




'''simple docstring'''





import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


SCREAMING_SNAKE_CASE__      					=     logging.getLogger(__name__)

SCREAMING_SNAKE_CASE__      					=     "pytorch_model.bin"


@dataclasses.dataclass
class 				snake_case :
       lowerCAmelCase__  :str						   =					dataclasses.field(
           metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}   )
       lowerCAmelCase__  :Optional[str]						   =					dataclasses.field(
           default=__UpperCAmelCase		,      metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."}		,      )





@dataclasses.dataclass
class 				snake_case :
       lowerCAmelCase__  :str						   =					dataclasses.field(metadata={"help": "A csv or a json file containing the training data."}   )
       lowerCAmelCase__  :str						   =					dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."}   )
       lowerCAmelCase__  :Optional[str]						   =					dataclasses.field(
           default=__UpperCAmelCase		,      metadata={"help": "A csv or a json file containing the validation data."}   )
       lowerCAmelCase__  :Optional[str]						   =					dataclasses.field(
           default=__UpperCAmelCase		,      metadata={"help": "The name of the task to train on."}		,      )
       lowerCAmelCase__  :Optional[List[str]]						   =					dataclasses.field(
           default=__UpperCAmelCase		,      metadata={"help": "The list of labels for the task."}   )





@dataclasses.dataclass
class 				snake_case :
       lowerCAmelCase__  :str						   =					dataclasses.field(
           metadata={"help": "The output directory where the model predictions and checkpoints will be written."}   )
       lowerCAmelCase__  :Optional[str]						   =					dataclasses.field(
           default="accuracy"		,      metadata={"help": "The evaluation metric used for the task."}   )
       lowerCAmelCase__  :Optional[str]						   =					dataclasses.field(
           default="no"		,      metadata={
               "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
           }		,      )
       lowerCAmelCase__  :Optional[int]						   =					dataclasses.field(
           default=10		,      metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}		,      )
       lowerCAmelCase__  :Optional[float]						   =					dataclasses.field(
           default=0.0		,      metadata={
               "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
           }		,      )
       lowerCAmelCase__  :Optional[bool]						   =					dataclasses.field(
           default=__UpperCAmelCase		,      metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}		,      )
       lowerCAmelCase__  :Optional[bool]						   =					dataclasses.field(
           default=__UpperCAmelCase		,      metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}		,      )
       lowerCAmelCase__  :Optional[bool]						   =					dataclasses.field(
           default=__UpperCAmelCase		,      metadata={"help": "Whether to fine-tune on labeled data after pseudo training."}		,      )
       lowerCAmelCase__  :Optional[float]						   =					dataclasses.field(
           default=0.0		,      metadata={"help": "Confidence threshold for pseudo-labeled data filtering."}		,      )
       lowerCAmelCase__  :Optional[int]						   =					dataclasses.field(
           default=100		,      metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}		,      )
       lowerCAmelCase__  :Optional[int]						   =					dataclasses.field(
           default=__UpperCAmelCase		,      metadata={"help": "Random seed for initialization."}		,      )





def        lowerCamelCase  (				_snake_case  :						Tuple     ,_snake_case  :						Any     ,_snake_case  :						Union[str, Any]     ,_snake_case  :						Any     ,_snake_case  :						Tuple     ,_snake_case  :						Union[str, Any]       ):





    '''simple docstring'''


    lowercase__		       =						datasets.concatenate_datasets([infer_input, infer_output]     ,axis=1       )

    if args.do_filter_by_confidence:
        lowercase__		       =						dataset.filter(lambda _snake_case       : example["probability"] > args.confidence_threshold       )

    if args.do_filter_by_val_performance:
        assert eval_result >= 0.0 and eval_result <= 1.0
        lowercase__		       =						int(eval_result * len(_lowerCAmelCase       )       )
        print(_lowerCAmelCase       )
        lowercase__		       =						dataset.sort("probability"     ,reverse=_lowerCAmelCase       )
        lowercase__		       =						dataset.select(range(_lowerCAmelCase       )       )

