



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    _lowercase (datasets.Metric   ):
				'''simple docstring'''







				def    _lowerCamelCase       (			self     ):

									'''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    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__=None  ,			snake_case__=1  ,			snake_case__="binary"  ,			snake_case__=None  ,			snake_case__="warn"  ,			):

									'''simple docstring'''


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






import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed




class        snake_case__				(_UpperCamelCase  ):
				"""simple docstring"""



				def __init__( self							:	Union[str, Any]   , __lowerCamelCase							:	Dict   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Optional[int]=False   , __lowerCamelCase							:	Optional[Any]=10_24   , __lowerCamelCase							:	str=10_24   , __lowerCamelCase							:	Any=3.6   , __lowerCamelCase							:	List[Any]=False   , )   ->	Optional[Any]:
							a       =     tokenizer
							a       =     tokenizer.bos_token_id
							a       =     dataset
							a       =     seq_length
							a       =     0
							a       =     infinite
							a       =     0
							a       =     tokenized

							if self.tokenized:
										a       =     seq_length * num_of_sequences
										a       =     "input_ids"
							else:
										a       =     seq_length * chars_per_token * num_of_sequences
										a       =     "content"





				def __iter__( self							:	Union[str, Any]				)   ->	List[str]:
							a       =     iter(self.dataset				)
							a       =     True
							while more_examples:
										a       ,						a       =     [], 0
										while True:
													if buffer_len >= self.max_buffer_size:
																break
													try:
																buffer.append(next(__lowerCamelCase				)[self.content_field]				)
																buffer_len += len(buffer[-1]				)
													except StopIteration:
																if self.infinite:
																			a       =     iter(self.dataset				)
																			self.epoch += 1
																			logger.info(f"""Dataset epoch: {self.epoch}"""				)
																else:
																			a       =     False
																			break
										if self.tokenized:
													a       =     buffer
										else:
													a       =     self.tokenizer(__lowerCamelCase   , truncation=__lowerCamelCase				)["input_ids"]
										a       =     []
										for tokenized_input in tokenized_inputs:
													all_token_ids.extend(tokenized_input + [self.concat_token_id]				)
										for i in range(0   , len(__lowerCamelCase				)   , self.seq_length				):
													a       =     all_token_ids[i : i + self.seq_length]
													if len(__lowerCamelCase				) == self.seq_length:
																self.current_size += 1
																yield torch.tensor(__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	List[str]   , __lowerCamelCase							:	Optional[int]=10_00				)   ->	Union[str, Any]:
							return ShufflerIterDataPipe(self   , buffer_size=__lowerCamelCase				)




def      __magic_name__							(					A						:   Any  ):






			'''simple docstring'''







			a       =     args.model_ckpt.split("/"  )[-1]
			a       =     logging.getLogger(__name__  )
			a       =     Path(args.save_dir  ) / "log/"
			log_dir.mkdir(exist_ok=A  )
			a       =     F"""debug_{accelerator.process_index}.log"""
			logging.basicConfig(
			    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",    datefmt="%m/%d/%Y %H:%M:%S",    level=logging.INFO,    handlers=[logging.FileHandler(log_dir / filename  ), logging.StreamHandler()],    )
			if accelerator.is_main_process:  # we only want to setup logging once
						accelerator.init_trackers(A,    vars(A  )  )
						a       =     accelerator.trackers[0].run.name
						logger.setLevel(logging.INFO  )
						datasets.utils.logging.set_verbosity_info()
						transformers.utils.logging.set_verbosity_info()
			else:
						a       =     ""
						logger.setLevel(logging.ERROR  )
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()
			return logger, run_name




def      __magic_name__							(					A						:   Any  ):






