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


UpperCamelCase  							=						logging.getLogger(__name__)

UpperCamelCase  							=						'''pytorch_model.bin'''



@dataclasses.dataclass
class   snake_case_       :
   __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   snake_case_       :
   __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   snake_case_       :
   __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=100  ,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 			lowercase_				(			_lowerCamelCase							:       Tuple		,	_lowerCamelCase							:       List[str]		,	_lowerCamelCase							:       Tuple		,	_lowerCamelCase							:       str		,	_lowerCamelCase							:       Any		,	_lowerCamelCase							:       List[Any]):
    lowercase__ :   Optional[int]    					=				datasets.concatenate_datasets([infer_input, infer_output]		,	axis=1)

    if args.do_filter_by_confidence:
        lowercase__ :   Dict    					=				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
        lowercase__ :   int    					=				int(eval_result * len(_lowerCamelCase))
        print(_lowerCamelCase)
        lowercase__ :   str    					=				dataset.sort("probability"		,	reverse=_lowerCamelCase)
        lowercase__ :   str    					=				dataset.select(range(_lowerCamelCase))

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

    lowercase__ :   List[str]    					=				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 			lowercase_				(			_lowerCamelCase							:       Optional[Any]		,	_lowerCamelCase							:       Any		,	_lowerCamelCase							:       Tuple		,	_lowerCamelCase							:       Optional[int]		,	**_lowerCamelCase							:       str):
    lowercase__ :   List[Any]    					=				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__ :   Optional[int]    					=				STModelArguments(model_name_or_path=_lowerCamelCase)
    lowercase__ :   Dict    					=				STDataArguments(train_file=_lowerCamelCase		,	infer_file=_lowerCamelCase)
    lowercase__ :   List[str]    					=				STTrainingArguments(output_dir=_lowerCamelCase)
    lowercase__ :   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
    lowercase__ :   Optional[int]    					=				{}
    lowercase__ :   List[Any]    					=				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__ :   int    					=				args.train_file
    lowercase__ :   Tuple    					=				args.infer_file

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

    for key in data_files:
        lowercase__ :   str    					=				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__ :   int    					=				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__ :   Optional[int]    					=				f'''{args.output_dir}/self-train_iter-{{}}'''.format
    lowercase__ :   str    					=				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__ :   Dict    					=				None
    lowercase__ :   Union[str, Any]    					=				None
    lowercase__ :   Union[str, Any]    					=				0
    lowercase__ :   Tuple    					=				False
    # Show the progress bar
    lowercase__ :   Optional[int]    					=				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__ :   List[str]    					=				data_dir_format(_lowerCamelCase)
        assert os.path.exists(_lowerCamelCase)

        # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
        # iteration > 0
        lowercase__ :   Dict    					=				os.path.join(_lowerCamelCase		,	"stage-1")
        lowercase__ :   List[str]    					=				{
            "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__ :   Optional[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
            lowercase__ :   Tuple    					=				os.path.join(_lowerCamelCase		,	"best-checkpoint")
            lowercase__ :   str    					=				os.path.join(_lowerCamelCase		,	"stage-2")
            # Update arguments_dict
            lowercase__ :   int    					=				model_path
            lowercase__ :   List[str]    					=				data_files["train"]
            lowercase__ :   List[str]    					=				current_output_dir

            lowercase__ :   str    					=				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__ :   Any    					=				iteration
        lowercase__ :   List[str]    					=				data_dir_format(iteration + 1)

        lowercase__ :   Optional[Any]    					=				AutoConfig.from_pretrained(os.path.join(_lowerCamelCase		,	"best-checkpoint"))
        lowercase__ :   Optional[Any]    					=				config.idalabel
        lowercase__ :   Optional[int]    					=				os.path.join(_lowerCamelCase		,	"eval_results_best-checkpoint.json")
        lowercase__ :   Dict    					=				os.path.join(_lowerCamelCase		,	"test_results_best-checkpoint.json")
        assert os.path.exists(_lowerCamelCase)

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

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

        if args.evaluation_strategy != IntervalStrategy.NO.value:
            lowercase__ :   Union[str, Any]    					=				eval_result

            if best_iteration is None:
                lowercase__ :   Union[str, Any]    					=				new_iteration
                lowercase__ :   List[Any]    					=				new_eval_result
            else:
                if new_eval_result - best_eval_result > args.early_stopping_threshold:
                    lowercase__ :   Dict    					=				new_iteration
                    lowercase__ :   Any    					=				new_eval_result
                    lowercase__ :   str    					=				0
                else:
                    if new_eval_result == best_eval_result:
                        lowercase__ :   str    					=				new_iteration
                        lowercase__ :   List[Any]    					=				new_eval_result
                    early_stopping_patience_counter += 1

                if early_stopping_patience_counter >= args.early_stopping_patience:
                    lowercase__ :   Dict    					=				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")		,	)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase					: Union[str, Any]      =       logging.get_logger(__name__)

_UpperCAmelCase					: List[str]      =       {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       List[str]	     =				"gpt_neox"



		def __init__( self				,      A_=50_432				,      A_=6_144				,      A_=44				,      A_=64				,      A_=24_576				,      A_="gelu"				,      A_=0.25				,      A_=10_000				,      A_=0.0				,      A_=0.0				,      A_=0.1				,      A_=2_048				,      A_=0.02				,      A_=1e-5				,      A_=True				,      A_=0				,      A_=2				,      A_=False				,      A_=True				,      A_=None				,      **A_				,      )  ->					Tuple:
				"""simple docstring"""
				super().__init__(bos_token_id=A_				,      eos_token_id=A_				,      **A_       )
				UpperCamelCase		=     vocab_size
				UpperCamelCase		=     max_position_embeddings
				UpperCamelCase		=     hidden_size
				UpperCamelCase		=     num_hidden_layers
				UpperCamelCase		=     num_attention_heads
				UpperCamelCase		=     intermediate_size
				UpperCamelCase		=     hidden_act
				UpperCamelCase		=     rotary_pct
				UpperCamelCase		=     rotary_emb_base
				UpperCamelCase		=     attention_dropout
				UpperCamelCase		=     hidden_dropout
				UpperCamelCase		=     classifier_dropout
				UpperCamelCase		=     initializer_range
				UpperCamelCase		=     layer_norm_eps
				UpperCamelCase		=     use_cache
				UpperCamelCase		=     tie_word_embeddings
				UpperCamelCase		=     use_parallel_residual
				UpperCamelCase		=     rope_scaling
				self._rope_scaling_validation()

				if self.hidden_size % self.num_attention_heads != 0:
						raise ValueError(
						    'The hidden size is not divisble by the number of attention heads! Make sure to update them!'       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				if self.rope_scaling is None:
						return

				if not isinstance(self.rope_scaling				,      A_       ) or len(self.rope_scaling       ) != 2:
						raise ValueError(
						    '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
						    F'''got {self.rope_scaling}'''       )
				UpperCamelCase		=     self.rope_scaling.get('type'				,      A_       )
				UpperCamelCase		=     self.rope_scaling.get('factor'				,      A_       )
				if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
						raise ValueError(
						    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''       )
				if rope_scaling_factor is None or not isinstance(A_				,      A_       ) or rope_scaling_factor <= 1.0:
						raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''       )
