



"""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


lowercase_						=       logging.getLogger(__name__)

lowercase_						=       """pytorch_model.bin"""
@dataclasses.dataclass
class 			_snake_case     :
				UpperCamelCase__			:   str       						=dataclasses.field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""})
				UpperCamelCase__			:   Optional[str]       						=dataclasses.field(
				    default=lowercase__      ,		metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""}      ,		)
@dataclasses.dataclass
class 			_snake_case     :
				UpperCamelCase__			:   str       						=dataclasses.field(metadata={"""help""": """A csv or a json file containing the training data."""})
				UpperCamelCase__			:   str       						=dataclasses.field(metadata={"""help""": """A csv or a json file containing the data to predict on."""})
				UpperCamelCase__			:   Optional[str]       						=dataclasses.field(
				    default=lowercase__      ,		metadata={"""help""": """A csv or a json file containing the validation data."""})
				UpperCamelCase__			:   Optional[str]       						=dataclasses.field(
				    default=lowercase__      ,		metadata={"""help""": """The name of the task to train on."""}      ,		)
				UpperCamelCase__			:   Optional[List[str]]       						=dataclasses.field(
				    default=lowercase__      ,		metadata={"""help""": """The list of labels for the task."""})
@dataclasses.dataclass
class 			_snake_case     :
				UpperCamelCase__			:   str       						=dataclasses.field(
				    metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""})
				UpperCamelCase__			:   Optional[str]       						=dataclasses.field(
				    default="""accuracy"""      ,		metadata={"""help""": """The evaluation metric used for the task."""})
				UpperCamelCase__			:   Optional[str]       						=dataclasses.field(
				    default="""no"""      ,		metadata={
				        """help""": """The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"""
				    }      ,		)
				UpperCamelCase__			:   Optional[int]       						=dataclasses.field(
				    default=1_0      ,		metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}      ,		)
				UpperCamelCase__			:   Optional[float]       						=dataclasses.field(
				    default=0.0      ,		metadata={
				        """help""": """How much the specified evaluation metric must improve to satisfy early stopping conditions."""
				    }      ,		)
				UpperCamelCase__			:   Optional[bool]       						=dataclasses.field(
				    default=lowercase__      ,		metadata={"""help""": """Whether to filter the pseudo-labeled data based on the confidence score."""}      ,		)
				UpperCamelCase__			:   Optional[bool]       						=dataclasses.field(
				    default=lowercase__      ,		metadata={"""help""": """Whether to filter the pseudo-labeled data based on the validation performance."""}      ,		)
				UpperCamelCase__			:   Optional[bool]       						=dataclasses.field(
				    default=lowercase__      ,		metadata={"""help""": """Whether to fine-tune on labeled data after pseudo training."""}      ,		)
				UpperCamelCase__			:   Optional[float]       						=dataclasses.field(
				    default=0.0      ,		metadata={"""help""": """Confidence threshold for pseudo-labeled data filtering."""}      ,		)
				UpperCamelCase__			:   Optional[int]       						=dataclasses.field(
				    default=1_0_0      ,		metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}      ,		)
				UpperCamelCase__			:   Optional[int]       						=dataclasses.field(
				    default=lowercase__      ,		metadata={"""help""": """Random seed for initialization."""}      ,		)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			datasets.concatenate_datasets([infer_input, infer_output]  ,					axis=1					)

			if args.do_filter_by_confidence:
						lowercase__   					=			dataset.filter(lambda SCREAMING_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(SCREAMING_SNAKE_CASE_					)					)
						print(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			dataset.sort("probability"  ,					reverse=SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			dataset.select(range(SCREAMING_SNAKE_CASE_					)					)

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

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




def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					**SCREAMING_SNAKE_CASE_					):
			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=SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			STDataArguments(train_file=SCREAMING_SNAKE_CASE_  ,					infer_file=SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			STTrainingArguments(output_dir=SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			argparse.Namespace()

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

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

    # 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=SCREAMING_SNAKE_CASE_					)
									os.makedirs(SCREAMING_SNAKE_CASE_  ,					exist_ok=SCREAMING_SNAKE_CASE_					)
			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(SCREAMING_SNAKE_CASE_					)
						assert os.path.exists(SCREAMING_SNAKE_CASE_					)

						# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
						# iteration > 0
						lowercase__   					=			os.path.join(SCREAMING_SNAKE_CASE_  ,					"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(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
												arguments_dict.update({key: value}					)

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

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

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

						lowercase__   					=			iteration
						lowercase__   					=			data_dir_format(iteration + 1					)

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

						with open(SCREAMING_SNAKE_CASE_  ,					"r"					) as f:
									lowercase__   					=			float(json.load(SCREAMING_SNAKE_CASE_					)[args.eval_metric]					)
						lowercase__   					=			os.path.join(SCREAMING_SNAKE_CASE_  ,					"infer_output_best-checkpoint.csv"					)
						assert os.path.exists(SCREAMING_SNAKE_CASE_					)
						# 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(SCREAMING_SNAKE_CASE_  ,					exist_ok=SCREAMING_SNAKE_CASE_					)
									shutil.copy(SCREAMING_SNAKE_CASE_  ,					os.path.join(SCREAMING_SNAKE_CASE_  ,					f'''eval_results_iter-{iteration}.json'''					)					)
									if os.path.exists(SCREAMING_SNAKE_CASE_					):
												shutil.copy(SCREAMING_SNAKE_CASE_  ,					os.path.join(SCREAMING_SNAKE_CASE_  ,					f'''test_results_iter-{iteration}.json'''					)					)
									create_pseudo_labeled_data(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
						accelerator.wait_for_everyone()

						lowercase__   					=			os.path.join(SCREAMING_SNAKE_CASE_  ,					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"  ,					SCREAMING_SNAKE_CASE_					)
						logger.info("Best evaluation result: %s = %f"  ,					args.eval_metric  ,					SCREAMING_SNAKE_CASE_					)
						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
									shutil.copy(
									    os.path.join(SCREAMING_SNAKE_CASE_  ,					f'''eval_results_iter-{iteration}.json'''					)  ,					os.path.join(SCREAMING_SNAKE_CASE_  ,					"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  ,					SCREAMING_SNAKE_CASE_					)
						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
									shutil.copy(
									    os.path.join(SCREAMING_SNAKE_CASE_  ,					f'''eval_results_iter-{args.max_selftrain_iterations - 1}.json'''					)  ,					os.path.join(SCREAMING_SNAKE_CASE_  ,					"eval_results_best-iteration.json"					)  ,					)









from pathlib import Path

import fire



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			Path(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			Path(SCREAMING_SNAKE_CASE_					)
			dest_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)
			for path in src_dir.iterdir():
						lowercase__   					=			[x.rstrip() for x in list(path.open().readlines()					)][:n]
						lowercase__   					=			dest_dir.joinpath(path.name					)
						print(SCREAMING_SNAKE_CASE_					)
						dest_path.open("w"					).write("\n".join(SCREAMING_SNAKE_CASE_					)					)


if __name__ == "__main__":
							fire.Fire(minify)




