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_:   str   =      logging.getLogger(__name__)

A_:   Tuple   =      '''pytorch_model.bin'''


@dataclasses.dataclass
class _lowercase       :





							"""simple docstring"""




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






@dataclasses.dataclass
class _lowercase       :





							"""simple docstring"""




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






@dataclasses.dataclass
class _lowercase       :





							"""simple docstring"""




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






def        __lowerCAmelCase      (	_A			,_A			,_A			,_A			,_A			,_A       ):



							"""simple docstring"""


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

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

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

							_lowercase					=						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        __lowerCAmelCase      (	_A			,_A			,_A			,_A			,**_A       ):



							"""simple docstring"""


							_lowercase					=						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()

							_lowercase					=						STModelArguments(model_name_or_path=UpperCAmelCase_       )
							_lowercase					=						STDataArguments(train_file=UpperCAmelCase_			,infer_file=UpperCAmelCase_       )
							_lowercase					=						STTrainingArguments(output_dir=UpperCAmelCase_       )
							_lowercase					=						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
							_lowercase					=						{}
							_lowercase					=						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
							_lowercase					=						args.train_file
							_lowercase					=						args.infer_file

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

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

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

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

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

																					_lowercase					=						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_       )

														_lowercase					=						iteration
														_lowercase					=						data_dir_format(iteration + 1       )

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

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

														_lowercase					=						os.path.join(UpperCAmelCase_			,f'''train_pseudo.{args.data_file_extension}'''       )

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

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

																												if early_stopping_patience_counter >= args.early_stopping_patience:
																																			_lowercase					=						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 argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


a__					:			Optional[int]          =     version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=False					,				)   ->Optional[int]:
	output_path.parent.mkdir(parents=UpperCAmelCase_					,				exist_ok=UpperCAmelCase_							)
	# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
	# so we check the torch version for backwards compatibility
	if is_torch_less_than_1_11:
		export(
		    UpperCAmelCase_					,				UpperCAmelCase_					,				f=output_path.as_posix()					,				input_names=UpperCAmelCase_					,				output_names=UpperCAmelCase_					,				dynamic_axes=UpperCAmelCase_					,				do_constant_folding=UpperCAmelCase_					,				use_external_data_format=UpperCAmelCase_					,				enable_onnx_checker=UpperCAmelCase_					,				opset_version=UpperCAmelCase_					,				)
	else:
		export(
		    UpperCAmelCase_					,				UpperCAmelCase_					,				f=output_path.as_posix()					,				input_names=UpperCAmelCase_					,				output_names=UpperCAmelCase_					,				dynamic_axes=UpperCAmelCase_					,				do_constant_folding=UpperCAmelCase_					,				opset_version=UpperCAmelCase_					,				)




@torch.no_grad()
def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_ = False							)   ->Union[str, Any]:
	snake_case__	       =   torch.floataa if fpaa else torch.floataa
	if fpaa and torch.cuda.is_available():
		snake_case__	       =   'cuda'
	elif fpaa and not torch.cuda.is_available():
		raise ValueError('`float16` model export is only supported on GPUs with CUDA'							)
	else:
		snake_case__	       =   'cpu'
	snake_case__	       =   Path(UpperCAmelCase_							)

	# VAE DECODER
	snake_case__	       =   AutoencoderKL.from_pretrained(model_path + '/vae'							)
	snake_case__	       =   vae_decoder.config.latent_channels
	# forward only through the decoder part
	snake_case__	       =   vae_decoder.decode
	onnx_export(
	    UpperCAmelCase_					,				model_args=(
	        torch.randn(1					,				UpperCAmelCase_					,				25					,				25							).to(device=UpperCAmelCase_					,				dtype=UpperCAmelCase_							),
	        False,
	    )					,				output_path=output_path / 'vae_decoder' / 'model.onnx'					,				ordered_input_names=['latent_sample', 'return_dict']					,				output_names=['sample']					,				dynamic_axes={
	        'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
	    }					,				opset=UpperCAmelCase_					,				)
	del vae_decoder


if __name__ == "__main__":
			a__					:			Optional[int]          =     argparse.ArgumentParser()

			parser.add_argument(
			    '''--model_path''',
			    type=str,
			    required=True,
			    help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
			)

			parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
			parser.add_argument(
			    '''--opset''',
			    default=14,
			    type=int,
			    help='''The version of the ONNX operator set to use.''',
			)
			parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')

			a__					:			List[str]          =     parser.parse_args()
			print(args.output_path)
			convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
			print('''SD: Done: ONNX''')



