
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_UpperCAmelCase :			Any              =	logging.getLogger(__name__)


@dataclass
class     lowercase :
		__lowercase	:	str     					=    field(
		    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}       )
		__lowercase	:	Optional[str]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Pretrained config name or path if not the same as model_name"}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="NER"  ,       metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}       )
		__lowercase	:	Optional[str]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}       )
		__lowercase	:	bool     					=    field(default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Set this flag to use fast tokenization."}       )
		# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
		# or just modify its tokenizer_config.json.
		__lowercase	:	Optional[str]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}  ,       )






@dataclass
class     lowercase :
		__lowercase	:	str     					=    field(
		    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}  ,       )
		__lowercase	:	int     					=    field(
		    default=128  ,       metadata={
		        "help": (
		            "The maximum total input sequence length after tokenization. Sequences longer "
		            "than this will be truncated, sequences shorter will be padded."
		        )
		    }  ,       )
		__lowercase	:	bool     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Overwrite the cached training and evaluation sets"}       )






def 				A	( )       ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase          =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
		if len(sys.argv			) == 2 and sys.argv[1].endswith('.json'			):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
		else:
				UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					parser.parse_args_into_dataclasses()

		if (
		    os.path.exists(training_args.output_dir			)
		    and os.listdir(training_args.output_dir			)
		    and training_args.do_train
		    and not training_args.overwrite_output_dir
		):
				raise ValueError(
				    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
				    ' --overwrite_output_dir to overcome.'			)

		UpperCamelCase          =					import_module('tasks'			)
		try:
				UpperCamelCase          =					getattr(lowercase   ,	model_args.task_type			)
				UpperCamelCase          =					token_classification_task_clazz()
		except AttributeError:
				raise ValueError(
				    f'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
				    f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''			)

		# Setup logging
		logging.basicConfig(
		    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'   ,	datefmt='%m/%d/%Y %H:%M:%S'   ,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN   ,	)
		logger.warning(
		    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'   ,	training_args.local_rank   ,	training_args.device   ,	training_args.n_gpu   ,	bool(training_args.local_rank != -1			)   ,	training_args.fpaa   ,	)
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank			):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info('Training/evaluation parameters %s'   ,	lowercase			)

		# Set seed
		set_seed(training_args.seed			)

		# Prepare CONLL-2003 task
		UpperCamelCase          =					token_classification_task.get_labels(data_args.labels			)
		UpperCamelCase          =					dict(enumerate(lowercase			)			)
		UpperCamelCase          =					len(lowercase			)

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		UpperCamelCase          =					AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,	num_labels=lowercase   ,	idalabel=lowercase   ,	labelaid={label: i for i, label in enumerate(lowercase			)}   ,	cache_dir=model_args.cache_dir   ,	)
		UpperCamelCase          =					AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   ,	cache_dir=model_args.cache_dir   ,	use_fast=model_args.use_fast   ,	)
		UpperCamelCase          =					AutoModelForTokenClassification.from_pretrained(
		    model_args.model_name_or_path   ,	from_tf=bool('.ckpt' in model_args.model_name_or_path			)   ,	config=lowercase   ,	cache_dir=model_args.cache_dir   ,	)

		# Get datasets
		UpperCamelCase          =					(
		    TokenClassificationDataset(
		        token_classification_task=lowercase   ,	data_dir=data_args.data_dir   ,	tokenizer=lowercase   ,	labels=lowercase   ,	model_type=config.model_type   ,	max_seq_length=data_args.max_seq_length   ,	overwrite_cache=data_args.overwrite_cache   ,	mode=Split.train   ,	)
		    if training_args.do_train
		    else None
		)
		UpperCamelCase          =					(
		    TokenClassificationDataset(
		        token_classification_task=lowercase   ,	data_dir=data_args.data_dir   ,	tokenizer=lowercase   ,	labels=lowercase   ,	model_type=config.model_type   ,	max_seq_length=data_args.max_seq_length   ,	overwrite_cache=data_args.overwrite_cache   ,	mode=Split.dev   ,	)
		    if training_args.do_eval
		    else None
		)

		def align_predictions(lowercase   ,	lowercase			) -> Tuple[List[int], List[int]]:
				UpperCamelCase          =					np.argmax(lowercase   ,	axis=2			)

