





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	:	List[Any]		     =       logging.getLogger(__name__)







@dataclass
class 				__lowerCAmelCase       :
      _lowercase				:					str						=			field(
          metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
      _lowercase				:					Optional[str]						=			field(
          default=UpperCamelCase__						,				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=UpperCamelCase__						,				metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
      _lowercase				:					bool						=			field(default=UpperCamelCase__						,				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=UpperCamelCase__						,				metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}						,				)







@dataclass
class 				__lowerCAmelCase       :
      _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=UpperCamelCase__						,				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=UpperCamelCase__						,				metadata={"""help""": """Overwrite the cached training and evaluation sets"""})
def      _A       (		):





  """simple docstring"""



  a__	:					Tuple										=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.
    a__					,							a__					,							a__	:					int										=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
  else:
    a__					,							a__					,							a__	:					Optional[Any]										=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."						)

  a__	:					List[Any]										=import_module("tasks"						)
  try:
    a__	:					Dict										=getattr(SCREAMING_SNAKE_CASE       ,       model_args.task_type						)
    a__	:					TokenClassificationTask										=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"       ,       SCREAMING_SNAKE_CASE						)

  # Set seed
  set_seed(training_args.seed						)

  # Prepare CONLL-2003 task
  a__	:					Dict										=token_classification_task.get_labels(data_args.labels						)
  a__	:					Dict[int, str]										=dict(enumerate(SCREAMING_SNAKE_CASE						)						)
  a__	:					List[Any]										=len(SCREAMING_SNAKE_CASE						)

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

  a__	:					List[Any]										=AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,       num_labels=SCREAMING_SNAKE_CASE       ,       idalabel=SCREAMING_SNAKE_CASE       ,       labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE						)}       ,       cache_dir=model_args.cache_dir       ,       )
  a__	:					Tuple										=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       ,       )
  a__	:					Optional[int]										=AutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path       ,       from_tf=bool(".ckpt" in model_args.model_name_or_path						)       ,       config=SCREAMING_SNAKE_CASE       ,       cache_dir=model_args.cache_dir       ,       )

  # Get datasets
  a__	:					int										=(
      TokenClassificationDataset(
          token_classification_task=SCREAMING_SNAKE_CASE       ,       data_dir=data_args.data_dir       ,       tokenizer=SCREAMING_SNAKE_CASE       ,       labels=SCREAMING_SNAKE_CASE       ,       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
  )
  a__	:					Optional[Any]										=(
      TokenClassificationDataset(
          token_classification_task=SCREAMING_SNAKE_CASE       ,       data_dir=data_args.data_dir       ,       tokenizer=SCREAMING_SNAKE_CASE       ,       labels=SCREAMING_SNAKE_CASE       ,       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(SCREAMING_SNAKE_CASE     :   np.ndarray       ,       SCREAMING_SNAKE_CASE     :   np.ndarray						) -> Tuple[List[int], List[int]]:
    a__	:					Any										=np.argmax(SCREAMING_SNAKE_CASE       ,       axis=2						)

    a__					,							a__	:					Dict										=preds.shape

    a__	:					List[str]										=[[] for _ in range(SCREAMING_SNAKE_CASE						)]
    a__	:					Optional[int]										=[[] for _ in range(SCREAMING_SNAKE_CASE						)]

    for i in range(SCREAMING_SNAKE_CASE						):
      for j in range(SCREAMING_SNAKE_CASE						):
        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(SCREAMING_SNAKE_CASE     :   EvalPrediction						) -> Dict:
    a__					,							a__	:					Optional[Any]										=align_predictions(p.predictions       ,       p.label_ids						)
    return {
        "accuracy_score": accuracy_score(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						),
        "precision": precision_score(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						),
        "recall": recall_score(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						),
        "f1": fa_score(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						),
    }

  # Data collator
  a__	:					Any										=DataCollatorWithPadding(SCREAMING_SNAKE_CASE       ,       pad_to_multiple_of=8						) if training_args.fpaa else None

  # Initialize our Trainer
  a__	:					str										=Trainer(
      model=SCREAMING_SNAKE_CASE       ,       args=SCREAMING_SNAKE_CASE       ,       train_dataset=SCREAMING_SNAKE_CASE       ,       eval_dataset=SCREAMING_SNAKE_CASE       ,       compute_metrics=SCREAMING_SNAKE_CASE       ,       data_collator=SCREAMING_SNAKE_CASE       ,       )

  # 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
  a__	:					int										={}
  if training_args.do_eval:
    logger.info("*** Evaluate ***"						)

    a__	:					str										=trainer.evaluate()

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

      results.update(SCREAMING_SNAKE_CASE						)

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

    a__					,							a__					,							a__	:					str										=trainer.predict(SCREAMING_SNAKE_CASE						)
    a__					,							a__	:					str										=align_predictions(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

    a__	:					Optional[int]										=os.path.join(training_args.output_dir       ,       "test_results.txt"						)
    if trainer.is_world_process_zero():
      with open(SCREAMING_SNAKE_CASE       ,       "w"						) as writer:
        for key, value in metrics.items():
          logger.info("  %s = %s"       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
          writer.write("%s = %s\n" % (key, value)						)

        # Save predictions
    a__	:					Union[str, Any]										=os.path.join(training_args.output_dir       ,       "test_predictions.txt"						)
    if trainer.is_world_process_zero():
      with open(SCREAMING_SNAKE_CASE       ,       "w"						) as writer:
        with open(os.path.join(data_args.data_dir       ,       "test.txt"						)       ,       "r"						) as f:
          token_classification_task.write_predictions_to_file(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

  return results
def      _A       (		SCREAMING_SNAKE_CASE     :   List[Any]						):





