



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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform 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
#
########################################################################


_lowercase						  =		16
_lowercase						  =		32



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Accelerator ,       UpperCamelCase_				:       int = 1_6	)->  List[Any]:
				A__		 =					AutoTokenizer.from_pretrained('''bert-base-cased'''	)
				A__		 =					load_dataset('''glue''' ,       '''mrpc'''	)

				def tokenize_function(UpperCamelCase_				:       List[Any]	):
								# max_length=None => use the model max length (it's actually the default)
								A__		 =					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():
								A__		 =					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
				A__		 =					tokenized_datasets.rename_column('''label''' ,       '''labels'''	)

				def collate_fn(UpperCamelCase_				:       Any	):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								A__		 =					1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												A__		 =					1_6
								elif accelerator.mixed_precision != "no":
												A__		 =					8
								else:
												A__		 =					None

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

				# Instantiate dataloaders.
				A__		 =					DataLoader(
				    tokenized_datasets['''train'''] ,       shuffle=UpperCamelCase_ ,       collate_fn=UpperCamelCase_ ,       batch_size=UpperCamelCase_	)
				A__		 =					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

			_lowercase						  =		mocked_dataloaders  # noqa: F811



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Optional[Any] ,       UpperCamelCase_				:       Optional[Any]	)->  str:
				# For testing only
				if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,       UpperCamelCase_	) == "1":
								A__		 =					2
				# New Code #
				A__		 =					int(args.gradient_accumulation_steps	)
				# Initialize accelerator
				A__		 =					Accelerator(
				    cpu=args.cpu ,       mixed_precision=args.mixed_precision ,       gradient_accumulation_steps=UpperCamelCase_	)
				if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
								raise NotImplementedError(
								    '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''	)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				A__		 =					config['''lr''']
				A__		 =					int(config['''num_epochs''']	)
				A__		 =					int(config['''seed''']	)
				A__		 =					int(config['''batch_size''']	)

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

				set_seed(UpperCamelCase_	)
				A__ ,	A__		 =					get_dataloaders(UpperCamelCase_ ,       UpperCamelCase_	)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				A__		 =					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).
				A__		 =					model.to(accelerator.device	)

				# Instantiate optimizer
				A__		 =					AdamW(params=model.parameters() ,       lr=UpperCamelCase_	)

				# Instantiate scheduler
				A__		 =					get_linear_schedule_with_warmup(
				    optimizer=UpperCamelCase_ ,       num_warmup_steps=1_0_0 ,       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.
				A__ ,	A__ ,	A__ ,	A__ ,	A__		 =					accelerator.prepare(
				    UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_	)

				# Now we train the model
				for epoch in range(UpperCamelCase_	):
								model.train()
								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_	):
																A__		 =					model(**UpperCamelCase_	)
																A__		 =					output.loss
																accelerator.backward(UpperCamelCase_	)
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								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():
																A__		 =					model(**UpperCamelCase_	)
												A__		 =					outputs.logits.argmax(dim=-1	)
												A__ ,	A__		 =					accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
												metric.add_batch(
												    predictions=UpperCamelCase_ ,       references=UpperCamelCase_ ,       )

								A__		 =					metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f"epoch {epoch}:" ,       UpperCamelCase_	)



def 		lowerCAmelCase__    (					)->  List[Any]:
				A__		 =					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('''--cpu''' ,       action='''store_true''' ,       help='''If passed, will train on the CPU.'''	)
				A__		 =					parser.parse_args()
				A__		 =					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
				training_function(UpperCamelCase_ ,       UpperCamelCase_	)


if __name__ == "__main__":
			main()





import numpy as np



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.array	)->  np.array:
    return 1 / (1 + np.exp(-vector	))



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.array	)->  np.array:
    return vector * sigmoid(1.702 * vector	)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