    lowercase__		       =						dataset.remove_columns(["label", "probability"]       )
    lowercase__		       =						dataset.rename_column("prediction"     ,"label"       )
    lowercase__		       =						dataset.map(lambda _snake_case       : {"label": idalabel[example["label"]]}       )
    lowercase__		       =						dataset.shuffle(seed=args.seed       )

    lowercase__		       =						os.path.join(_lowerCAmelCase     ,f'''train_pseudo.{args.data_file_extension}'''       )
    if args.data_file_extension == "csv":
        dataset.to_csv(_lowerCAmelCase     ,index=_lowerCAmelCase       )
    else:
        dataset.to_json(_lowerCAmelCase       )




def        lowerCamelCase  (				_snake_case  :						List[str]     ,_snake_case  :						List[Any]     ,_snake_case  :						List[str]     ,_snake_case  :						str     ,**_snake_case  :						List[Any]       ):





    '''simple docstring'''


    lowercase__		       =						Accelerator()
    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"     ,datefmt="%m/%d/%Y %H:%M:%S"     ,level=logging.INFO     ,)
    logger.info(accelerator.state       )

    # Setup logging, we only want one process per machine to log things on the
    # screen. accelerator.is_local_main_process is only True for one process per
    # machine.
    logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR       )

    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_info()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()

    lowercase__		       =						STModelArguments(model_name_or_path=_lowerCAmelCase       )
    lowercase__		       =						STDataArguments(train_file=_lowerCAmelCase     ,infer_file=_lowerCAmelCase       )
    lowercase__		       =						STTrainingArguments(output_dir=_lowerCAmelCase       )
    lowercase__		       =						argparse.Namespace()

    for arg_class in (model_args, data_args, training_args):
        for key, value in vars(_lowerCAmelCase       ).items():
            setattr(_lowerCAmelCase     ,_lowerCAmelCase     ,_lowerCAmelCase       )

    for key, value in kwargs.items():
        if hasattr(_lowerCAmelCase     ,_lowerCAmelCase       ):
            setattr(_lowerCAmelCase     ,_lowerCAmelCase     ,_lowerCAmelCase       )

    # Sanity checks
    lowercase__		       =						{}
    lowercase__		       =						None

    # You need to provide the training data and the data to predict on
    assert args.train_file is not None
    assert args.infer_file is not None
    lowercase__		       =						args.train_file
    lowercase__		       =						args.infer_file

    if args.evaluation_strategy != IntervalStrategy.NO.value:
        assert args.eval_file is not None
        lowercase__		       =						args.eval_file

    for key in data_files:
        lowercase__		       =						data_files[key].split("."       )[-1]
        assert extension in ["csv", "json"], f'''`{key}_file` should be a csv or a json file.'''
        if args.data_file_extension is None:
            lowercase__		       =						extension
        else:
            assert extension == args.data_file_extension, f'''`{key}_file` should be a {args.data_file_extension} file`.'''

    assert (
        args.eval_metric in datasets.list_metrics()
    ), f'''{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'''

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed       )

    logger.info("Creating the initial data directory for self-training..."       )
    lowercase__		       =						f'''{args.output_dir}/self-train_iter-{{}}'''.format
    lowercase__		       =						data_dir_format(0       )

    if accelerator.is_main_process:
        if args.output_dir is not None:
            os.makedirs(args.output_dir     ,exist_ok=_lowerCAmelCase       )
            os.makedirs(_lowerCAmelCase     ,exist_ok=_lowerCAmelCase       )
    accelerator.wait_for_everyone()

    lowercase__		       =						None
    lowercase__		       =						None
    lowercase__		       =						0
    lowercase__		       =						False
    # Show the progress bar
    lowercase__		       =						tqdm(range(args.max_selftrain_iterations       )     ,disable=not accelerator.is_local_main_process       )

    # Self-train
    for iteration in range(0     ,int(args.max_selftrain_iterations       )       ):
        lowercase__		       =						data_dir_format(_lowerCAmelCase       )
        assert os.path.exists(_lowerCAmelCase       )

        # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
        # iteration > 0
        lowercase__		       =						os.path.join(_lowerCAmelCase     ,"stage-1"       )
        lowercase__		       =						{
            "accelerator": accelerator,
            "model_name_or_path": args.model_name_or_path,
            "cache_dir": args.cache_dir,
            "do_train": True,
            "train_file": data_files["train"] if iteration == 0 else data_files["train_pseudo"],
            "do_eval": True if args.eval_file is not None else False,
            "eval_file": data_files["eval"],
            "do_predict": True,
            "infer_file": data_files["infer"],
            "task_name": args.task_name,
            "label_list": args.label_list,
            "output_dir": current_output_dir,
            "eval_metric": args.eval_metric,
            "evaluation_strategy": args.evaluation_strategy,
            "early_stopping_patience": args.early_stopping_patience,
            "early_stopping_threshold": args.early_stopping_threshold,
            "seed": args.seed,
        }
        # Add additional training arguments
        for key, value in kwargs.items():
            if key not in arguments_dict and not hasattr(_lowerCAmelCase     ,_lowerCAmelCase       ):
                arguments_dict.update({key: value}       )

        lowercase__		       =						os.path.join(_lowerCAmelCase     ,"best-checkpoint"     ,_lowerCAmelCase       )
        if os.path.exists(_lowerCAmelCase       ):
            logger.info(
                "Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1."     ,_lowerCAmelCase     ,_lowerCAmelCase     ,)
        else:
            logger.info("***** Running self-training: iteration: %d, stage: 1 *****"     ,_lowerCAmelCase       )
            finetune(**_lowerCAmelCase       )
            accelerator.wait_for_everyone()
            assert os.path.exists(_lowerCAmelCase       )
            logger.info("Self-training job completed: iteration: %d, stage: 1."     ,_lowerCAmelCase       )

        if iteration > 0 and args.finetune_on_labeled_data:
            # Stage 2 (optional): fine-tuning on the original labeled data
            lowercase__		       =						os.path.join(_lowerCAmelCase     ,"best-checkpoint"       )
            lowercase__		       =						os.path.join(_lowerCAmelCase     ,"stage-2"       )
            # Update arguments_dict
            lowercase__		       =						model_path
            lowercase__		       =						data_files["train"]
            lowercase__		       =						current_output_dir

            lowercase__		       =						os.path.join(_lowerCAmelCase     ,"best-checkpoint"     ,_lowerCAmelCase       )
            if os.path.exists(_lowerCAmelCase       ):
                logger.info(
                    "Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2."     ,_lowerCAmelCase     ,_lowerCAmelCase     ,)
            else:
                logger.info("***** Running self-training: iteration: %d, stage: 2 *****"     ,_lowerCAmelCase       )
                finetune(**_lowerCAmelCase       )
                accelerator.wait_for_everyone()
                assert os.path.exists(_lowerCAmelCase       )
                logger.info("Self-training job completed: iteration: %d, stage: 2."     ,_lowerCAmelCase       )

        lowercase__		       =						iteration
        lowercase__		       =						data_dir_format(iteration + 1       )

        lowercase__		       =						AutoConfig.from_pretrained(os.path.join(_lowerCAmelCase     ,"best-checkpoint"       )       )
        lowercase__		       =						config.idalabel
        lowercase__		       =						os.path.join(_lowerCAmelCase     ,"eval_results_best-checkpoint.json"       )
        lowercase__		       =						os.path.join(_lowerCAmelCase     ,"test_results_best-checkpoint.json"       )
        assert os.path.exists(_lowerCAmelCase       )

        with open(_lowerCAmelCase     ,"r"       ) as f:
            lowercase__		       =						float(json.load(_lowerCAmelCase       )[args.eval_metric]       )
        lowercase__		       =						os.path.join(_lowerCAmelCase     ,"infer_output_best-checkpoint.csv"       )
        assert os.path.exists(_lowerCAmelCase       )
        # Loading the dataset from local csv or json files.
        lowercase__		       =						load_dataset(args.data_file_extension     ,data_files={"data": data_files["infer"]}       )["data"]
        lowercase__		       =						load_dataset("csv"     ,data_files={"data": infer_output_file}       )["data"]

