
'''simple docstring'''







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 					lowerCAmelCase__  (    lowerCamelCase_	):






					def __init__(  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE=False							,				__SCREAMING_SNAKE_CASE=10_24							,				__SCREAMING_SNAKE_CASE=10_24							,				__SCREAMING_SNAKE_CASE=3.6							,				__SCREAMING_SNAKE_CASE=False							,				):


											"""simple docstring"""





											lowercase_      :	List[Any]						=			tokenizer
											lowercase_      :	List[Any]						=			tokenizer.bos_token_id
											lowercase_      :	int						=			dataset
											lowercase_      :	Tuple						=			seq_length
											lowercase_      :	int						=			0
											lowercase_      :	str						=			infinite
											lowercase_      :	Tuple						=			0
											lowercase_      :	Union[str, Any]						=			tokenized

											if self.tokenized:
																	lowercase_      :	Any						=			seq_length * num_of_sequences
																	lowercase_      :	Dict						=			'''input_ids'''
											else:
																	lowercase_      :	Tuple						=			seq_length * chars_per_token * num_of_sequences
																	lowercase_      :	List[Any]						=			'''content'''






					def __iter__(  self						):


											"""simple docstring"""





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






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE=10_00						):


											"""simple docstring"""





											return ShufflerIterDataPipe(self							,				buffer_size=__SCREAMING_SNAKE_CASE						)







def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Tuple				):



						"""simple docstring"""





						lowercase_      :	Optional[Any]						=			args.model_ckpt.split('''/'''				)[-1]
						lowercase_      :	str						=			logging.getLogger(__name__				)
						lowercase_      :	str						=			Path(args.save_dir				) / '''log/'''
						log_dir.mkdir(exist_ok=__SCREAMING_SNAKE_CASE				)
						lowercase_      :	List[Any]						=			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(__SCREAMING_SNAKE_CASE , vars(__SCREAMING_SNAKE_CASE				)				)
												lowercase_      :	Dict						=			accelerator.trackers[0].run.name
												logger.setLevel(logging.INFO				)
												datasets.utils.logging.set_verbosity_info()
												transformers.utils.logging.set_verbosity_info()
						else:
												lowercase_      :	List[Any]						=			''''''
												logger.setLevel(logging.ERROR				)
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						return logger, run_name
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Optional[int]				):



						"""simple docstring"""





						lowercase_      :	Dict						=			{'''streaming''': True}
						lowercase_      :	Tuple						=			load_dataset(args.dataset_name_train , split='''train''' , **__SCREAMING_SNAKE_CASE				)
						lowercase_      :	Optional[int]						=			train_data.shuffle(buffer_size=args.shuffle_buffer , seed=args.seed				)
						lowercase_      :	int						=			load_dataset(args.dataset_name_valid , split='''train''' , **__SCREAMING_SNAKE_CASE				)
						lowercase_      :	Dict						=			ConstantLengthDataset(
						    __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , infinite=__SCREAMING_SNAKE_CASE , seq_length=args.seq_length , tokenized=args.tokenized				)
						lowercase_      :	Optional[int]						=			ConstantLengthDataset(
						    __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , infinite=__SCREAMING_SNAKE_CASE , seq_length=args.seq_length , tokenized=args.tokenized				)
						lowercase_      :	Union[str, Any]						=			train_dataset.shuffle(buffer_size=args.shuffle_buffer				)
						lowercase_      :	Union[str, Any]						=			DataLoader(__SCREAMING_SNAKE_CASE , batch_size=args.train_batch_size , shuffle=__SCREAMING_SNAKE_CASE				)
						lowercase_      :	Optional[Any]						=			DataLoader(__SCREAMING_SNAKE_CASE , batch_size=args.valid_batch_size				)
						return train_dataloader, eval_dataloader
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Optional[Any] , __SCREAMING_SNAKE_CASE   :					Optional[int] , __SCREAMING_SNAKE_CASE   :					List[Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]				):



