




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								=	logging.getLogger(__name__)





@dataclass
class 					__lowerCamelCase			:




    """simple docstring"""

    snake_case__   = field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}       )
    snake_case__   = field(
        default=UpperCamelCase__      ,							metadata={"help": "Pretrained config name or path if not the same as model_name"}       )
    snake_case__   = field(
        default="NER"      ,							metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}       )
    snake_case__   = field(
        default=UpperCamelCase__      ,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}       )
    snake_case__   = 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.
    snake_case__   = field(
        default=UpperCamelCase__      ,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}      ,							)






@dataclass
class 					__lowerCamelCase			:




    """simple docstring"""

    snake_case__   = field(
        metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}       )
    snake_case__   = field(
        default=UpperCamelCase__      ,							metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}      ,							)
    snake_case__   = field(
        default=1_2_8      ,							metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        }      ,							)
    snake_case__   = field(
        default=UpperCamelCase__      ,							metadata={"help": "Overwrite the cached training and evaluation sets"}       )






def 				_A  (		):
     """simple docstring"""

     lowerCAmelCase__            =					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.
          lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__            =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
     else:
          lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__            =					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."     )

     lowerCAmelCase__            =					import_module("tasks"     )
     try:
          lowerCAmelCase__            =					getattr(lowerCAmelCase_  ,							model_args.task_type     )
          lowerCAmelCase__            =					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"  ,							lowerCAmelCase_     )

     # Set seed
     set_seed(training_args.seed     )

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

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

     lowerCAmelCase__            =					AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,							num_labels=lowerCAmelCase_  ,							idalabel=lowerCAmelCase_  ,							labelaid={label: i for i, label in enumerate(lowerCAmelCase_     )}  ,							cache_dir=model_args.cache_dir  ,							)
     lowerCAmelCase__            =					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  ,							)
     lowerCAmelCase__            =					AutoModelForTokenClassification.from_pretrained(
         model_args.model_name_or_path  ,							from_tf=bool(".ckpt" in model_args.model_name_or_path     )  ,							config=lowerCAmelCase_  ,							cache_dir=model_args.cache_dir  ,							)

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

          lowerCAmelCase__      ,						lowerCAmelCase__            =					preds.shape

          lowerCAmelCase__            =					[[] for _ in range(lowerCAmelCase_     )]
          lowerCAmelCase__            =					[[] for _ in range(lowerCAmelCase_     )]

          for i in range(lowerCAmelCase_     ):
               for j in range(lowerCAmelCase_     ):
                    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(lowerCAmelCase_	:   EvalPrediction     ) -> Dict:
          lowerCAmelCase__      ,						lowerCAmelCase__            =					align_predictions(p.predictions  ,							p.label_ids     )
          return {
              "accuracy_score": accuracy_score(lowerCAmelCase_  ,							lowerCAmelCase_     ),
              "precision": precision_score(lowerCAmelCase_  ,							lowerCAmelCase_     ),
              "recall": recall_score(lowerCAmelCase_  ,							lowerCAmelCase_     ),
              "f1": fa_score(lowerCAmelCase_  ,							lowerCAmelCase_     ),
          }

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

     # Initialize our Trainer
     lowerCAmelCase__            =					Trainer(
         model=lowerCAmelCase_  ,							args=lowerCAmelCase_  ,							train_dataset=lowerCAmelCase_  ,							eval_dataset=lowerCAmelCase_  ,							compute_metrics=lowerCAmelCase_  ,							data_collator=lowerCAmelCase_  ,							)

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

          lowerCAmelCase__            =					trainer.evaluate()

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

               results.update(lowerCAmelCase_     )

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

          lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__            =					trainer.predict(lowerCAmelCase_     )
          lowerCAmelCase__      ,						lowerCAmelCase__            =					align_predictions(lowerCAmelCase_  ,							lowerCAmelCase_     )

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

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

     return results



def 				_A  (		lowerCAmelCase_	:   Tuple     ):
     """simple docstring"""

     main()


if __name__ == "__main__":
     main()




'''simple docstring'''


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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 							=  16
_UpperCamelCase 							=  32


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE = 16							):




     '''simple docstring'''





