






import argparse

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
#
# 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__		(						lowerCamelCase_       :       Accelerator   ,  lowerCamelCase_       :       int = 1_6				):
			__a						:  Tuple									=     AutoTokenizer.from_pretrained('bert-base-cased'				)
			__a						:  int									=     load_dataset('glue'   ,  'mrpc'				)

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

			def collate_fn(lowerCamelCase_       :       Dict				):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						__a						:  Optional[int]									=     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						:  List[Any]									=     1_6
						elif accelerator.mixed_precision != "no":
									__a						:  Union[str, Any]									=     8
						else:
									__a						:  List[str]									=     None

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

			# Instantiate dataloaders.
			__a						:  List[str]									=     DataLoader(
			    tokenized_datasets['train']   ,  shuffle=lowerCamelCase_   ,  collate_fn=lowerCamelCase_   ,  batch_size=lowerCamelCase_   ,  drop_last=lowerCamelCase_				)
			__a						:  List[str]									=     DataLoader(
			    tokenized_datasets['validation']   ,  shuffle=lowerCamelCase_   ,  collate_fn=lowerCamelCase_   ,  batch_size=lowerCamelCase_   ,  drop_last=(accelerator.mixed_precision == 'fp8')   ,  )

			return train_dataloader, eval_dataloader

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[str]   ,  lowerCamelCase_       :       List[str]				):
			# Initialize accelerator
			__a						:  Tuple									=     Accelerator(cpu=args.cpu   ,  mixed_precision=args.mixed_precision				)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			__a						:  List[str]									=     config['lr']
			__a						:  Tuple									=     int(config['num_epochs']				)
			__a						:  Tuple									=     int(config['seed']				)
			__a						:  Union[str, Any]									=     int(config['batch_size']				)

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

			# If the batch size is too big we use gradient accumulation
			__a						:  int									=     1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						__a						:  str									=     batch_size // MAX_GPU_BATCH_SIZE
						__a						:  Optional[int]									=     MAX_GPU_BATCH_SIZE

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

			# 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						:  int									=     model.to(accelerator.device				)
			# Instantiate optimizer
			__a						:  Any									=     AdamW(params=model.parameters()   ,  lr=lowerCamelCase_				)

			# Instantiate scheduler
			__a						:  Optional[int]									=     get_linear_schedule_with_warmup(
			    optimizer=lowerCamelCase_   ,  num_warmup_steps=1_0_0   ,  num_training_steps=(len(lowerCamelCase_				) * num_epochs) // gradient_accumulation_steps   ,  )

			# 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						:  Dict									=     accelerator.prepare(
			    lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)

			# Now we train the model
			for epoch in range(lowerCamelCase_				):
						model.train()
						for step, batch in enumerate(lowerCamelCase_				):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device				)
									__a						:  int									=     model(**lowerCamelCase_				)
									__a						:  Optional[Any]									=     outputs.loss
									__a						:  Optional[int]									=     loss / gradient_accumulation_steps
									accelerator.backward(lowerCamelCase_				)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

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

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

def        UpperCAmelCase__		(						):
			__a						:  Optional[int]									=     argparse.ArgumentParser(description='Simple example of training script.'				)
			parser.add_argument(
			    '--mixed_precision'   ,  type=lowerCamelCase_   ,  default=lowerCamelCase_   ,  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.'   ,  )
			parser.add_argument('--cpu'   ,  action='store_true'   ,  help='If passed, will train on the CPU.'				)
			__a						:  List[Any]									=     parser.parse_args()
			__a						:  Optional[int]									=     {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
			training_function(lowerCamelCase_   ,  lowerCamelCase_				)


if __name__ == "__main__":
							main()













import numpy as np

import datasets


A__      :     int       =					'\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n'

A__      :     Optional[int]       =					'\\n@article{de2000mahalanobis,\n  title={The mahalanobis distance},\n  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n  journal={Chemometrics and intelligent laboratory systems},\n  volume={50},\n  number={1},\n  pages={1--18},\n  year={2000},\n  publisher={Elsevier}\n}\n'

A__      :     Optional[int]       =					'\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric("mahalanobis")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {\'mahalanobis\': array([0.5])}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,_KWARGS_DESCRIPTION      )
class  _UpperCAmelCase   (			datasets.Metric      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								return datasets.MetricInfo(
								    description=_DESCRIPTION,    citation=_CITATION,    inputs_description=_KWARGS_DESCRIPTION,    features=datasets.Features(
								        {
								            '''X''': datasets.Sequence(datasets.Value('''float''',    id='''sequence'''    ),    id='''X'''    ),
								        }    ),    )




				def 							lowercase__  (	self		:       Dict,    lowerCamelCase		:       int,    lowerCamelCase		:       Optional[int]    ):

								'''simple docstring'''
								# convert to numpy arrays
								lowercase__  					=     np.array(lowerCamelCase    )
								lowercase__  					=     np.array(lowerCamelCase    )

								# Assert that arrays are 2D
								if len(X.shape    ) != 2:
												raise ValueError('''Expected `X` to be a 2D vector'''    )
								if len(reference_distribution.shape    ) != 2:
												raise ValueError('''Expected `reference_distribution` to be a 2D vector'''    )
								if reference_distribution.shape[0] < 2:
												raise ValueError(
												    '''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension'''    )

								# Get mahalanobis distance for each prediction
								lowercase__  					=     X - np.mean(lowerCamelCase    )
								lowercase__  					=     np.cov(reference_distribution.T    )
								try:
												lowercase__  					=     np.linalg.inv(lowerCamelCase    )
								except np.linalg.LinAlgError:
												lowercase__  					=     np.linalg.pinv(lowerCamelCase    )
								lowercase__  					=     np.dot(lowerCamelCase,    lowerCamelCase    )
								lowercase__  					=     np.dot(lowerCamelCase,    X_minus_mu.T    ).diagonal()

								return {"mahalanobis": mahal_dist}




