"""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


lowerCAmelCase__  :  List[Any]						     =	logging.getLogger(__name__)

lowerCAmelCase__  :  Any						     =	'pytorch_model.bin'
@dataclasses.dataclass
class 				snake_case       :




					"""simple docstring"""

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






@dataclasses.dataclass
class 				snake_case       :




					"""simple docstring"""

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






@dataclasses.dataclass
class 				snake_case       :




					"""simple docstring"""

					snake_case__				    = dataclasses.field(
					    metadata={"help": "The output directory where the model predictions and checkpoints will be written."}				)
					snake_case__				    = dataclasses.field(
					    default="accuracy"      ,    metadata={"help": "The evaluation metric used for the task."}				)
					snake_case__				    = dataclasses.field(
					    default="no"      ,    metadata={
					        "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
					    }      ,    )
					snake_case__				    = dataclasses.field(
					    default=10      ,    metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}      ,    )
					snake_case__				    = dataclasses.field(
					    default=0.0      ,    metadata={
					        "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
					    }      ,    )
					snake_case__				    = dataclasses.field(
					    default=__UpperCAmelCase      ,    metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}      ,    )
					snake_case__				    = dataclasses.field(
					    default=__UpperCAmelCase      ,    metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}      ,    )
					snake_case__				    = dataclasses.field(
					    default=__UpperCAmelCase      ,    metadata={"help": "Whether to fine-tune on labeled data after pseudo training."}      ,    )
					snake_case__				    = dataclasses.field(
					    default=0.0      ,    metadata={"help": "Confidence threshold for pseudo-labeled data filtering."}      ,    )
					snake_case__				    = dataclasses.field(
					    default=1_00      ,    metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}      ,    )
					snake_case__				    = dataclasses.field(
					    default=__UpperCAmelCase      ,    metadata={"help": "Random seed for initialization."}      ,    )






def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							datasets.concatenate_datasets([infer_input, infer_output]					,			axis=1       )

				if args.do_filter_by_confidence:
								UpperCAmelCase__             =							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
								UpperCAmelCase__             =							int(eval_result * len(lowerCamelCase       )       )
								print(lowerCamelCase       )
								UpperCAmelCase__             =							dataset.sort('probability'					,			reverse=lowerCamelCase       )
								UpperCAmelCase__             =							dataset.select(range(lowerCamelCase       )       )

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

				UpperCAmelCase__             =							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					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			**lowerCamelCase       ):
				UpperCAmelCase__             =							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__             =							STModelArguments(model_name_or_path=lowerCamelCase       )
				UpperCAmelCase__             =							STDataArguments(train_file=lowerCamelCase					,			infer_file=lowerCamelCase       )
				UpperCAmelCase__             =							STTrainingArguments(output_dir=lowerCamelCase       )
				UpperCAmelCase__             =							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
				UpperCAmelCase__             =							{}
				UpperCAmelCase__             =							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__             =							args.train_file
				UpperCAmelCase__             =							args.infer_file

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

				for key in data_files:
								UpperCAmelCase__             =							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__             =							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__             =							f'''{args.output_dir}/self-train_iter-{{}}'''.format
				UpperCAmelCase__             =							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()

				UpperCAmelCase__             =							None
				UpperCAmelCase__             =							None
				UpperCAmelCase__             =							0
				UpperCAmelCase__             =							False
				# Show the progress bar
				UpperCAmelCase__             =							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__             =							data_dir_format(lowerCamelCase       )
								assert os.path.exists(lowerCamelCase       )

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

								UpperCAmelCase__             =							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
												UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			'best-checkpoint'       )
												UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			'stage-2'       )
												# Update arguments_dict
												UpperCAmelCase__             =							model_path
												UpperCAmelCase__             =							data_files['train']
												UpperCAmelCase__             =							current_output_dir

												UpperCAmelCase__             =							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       )

								UpperCAmelCase__             =							iteration
								UpperCAmelCase__             =							data_dir_format(iteration + 1       )

