






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			:		Optional[int]   				=    16
_UpperCAmelCase			:		Tuple   				=    32





def 	lowerCAmelCase_		(lowercase__						:       Accelerator     ,					lowercase__						:       int = 16       )		->    int:


				'''simple docstring'''




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

				def tokenize_function(lowercase__						:       Dict       ):
								# max_length=None => use the model max length (it's actually the default)
								lowerCAmelCase__      			=						tokenizer(examples['''sentence1''']     ,					examples['''sentence2''']     ,					truncation=lowercase__     ,					max_length=lowercase__       )
								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(
								    lowercase__     ,					batched=lowercase__     ,					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(lowercase__						:       List[Any]       ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowerCAmelCase__      			=						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":
												lowerCAmelCase__      			=						16
								elif accelerator.mixed_precision != "no":
												lowerCAmelCase__      			=						8
								else:
												lowerCAmelCase__      			=						None

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

				# Instantiate dataloaders.
				lowerCAmelCase__      			=						DataLoader(
				    tokenized_datasets['''train''']     ,					shuffle=lowercase__     ,					collate_fn=lowercase__     ,					batch_size=lowercase__       )
				lowerCAmelCase__      			=						DataLoader(
				    tokenized_datasets['''validation''']     ,					shuffle=lowercase__     ,					collate_fn=lowercase__     ,					batch_size=lowercase__       )

				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			:		str   				=    mocked_dataloaders  # noqa: F811





def 	lowerCAmelCase_		(lowercase__						:       Dict     ,					lowercase__						:       int       )		->    Any:


				'''simple docstring'''




				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,					lowercase__       ) == "1":
								lowerCAmelCase__      			=						2
				# New Code #
				lowerCAmelCase__      			=						int(args.gradient_accumulation_steps       )
				lowerCAmelCase__      			=						int(args.local_sgd_steps       )
				# Initialize accelerator
				lowerCAmelCase__      			=						Accelerator(
				    cpu=args.cpu     ,					mixed_precision=args.mixed_precision     ,					gradient_accumulation_steps=lowercase__       )
				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
				lowerCAmelCase__      			=						config['''lr''']
				lowerCAmelCase__      			=						int(config['''num_epochs''']       )
				lowerCAmelCase__      			=						int(config['''seed''']       )
				lowerCAmelCase__      			=						int(config['''batch_size''']       )

				lowerCAmelCase__      			=						evaluate.load('''glue'''     ,					'''mrpc'''       )

				set_seed(lowercase__       )
				lowerCAmelCase__			,		lowerCAmelCase__      			=						get_dataloaders(lowercase__     ,					lowercase__       )
				# 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=lowercase__       )

				# 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=lowercase__       )

				# Instantiate scheduler
				lowerCAmelCase__      			=						get_linear_schedule_with_warmup(
				    optimizer=lowercase__     ,					num_warmup_steps=1_00     ,					num_training_steps=(len(lowercase__       ) * 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(
				    lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__       )

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

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

								lowerCAmelCase__      			=						metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'epoch {epoch}:'     ,					lowercase__       )





def 	lowerCAmelCase_		()		->    Optional[Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						argparse.ArgumentParser(description='''Simple example of training script.'''       )
				parser.add_argument(
				    '''--mixed_precision'''     ,					type=lowercase__     ,					default=lowercase__     ,					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=lowercase__     ,					default=1     ,					help='''The number of minibatches to be ran before gradients are accumulated.'''     ,					)
				parser.add_argument(
				    '''--local_sgd_steps'''     ,					type=lowercase__     ,					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.'''       )
				lowerCAmelCase__      			=						parser.parse_args()
				lowerCAmelCase__      			=						{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowercase__     ,					lowercase__       )


if __name__ == "__main__":
			main()










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCAmelCase			:		str   				=    {
    "configuration_blenderbot_small": [
        "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotSmallConfig",
        "BlenderbotSmallOnnxConfig",
    ],
    "tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    ["BlenderbotSmallTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    [
			    "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "BlenderbotSmallForCausalLM",
			    "BlenderbotSmallForConditionalGeneration",
			    "BlenderbotSmallModel",
			    "BlenderbotSmallPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Dict   				=    [
			    "TFBlenderbotSmallForConditionalGeneration",
			    "TFBlenderbotSmallModel",
			    "TFBlenderbotSmallPreTrainedModel",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    [
			    "FlaxBlenderbotSmallForConditionalGeneration",
			    "FlaxBlenderbotSmallModel",
			    "FlaxBlenderbotSmallPreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_blenderbot_small import (
			    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    BlenderbotSmallConfig,
			    BlenderbotSmallOnnxConfig,
			)
			from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_blenderbot_small import (
						    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BlenderbotSmallForCausalLM,
						    BlenderbotSmallForConditionalGeneration,
						    BlenderbotSmallModel,
						    BlenderbotSmallPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_blenderbot_small import (
						    TFBlenderbotSmallForConditionalGeneration,
						    TFBlenderbotSmallModel,
						    TFBlenderbotSmallPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_blenderbot_small import (
						    FlaxBlenderbotSmallForConditionalGeneration,
						    FlaxBlenderbotSmallModel,
						    FlaxBlenderbotSmallPreTrainedModel,
						)

else:
			import sys

			_UpperCAmelCase			:		int   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





