




"""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   =       logging.getLogger(__name__)

A   =       """pytorch_model.bin"""



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






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






@dataclasses.dataclass
class      A				:
			lowercase_    =	dataclasses.field(
			    metadata={"help": "The output directory where the model predictions and checkpoints will be written."}   )
			lowercase_    =	dataclasses.field(
			    default="accuracy"							,    metadata={"help": "The evaluation metric used for the task."}   )
			lowercase_    =	dataclasses.field(
			    default="no"							,    metadata={
			        "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
			    }							,    )
			lowercase_    =	dataclasses.field(
			    default=1_0							,    metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}							,    )
			lowercase_    =	dataclasses.field(
			    default=0.0							,    metadata={
			        "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
			    }							,    )
			lowercase_    =	dataclasses.field(
			    default=__magic_name__							,    metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}							,    )
			lowercase_    =	dataclasses.field(
			    default=__magic_name__							,    metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}							,    )
			lowercase_    =	dataclasses.field(
			    default=__magic_name__							,    metadata={"help": "Whether to fine-tune on labeled data after pseudo training."}							,    )
			lowercase_    =	dataclasses.field(
			    default=0.0							,    metadata={"help": "Confidence threshold for pseudo-labeled data filtering."}							,    )
			lowercase_    =	dataclasses.field(
			    default=1_0_0							,    metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}							,    )
			lowercase_    =	dataclasses.field(
			    default=__magic_name__							,    metadata={"help": "Random seed for initialization."}							,    )






def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Dict:


			"""simple docstring"""
			__UpperCAmelCase	: List[Any]		=					datasets.concatenate_datasets([infer_input, infer_output]				,       axis=1							)

			if args.do_filter_by_confidence:
						__UpperCAmelCase	: Optional[int]		=					dataset.filter(lambda UpperCamelCase							: example["probability"] > args.confidence_threshold							)

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

			__UpperCAmelCase	: Tuple		=					dataset.remove_columns(["label", "probability"]							)
			__UpperCAmelCase	: int		=					dataset.rename_column("prediction"				,       "label"							)
			__UpperCAmelCase	: Optional[Any]		=					dataset.map(lambda UpperCamelCase							: {"label": idalabel[example["label"]]}							)
			__UpperCAmelCase	: int		=					dataset.shuffle(seed=args.seed							)

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





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       **UpperCamelCase							)			->			Optional[int]:


			"""simple docstring"""
			__UpperCAmelCase	: 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()

			__UpperCAmelCase	: Any		=					STModelArguments(model_name_or_path=UpperCamelCase							)
			__UpperCAmelCase	: List[str]		=					STDataArguments(train_file=UpperCamelCase				,       infer_file=UpperCamelCase							)
			__UpperCAmelCase	: List[Any]		=					STTrainingArguments(output_dir=UpperCamelCase							)
			__UpperCAmelCase	: str		=					argparse.Namespace()

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

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

    # Sanity checks
			__UpperCAmelCase	: str		=					{}
			__UpperCAmelCase	: 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
			__UpperCAmelCase	: str		=					args.train_file
			__UpperCAmelCase	: Union[str, Any]		=					args.infer_file

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

			for key in data_files:
						__UpperCAmelCase	: Optional[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:
									__UpperCAmelCase	: 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..."							)
			__UpperCAmelCase	: Any		=					f"{args.output_dir}/self-train_iter-{{}}".format
			__UpperCAmelCase	: Optional[int]		=					data_dir_format(0							)

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

			__UpperCAmelCase	: List[str]		=					None
			__UpperCAmelCase	: Tuple		=					None
			__UpperCAmelCase	: List[Any]		=					0
			__UpperCAmelCase	: int		=					False
			# Show the progress bar
			__UpperCAmelCase	: str		=					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							)							):
						__UpperCAmelCase	: List[Any]		=					data_dir_format(UpperCamelCase							)
						assert os.path.exists(UpperCamelCase							)

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

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

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

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

						__UpperCAmelCase	: Any		=					iteration
						__UpperCAmelCase	: Tuple		=					data_dir_format(iteration + 1							)

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

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

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

						__UpperCAmelCase	: Optional[int]		=					os.path.join(UpperCamelCase				,       f"train_pseudo.{args.data_file_extension}"							)