						"""simple docstring"""





						lowercase_		,					lowercase_      :	int						=			[], []
						for n, p in model.named_parameters():
												if any(nd in n for nd in no_decay				):
																		params_without_wd.append(__SCREAMING_SNAKE_CASE				)
												else:
																		params_with_wd.append(__SCREAMING_SNAKE_CASE				)
						return [
						    {"params": params_with_wd, "weight_decay": args.weight_decay},
						    {"params": params_without_wd, "weight_decay": 0.0},
						]
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					List[Any] , __SCREAMING_SNAKE_CASE   :					Optional[Any]				):



						"""simple docstring"""





						logger.info(F'''Step {step}: {metrics}'''				)
						if accelerator.is_main_process:
												accelerator.log(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Dict , __SCREAMING_SNAKE_CASE   :					str , __SCREAMING_SNAKE_CASE   :					Union[str, Any]				):



						"""simple docstring"""





						lowercase_      :	List[Any]						=			accelerator.unwrap_model(__SCREAMING_SNAKE_CASE				).config
						lowercase_      :	Union[str, Any]						=			4 if args.gradient_checkpointing else 3
						lowercase_      :	Tuple						=			args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
						lowercase_      :	Union[str, Any]						=			24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
						lowercase_      :	Dict						=			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))
						)
						lowercase_      :	Any						=			flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
						return tflops
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Optional[Any]				):



						"""simple docstring"""





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


# Settings
_lowercase					:    Tuple       				=			HfArgumentParser(TrainingArguments)
_lowercase					:    int       				=			parser.parse_args()

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

_lowercase					:    int       				=			Namespace(**vars(args), **acc_state)
_lowercase					:    Dict       				=			accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
_lowercase				,    _lowercase					:    int       				=			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
_lowercase					:    Union[str, Any]       				=			AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
							model.gradient_checkpointing_enable()
_lowercase					:    str       				=			AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
_lowercase				,    _lowercase					:    Optional[int]       				=			create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
_lowercase					:    int       				=			AdamW(get_grouped_params(model, args), lr=args.learning_rate)
_lowercase					:    int       				=			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    snake_case_						(							):



						"""simple docstring"""





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


# Prepare everything with our `accelerator`.
_lowercase				,    _lowercase				,    _lowercase				,    _lowercase					:    int       				=			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)
														_lowercase					:    Optional[Any]       				=			os.path.basename(args.resume_from_checkpoint)
							else:
														# Get the most recent checkpoint
														_lowercase					:    int       				=			[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)
														_lowercase					:    str       				=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
							# Extract the step of the checkpoint to continue from there
							_lowercase					:    Optional[Any]       				=			os.path.splitext(path)[0]
							_lowercase					:    Tuple       				=			int(training_difference.replace("step_", ""))

# Train model
model.train()
_lowercase					:    Union[str, Any]       				=			0
_lowercase					:    List[Any]       				=			time.time()
_lowercase					:    Optional[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
							_lowercase					:    Optional[Any]       				=			model(batch, labels=batch, use_cache=False).loss
							_lowercase					:    Dict       				=			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()})
							_lowercase					:    Any       				=			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:
														_lowercase					:    Optional[int]       				=			get_lr()
														accelerator.backward(loss)
														accelerator.clip_grad_norm_(model.parameters(), 1.0)
														optimizer.step()
														lr_scheduler.step()
														optimizer.zero_grad()
														_lowercase					:    List[str]       				=			time.time() - t_start
														_lowercase					:    Union[str, 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,
														    },
														)
														_lowercase					:    int       				=			time.time()
														_lowercase					:    Union[str, Any]       				=			0
														completed_steps += 1
							if step % args.save_checkpoint_steps == 0:
														logger.info("Evaluating and saving model checkpoint")
														_lowercase				,    _lowercase					:    str       				=			evaluate(args)
														log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
														accelerator.wait_for_everyone()
														_lowercase					:    List[str]       				=			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")
_lowercase				,    _lowercase					:    List[str]       				=			evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
_lowercase					:    Any       				=			accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
_lowercase					:    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="final model")





