


from sklearn.metrics import precision_score

import datasets


UpperCamelCase :				Dict						    =       '''
Precision is the fraction of correctly labeled positive examples out of all of the examples that were labeled as positive. It is computed via the equation:
Precision = TP / (TP + FP)
where TP is the True positives (i.e. the examples correctly labeled as positive) and FP is the False positive examples (i.e. the examples incorrectly labeled as positive).
'''


UpperCamelCase :				Tuple						    =       '''
Args:
    predictions (`list` of `int`): Predicted class labels.
    references (`list` of `int`): Actual class labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`. If `average` is `None`, it should be the label order. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.
    zero_division (`int` or `string`): Sets the value to return when there is a zero division. Defaults to \'warn\'.

        - 0: Returns 0 when there is a zero division.
        - 1: Returns 1 when there is a zero division.
        - \'warn\': Raises warnings and then returns 0 when there is a zero division.

Returns:
    precision (`float` or `array` of `float`): Precision score or list of precision scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate that fewer negative examples were incorrectly labeled as positive, which means that, generally, higher scores are better.

Examples:

    Example 1-A simple binary example
        >>> precision_metric = datasets.load_metric("precision")
        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'precision\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> precision_metric = datasets.load_metric("precision")
        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'precision\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> precision_metric = datasets.load_metric("precision")
        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(results)
        {\'precision\': 0.23529411764705882}

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'macro\')
        >>> print(results)
        {\'precision\': 0.2222222222222222}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'micro\')
        >>> print(results)
        {\'precision\': 0.3333333333333333}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'weighted\')
        >>> print(results)
        {\'precision\': 0.2222222222222222}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)
        >>> print([round(res, 2) for res in results[\'precision\']])
        [0.67, 0.0, 0.0]
'''


UpperCamelCase :				str						    =       '''
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
    and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
    and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
    Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,			_KWARGS_DESCRIPTION)
class     __snake_case							(      datasets.Metric):
					def   a (	self					:	Union[str, Any]     )       ->				Optional[int]:



											'''simple docstring'''





											return datasets.MetricInfo(
											    description=_DESCRIPTION  ,			citation=_CITATION  ,			inputs_description=_KWARGS_DESCRIPTION  ,			features=datasets.Features(
											        {
											            'predictions': datasets.Sequence(datasets.Value('int32'     )     ),
											            'references': datasets.Sequence(datasets.Value('int32'     )     ),
											        }
											        if self.config_name == 'multilabel'
											        else {
											            'predictions': datasets.Value('int32'     ),
											            'references': datasets.Value('int32'     ),
											        }     )  ,			reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html']  ,			)







					def   a (	self					:	int  ,			_lowerCAmelCase					:	Optional[int]  ,			_lowerCAmelCase					:	int  ,			_lowerCAmelCase					:	Optional[int]=None  ,			_lowerCAmelCase					:	Dict=1  ,			_lowerCAmelCase					:	Union[str, Any]="binary"  ,			_lowerCAmelCase					:	Any=None  ,			_lowerCAmelCase					:	List[str]="warn"  ,			)       ->				List[Any]:



											'''simple docstring'''





											__magic_name__ 		=			precision_score(
											    _lowerCAmelCase  ,			_lowerCAmelCase  ,			labels=_lowerCAmelCase  ,			pos_label=_lowerCAmelCase  ,			average=_lowerCAmelCase  ,			sample_weight=_lowerCAmelCase  ,			zero_division=_lowerCAmelCase  ,			)
											return {"precision": float(_lowerCAmelCase     ) if score.size == 1 else score}





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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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 :				List[Any]						    =       16
UpperCamelCase :				Dict						    =       32
def 							UpperCamelCase_  (				__snake_case			:						Any	,			__snake_case			:						Tuple     )->      List[Any]:




						'''simple docstring'''
						# Initialize accelerator
						if args.with_tracking:
												__magic_name__ 		=			Accelerator(
												    cpu=args.cpu	,			mixed_precision=args.mixed_precision	,			log_with='all'	,			project_dir=args.project_dir     )
						else:
												__magic_name__ 		=			Accelerator(cpu=args.cpu	,			mixed_precision=args.mixed_precision     )

						if hasattr(args.checkpointing_steps	,			'isdigit'     ):
												if args.checkpointing_steps == "epoch":
																		__magic_name__ 		=			args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		__magic_name__ 		=			int(args.checkpointing_steps     )
												else:
																		raise ValueError(
																		    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''     )
						else:
												__magic_name__ 		=			None
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__magic_name__ 		=			config['lr']
						__magic_name__ 		=			int(config['num_epochs']     )
						__magic_name__ 		=			int(config['seed']     )
						__magic_name__ 		=			int(config['batch_size']     )

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												__magic_name__ 		=			os.path.split(__snake_case     )[-1].split('.'     )[0]
												accelerator.init_trackers(__snake_case	,			__snake_case     )

						__magic_name__ 		=			AutoTokenizer.from_pretrained('bert-base-cased'     )
						__magic_name__ 		=			load_dataset('glue'	,			'mrpc'     )
						__magic_name__ 		=			evaluate.load('glue'	,			'mrpc'     )