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

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

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








"""simple docstring"""





import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
A   =       logging.getLogger(__name__)

def 					_UpperCamelCase    (			UpperCamelCase							)			->			Any:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[Any]		=					git.Repo(search_parent_directories=UpperCamelCase							)
			__UpperCAmelCase	: Any		=					{
			    "repo_id": str(UpperCamelCase							),
			    "repo_sha": str(repo.head.object.hexsha							),
			    "repo_branch": str(repo.active_branch							),
			}

			with open(os.path.join(UpperCamelCase				,       "git_log.json"							)				,       "w"							) as f:
						json.dump(UpperCamelCase				,       UpperCamelCase				,       indent=4							)

def 					_UpperCamelCase    (			UpperCamelCase							)			->			str:


			"""simple docstring"""
			if params.n_gpu <= 0:
						__UpperCAmelCase	: str		=					0
						__UpperCAmelCase	: Dict		=					-1
						__UpperCAmelCase	: Tuple		=					True
						__UpperCAmelCase	: List[str]		=					False
						return

			assert torch.cuda.is_available()

			logger.info("Initializing GPUs"							)
			if params.n_gpu > 1:
						assert params.local_rank != -1

						__UpperCAmelCase	: Optional[int]		=					int(os.environ["WORLD_SIZE"]							)
						__UpperCAmelCase	: Union[str, Any]		=					int(os.environ["N_GPU_NODE"]							)
						__UpperCAmelCase	: Optional[int]		=					int(os.environ["RANK"]							)

						# number of nodes / node ID
						__UpperCAmelCase	: int		=					params.world_size // params.n_gpu_per_node
						__UpperCAmelCase	: Optional[int]		=					params.global_rank // params.n_gpu_per_node
						__UpperCAmelCase	: Tuple		=					True

						assert params.n_nodes == int(os.environ["N_NODES"]							)
						assert params.node_id == int(os.environ["NODE_RANK"]							)

			# local job (single GPU)
			else:
						assert params.local_rank == -1

						__UpperCAmelCase	: Union[str, Any]		=					1
						__UpperCAmelCase	: Dict		=					0
						__UpperCAmelCase	: List[Any]		=					0
						__UpperCAmelCase	: Tuple		=					0
						__UpperCAmelCase	: Any		=					1
						__UpperCAmelCase	: str		=					1
						__UpperCAmelCase	: Dict		=					False

			# sanity checks
			assert params.n_nodes >= 1
			assert 0 <= params.node_id < params.n_nodes
			assert 0 <= params.local_rank <= params.global_rank < params.world_size
			assert params.world_size == params.n_nodes * params.n_gpu_per_node

			# define whether this is the master process / if we are in multi-node distributed mode
			__UpperCAmelCase	: Union[str, Any]		=					params.node_id == 0 and params.local_rank == 0
			__UpperCAmelCase	: Dict		=					params.n_nodes > 1

			# summary
			__UpperCAmelCase	: Any		=					f"--- Global rank: {params.global_rank} - "
			logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes							)
			logger.info(PREFIX + "Node ID        : %i" % params.node_id							)
			logger.info(PREFIX + "Local rank     : %i" % params.local_rank							)
			logger.info(PREFIX + "World size     : %i" % params.world_size							)
			logger.info(PREFIX + "GPUs per node  : %i" % params.n_gpu_per_node							)
			logger.info(PREFIX + "Master         : %s" % str(params.is_master							)							)
			logger.info(PREFIX + "Multi-node     : %s" % str(params.multi_node							)							)
			logger.info(PREFIX + "Multi-GPU      : %s" % str(params.multi_gpu							)							)
			logger.info(PREFIX + "Hostname       : %s" % socket.gethostname()							)

			# set GPU device
			torch.cuda.set_device(params.local_rank							)

			# initialize multi-GPU
			if params.multi_gpu:
						logger.info("Initializing PyTorch distributed"							)
						torch.distributed.init_process_group(
						    init_method="env://"				,       backend="nccl"				,       )





def 					_UpperCamelCase    (			UpperCamelCase							)			->			Tuple:


			"""simple docstring"""
			np.random.seed(args.seed							)
			torch.manual_seed(args.seed							)
			if args.n_gpu > 0:
						torch.cuda.manual_seed_all(args.seed							)