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

     def tokenize_function(SCREAMING_SNAKE_CASE							):
          # max_length=None => use the model max length (it's actually the default)
          __A	:       Any      =							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	:       Optional[Any]      =							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	:       List[Any]      =							tokenized_datasets.rename_column("label"       ,					"labels"							)

     def collate_fn(SCREAMING_SNAKE_CASE							):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __A	:       str      =							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	:       List[str]      =							16
          elif accelerator.mixed_precision != "no":
               __A	:       Tuple      =							8
          else:
               __A	:       Optional[Any]      =							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	:       Optional[int]      =							DataLoader(
         tokenized_datasets["train"]       ,					shuffle=SCREAMING_SNAKE_CASE       ,					collate_fn=SCREAMING_SNAKE_CASE       ,					batch_size=SCREAMING_SNAKE_CASE							)
     __A	:       Tuple      =							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 							=  mocked_dataloaders  # noqa: F811


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,					SCREAMING_SNAKE_CASE							) == "1":
          __A	:       int      =							2
     # New Code #
     __A	:       List[str]      =							int(args.gradient_accumulation_steps							)
     __A	:       int      =							int(args.local_sgd_steps							)
     # Initialize accelerator
     __A	:       int      =							Accelerator(
         cpu=args.cpu       ,					mixed_precision=args.mixed_precision       ,					gradient_accumulation_steps=SCREAMING_SNAKE_CASE							)
     if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
          raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"							)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __A	:       Tuple      =							config["lr"]
     __A	:       Union[str, Any]      =							int(config["num_epochs"]							)
     __A	:       Optional[int]      =							int(config["seed"]							)
     __A	:       Optional[Any]      =							int(config["batch_size"]							)

     __A	:       Optional[int]      =							evaluate.load("glue"       ,					"mrpc"							)

     set_seed(SCREAMING_SNAKE_CASE							)
     __A  ,__A	:       Tuple      =							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	:       Union[str, Any]      =							model.to(accelerator.device							)

     # Instantiate optimizer
     __A	:       str      =							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)       ,					)

     # 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	:       List[str]      =							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()
          with LocalSGD(
              accelerator=SCREAMING_SNAKE_CASE       ,					model=SCREAMING_SNAKE_CASE       ,					local_sgd_steps=SCREAMING_SNAKE_CASE       ,					enabled=local_sgd_steps is not None							) as local_sgd:
               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							)
                    # New code #
                    # We use the new `accumulate` context manager to perform gradient accumulation
                    # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                    with accelerator.accumulate(SCREAMING_SNAKE_CASE							):
                         __A	:       List[Any]      =							model(**SCREAMING_SNAKE_CASE							)
                         __A	:       List[str]      =							output.loss
                         accelerator.backward(SCREAMING_SNAKE_CASE							)
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # LocalSGD-specific line
                         local_sgd.step()

          model.eval()
          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	:       Tuple      =							model(**SCREAMING_SNAKE_CASE							)
               __A	:       Union[str, Any]      =							outputs.logits.argmax(dim=-1							)
               __A  ,__A	:       Tuple      =							accelerator.gather_for_metrics((predictions, batch["labels"])							)
               metric.add_batch(
                   predictions=SCREAMING_SNAKE_CASE       ,					references=SCREAMING_SNAKE_CASE       ,					)

          __A	:       List[Any]      =							metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f"epoch {epoch}:"       ,					SCREAMING_SNAKE_CASE							)


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Dict      =							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."       ,					)
     # New Code #
     parser.add_argument(
         "--gradient_accumulation_steps"       ,					type=SCREAMING_SNAKE_CASE       ,					default=1       ,					help="The number of minibatches to be ran before gradients are accumulated."       ,					)
     parser.add_argument(
         "--local_sgd_steps"       ,					type=SCREAMING_SNAKE_CASE       ,					default=8       ,					help="Number of local SGD steps or None to disable local SGD"							)
     parser.add_argument("--cpu"       ,					action="store_true"       ,					help="If passed, will train on the CPU."							)
     __A	:       List[str]      =							parser.parse_args()
     __A	:       List[str]      =							{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
      main()

