str								=AutoTokenizer.from_pretrained('''bert-base-cased'''	)
						lowercase   :       Optional[int]								=load_dataset('''glue'''      ,   '''mrpc'''	)

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

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

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

						# Instantiate dataloaders.
						lowercase   :       str								=DataLoader(
						    tokenized_datasets['''train''']      ,   shuffle=__magic_name__      ,   collate_fn=__magic_name__      ,   batch_size=__magic_name__	)
						lowercase   :       Any								=DataLoader(
						    tokenized_datasets['''validation''']      ,   shuffle=__magic_name__      ,   collate_fn=__magic_name__      ,   batch_size=__magic_name__	)

						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_							=					mocked_dataloaders  # noqa: F811






def 							_lowerCAmelCase (      __magic_name__	:    str      ,   __magic_name__	:    Tuple	)							->     List[Any]:
						# For testing only
						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''      ,   __magic_name__	) == "1":
												lowercase   :       Union[str, Any]								=2
						# New Code #
						lowercase   :       int								=int(args.gradient_accumulation_steps	)
						lowercase   :       Optional[Any]								=int(args.local_sgd_steps	)
						# Initialize accelerator
						lowercase   :       Optional[Any]								=Accelerator(
						    cpu=args.cpu      ,   mixed_precision=args.mixed_precision      ,   gradient_accumulation_steps=__magic_name__	)
						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
						lowercase   :       Dict								=config['lr']
						lowercase   :       int								=int(config['''num_epochs''']	)
						lowercase   :       List[Any]								=int(config['''seed''']	)
						lowercase   :       Optional[int]								=int(config['''batch_size''']	)

						lowercase   :       str								=evaluate.load('''glue'''      ,   '''mrpc'''	)

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

						# 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).
						lowercase   :       Dict								=model.to(accelerator.device	)

						# Instantiate optimizer
						lowercase   :       int								=AdamW(params=model.parameters()      ,   lr=__magic_name__	)

						# Instantiate scheduler
						lowercase   :       Tuple								=get_linear_schedule_with_warmup(
						    optimizer=__magic_name__      ,   num_warmup_steps=100      ,   num_training_steps=(len(__magic_name__	) * 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.
						lowercase   :       int								=accelerator.prepare(
						    __magic_name__      ,   __magic_name__      ,   __magic_name__      ,   __magic_name__      ,   __magic_name__	)

						# Now we train the model
						for epoch in range(__magic_name__	):
												model.train()
												with LocalSGD(
												    accelerator=__magic_name__      ,   model=__magic_name__      ,   local_sgd_steps=__magic_name__      ,   enabled=local_sgd_steps is not None	) as local_sgd:
																		for step, batch in enumerate(__magic_name__	):
																								# 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(__magic_name__	):
																														lowercase   :       int								=model(**__magic_name__	)
																														lowercase   :       List[Any]								=output.loss
																														accelerator.backward(__magic_name__	)
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()
																														# LocalSGD-specific line
																														local_sgd.step()

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

												lowercase   :       Optional[int]								=metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'''epoch {epoch}:'''      ,   __magic_name__	)






def 							_lowerCAmelCase (      )							->     List[Any]:
						lowercase   :       Any								=argparse.ArgumentParser(description='''Simple example of training script.'''	)
						parser.add_argument(
						    '''--mixed_precision'''      ,   type=__magic_name__      ,   default=__magic_name__      ,   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=__magic_name__      ,   default=1      ,   help='''The number of minibatches to be ran before gradients are accumulated.'''      ,   )
						parser.add_argument(
						    '''--local_sgd_steps'''      ,   type=__magic_name__      ,   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.'''	)
						lowercase   :       Any								=parser.parse_args()
						lowercase   :       List[Any]								={'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(__magic_name__      ,   __magic_name__	)


if __name__ == "__main__":
							main()








'''simple docstring'''





import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
							from ... import PreTrainedTokenizerBase, TensorType

UpperCamelCase_							=					logging.get_logger(__name__)