								UpperCAmelCase__             =							AutoConfig.from_pretrained(os.path.join(lowerCamelCase					,			'best-checkpoint'       )       )
								UpperCAmelCase__             =							config.idalabel
								UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			'eval_results_best-checkpoint.json'       )
								UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			'test_results_best-checkpoint.json'       )
								assert os.path.exists(lowerCamelCase       )

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

								UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			f'''train_pseudo.{args.data_file_extension}'''       )

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

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

																if early_stopping_patience_counter >= args.early_stopping_patience:
																				UpperCAmelCase__             =							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"""






import os

import numpy
import onnx



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							a.name
				UpperCAmelCase__             =							b.name

				UpperCAmelCase__             =							''
				UpperCAmelCase__             =							''

				UpperCAmelCase__             =							a == b

				UpperCAmelCase__             =							name_a
				UpperCAmelCase__             =							name_b

				return res



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				for i, input_name in enumerate(node_proto.input       ):
								if input_name == name:
												node_proto.input.insert(lowerCamelCase					,			lowerCamelCase       )
												node_proto.input.pop(i + 1       )

				if node_proto.op_type == "If":
								_graph_replace_input_with(node_proto.attribute[0].g					,			lowerCamelCase					,			lowerCamelCase       )
								_graph_replace_input_with(node_proto.attribute[1].g					,			lowerCamelCase					,			lowerCamelCase       )
				if node_proto.op_type == "Loop":
								_graph_replace_input_with(node_proto.attribute[0].g					,			lowerCamelCase					,			lowerCamelCase       )



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				for n in graph_proto.node:
								_node_replace_input_with(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							list(model.graph.initializer       )
				UpperCAmelCase__             =							list(model_without_ext.graph.initializer       )
				for i, ref_i in ind_to_replace:
								assert inits_with_data[i].name == inits[i].name
								assert inits_with_data[ref_i].name == inits[ref_i].name
								assert i > ref_i

								UpperCAmelCase__             =							inits[i].name
								UpperCAmelCase__             =							inits[ref_i].name

								model_without_ext.graph.initializer.remove(inits[i]       )

								# for n in model.graph.node:
								_graph_replace_input_with(model_without_ext.graph					,			lowerCamelCase					,			lowerCamelCase       )







def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							os.path.dirname(lowerCamelCase       )
				UpperCAmelCase__             =							os.path.basename(lowerCamelCase       )

				UpperCAmelCase__             =							onnx.load(os.path.join(lowerCamelCase					,			lowerCamelCase       )       )

				UpperCAmelCase__             =							list(model.graph.initializer       )

				UpperCAmelCase__             =							set()
				UpperCAmelCase__             =							{}
				UpperCAmelCase__             =							[]

				UpperCAmelCase__             =							0

				for i in range(len(lowerCamelCase       )       ):
								if i in dup_set:
												continue

								for j in range(i + 1					,			len(lowerCamelCase       )       ):
												if j in dup_set:
																continue
												if _is_equal_tensor_proto(inits[i]					,			inits[j]       ):
																dup_set.add(lowerCamelCase       )
																dup_set.add(lowerCamelCase       )

																UpperCAmelCase__             =							inits[j].data_type
																UpperCAmelCase__             =							numpy.prod(inits[j].dims       )
																if dtype == 1:
																				mem_size *= 4
																elif dtype == 6:
																				mem_size *= 4
																elif dtype == 7 or dtype == 1_1:
																				mem_size *= 8
																else:
																				print('unexpected data type: '					,			lowerCamelCase       )
																total_reduced_size += mem_size

																UpperCAmelCase__             =							inits[i].name
																UpperCAmelCase__             =							inits[j].name

																if name_i in dup_map:
																				dup_map[name_i].append(lowerCamelCase       )
																else:
																				UpperCAmelCase__             =							[name_j]
																ind_to_replace.append((j, i)       )

				print('total reduced size: '					,			total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4					,			'GB'       )

				UpperCAmelCase__             =							sorted(lowerCamelCase       )
				_remove_dup_initializers_from_model(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

				UpperCAmelCase__             =							'optimized_' + model_file_name
				UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			lowerCamelCase       )
				onnx.save(lowerCamelCase					,			lowerCamelCase       )

				return new_model



