

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 							UpperCAmelCase_						:
							"""simple docstring"""







							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 							UpperCAmelCase_						:
							"""simple docstring"""







							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 							UpperCAmelCase_						:
							"""simple docstring"""







							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=10							,			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=100							,			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   __UpperCAmelCase					(      __a :     Tuple						,__a :     Optional[int]						,__a :     List[str]						,__a :     Dict						,__a :     str						,__a :     Any      )     ->	List[str]:

					"""simple docstring"""

					_a      :       Union[str, Any]   =						datasets.concatenate_datasets([infer_input, infer_output]						,axis=1      )

					if args.do_filter_by_confidence:
										_a      :       Any   =						dataset.filter(lambda __a      : example["probability"] > args.confidence_threshold      )

					if args.do_filter_by_val_performance:
										assert eval_result >= 0.0 and eval_result <= 1.0
										_a      :       List[Any]   =						int(eval_result * len(__a      )      )
										print(__a      )
										_a      :       int   =						dataset.sort('''probability'''						,reverse=__a      )
										_a      :       Union[str, Any]   =						dataset.select(range(__a      )      )

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

					_a      :       int   =						os.path.join(__a						,F"""train_pseudo.{args.data_file_extension}"""      )
					if args.data_file_extension == "csv":
										dataset.to_csv(__a						,index=__a      )
					else:
										dataset.to_json(__a      )







def   __UpperCAmelCase					(      __a :     Optional[Any]						,__a :     str						,__a :     Union[str, Any]						,__a :     Dict						,**__a :     List[Any]      )     ->	Tuple:

					"""simple docstring"""

					_a      :       Optional[int]   =						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()

					_a      :       List[Any]   =						STModelArguments(model_name_or_path=__a      )
					_a      :       Union[str, Any]   =						STDataArguments(train_file=__a						,infer_file=__a      )
					_a      :       Optional[int]   =						STTrainingArguments(output_dir=__a      )
					_a      :       Optional[int]   =						argparse.Namespace()

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

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

    # Sanity checks
					_a      :       List[str]   =						{}
					_a      :       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
					_a      :       int   =						args.train_file
					_a      :       Optional[int]   =						args.infer_file

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

					for key in data_files:
										_a      :       List[str]   =						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:
															_a      :       List[str]   =						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...'''      )
					_a      :       Union[str, Any]   =						F"""{args.output_dir}/self-train_iter-{{}}""".format
					_a      :       str   =						data_dir_format(0      )

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

					_a      :       str   =						None
					_a      :       Union[str, Any]   =						None
					_a      :       Any   =						0
					_a      :       Any   =						False
					# Show the progress bar
					_a      :       Optional[int]   =						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      )      ):
										_a      :       Dict   =						data_dir_format(__a      )
										assert os.path.exists(__a      )

										# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
										# iteration > 0
										_a      :       Any   =						os.path.join(__a						,'''stage-1'''      )
										_a      :       Union[str, 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(__a						,__a      ):
																				arguments_dict.update({key: value}      )

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

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

															_a      :       Optional[Any]   =						os.path.join(__a						,'''best-checkpoint'''						,__a      )
															if os.path.exists(__a      ):
																				logger.info(
																				    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'''						,__a						,__a						,)
															else:
																				logger.info('''***** Running self-training: iteration: %d, stage: 2 *****'''						,__a      )
																				finetune(**__a      )
																				accelerator.wait_for_everyone()
																				assert os.path.exists(__a      )
																				logger.info('''Self-training job completed: iteration: %d, stage: 2.'''						,__a      )

										_a      :       List[str]   =						iteration
										_a      :       Tuple   =						data_dir_format(iteration + 1      )

										_a      :       List[Any]   =						AutoConfig.from_pretrained(os.path.join(__a						,'''best-checkpoint'''      )      )
										_a      :       Optional[int]   =						config.idalabel
										_a      :       Optional[Any]   =						os.path.join(__a						,'''eval_results_best-checkpoint.json'''      )
										_a      :       Union[str, Any]   =						os.path.join(__a						,'''test_results_best-checkpoint.json'''      )
										assert os.path.exists(__a      )

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

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

										_a      :       Optional[Any]   =						os.path.join(__a						,F"""train_pseudo.{args.data_file_extension}"""      )

										if args.evaluation_strategy != IntervalStrategy.NO.value:
															_a      :       Optional[int]   =						eval_result

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

																				if early_stopping_patience_counter >= args.early_stopping_patience:
																									_a      :       Optional[Any]   =						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'''						,__a      )
										logger.info('''Best evaluation result: %s = %f'''						,args.eval_metric						,__a      )
										accelerator.wait_for_everyone()
										if accelerator.is_main_process:
															shutil.copy(
															    os.path.join(__a						,F"""eval_results_iter-{iteration}.json"""      )						,os.path.join(__a						,'''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						,__a      )
										accelerator.wait_for_everyone()
										if accelerator.is_main_process:
															shutil.copy(
															    os.path.join(__a						,F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json"""      )						,os.path.join(__a						,'''eval_results_best-iteration.json'''      )						,)






from random import randint
from tempfile import TemporaryFile

import numpy as np



def   __UpperCAmelCase					(      __a :     Optional[Any]						,__a :     int						,__a :     Any      )     ->	int:

					"""simple docstring"""

					_a      :       int   =						0
					if start < end:
										_a      :       Tuple   =						randint(__a						,__a      )
										_a      :       Tuple   =						a[end]
										_a      :       List[str]   =						a[pivot]
										_a      :       Any   =						temp

										_a ,   _a      :       Optional[int]   =						_in_place_partition(__a						,__a						,__a      )
										count += _in_place_quick_sort(__a						,__a						,p - 1      )
										count += _in_place_quick_sort(__a						,p + 1						,__a      )
					return count



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Tuple						,__a :     Dict      )     ->	Dict:

					"""simple docstring"""

					_a      :       Dict   =						0
					_a      :       Tuple   =						randint(__a						,__a      )
					_a      :       List[Any]   =						a[end]
					_a      :       str   =						a[pivot]
					_a      :       str   =						temp
					_a      :       Dict   =						start - 1
					for index in range(__a						,__a      ):
										count += 1
										if a[index] < a[end]:  # check if current val is less than pivot value
															_a      :       int   =						new_pivot_index + 1
															_a      :       Any   =						a[new_pivot_index]
															_a      :       Optional[int]   =						a[index]
															_a      :       str   =						temp

					_a      :       Union[str, Any]   =						a[new_pivot_index + 1]
					_a      :       Tuple   =						a[end]
					_a      :       Any   =						temp
					return new_pivot_index + 1, count


a__												=						TemporaryFile()
a__												=						100  # 1000 elements are to be sorted


a__						,     a__												=						0, 1  # mean and standard deviation
a__												=						np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a__												=						np.load(outfile)
a__												=						len(M) - 1
a__												=						_in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)







print(z)



