





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       :    Tuple				,			__lowerCamelCase       :    Union[str, Any]				,			__lowerCamelCase       :    int				,			__lowerCamelCase       :    Dict=False				,			__lowerCamelCase       :    int=1_024				,			__lowerCamelCase       :    List[str]=1_024				,			__lowerCamelCase       :    List[str]=3.6				,			__lowerCamelCase       :    Union[str, Any]=False				,			)					->   Optional[int]:

			'''simple docstring'''




			__lowercase					=    tokenizer
			__lowercase					=    tokenizer.bos_token_id
			__lowercase					=    dataset
			__lowercase					=    seq_length
			__lowercase					=    0
			__lowercase					=    infinite
			__lowercase					=    0
			__lowercase					=    tokenized

			if self.tokenized:
					__lowercase					=    seq_length * num_of_sequences
					__lowercase					=    'input_ids'
			else:
					__lowercase					=    seq_length * chars_per_token * num_of_sequences
					__lowercase					=    'content'




	def __iter__(	self       :    Dict      )					->   Any:

			'''simple docstring'''




			__lowercase					=    iter(self.dataset      )
			__lowercase					=    True
			while more_examples:
					__lowercase	,			__lowercase					=    [], 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:
											__lowercase					=    iter(self.dataset      )
											self.epoch += 1
											logger.info(F"Dataset epoch: {self.epoch}"      )
									else:
											__lowercase					=    False
											break
					if self.tokenized:
							__lowercase					=    buffer
					else:
							__lowercase					=    self.tokenizer(__lowerCamelCase				,			truncation=__lowerCamelCase      )['input_ids']
					__lowercase					=    []
					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      ):
							__lowercase					=    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       :    Optional[Any]				,			__lowerCamelCase       :    Dict=1_000      )					->   Union[str, Any]:

			'''simple docstring'''




			return ShufflerIterDataPipe(self				,			buffer_size=__lowerCamelCase      )





def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						Dict:
		__lowercase					=    args.model_ckpt.split('/'       )[-1]
		__lowercase					=    logging.getLogger(__name__       )
		__lowercase					=    Path(args.save_dir       ) / 'log/'
		log_dir.mkdir(exist_ok=snake_case       )
		__lowercase					=    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(snake_case	,      vars(snake_case       )       )
				__lowercase					=    accelerator.trackers[0].run.name
				logger.setLevel(logging.INFO       )
				datasets.utils.logging.set_verbosity_info()
				transformers.utils.logging.set_verbosity_info()
		else:
				__lowercase					=    ''
				logger.setLevel(logging.ERROR       )
				datasets.utils.logging.set_verbosity_error()
				transformers.utils.logging.set_verbosity_error()
		return logger, run_name



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						Tuple:
		__lowercase					=    {'streaming': True}
		__lowercase					=    load_dataset(args.dataset_name_train	,      split='train'	,      **snake_case       )
		__lowercase					=    train_data.shuffle(buffer_size=args.shuffle_buffer	,      seed=args.seed       )
		__lowercase					=    load_dataset(args.dataset_name_valid	,      split='train'	,      **snake_case       )
		__lowercase					=    ConstantLengthDataset(
		    snake_case	,      snake_case	,      infinite=snake_case	,      seq_length=args.seq_length	,      tokenized=args.tokenized       )
		__lowercase					=    ConstantLengthDataset(
		    snake_case	,      snake_case	,      infinite=snake_case	,      seq_length=args.seq_length	,      tokenized=args.tokenized       )
		__lowercase					=    train_dataset.shuffle(buffer_size=args.shuffle_buffer       )
		__lowercase					=    DataLoader(snake_case	,      batch_size=args.train_batch_size	,      shuffle=snake_case       )
		__lowercase					=    DataLoader(snake_case	,      batch_size=args.valid_batch_size       )
		return train_dataloader, eval_dataloader



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]       )		->						Optional[int]:
		__lowercase	,			__lowercase					=    [], []
		for n, p in model.named_parameters():
				if any(nd in n for nd in no_decay       ):
						params_without_wd.append(snake_case       )
				else:
						params_with_wd.append(snake_case       )
		return [
		    {"params": params_with_wd, "weight_decay": args.weight_decay},
		    {"params": params_without_wd, "weight_decay": 0.0},
		]



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						int:
		logger.info(F"Step {step}: {metrics}"       )
		if accelerator.is_main_process:
				accelerator.log(snake_case	,      snake_case       )



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case       )		->						Tuple:
		# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
		__lowercase					=    accelerator.unwrap_model(snake_case       ).config
		__lowercase					=    4 if args.gradient_checkpointing else 3
		__lowercase					=    args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
		__lowercase					=    24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
		__lowercase					=    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					=    flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
		return tflops



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


# Settings
SCREAMING_SNAKE_CASE_      :			int		  =      HfArgumentParser(TrainingArguments)
SCREAMING_SNAKE_CASE_      :			List[str]		  =      parser.parse_args()

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

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

# Clone model repository
if accelerator.is_main_process:
		SCREAMING_SNAKE_CASE_      :			int		  =      Repository(args.save_dir, clone_from=args.model_ckpt)

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

# Load dataset and dataloader
SCREAMING_SNAKE_CASE_  ,SCREAMING_SNAKE_CASE_      :			List[str]		  =      create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
SCREAMING_SNAKE_CASE_      :			List[Any]		  =      AdamW(get_grouped_params(model, args), lr=args.learning_rate)
SCREAMING_SNAKE_CASE_      :			List[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    SCREAMING_SNAKE_CASE							(			)		->						Dict:
		return optimizer.param_groups[0]["lr"]


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

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











import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


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

SCREAMING_SNAKE_CASE_      :			Tuple		  =      '''\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
'''

SCREAMING_SNAKE_CASE_      :			Dict		  =      '''
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode="strict". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        "warn". "warn" acts as 0, but the warning is raised.

Returns:
    \'scores\': dict. Summary of the scores for overall and per type
        Overall:
            \'accuracy\': accuracy,
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure,
        Per type:
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> seqeval = datasets.load_metric("seqeval")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']
    >>> print(results["overall_f1"])
    0.5
    >>> print(results["PER"]["f1"])
    1.0
'''

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







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    Any      )					->   str:

			'''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       UpperCAmelCase (	self       :    Optional[Any]				,			__lowerCamelCase       :    int				,			__lowerCamelCase       :    str				,			__lowerCamelCase       :    bool = False				,			__lowerCamelCase       :    Optional[str] = None				,			__lowerCamelCase       :    Optional[str] = None				,			__lowerCamelCase       :    Optional[List[int]] = None				,			__lowerCamelCase       :    Union[str, int] = "warn"				,			)					->   Tuple:

			'''simple docstring'''




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

			__lowercase					=    {
			    type_name: {
			        'precision': score['precision'],
			        'recall': score['recall'],
			        'f1': score['f1-score'],
			        'number': score['support'],
			    }
			    for type_name, score in report.items()
			}
			__lowercase					=    overall_score['precision']
			__lowercase					=    overall_score['recall']
			__lowercase					=    overall_score['f1-score']
			__lowercase					=    accuracy_score(y_true=__lowerCamelCase				,			y_pred=__lowerCamelCase      )

			return scores