				UpperCamelCase     ,  UpperCamelCase          =					preds.shape

				UpperCamelCase          =					[[] for _ in range(lowercase			)]
				UpperCamelCase          =					[[] for _ in range(lowercase			)]

				for i in range(lowercase			):
						for j in range(lowercase			):
								if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
										out_label_list[i].append(label_map[label_ids[i][j]]			)
										preds_list[i].append(label_map[preds[i][j]]			)

				return preds_list, out_label_list

		def compute_metrics(lowercase			) -> Dict:
				UpperCamelCase     ,  UpperCamelCase          =					align_predictions(p.predictions   ,	p.label_ids			)
				return {
				    "accuracy_score": accuracy_score(lowercase   ,	lowercase			),
				    "precision": precision_score(lowercase   ,	lowercase			),
				    "recall": recall_score(lowercase   ,	lowercase			),
				    "f1": fa_score(lowercase   ,	lowercase			),
				}

		# Data collator
		UpperCamelCase          =					DataCollatorWithPadding(lowercase   ,	pad_to_multiple_of=8			) if training_args.fpaa else None

		# Initialize our Trainer
		UpperCamelCase          =					Trainer(
		    model=lowercase   ,	args=lowercase   ,	train_dataset=lowercase   ,	eval_dataset=lowercase   ,	compute_metrics=lowercase   ,	data_collator=lowercase   ,	)

		# Training
		if training_args.do_train:
				trainer.train(
				    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
				trainer.save_model()
				# For convenience, we also re-save the tokenizer to the same directory,
				# so that you can share your model easily on huggingface.co/models =)
				if trainer.is_world_process_zero():
						tokenizer.save_pretrained(training_args.output_dir			)

    # Evaluation
		UpperCamelCase          =					{}
		if training_args.do_eval:
				logger.info('*** Evaluate ***'			)

				UpperCamelCase          =					trainer.evaluate()

				UpperCamelCase          =					os.path.join(training_args.output_dir   ,	'eval_results.txt'			)
				if trainer.is_world_process_zero():
						with open(lowercase   ,	'w'			) as writer:
								logger.info('***** Eval results *****'			)
								for key, value in result.items():
										logger.info('  %s = %s'   ,	lowercase   ,	lowercase			)
										writer.write('%s = %s\n' % (key, value)			)

						results.update(lowercase			)

    # Predict
		if training_args.do_predict:
				UpperCamelCase          =					TokenClassificationDataset(
				    token_classification_task=lowercase   ,	data_dir=data_args.data_dir   ,	tokenizer=lowercase   ,	labels=lowercase   ,	model_type=config.model_type   ,	max_seq_length=data_args.max_seq_length   ,	overwrite_cache=data_args.overwrite_cache   ,	mode=Split.test   ,	)

				UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					trainer.predict(lowercase			)
				UpperCamelCase     ,  UpperCamelCase          =					align_predictions(lowercase   ,	lowercase			)

				UpperCamelCase          =					os.path.join(training_args.output_dir   ,	'test_results.txt'			)
				if trainer.is_world_process_zero():
						with open(lowercase   ,	'w'			) as writer:
								for key, value in metrics.items():
										logger.info('  %s = %s'   ,	lowercase   ,	lowercase			)
										writer.write('%s = %s\n' % (key, value)			)

