

"""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


A_				:      Any           =			logging.getLogger(__name__)

A_				:      Optional[Any]           =			"pytorch_model.bin"





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





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





@dataclasses.dataclass
class  lowerCamelCase    :
			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=1_0      ,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=A__      ,metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'}      ,)
			lowerCamelCase__ :		Optional[bool]       		=				dataclasses.field(
			    default=A__      ,metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'}      ,)
			lowerCamelCase__ :		Optional[bool]       		=				dataclasses.field(
			    default=A__      ,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=1_0_0      ,metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}      ,)
			lowerCamelCase__ :		Optional[int]       		=				dataclasses.field(
			    default=A__      ,metadata={'help': 'Random seed for initialization.'}      ,)

def 						A							(  snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__     ):




			'''simple docstring'''


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

			if args.do_filter_by_confidence:
						SCREAMING_SNAKE_CASE__										=   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
						SCREAMING_SNAKE_CASE__										=   int(eval_result * len(snake_case__     )     )
						print(snake_case__     )
						SCREAMING_SNAKE_CASE__										=   dataset.sort("""probability"""		,					reverse=snake_case__     )
						SCREAMING_SNAKE_CASE__										=   dataset.select(range(snake_case__     )     )

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

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




def 						A							(  snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   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()

			SCREAMING_SNAKE_CASE__										=   STModelArguments(model_name_or_path=snake_case__     )
			SCREAMING_SNAKE_CASE__										=   STDataArguments(train_file=snake_case__		,					infer_file=snake_case__     )
			SCREAMING_SNAKE_CASE__										=   STTrainingArguments(output_dir=snake_case__     )
			SCREAMING_SNAKE_CASE__										=   argparse.Namespace()

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

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

    # Sanity checks
			SCREAMING_SNAKE_CASE__										=   {}
			SCREAMING_SNAKE_CASE__										=   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
			SCREAMING_SNAKE_CASE__										=   args.train_file
			SCREAMING_SNAKE_CASE__										=   args.infer_file

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

			for key in data_files:
						SCREAMING_SNAKE_CASE__										=   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:
									SCREAMING_SNAKE_CASE__										=   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..."""     )
			SCREAMING_SNAKE_CASE__										=   f"""{args.output_dir}/self-train_iter-{{}}""".format
			SCREAMING_SNAKE_CASE__										=   data_dir_format(0     )

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

			SCREAMING_SNAKE_CASE__										=   None
			SCREAMING_SNAKE_CASE__										=   None
			SCREAMING_SNAKE_CASE__										=   0
			SCREAMING_SNAKE_CASE__										=   False
			# Show the progress bar
			SCREAMING_SNAKE_CASE__										=   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     )     ):
						SCREAMING_SNAKE_CASE__										=   data_dir_format(snake_case__     )
						assert os.path.exists(snake_case__     )

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

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

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

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

						SCREAMING_SNAKE_CASE__										=   iteration
						SCREAMING_SNAKE_CASE__										=   data_dir_format(iteration + 1     )

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

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

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

						SCREAMING_SNAKE_CASE__										=   os.path.join(snake_case__		,					f"""train_pseudo.{args.data_file_extension}"""     )

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

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

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





"""simple docstring"""




from typing import List

from .keymap import KEYMAP, get_character

def 						A							(  snake_case__     ):




			'''simple docstring'''


			def decorator(snake_case__     ):
						SCREAMING_SNAKE_CASE__										=   getattr(snake_case__		,					"""handle_key"""		,					[]     )
						handle += [key]
						setattr(snake_case__		,					"""handle_key"""		,					snake_case__     )
						return func

			return decorator

def 						A							(  *snake_case__     ):




			'''simple docstring'''


			def decorator(snake_case__     ):
						SCREAMING_SNAKE_CASE__										=   getattr(snake_case__		,					"""handle_key"""		,					[]     )
						handle += keys
						setattr(snake_case__		,					"""handle_key"""		,					snake_case__     )
						return func

			return decorator





class  lowerCamelCase    (A__      ):

			def __new__(       cls					:    Dict     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    Optional[Any]							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   super().__new__(cls     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase							)
						if not hasattr(__UpperCAmelCase     ,					"""key_handler"""							):
									setattr(__UpperCAmelCase     ,					"""key_handler"""     ,					{}							)
						setattr(__UpperCAmelCase     ,					"""handle_input"""     ,					KeyHandler.handle_input							)

						for value in attrs.values():
									SCREAMING_SNAKE_CASE__										=   getattr(__UpperCAmelCase     ,					"""handle_key"""     ,					[]							)
									for key in handled_keys:
												SCREAMING_SNAKE_CASE__										=   value
						return new_cls





			@staticmethod
			def        SCREAMING_SNAKE_CASE					(       cls					:    Tuple							) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   get_character()
						if char != KEYMAP["undefined"]:
									SCREAMING_SNAKE_CASE__										=   ord(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   cls.key_handler.get(__UpperCAmelCase							)
						if handler:
									SCREAMING_SNAKE_CASE__										=   char
									return handler(cls							)
						else:
									return None

def 						A							(  cls     ):




			'''simple docstring'''


			return KeyHandler(cls.__name__		,					cls.__bases__		,					cls.__dict__.copy()     )


