
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


__lowerCamelCase				=			logging.getLogger(__name__)

__lowerCamelCase				=			"""pytorch_model.bin"""






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






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






@dataclasses.dataclass
class     UpperCAmelCase						:
   A__   :						str			=	dataclasses.field(
       metadata={"help": "The output directory where the model predictions and checkpoints will be written."}					)
   A__   :						Optional[str]			=	dataclasses.field(
       default="accuracy"			,metadata={"help": "The evaluation metric used for the task."}					)
   A__   :						Optional[str]			=	dataclasses.field(
       default="no"			,metadata={
           "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
       }			,)
   A__   :						Optional[int]			=	dataclasses.field(
       default=10			,metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}			,)
   A__   :						Optional[float]			=	dataclasses.field(
       default=0.0			,metadata={
           "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
       }			,)
   A__   :						Optional[bool]			=	dataclasses.field(
       default=A_			,metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}			,)
   A__   :						Optional[bool]			=	dataclasses.field(
       default=A_			,metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}			,)
   A__   :						Optional[bool]			=	dataclasses.field(
       default=A_			,metadata={"help": "Whether to fine-tune on labeled data after pseudo training."}			,)
   A__   :						Optional[float]			=	dataclasses.field(
       default=0.0			,metadata={"help": "Confidence threshold for pseudo-labeled data filtering."}			,)
   A__   :						Optional[int]			=	dataclasses.field(
       default=1_00			,metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}			,)
   A__   :						Optional[int]			=	dataclasses.field(
       default=A_			,metadata={"help": "Random seed for initialization."}			,)



def      UpperCamelCase   (       __lowerCamelCase	:					List[Any]							,   __lowerCamelCase	:					Optional[Any]							,   __lowerCamelCase	:					Any							,   __lowerCamelCase	:					List[Any]							,   __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					Optional[int]       ):
       snake_case			:  str     							= datasets.concatenate_datasets([infer_input, infer_output]							,   axis=1       )

       if args.do_filter_by_confidence:
              snake_case			:  Optional[Any]     							= dataset.filter(lambda __lowerCamelCase       : example["probability"] > args.confidence_threshold       )

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

       snake_case			:  Tuple     							= dataset.remove_columns(["label", "probability"]       )
       snake_case			:  List[Any]     							= dataset.rename_column("prediction"							,   "label"       )
       snake_case			:  List[str]     							= dataset.map(lambda __lowerCamelCase       : {"label": idalabel[example["label"]]}       )
       snake_case			:  Tuple     							= dataset.shuffle(seed=args.seed       )

       snake_case			:  Optional[int]     							= 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      UpperCamelCase   (       __lowerCamelCase	:					Dict							,   __lowerCamelCase	:					Any							,   __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					List[Any]							,   **__lowerCamelCase	:					str       ):
       snake_case			:  List[str]     							= 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()

       snake_case			:  int     							= STModelArguments(model_name_or_path=__lowerCamelCase       )
       snake_case			:  Dict     							= STDataArguments(train_file=__lowerCamelCase							,   infer_file=__lowerCamelCase       )
       snake_case			:  Union[str, Any]     							= STTrainingArguments(output_dir=__lowerCamelCase       )
       snake_case			:  Union[str, Any]     							= 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
       snake_case			:  Optional[int]     							= {}
       snake_case			:  int     							= 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
       snake_case			:  Union[str, Any]     							= args.train_file
       snake_case			:  int     							= args.infer_file

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

       for key in data_files:
              snake_case			:  Optional[int]     							= 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:
                     snake_case			:  Optional[Any]     							= 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..."       )
       snake_case			:  List[Any]     							= f"""{args.output_dir}/self-train_iter-{{}}""".format
       snake_case			:  Union[str, Any]     							= 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()

       snake_case			:  Tuple     							= None
       snake_case			:  List[Any]     							= None
       snake_case			:  Tuple     							= 0
       snake_case			:  List[Any]     							= False
       # Show the progress bar
       snake_case			:  Dict     							= 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       )       ):
              snake_case			:  Tuple     							= data_dir_format(__lowerCamelCase       )
              assert os.path.exists(__lowerCamelCase       )

              # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
              # iteration > 0
              snake_case			:  Tuple     							= os.path.join(__lowerCamelCase							,   "stage-1"       )
              snake_case			:  Tuple     							= {
                  "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}       )

              snake_case			:  Any     							= 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
                     snake_case			:  List[Any]     							= os.path.join(__lowerCamelCase							,   "best-checkpoint"       )
                     snake_case			:  Any     							= os.path.join(__lowerCamelCase							,   "stage-2"       )
                     # Update arguments_dict
                     snake_case			:  Tuple     							= model_path
                     snake_case			:  Optional[Any]     							= data_files["train"]
                     snake_case			:  Optional[int]     							= current_output_dir

                     snake_case			:  Dict     							= 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       )

              snake_case			:  Tuple     							= iteration
              snake_case			:  Tuple     							= data_dir_format(iteration + 1       )

              snake_case			:  int     							= AutoConfig.from_pretrained(os.path.join(__lowerCamelCase							,   "best-checkpoint"       )       )
              snake_case			:  Tuple     							= config.idalabel
              snake_case			:  List[str]     							= os.path.join(__lowerCamelCase							,   "eval_results_best-checkpoint.json"       )
              snake_case			:  List[str]     							= os.path.join(__lowerCamelCase							,   "test_results_best-checkpoint.json"       )
              assert os.path.exists(__lowerCamelCase       )

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

              snake_case			:  Union[str, Any]     							= os.path.join(__lowerCamelCase							,   f"""train_pseudo.{args.data_file_extension}"""       )

              if args.evaluation_strategy != IntervalStrategy.NO.value:
                     snake_case			:  Dict     							= eval_result

                     if best_iteration is None:
                            snake_case			:  int     							= new_iteration
                            snake_case			:  Tuple     							= new_eval_result
                     else:
                            if new_eval_result - best_eval_result > args.early_stopping_threshold:
                                   snake_case			:  str     							= new_iteration
                                   snake_case			:  Any     							= new_eval_result
                                   snake_case			:  List[str]     							= 0
                            else:
                                   if new_eval_result == best_eval_result:
                                          snake_case			:  List[str]     							= new_iteration
                                          snake_case			:  List[Any]     							= new_eval_result
                                   early_stopping_patience_counter += 1

                            if early_stopping_patience_counter >= args.early_stopping_patience:
                                   snake_case			:  List[Any]     							= 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"       )							,   )







__lowerCamelCase				=			{
    0: """0""",
    1: """1""",
    2: """2""",
    3: """3""",
    4: """4""",
    5: """5""",
    6: """6""",
    7: """7""",
    8: """8""",
    9: """9""",
    10: """a""",
    11: """b""",
    12: """c""",
    13: """d""",
    14: """e""",
    15: """f""",
}


def      UpperCamelCase   (       __lowerCamelCase	:					float       ):
       assert type(__lowerCamelCase       ) in (int, float) and decimal == int(__lowerCamelCase       )
       snake_case			:  str     							= int(__lowerCamelCase       )
       snake_case			:  str     							= ""
       snake_case			:  str     							= False
       if decimal < 0:
              snake_case			:  Optional[Any]     							= True
              decimal *= -1
       while decimal > 0:
              snake_case							,   snake_case			:  Union[str, Any]     							= divmod(__lowerCamelCase							,   16       )
              snake_case			:  Tuple     							= values[remainder] + hexadecimal
       snake_case			:  Tuple     							= "0x" + hexadecimal
       if negative:
              snake_case			:  Any     							= "-" + hexadecimal
       return hexadecimal


if __name__ == "__main__":
   import doctest

   doctest.testmod()