        # Save predictions
				UpperCamelCase          =					os.path.join(training_args.output_dir   ,	'test_predictions.txt'			)
				if trainer.is_world_process_zero():
						with open(lowercase   ,	'w'			) as writer:
								with open(os.path.join(data_args.data_dir   ,	'test.txt'			)   ,	'r'			) as f:
										token_classification_task.write_predictions_to_file(lowercase   ,	lowercase   ,	lowercase			)

		return results


def 				A	( lowercase			)       ->  Union[str, Any]:
		'''simple docstring'''

		main()


if __name__ == "__main__":
					main()


import argparse

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)
#
# 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 :			Union[str, Any]              =	16
_UpperCAmelCase :			Dict              =	32


def 				A	( lowercase   ,	lowercase = 16			)       ->  str:
		'''simple docstring'''

		UpperCamelCase          =					AutoTokenizer.from_pretrained('bert-base-cased'			)
		UpperCamelCase          =					load_dataset('glue'   ,	'mrpc'			)

		def tokenize_function(lowercase			):
				# max_length=None => use the model max length (it's actually the default)
				UpperCamelCase          =					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():
				UpperCamelCase          =					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
		UpperCamelCase          =					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.
				UpperCamelCase          =					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":
						UpperCamelCase          =					16
				elif accelerator.mixed_precision != "no":
						UpperCamelCase          =					8
				else:
						UpperCamelCase          =					None

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

		# Instantiate dataloaders.
		UpperCamelCase          =					DataLoader(
		    tokenized_datasets['train']   ,	shuffle=lowercase   ,	collate_fn=lowercase   ,	batch_size=lowercase   ,	drop_last=lowercase			)
		UpperCamelCase          =					DataLoader(
		    tokenized_datasets['validation']   ,	shuffle=lowercase   ,	collate_fn=lowercase   ,	batch_size=lowercase   ,	drop_last=(accelerator.mixed_precision == 'fp8')   ,	)

		return train_dataloader, eval_dataloader


def 				A	( lowercase   ,	lowercase			)       ->  Optional[Any]:
		'''simple docstring'''

		UpperCamelCase          =					Accelerator(cpu=args.cpu   ,	mixed_precision=args.mixed_precision			)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		UpperCamelCase          =					config['lr']
		UpperCamelCase          =					int(config['num_epochs']			)
		UpperCamelCase          =					int(config['seed']			)
		UpperCamelCase          =					int(config['batch_size']			)

		UpperCamelCase          =					evaluate.load('glue'   ,	'mrpc'			)

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

		set_seed(lowercase			)
		UpperCamelCase     ,  UpperCamelCase          =					get_dataloaders(lowercase   ,	lowercase			)
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		UpperCamelCase          =					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).
		UpperCamelCase          =					model.to(accelerator.device			)
		# Instantiate optimizer
		UpperCamelCase          =					AdamW(params=model.parameters()   ,	lr=lowercase			)

		# Instantiate scheduler
		UpperCamelCase          =					get_linear_schedule_with_warmup(
		    optimizer=lowercase   ,	num_warmup_steps=100   ,	num_training_steps=(len(lowercase			) * 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.

		UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					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			)
						UpperCamelCase          =					model(**lowercase			)
						UpperCamelCase          =					outputs.loss
						UpperCamelCase          =					loss / gradient_accumulation_steps
						accelerator.backward(lowercase			)
						if step % gradient_accumulation_steps == 0:
								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():
								UpperCamelCase          =					model(**lowercase			)
						UpperCamelCase          =					outputs.logits.argmax(dim=-1			)
						UpperCamelCase     ,  UpperCamelCase          =					accelerator.gather_for_metrics((predictions, batch['labels'])			)
						metric.add_batch(
						    predictions=lowercase   ,	references=lowercase   ,	)

				UpperCamelCase          =					metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'''epoch {epoch}:'''   ,	lowercase			)


def 				A	( )       ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase          =					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.'   ,	)
		parser.add_argument('--cpu'   ,	action='store_true'   ,	help='If passed, will train on the CPU.'			)
		UpperCamelCase          =					parser.parse_args()
		UpperCamelCase          =					{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
		training_function(lowercase   ,	lowercase			)


if __name__ == "__main__":
					main()