			'''simple docstring'''







			a       =     {"streaming": True}
			a       =     load_dataset(args.dataset_name_train,    split="train",    **A  )
			a       =     train_data.shuffle(buffer_size=args.shuffle_buffer,    seed=args.seed  )
			a       =     load_dataset(args.dataset_name_valid,    split="train",    **A  )
			a       =     ConstantLengthDataset(
			    A,    A,    infinite=A,    seq_length=args.seq_length,    tokenized=args.tokenized  )
			a       =     ConstantLengthDataset(
			    A,    A,    infinite=A,    seq_length=args.seq_length,    tokenized=args.tokenized  )
			a       =     train_dataset.shuffle(buffer_size=args.shuffle_buffer  )
			a       =     DataLoader(A,    batch_size=args.train_batch_size,    shuffle=A  )
			a       =     DataLoader(A,    batch_size=args.valid_batch_size  )
			return train_dataloader, eval_dataloader




def      __magic_name__							(					A						:   Union[str, Any],    A						:   Optional[Any],    A						:   Tuple=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  ):






			'''simple docstring'''







			a       ,						a       =     [], []
			for n, p in model.named_parameters():
						if any(nd in n for nd in no_decay  ):
									params_without_wd.append(A  )
						else:
									params_with_wd.append(A  )
			return [
			    {"params": params_with_wd, "weight_decay": args.weight_decay},
			    {"params": params_without_wd, "weight_decay": 0.0},
			]




def      __magic_name__							(					A						:   List[str],    A						:   List[str]  ):






			'''simple docstring'''







			logger.info(F"""Step {step}: {metrics}"""  )
			if accelerator.is_main_process:
						accelerator.log(A,    A  )




def      __magic_name__							(					A						:   Union[str, Any],    A						:   Optional[int],    A						:   List[str]  ):






			'''simple docstring'''







			a       =     accelerator.unwrap_model(A  ).config
			a       =     4 if args.gradient_checkpointing else 3
			a       =     args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
			a       =     24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
			a       =     factor * (
			    1.0
			    + (args.seq_length / (6.0 * config_model.n_embd))
			    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
			)
			a       =     flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
			return tflops




def      __magic_name__							(					A						:   List[Any]  ):






			'''simple docstring'''







			model.eval()
			a       =     []
			for step, batch in enumerate(A  ):
						with torch.no_grad():
									a       =     model(A,    labels=A  )
						a       =     outputs.loss.repeat(args.valid_batch_size  )
						losses.append(accelerator.gather(A  )  )
						if args.max_eval_steps > 0 and step >= args.max_eval_steps:
									break
			a       =     torch.cat(A  )
			a       =     losses[: eval_dataloader.dataset.current_size].mean()
			try:
						a       =     torch.exp(A  )
			except OverflowError:
						a       =     float("inf"  )
			return loss.item(), perplexity.item()


# Settings
__lowerCAmelCase						:							Optional[int]			     =						HfArgumentParser(TrainingArguments)
__lowerCAmelCase						:							Optional[Any]			     =						parser.parse_args()

# Accelerator
__lowerCAmelCase						:							int			     =						ProjectConfiguration(project_dir=args.save_dir, logging_dir='log')
__lowerCAmelCase						:							Union[str, Any]			     =						Accelerator(log_with=['wandb', 'tensorboard'], project_config=config)
__lowerCAmelCase						:							Tuple			     =						{str(k): str(v) for k, v in accelerator.state.__dict__.items()}

__lowerCAmelCase						:							Dict			     =						Namespace(**vars(args), **acc_state)
__lowerCAmelCase						:							Optional[int]			     =						accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
		__lowerCAmelCase						:							List[Any]			     =						Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
__lowerCAmelCase     ,	__lowerCAmelCase						:							Dict			     =						setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
		hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
__lowerCAmelCase						:							Union[str, Any]			     =						AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
		model.gradient_checkpointing_enable()
__lowerCAmelCase						:							str			     =						AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
__lowerCAmelCase     ,	__lowerCAmelCase						:							Tuple			     =						create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
__lowerCAmelCase						:							Any			     =						AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__lowerCAmelCase						:							Optional[Any]			     =						get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)




def      __magic_name__							(					):






