


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
#
########################################################################


__lowerCAmelCase	: Optional[Any]  				=1_6
__lowerCAmelCase	: Union[str, Any]  				=3_2


def        _UpperCamelCase							(      lowercase__    ,       lowercase__ = 16      ):
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		AutoTokenizer.from_pretrained('''bert-base-cased'''      )
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		load_dataset('''glue'''    ,       '''mrpc'''      )

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

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

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

						# Instantiate dataloaders.
						__SCREAMING_SNAKE_CASE						:     Tuple					=		DataLoader(
						    tokenized_datasets['''train''']    ,       shuffle=lowercase__    ,       collate_fn=lowercase__    ,       batch_size=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Tuple					=		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

						__lowerCAmelCase	: Optional[int]  				=mocked_dataloaders  # noqa: F811


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

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

						set_seed(lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[int]					=		get_dataloaders(lowercase__    ,       lowercase__      )
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		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).
						__SCREAMING_SNAKE_CASE						:     int					=		model.to(accelerator.device      )

						# Instantiate optimizer
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		AdamW(params=model.parameters()    ,       lr=lowercase__      )

						# Instantiate scheduler
						__SCREAMING_SNAKE_CASE						:     Tuple					=		get_linear_schedule_with_warmup(
						    optimizer=lowercase__    ,       num_warmup_steps=100    ,       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.
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     str					=		accelerator.prepare(
						    lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      )

						# Now we train the model
						for epoch in range(lowercase__      ):
												model.train()
												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__      ):
																								__SCREAMING_SNAKE_CASE						:     Tuple					=		model(**lowercase__      )
																								__SCREAMING_SNAKE_CASE						:     str					=		output.loss
																								accelerator.backward(lowercase__      )
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

												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():
																								__SCREAMING_SNAKE_CASE						:     Dict					=		model(**lowercase__      )
																		__SCREAMING_SNAKE_CASE						:     List[str]					=		outputs.logits.argmax(dim=-1      )
																		__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
																		metric.add_batch(
																		    predictions=lowercase__    ,       references=lowercase__    ,       )

												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:'''    ,       lowercase__      )


def        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     str					=		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('''--cpu'''    ,       action='''store_true'''    ,       help='''If passed, will train on the CPU.'''      )
						__SCREAMING_SNAKE_CASE						:     Any					=		parser.parse_args()
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(lowercase__    ,       lowercase__      )


if __name__ == "__main__":
						main()




from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

__lowerCAmelCase	: List[Any]  				=datasets.load_iris()

__lowerCAmelCase	: Tuple  				=np.array(data['data'])
__lowerCAmelCase	: Dict  				=np.array(data['target'])
__lowerCAmelCase	: List[str]  				=data['target_names']

__lowerCAmelCase				,__lowerCAmelCase				,__lowerCAmelCase				,__lowerCAmelCase	: str  				=train_test_split(X, y)


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						return np.linalg.norm(np.array(lowercase__      ) - np.array(lowercase__      )      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__=5      ):
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		zip(lowercase__    ,       lowercase__      )
						# List of distances of all points from the point to be classified
						__SCREAMING_SNAKE_CASE						:     Dict					=		[]
						for data_point in data:
												__SCREAMING_SNAKE_CASE						:     Tuple					=		euclidean_distance(data_point[0]    ,       lowercase__      )
												distances.append((distance, data_point[1])      )
						# Choosing 'k' points with the least distances.
						__SCREAMING_SNAKE_CASE						:     int					=		[i[1] for i in sorted(lowercase__      )[:k]]
						# Most commonly occurring class among them
						# is the class into which the point is classified
						__SCREAMING_SNAKE_CASE						:     Any					=		Counter(lowercase__      ).most_common(1      )[0][0]
						return classes[result]


if __name__ == "__main__":
						print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