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

						# If the batch size is too big we use gradient accumulation
						__magic_name__ 		=			1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												__magic_name__ 		=			batch_size // MAX_GPU_BATCH_SIZE
												__magic_name__ 		=			MAX_GPU_BATCH_SIZE

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

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

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

						set_seed(__snake_case     )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__magic_name__ 		=			AutoModelForSequenceClassification.from_pretrained('bert-base-cased'	,			return_dict=__snake_case     )

						# 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).
						__magic_name__ 		=			model.to(accelerator.device     )

						# Instantiate optimizer
						__magic_name__ 		=			AdamW(params=model.parameters()	,			lr=__snake_case     )

						# Instantiate scheduler
						__magic_name__ 		=			get_linear_schedule_with_warmup(
						    optimizer=__snake_case	,			num_warmup_steps=1_00	,			num_training_steps=(len(__snake_case     ) * 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.
						__magic_name__		,			__magic_name__		,			__magic_name__		,			__magic_name__		,			__magic_name__ 		=			accelerator.prepare(
						    __snake_case	,			__snake_case	,			__snake_case	,			__snake_case	,			__snake_case     )

						# We need to keep track of how many total steps we have iterated over
						__magic_name__ 		=			0
						# We also need to keep track of the stating epoch so files are named properly
						__magic_name__ 		=			0

						# Potentially load in the weights and states from a previous save
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''     )
																		accelerator.load_state(args.resume_from_checkpoint     )
																		__magic_name__ 		=			os.path.basename(args.resume_from_checkpoint     )
												else:
																		# Get the most recent checkpoint
																		__magic_name__ 		=			[f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
																		dirs.sort(key=os.path.getctime     )
																		__magic_name__ 		=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												__magic_name__ 		=			os.path.splitext(__snake_case     )[0]

												if "epoch" in training_difference:
																		__magic_name__ 		=			int(training_difference.replace('epoch_'	,			''     )     ) + 1
																		__magic_name__ 		=			None
												else:
																		__magic_name__ 		=			int(training_difference.replace('step_'	,			''     )     )
																		__magic_name__ 		=			resume_step // len(__snake_case     )
																		resume_step -= starting_epoch * len(__snake_case     )

    # Now we train the model
						for epoch in range(__snake_case	,			__snake_case     ):
												model.train()
												if args.with_tracking:
																		__magic_name__ 		=			0
												if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																		# We need to skip steps until we reach the resumed step
																		__magic_name__ 		=			accelerator.skip_first_batches(__snake_case	,			__snake_case     )
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		__magic_name__ 		=			train_dataloader
												for step, batch in enumerate(__snake_case     ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device     )
																		__magic_name__ 		=			model(**__snake_case     )
																		__magic_name__ 		=			outputs.loss
																		__magic_name__ 		=			loss / gradient_accumulation_steps
																		# We keep track of the loss at each epoch
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		accelerator.backward(__snake_case     )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

																		if isinstance(__snake_case	,			__snake_case     ):
																								__magic_name__ 		=			F'''step_{overall_step}'''
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				__magic_name__ 		=			os.path.join(args.output_dir	,			__snake_case     )
																														accelerator.save_state(__snake_case     )

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

												__magic_name__ 		=			metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:'''	,			__snake_case     )
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        'accuracy': eval_metric['accuracy'],
																		        'f1': eval_metric['f1'],
																		        'train_loss': total_loss.item() / len(__snake_case     ),
																		        'epoch': epoch,
																		    }	,			step=__snake_case	,			)

												if checkpointing_steps == "epoch":
																		__magic_name__ 		=			F'''epoch_{epoch}'''
																		if args.output_dir is not None:
																								__magic_name__ 		=			os.path.join(args.output_dir	,			__snake_case     )
																		accelerator.save_state(__snake_case     )

						if args.with_tracking:
												accelerator.end_training()
def 							UpperCamelCase_  (				)->      Dict:




						'''simple docstring'''
						__magic_name__ 		=			argparse.ArgumentParser(description='Simple example of training script.'     )
						parser.add_argument(
						    '--mixed_precision'	,			type=__snake_case	,			default=__snake_case	,			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.'     )
						parser.add_argument(
						    '--checkpointing_steps'	,			type=__snake_case	,			default=__snake_case	,			help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'	,			)
						parser.add_argument(
						    '--resume_from_checkpoint'	,			type=__snake_case	,			default=__snake_case	,			help='If the training should continue from a checkpoint folder.'	,			)
						parser.add_argument(
						    '--with_tracking'	,			action='store_true'	,			help='Whether to load in all available experiment trackers from the environment and use them for logging.'	,			)
						parser.add_argument(
						    '--output_dir'	,			type=__snake_case	,			default='.'	,			help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'	,			)
						parser.add_argument(
						    '--project_dir'	,			type=__snake_case	,			default='logs'	,			help='Location on where to store experiment tracking logs` and relevent project information'	,			)
						__magic_name__ 		=			parser.parse_args()
						__magic_name__ 		=			{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(__snake_case	,			__snake_case     )


if __name__ == "__main__":
					main()