			'''simple docstring'''







			return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
__lowerCAmelCase     ,	__lowerCAmelCase     ,	__lowerCAmelCase     ,	__lowerCAmelCase						:							List[str]			     =						accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# 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)
				__lowerCAmelCase						:							Dict			     =						os.path.basename(args.resume_from_checkpoint)
		else:
				# Get the most recent checkpoint
				__lowerCAmelCase						:							Optional[Any]			     =						[f.name for f in os.scandir(args.save_dir) if f.is_dir() and 'step' in str(f)]
				dirs.sort(key=os.path.getctime)
				__lowerCAmelCase						:							List[Any]			     =						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract the step of the checkpoint to continue from there
		__lowerCAmelCase						:							Dict			     =						os.path.splitext(path)[0]
		__lowerCAmelCase						:							List[Any]			     =						int(training_difference.replace('step_', ''))

# Train model
model.train()
__lowerCAmelCase						:							int			     =						0
__lowerCAmelCase						:							Union[str, Any]			     =						time.time()
__lowerCAmelCase						:							List[Any]			     =						0
for step, batch in enumerate(train_dataloader, start=1):
		if args.resume_from_checkpoint and step < resume_step:
				continue  # we need to skip steps until we reach the resumed step
		__lowerCAmelCase						:							List[str]			     =						model(batch, labels=batch, use_cache=False).loss
		__lowerCAmelCase						:							Any			     =						accelerator.gather(loss.repeat(args.train_batch_size)).mean()
		loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
		log_metrics(step, {'samples': step * samples_per_step, 'loss_per_step/train': loss.item()})
		__lowerCAmelCase						:							Tuple			     =						loss / args.gradient_accumulation_steps
		if step % args.gradient_accumulation_steps != 0:
				# Prevent backward from doing gradient all_reduce in every step
				if accelerator.distributed_type == DistributedType.MULTI_GPU:
						with model.no_sync():
								accelerator.backward(loss)
				else:
						accelerator.backward(loss)
		else:
				__lowerCAmelCase						:							Optional[Any]			     =						get_lr()
				accelerator.backward(loss)
				accelerator.clip_grad_norm_(model.parameters(), 1.0)
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()
				__lowerCAmelCase						:							str			     =						time.time() - t_start
				__lowerCAmelCase						:							Optional[Any]			     =						compute_tflops(elapsed_time, accelerator, args)
				log_metrics(
				    step,
				    {
				        'steps': completed_steps,
				        'loss/train': loss_tracking,
				        'lr': lr,
				        'tflops': tflops,
				        'time_per_iteration': elapsed_time,
				    },
				)
				__lowerCAmelCase						:							Dict			     =						time.time()
				__lowerCAmelCase						:							str			     =						0
				completed_steps += 1
		if step % args.save_checkpoint_steps == 0:
				logger.info('Evaluating and saving model checkpoint')
				__lowerCAmelCase     ,	__lowerCAmelCase						:							Any			     =						evaluate(args)
				log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
				accelerator.wait_for_everyone()
				__lowerCAmelCase						:							Tuple			     =						os.path.join(args.save_dir, F'''step_{step}''')
				accelerator.save_state(save_dir)
				if accelerator.is_main_process:
						hf_repo.push_to_hub(commit_message=F'''step {step}''')
				model.train()
		if completed_steps >= args.max_train_steps:
				break

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
__lowerCAmelCase     ,	__lowerCAmelCase						:							str			     =						evaluate(args)
log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
accelerator.wait_for_everyone()
__lowerCAmelCase						:							Optional[Any]			     =						accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
__lowerCAmelCase						:							Any			     =						os.path.join(args.save_dir, F'''step_{step}''')
accelerator.save_state(save_dir)
if accelerator.is_main_process:
		hf_repo.push_to_hub(commit_message='final model')