  """simple docstring"""



  main()


if __name__ == "__main__":
     main()













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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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	:	Any		     =       16
UpperCAmelCase	:	str		     =       32
def      _A       (		SCREAMING_SNAKE_CASE     :   Accelerator       ,       SCREAMING_SNAKE_CASE     :   int = 16						):





  """simple docstring"""



  a__	:					int										=AutoTokenizer.from_pretrained("bert-base-cased"						)
  a__	:					List[str]										=load_dataset("glue"       ,       "mrpc"						)

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

  def collate_fn(SCREAMING_SNAKE_CASE     :   str						):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    a__	:					Optional[Any]										=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":
      a__	:					str										=16
    elif accelerator.mixed_precision != "no":
      a__	:					Union[str, Any]										=8
    else:
      a__	:					List[str]										=None

    return tokenizer.pad(
        SCREAMING_SNAKE_CASE       ,       padding="longest"       ,       max_length=SCREAMING_SNAKE_CASE       ,       pad_to_multiple_of=SCREAMING_SNAKE_CASE       ,       return_tensors="pt"       ,       )

  # Instantiate dataloaders.
  a__	:					Any										=DataLoader(
      tokenized_datasets["train"]       ,       shuffle=SCREAMING_SNAKE_CASE       ,       collate_fn=SCREAMING_SNAKE_CASE       ,       batch_size=SCREAMING_SNAKE_CASE						)
  a__	:					int										=DataLoader(
      tokenized_datasets["validation"]       ,       shuffle=SCREAMING_SNAKE_CASE       ,       collate_fn=SCREAMING_SNAKE_CASE       ,       batch_size=SCREAMING_SNAKE_CASE						)

  return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
     from accelerate.test_utils.training import mocked_dataloaders

     UpperCAmelCase	:	str		     =       mocked_dataloaders  # noqa: F811
def      _A       (		SCREAMING_SNAKE_CASE     :   List[Any]       ,       SCREAMING_SNAKE_CASE     :   str						):





  """simple docstring"""



  if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,       SCREAMING_SNAKE_CASE						) == "1":
    a__	:					Tuple										=2
  # Initialize accelerator
  a__	:					int										=Accelerator(cpu=args.cpu       ,       mixed_precision=args.mixed_precision						)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  a__	:					Optional[int]										=config["lr"]
  a__	:					Union[str, Any]										=int(config["num_epochs"]						)
  a__	:					Any										=int(config["seed"]						)
  a__	:					Dict										=int(config["batch_size"]						)

  a__	:					int										=evaluate.load("glue"       ,       "mrpc"						)

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

  set_seed(SCREAMING_SNAKE_CASE						)
  a__					,							a__	:					Optional[int]										=get_dataloaders(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  a__	:					List[str]										=AutoModelForSequenceClassification.from_pretrained("bert-base-cased"       ,       return_dict=SCREAMING_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).
  a__	:					List[str]										=model.to(accelerator.device						)

  # Instantiate optimizer
  a__	:					List[Any]										=AdamW(params=model.parameters()       ,       lr=SCREAMING_SNAKE_CASE						)

  # Instantiate scheduler
  a__	:					Optional[int]										=get_linear_schedule_with_warmup(
      optimizer=SCREAMING_SNAKE_CASE       ,       num_warmup_steps=100       ,       num_training_steps=(len(SCREAMING_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.
  a__					,							a__					,							a__					,							a__					,							a__	:					Optional[int]										=accelerator.prepare(
      SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

  # Now we train the model
  for epoch in range(SCREAMING_SNAKE_CASE						):
    model.train()
    for step, batch in enumerate(SCREAMING_SNAKE_CASE						):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device						)
      a__	:					Dict										=model(**SCREAMING_SNAKE_CASE						)
      a__	:					List[Any]										=outputs.loss
      a__	:					List[str]										=loss / gradient_accumulation_steps
      accelerator.backward(SCREAMING_SNAKE_CASE						)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    a__	:					Optional[Any]										=0
    for step, batch in enumerate(SCREAMING_SNAKE_CASE						):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device						)
      with torch.no_grad():
        a__	:					Any										=model(**SCREAMING_SNAKE_CASE						)
      a__	:					str										=outputs.logits.argmax(dim=-1						)
      a__					,							a__	:					List[str]										=accelerator.gather((predictions, batch["labels"])						)
      # New Code #
      # First we check if it's a distributed system
      if accelerator.use_distributed:
        # Then see if we're on the last batch of our eval dataloader
        if step == len(SCREAMING_SNAKE_CASE						) - 1:
          # Last batch needs to be truncated on distributed systems as it contains additional samples
          a__	:					Optional[Any]										=predictions[: len(eval_dataloader.dataset						) - samples_seen]
          a__	:					Dict										=references[: len(eval_dataloader.dataset						) - samples_seen]
        else:
          # Otherwise we add the number of samples seen
          samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
      metric.add_batch(
          predictions=SCREAMING_SNAKE_CASE       ,       references=SCREAMING_SNAKE_CASE       ,       )

    a__	:					Tuple										=metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:'''       ,       SCREAMING_SNAKE_CASE						)
def      _A       (		):





  """simple docstring"""



  a__	:					List[str]										=argparse.ArgumentParser(description="Simple example of training script."						)
  parser.add_argument(
      "--mixed_precision"       ,       type=SCREAMING_SNAKE_CASE       ,       default=SCREAMING_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."						)
  a__	:					str										=parser.parse_args()
  a__	:					Optional[int]										={"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
     main()