'''simple docstring'''







import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


_lowercase					:    Optional[int]       				=			"\\n@inproceedings{ramshaw-marcus-1995-text,\n    title = \"Text Chunking using Transformation-Based Learning\",\n    author = \"Ramshaw, Lance  and\n      Marcus, Mitch\",\n    booktitle = \"Third Workshop on Very Large Corpora\",\n    year = \"1995\",\n    url = \"https://www.aclweb.org/anthology/W95-0107\",\n}\n@misc{seqeval,\n  title={{seqeval}: A Python framework for sequence labeling evaluation},\n  url={https://github.com/chakki-works/seqeval},\n  note={Software available from https://github.com/chakki-works/seqeval},\n  author={Hiroki Nakayama},\n  year={2018},\n}\n"

_lowercase					:    List[Any]       				=			"\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n"

_lowercase					:    int       				=			"\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode=\"strict\". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        \"warn\". \"warn\" acts as 0, but the warning is raised.\n\nReturns:\n    'scores': dict. Summary of the scores for overall and per type\n        Overall:\n            'accuracy': accuracy,\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> seqeval = datasets.load_metric(\"seqeval\")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']\n    >>> print(results[\"overall_f1\"])\n    0.5\n    >>> print(results[\"PER\"][\"f1\"])\n    1.0\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,		_KWARGS_DESCRIPTION	)
class 					lowerCAmelCase__  (    datasets.Metric	):






					def       _snake_case       (  self						):


											"""simple docstring"""





											return datasets.MetricInfo(
											    description=_DESCRIPTION							,				citation=_CITATION							,				homepage='''https://github.com/chakki-works/seqeval'''							,				inputs_description=_KWARGS_DESCRIPTION							,				features=datasets.Features(
											        {
											            '''predictions''': datasets.Sequence(datasets.Value('''string'''							,				id='''label'''						)							,				id='''sequence'''						),
											            '''references''': datasets.Sequence(datasets.Value('''string'''							,				id='''label'''						)							,				id='''sequence'''						),
											        }						)							,				codebase_urls=['''https://github.com/chakki-works/seqeval''']							,				reference_urls=['''https://github.com/chakki-works/seqeval''']							,				)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE = False							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = "warn"							,				):


											"""simple docstring"""





											if scheme is not None:
																	try:
																							lowercase_      :	List[str]						=			importlib.import_module('''seqeval.scheme'''						)
																							lowercase_      :	int						=			getattr(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)
																	except AttributeError:
																							raise ValueError(F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''						)
											lowercase_      :	List[Any]						=			classification_report(
											    y_true=__SCREAMING_SNAKE_CASE							,				y_pred=__SCREAMING_SNAKE_CASE							,				suffix=__SCREAMING_SNAKE_CASE							,				output_dict=__SCREAMING_SNAKE_CASE							,				scheme=__SCREAMING_SNAKE_CASE							,				mode=__SCREAMING_SNAKE_CASE							,				sample_weight=__SCREAMING_SNAKE_CASE							,				zero_division=__SCREAMING_SNAKE_CASE							,				)
											report.pop('''macro avg'''						)
											report.pop('''weighted avg'''						)
											lowercase_      :	int						=			report.pop('''micro avg'''						)

											lowercase_      :	List[Any]						=			{
											    type_name: {
											        '''precision''': score['''precision'''],
											        '''recall''': score['''recall'''],
											        '''f1''': score['''f1-score'''],
											        '''number''': score['''support'''],
											    }
											    for type_name, score in report.items()
											}
											lowercase_      :	Optional[Any]						=			overall_score['''precision''']
											lowercase_      :	Optional[Any]						=			overall_score['''recall''']
											lowercase_      :	Optional[int]						=			overall_score['''f1-score''']
											lowercase_      :	Optional[int]						=			accuracy_score(y_true=__SCREAMING_SNAKE_CASE							,				y_pred=__SCREAMING_SNAKE_CASE						)

											return scores



