






'''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


UpperCamelCase    :      Optional[Any]  			=		logging.getLogger(__name__)

UpperCamelCase    :      Union[str, Any]  			=		'pytorch_model.bin'




@dataclasses.dataclass
class 		UpperCamelCase__    :




		'''simple docstring'''





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




@dataclasses.dataclass
class 		UpperCamelCase__    :




		'''simple docstring'''





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




@dataclasses.dataclass
class 		UpperCamelCase__    :




		'''simple docstring'''





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



def 						A__			(				__lowerCAmelCase		:			List[Any]   ,    __lowerCAmelCase		:			Optional[Any]   ,    __lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			Optional[int]						):
						lowerCamelCase__         =  datasets.concatenate_datasets([infer_input, infer_output]   ,    axis=1						)

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

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

						lowerCamelCase__         =  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 						A__			(				__lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			Any   ,    __lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Optional[int]   ,    **__lowerCAmelCase		:			str						):
						lowerCamelCase__         =  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()

						lowerCamelCase__         =  STModelArguments(model_name_or_path=__lowerCAmelCase						)
						lowerCamelCase__         =  STDataArguments(train_file=__lowerCAmelCase   ,    infer_file=__lowerCAmelCase						)
						lowerCamelCase__         =  STTrainingArguments(output_dir=__lowerCAmelCase						)
						lowerCamelCase__         =  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
						lowerCamelCase__         =  {}
						lowerCamelCase__         =  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
						lowerCamelCase__         =  args.train_file
						lowerCamelCase__         =  args.infer_file

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

						for key in data_files:
												lowerCamelCase__         =  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:
																		lowerCamelCase__         =  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..."""						)
						lowerCamelCase__         =  F'''{args.output_dir}/self-train_iter-{{}}'''.format
						lowerCamelCase__         =  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()

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

												# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
												# iteration > 0
												lowerCamelCase__         =  os.path.join(__lowerCAmelCase   ,    """stage-1"""						)
												lowerCamelCase__         =  {
												    """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}						)

												lowerCamelCase__         =  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
																		lowerCamelCase__         =  os.path.join(__lowerCAmelCase   ,    """best-checkpoint"""						)
																		lowerCamelCase__         =  os.path.join(__lowerCAmelCase   ,    """stage-2"""						)
																		# Update arguments_dict
																		lowerCamelCase__         =  model_path
																		lowerCamelCase__         =  data_files["""train"""]
																		lowerCamelCase__         =  current_output_dir

																		lowerCamelCase__         =  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						)

												lowerCamelCase__         =  iteration
												lowerCamelCase__         =  data_dir_format(iteration + 1						)

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

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

												lowerCamelCase__         =  os.path.join(__lowerCAmelCase   ,    F'''train_pseudo.{args.data_file_extension}'''						)

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

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

																								if early_stopping_patience_counter >= args.early_stopping_patience:
																														lowerCamelCase__         =  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"""						)   ,    )









'''simple docstring'''







from manim import *




class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  Rectangle(height=0.5   ,width=0.5       )
								lowerCamelCase__         =  Rectangle(height=0.46   ,width=0.46       ).set_stroke(width=0       )

								lowerCamelCase__         =  [mem.copy() for i in range(6       )]
								lowerCamelCase__         =  [mem.copy() for i in range(6       )]
								lowerCamelCase__         =  VGroup(*_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )
								lowerCamelCase__         =  VGroup(*_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )
								lowerCamelCase__         =  VGroup(_lowerCAmelCase   ,_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )
								lowerCamelCase__         =  Text("""CPU"""   ,font_size=24       )
								lowerCamelCase__         =  Group(_lowerCAmelCase   ,_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0.5   ,aligned_edge=_lowerCAmelCase       )
								cpu.move_to([-2.5, -0.5, 0]       )
								self.add(_lowerCAmelCase       )

								lowerCamelCase__         =  [mem.copy() for i in range(1       )]
								lowerCamelCase__         =  VGroup(*_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )
								lowerCamelCase__         =  Text("""GPU"""   ,font_size=24       )
								lowerCamelCase__         =  Group(_lowerCAmelCase   ,_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0.5   ,aligned_edge=_lowerCAmelCase       )
								gpu.align_to(_lowerCAmelCase   ,_lowerCAmelCase       )
								gpu.set_x(gpu.get_x() - 1       )

								self.add(_lowerCAmelCase       )

								lowerCamelCase__         =  [mem.copy() for i in range(6       )]
								lowerCamelCase__         =  VGroup(*_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )

								lowerCamelCase__         =  Text("""Model"""   ,font_size=24       )
								lowerCamelCase__         =  Group(_lowerCAmelCase   ,_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0.5   ,aligned_edge=_lowerCAmelCase       )
								model.move_to([3, -1.0, 0]       )

								self.play(
								    Create(_lowerCAmelCase   ,run_time=1       )   ,Create(_lowerCAmelCase   ,run_time=1       )   ,Create(_lowerCAmelCase   ,run_time=1       )   ,)

								lowerCamelCase__         =  MarkupText(
								    F'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'''   ,font_size=24   ,)

								lowerCamelCase__         =  Square(side_length=2.2       )
								key.move_to([-5, 2, 0]       )

								lowerCamelCase__         =  MarkupText(
								    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''   ,font_size=18   ,)

								key_text.move_to([-5, 2.4, 0]       )

								step_a.move_to([2, 2, 0]       )
								self.play(Write(_lowerCAmelCase   ,run_time=2.5       )   ,Write(_lowerCAmelCase       )   ,Write(_lowerCAmelCase       )       )

								self.add(_lowerCAmelCase       )

								lowerCamelCase__         =  []
								lowerCamelCase__         =  []
								lowerCamelCase__         =  []
								for i, rect in enumerate(_lowerCAmelCase       ):
														lowerCamelCase__         =  Rectangle(height=0.46   ,width=0.46       ).set_stroke(width=0.0       ).set_fill(_lowerCAmelCase   ,opacity=0.7       )
														cpu_target.move_to(_lowerCAmelCase       )
														cpu_target.generate_target()
														lowerCamelCase__         =  0.46 / 4
														lowerCamelCase__         =  0.46 / 3

														if i == 0:
																				cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT       )   ,buff=0.02   ,direction=_lowerCAmelCase       )
																				cpu_target.target.set_x(cpu_target.target.get_x() + 0.1       )
														elif i == 3:
																				cpu_target.target.next_to(cpu_targs[0].target   ,direction=_lowerCAmelCase   ,buff=0.0       )
														else:
																				cpu_target.target.next_to(cpu_targs[i - 1].target   ,direction=_lowerCAmelCase   ,buff=0.0       )
														cpu_targs.append(_lowerCAmelCase       )

														first_animations.append(rect.animate(run_time=0.5       ).set_stroke(_lowerCAmelCase       )       )
														second_animations.append(MoveToTarget(_lowerCAmelCase   ,run_time=1.5       )       )

								self.play(*_lowerCAmelCase       )
								self.play(*_lowerCAmelCase       )

								self.wait()

