






"""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
#
########################################################################


__SCREAMING_SNAKE_CASE   =   16
__SCREAMING_SNAKE_CASE   =   32


def        UpperCAmelCase      (					a__     ,      a__ = 16 ):





						'''simple docstring'''






						lowerCAmelCase   :List[str]						=			AutoTokenizer.from_pretrained('bert-base-cased' )
						lowerCAmelCase   :str						=			load_dataset('glue'     ,      'mrpc' )

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

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

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

						# Instantiate dataloaders.
						lowerCAmelCase   :Tuple						=			DataLoader(
						    tokenized_datasets['train']     ,      shuffle=a__     ,      collate_fn=a__     ,      batch_size=a__ )
						lowerCAmelCase   :Dict						=			DataLoader(
						    tokenized_datasets['validation']     ,      shuffle=a__     ,      collate_fn=a__     ,      batch_size=a__ )

						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

		__SCREAMING_SNAKE_CASE   =   mocked_dataloaders  # noqa: F811


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,      a__ ) == "1":
												lowerCAmelCase   :Tuple						=			2
						# New Code #
						lowerCAmelCase   :List[Any]						=			int(args.gradient_accumulation_steps )
						lowerCAmelCase   :int						=			int(args.local_sgd_steps )
						# Initialize accelerator
						lowerCAmelCase   :List[str]						=			Accelerator(
						    cpu=args.cpu     ,      mixed_precision=args.mixed_precision     ,      gradient_accumulation_steps=a__ )
						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   :List[str]						=			config["""lr"""]
						lowerCAmelCase   :Dict						=			int(config['num_epochs'] )
						lowerCAmelCase   :Optional[int]						=			int(config['seed'] )
						lowerCAmelCase   :Tuple						=			int(config['batch_size'] )

						lowerCAmelCase   :Dict						=			evaluate.load('glue'     ,      'mrpc' )

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

						# 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   :List[str]						=			model.to(accelerator.device )

						# Instantiate optimizer
						lowerCAmelCase   :Tuple						=			AdamW(params=model.parameters()     ,      lr=a__ )

						# Instantiate scheduler
						lowerCAmelCase   :Dict						=			get_linear_schedule_with_warmup(
						    optimizer=a__     ,      num_warmup_steps=1_00     ,      num_training_steps=(len(a__ ) * 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   :List[Any]						=			accelerator.prepare(
						    a__     ,      a__     ,      a__     ,      a__     ,      a__ )

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

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

												lowerCAmelCase   :Tuple						=			metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}:"""     ,      a__ )


def        UpperCAmelCase      (					):





						'''simple docstring'''






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


if __name__ == "__main__":
		main()





from typing import List

from .keymap import KEYMAP, get_character




def 		lowerCAmelCase_ (						lowerCamelCase ):

				def decorator(lowerCamelCase ):
								__magic_name__						:						str										=getattr(lowerCamelCase						,  """handle_key"""						,  [] )
								handle += [key]
								setattr(lowerCamelCase						,  """handle_key"""						,  lowerCamelCase )
								return func

				return decorator




def 		lowerCAmelCase_ (						*lowerCamelCase ):

				def decorator(lowerCamelCase ):
								__magic_name__						:						Dict										=getattr(lowerCamelCase						,  """handle_key"""						,  [] )
								handle += keys
								setattr(lowerCamelCase						,  """handle_key"""						,  lowerCamelCase )
								return func

				return decorator




class 		__A   (      UpperCamelCase__      ):

						def __new__(		cls :Dict     ,			__snake_case :Optional[Any]     ,			__snake_case :Union[str, Any]     ,			__snake_case :List[str]			):
										'''simple docstring'''

										__magic_name__						:						int										=super().__new__(cls     ,			__snake_case     ,			__snake_case     ,			__snake_case			)
										if not hasattr(__snake_case     ,			"""key_handler"""			):
														setattr(__snake_case     ,			"""key_handler"""     ,			{}			)
										setattr(__snake_case     ,			"""handle_input"""     ,			KeyHandler.handle_input			)

										for value in attrs.values():
														__magic_name__						:						int										=getattr(__snake_case     ,			"""handle_key"""     ,			[]			)
														for key in handled_keys:
																		__magic_name__						:						List[str]										=value
										return new_cls


						@staticmethod
						def      A__    (		cls :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=get_character()
										if char != KEYMAP["undefined"]:
														__magic_name__						:						Optional[int]										=ord(__snake_case			)
										__magic_name__						:						int										=cls.key_handler.get(__snake_case			)
										if handler:
														__magic_name__						:						Dict										=char
														return handler(cls			)
										else:
														return None




def 		lowerCAmelCase_ (						cls ):
				return KeyHandler(cls.__name__						,  cls.__bases__						,  cls.__dict__.copy() )




