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						:   Tuple         =			logging.getLogger(__name__)

__SCREAMING_SNAKE_CASE						:   Any         =			'pytorch_model.bin'



@dataclasses.dataclass
class        lowercase_   :
							_lowerCamelCase						=		dataclasses.field(
							    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'}		)
							_lowerCamelCase						=		dataclasses.field(
							    default=__snake_case						,			metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'}						,			)




@dataclasses.dataclass
class        lowercase_   :
							_lowerCamelCase						=		dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'}		)
							_lowerCamelCase						=		dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'}		)
							_lowerCamelCase						=		dataclasses.field(
							    default=__snake_case						,			metadata={'help': 'A csv or a json file containing the validation data.'}		)
							_lowerCamelCase						=		dataclasses.field(
							    default=__snake_case						,			metadata={'help': 'The name of the task to train on.'}						,			)
							_lowerCamelCase						=		dataclasses.field(
							    default=__snake_case						,			metadata={'help': 'The list of labels for the task.'}		)




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




def  snake_case						(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      __lowercase						)	->	Tuple:

						'''simple docstring'''



						_snake_case						:					List[Any]    =   datasets.concatenate_datasets([infer_input, infer_output] ,      axis=1						)

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

						if args.do_filter_by_val_performance:
												assert eval_result >= 0.0 and eval_result <= 1.0
												_snake_case						:					List[str]    =   int(eval_result * len(__lowercase						)						)
												print(__lowercase						)
												_snake_case						:					Dict    =   dataset.sort("probability" ,      reverse=__lowercase						)
												_snake_case						:					List[str]    =   dataset.select(range(__lowercase						)						)

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

						_snake_case						:					List[Any]    =   os.path.join(__lowercase ,      F"""train_pseudo.{args.data_file_extension}"""						)
						if args.data_file_extension == "csv":
												dataset.to_csv(__lowercase ,      index=__lowercase						)
						else:
												dataset.to_json(__lowercase						)

def  snake_case						(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      **__lowercase						)	->	Optional[int]:

						'''simple docstring'''



						_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						:					Tuple    =   STModelArguments(model_name_or_path=__lowercase						)
						_snake_case						:					Optional[int]    =   STDataArguments(train_file=__lowercase ,      infer_file=__lowercase						)
						_snake_case						:					List[str]    =   STTrainingArguments(output_dir=__lowercase						)
						_snake_case						:					str    =   argparse.Namespace()

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

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

    # Sanity checks
						_snake_case						:					Optional[Any]    =   {}
						_snake_case						:					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
						_snake_case						:					Dict    =   args.train_file
						_snake_case						:					Dict    =   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						:					List[Any]    =   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[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..."						)
						_snake_case						:					List[str]    =   F"""{args.output_dir}/self-train_iter-{{}}""".format
						_snake_case						:					int    =   data_dir_format(0						)

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

						_snake_case						:					str    =   None
						_snake_case						:					List[str]    =   None
						_snake_case						:					Dict    =   0
						_snake_case						:					int    =   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						:					List[Any]    =   data_dir_format(__lowercase						)
												assert os.path.exists(__lowercase						)

												# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
												# iteration > 0
												_snake_case						:					int    =   os.path.join(__lowercase ,      "stage-1"						)
												_snake_case						:					Optional[int]    =   {
												    "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(__lowercase ,      __lowercase						):
																								arguments_dict.update({key: value}						)

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

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

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

												_snake_case						:					int    =   iteration
												_snake_case						:					Union[str, Any]    =   data_dir_format(iteration + 1						)

												_snake_case						:					Union[str, Any]    =   AutoConfig.from_pretrained(os.path.join(__lowercase ,      "best-checkpoint"						)						)
												_snake_case						:					List[Any]    =   config.idalabel
												_snake_case						:					List[str]    =   os.path.join(__lowercase ,      "eval_results_best-checkpoint.json"						)
												_snake_case						:					List[Any]    =   os.path.join(__lowercase ,      "test_results_best-checkpoint.json"						)
												assert os.path.exists(__lowercase						)

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

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

												_snake_case						:					Optional[Any]    =   os.path.join(__lowercase ,      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						:					Tuple    =   new_iteration
																								_snake_case						:					Optional[Any]    =   new_eval_result
																		else:
																								if new_eval_result - best_eval_result > args.early_stopping_threshold:
																														_snake_case						:					Dict    =   new_iteration
																														_snake_case						:					Tuple    =   new_eval_result
																														_snake_case						:					Tuple    =   0
																								else:
																														if new_eval_result == best_eval_result:
																																				_snake_case						:					List[Any]    =   new_iteration
																																				_snake_case						:					List[str]    =   new_eval_result
																														early_stopping_patience_counter += 1

																								if early_stopping_patience_counter >= args.early_stopping_patience:
																														_snake_case						:					str    =   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" ,      __lowercase						)
												logger.info("Best evaluation result: %s = %f" ,      args.eval_metric ,      __lowercase						)
												accelerator.wait_for_everyone()
												if accelerator.is_main_process:
																		shutil.copy(
																		    os.path.join(__lowercase ,      F"""eval_results_iter-{iteration}.json"""						) ,      os.path.join(__lowercase ,      "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 ,      __lowercase						)
												accelerator.wait_for_everyone()
												if accelerator.is_main_process:
																		shutil.copy(
																		    os.path.join(__lowercase ,      F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json"""						) ,      os.path.join(__lowercase ,      "eval_results_best-iteration.json"						) ,      )
import logging

from transformers import PretrainedConfig


__SCREAMING_SNAKE_CASE						:   Any         =			logging.getLogger(__name__)


__SCREAMING_SNAKE_CASE						:   int         =			{
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'bertabs'



							def __init__(						self				,    lowercase_=30_522				,    lowercase_=512				,    lowercase_=6				,    lowercase_=512				,    lowercase_=8				,    lowercase_=512				,    lowercase_=0.2				,    lowercase_=6				,    lowercase_=768				,    lowercase_=8				,    lowercase_=2_048				,    lowercase_=0.2				,    **lowercase_				,    ):
													super().__init__(**lowercase_    )

													_snake_case						:					List[Any]    =   vocab_size
													_snake_case						:					int    =   max_pos

													_snake_case						:					Tuple    =   enc_layers
													_snake_case						:					Optional[Any]    =   enc_hidden_size
													_snake_case						:					Union[str, Any]    =   enc_heads
													_snake_case						:					str    =   enc_ff_size
													_snake_case						:					Any    =   enc_dropout

													_snake_case						:					Tuple    =   dec_layers
													_snake_case						:					Optional[Any]    =   dec_hidden_size
													_snake_case						:					Dict    =   dec_heads
													_snake_case						:					str    =   dec_ff_size
													_snake_case						:					List[str]    =   dec_dropout