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
				lowerCamelCase_							=						'vision-encoder-decoder'
				lowerCamelCase_							=						True







				def __init__(					self    :		Optional[int]		,				**UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										super().__init__(**UpperCAmelCase__      )
										if "encoder" not in kwargs or "decoder" not in kwargs:
																raise ValueError(
																    F'''A configuraton of type {self.model_type} cannot be instantiated because '''
																    F'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''      )

										lowercase   :       Optional[Any]								=kwargs.pop('''encoder'''      )
										lowercase   :       List[Any]								=encoder_config.pop('''model_type'''      )
										lowercase   :       List[str]								=kwargs.pop('''decoder'''      )
										lowercase   :       Dict								=decoder_config.pop('''model_type'''      )

										lowercase   :       Union[str, Any]								=AutoConfig.for_model(UpperCAmelCase__		,				**UpperCAmelCase__      )
										lowercase   :       List[str]								=AutoConfig.for_model(UpperCAmelCase__		,				**UpperCAmelCase__      )
										lowercase   :       str								=True







				@classmethod
				def    lowerCamelCase_  (					cls    :		List[str]		,				UpperCAmelCase__    :		PretrainedConfig		,				UpperCAmelCase__    :		PretrainedConfig		,				**UpperCAmelCase__    :		Optional[int]      ):
										'''simple docstring'''
										logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''      )
										lowercase   :       int								=True
										lowercase   :       Optional[Any]								=True

										return cls(encoder=encoder_config.to_dict()		,				decoder=decoder_config.to_dict()		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       int								=copy.deepcopy(self.__dict__      )
										lowercase   :       Union[str, Any]								=self.encoder.to_dict()
										lowercase   :       Union[str, Any]								=self.decoder.to_dict()
										lowercase   :       int								=self.__class__.model_type
										return output





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
				lowerCamelCase_							=						version.parse('1.11'			)







				@property
				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										return OrderedDict(
										    [
										        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
										    ]      )







				@property
				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										return 1E-4







				@property
				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										return OrderedDict({'''last_hidden_state''': {0: '''batch''', 1: '''encoder_sequence'''}}      )





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				@property
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       List[str]								=OrderedDict()
										lowercase   :       Tuple								={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
										lowercase   :       Optional[int]								={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
										lowercase   :       int								={0: '''batch''', 1: '''encoder_sequence'''}

										return common_inputs







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		"PreTrainedTokenizerBase"		,				UpperCAmelCase__    :		int = -1		,				UpperCAmelCase__    :		int = -1		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		Optional["TensorType"] = None		,				):
										'''simple docstring'''
										import torch

										lowercase   :       Optional[Any]								=OrderedDict()

										lowercase   :       List[Any]								=super().generate_dummy_inputs(
										    UpperCAmelCase__		,				batch_size=UpperCAmelCase__		,				seq_length=UpperCAmelCase__		,				is_pair=UpperCAmelCase__		,				framework=UpperCAmelCase__      )

										lowercase		,  lowercase   :       Optional[int]								=dummy_input['''input_ids'''].shape
										lowercase   :       Union[str, Any]								=(batch, encoder_sequence, self._config.encoder_hidden_size)
										lowercase   :       List[str]								=dummy_input.pop('''input_ids'''      )
										lowercase   :       Tuple								=dummy_input.pop('''attention_mask'''      )
										lowercase   :       Union[str, Any]								=torch.zeros(UpperCAmelCase__      )

										return common_inputs





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				@property
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										pass







				def    lowerCamelCase_  (					self    :		List[Any]		,				UpperCAmelCase__    :		PretrainedConfig      ):
										'''simple docstring'''
										return VisionEncoderDecoderEncoderOnnxConfig(UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		PretrainedConfig		,				UpperCAmelCase__    :		PretrainedConfig		,				UpperCAmelCase__    :		str = "default"      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=encoder_config.hidden_size
										return VisionEncoderDecoderDecoderOnnxConfig(UpperCAmelCase__		,				UpperCAmelCase__      )