        if accelerator.is_main_process:
            os.makedirs(_lowerCAmelCase     ,exist_ok=_lowerCAmelCase       )
            shutil.copy(_lowerCAmelCase     ,os.path.join(_lowerCAmelCase     ,f'''eval_results_iter-{iteration}.json'''       )       )
            if os.path.exists(_lowerCAmelCase       ):
                shutil.copy(_lowerCAmelCase     ,os.path.join(_lowerCAmelCase     ,f'''test_results_iter-{iteration}.json'''       )       )
            create_pseudo_labeled_data(_lowerCAmelCase     ,_lowerCAmelCase     ,_lowerCAmelCase     ,_lowerCAmelCase     ,_lowerCAmelCase     ,_lowerCAmelCase       )
        accelerator.wait_for_everyone()

        lowercase__		       =						os.path.join(_lowerCAmelCase     ,f'''train_pseudo.{args.data_file_extension}'''       )

        if args.evaluation_strategy != IntervalStrategy.NO.value:
            lowercase__		       =						eval_result

            if best_iteration is None:
                lowercase__		       =						new_iteration
                lowercase__		       =						new_eval_result
            else:
                if new_eval_result - best_eval_result > args.early_stopping_threshold:
                    lowercase__		       =						new_iteration
                    lowercase__		       =						new_eval_result
                    lowercase__		       =						0
                else:
                    if new_eval_result == best_eval_result:
                        lowercase__		       =						new_iteration
                        lowercase__		       =						new_eval_result
                    early_stopping_patience_counter += 1

                if early_stopping_patience_counter >= args.early_stopping_patience:
                    lowercase__		       =						True

        progress_bar.update(1       )

        if should_training_stop:
            break

    if best_iteration is not None:
        # Save the best iteration
        logger.info("Best iteration: %d"     ,_lowerCAmelCase       )
        logger.info("Best evaluation result: %s = %f"     ,args.eval_metric     ,_lowerCAmelCase       )
        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            shutil.copy(
                os.path.join(_lowerCAmelCase     ,f'''eval_results_iter-{iteration}.json'''       )     ,os.path.join(_lowerCAmelCase     ,"eval_results_best-iteration.json"       )     ,)
    else:
        # Assume that the last iteration is the best
        logger.info("Best iteration: %d"     ,args.max_selftrain_iterations - 1       )
        logger.info("Best evaluation result: %s = %f"     ,args.eval_metric     ,_lowerCAmelCase       )
        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            shutil.copy(
                os.path.join(_lowerCAmelCase     ,f'''eval_results_iter-{args.max_selftrain_iterations - 1}.json'''       )     ,os.path.join(_lowerCAmelCase     ,"eval_results_best-iteration.json"       )     ,)









'''simple docstring'''





import os
import time

import numpy as np
import onnxruntime as ort


SCREAMING_SNAKE_CASE__      					=     "1"
SCREAMING_SNAKE_CASE__      					=     "0"
SCREAMING_SNAKE_CASE__      					=     "1"

SCREAMING_SNAKE_CASE__      					=     ort.SessionOptions()
SCREAMING_SNAKE_CASE__      					=     ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
SCREAMING_SNAKE_CASE__      					=     ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
SCREAMING_SNAKE_CASE__      					=     ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
SCREAMING_SNAKE_CASE__      					=     ort.RunOptions()

SCREAMING_SNAKE_CASE__      					=     128
SCREAMING_SNAKE_CASE__      					=     1
SCREAMING_SNAKE_CASE__      					=     np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_SNAKE_CASE__      					=     np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_SNAKE_CASE__      					=     np.ones((batch, sequence), dtype=np.intaa)

print("Warm up phase...")
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print("Start inference...")
SCREAMING_SNAKE_CASE__      					=     time.time()
SCREAMING_SNAKE_CASE__      					=     2000
SCREAMING_SNAKE_CASE__      					=     {}
for iter in range(max_iters):
 SCREAMING_SNAKE_CASE__      					=     sess.run(
     None,
     {
         sess.get_inputs()[0].name: input_ids,
         sess.get_inputs()[1].name: attention_mask,
         sess.get_inputs()[2].name: token_type_ids,
     },
     run_options=run_opt,
 )
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1000 / max_iters))




