


'''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				: Tuple							    =				logging.getLogger(__name__)

_UpperCAmelCase				: Optional[Any]							    =				"""pytorch_model.bin"""


@dataclasses.dataclass
class       a__				:







				"""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=__A							,						metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'}							,						)


@dataclasses.dataclass
class       a__				:







				"""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=__A							,						metadata={'help': 'A csv or a json file containing the validation data.'}      )
				__UpperCamelCase    :		Optional[str]	=	dataclasses.field(
				    default=__A							,						metadata={'help': 'The name of the task to train on.'}							,						)
				__UpperCamelCase    :		Optional[List[str]]	=	dataclasses.field(
				    default=__A							,						metadata={'help': 'The list of labels for the task.'}      )


@dataclasses.dataclass
class       a__				:







				"""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=__A							,						metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'}							,						)
				__UpperCamelCase    :		Optional[bool]	=	dataclasses.field(
				    default=__A							,						metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'}							,						)
				__UpperCamelCase    :		Optional[bool]	=	dataclasses.field(
				    default=__A							,						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=__A							,						metadata={'help': 'Random seed for initialization.'}							,						)

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						__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 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				**lowerCamelCase):
						__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 typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			):
										super().__init__()
										self.register_modules(transformer=__lowercase       ,			vae=__lowercase       ,			scheduler=__lowercase		)

										# create a imagenet -> id dictionary for easier use
										__lowerCAmelCase							       =				{}
										if idalabel is not None:
																for key, value in idalabel.items():
																						for label in value.split(''','''		):
																												__lowerCAmelCase							       =				int(__lowercase		)
																__lowerCAmelCase							       =				dict(sorted(self.labels.items()		)		)




				def 					_snake_case  (self       ,			__lowercase		):

										if not isinstance(__lowercase       ,			__lowercase		):
																__lowerCAmelCase							       =				list(__lowercase		)

										for l in label:
																if l not in self.labels:
																						raise ValueError(
																						    F"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""		)

										return [self.labels[l] for l in label]




				@torch.no_grad()
				def __call__(self       ,			__lowercase       ,			__lowercase = 4.0       ,			__lowercase = None       ,			__lowercase = 50       ,			__lowercase = "pil"       ,			__lowercase = True       ,			):
										__lowerCAmelCase							       =				len(__lowercase		)
										__lowerCAmelCase							       =				self.transformer.config.sample_size
										__lowerCAmelCase							       =				self.transformer.config.in_channels

										__lowerCAmelCase							       =				randn_tensor(
										    shape=(batch_size, latent_channels, latent_size, latent_size)       ,			generator=__lowercase       ,			device=self.device       ,			dtype=self.transformer.dtype       ,			)
										__lowerCAmelCase							       =				torch.cat([latents] * 2		) if guidance_scale > 1 else latents

										__lowerCAmelCase							       =				torch.tensor(__lowercase       ,			device=self.device		).reshape(-1		)
										__lowerCAmelCase							       =				torch.tensor([10_00] * batch_size       ,			device=self.device		)
										__lowerCAmelCase							       =				torch.cat([class_labels, class_null]       ,			0		) if guidance_scale > 1 else class_labels

										# set step values
										self.scheduler.set_timesteps(__lowercase		)

										for t in self.progress_bar(self.scheduler.timesteps		):
																if guidance_scale > 1:
																						__lowerCAmelCase							       =				latent_model_input[: len(__lowercase		) // 2]
																						__lowerCAmelCase							       =				torch.cat([half, half]       ,			dim=0		)
																__lowerCAmelCase							       =				self.scheduler.scale_model_input(__lowercase       ,			__lowercase		)

																__lowerCAmelCase							       =				t
																if not torch.is_tensor(__lowercase		):
																						# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																						# This would be a good case for the `match` statement (Python 3.10+)
																						__lowerCAmelCase							       =				latent_model_input.device.type == '''mps'''
																						if isinstance(__lowercase       ,			__lowercase		):
																												__lowerCAmelCase							       =				torch.floataa if is_mps else torch.floataa
																						else:
																												__lowerCAmelCase							       =				torch.intaa if is_mps else torch.intaa
																						__lowerCAmelCase							       =				torch.tensor([timesteps]       ,			dtype=__lowercase       ,			device=latent_model_input.device		)
																elif len(timesteps.shape		) == 0:
																						__lowerCAmelCase							       =				timesteps[None].to(latent_model_input.device		)
																# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																__lowerCAmelCase							       =				timesteps.expand(latent_model_input.shape[0]		)
																# predict noise model_output
																__lowerCAmelCase							       =				self.transformer(
																    __lowercase       ,			timestep=__lowercase       ,			class_labels=__lowercase		).sample

																# perform guidance
																if guidance_scale > 1:
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				torch.split(__lowercase       ,			len(__lowercase		) // 2       ,			dim=0		)

																						__lowerCAmelCase							       =				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																						__lowerCAmelCase							       =				torch.cat([half_eps, half_eps]       ,			dim=0		)

																						__lowerCAmelCase							       =				torch.cat([eps, rest]       ,			dim=1		)

																# learned sigma
																if self.transformer.config.out_channels // 2 == latent_channels:
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				torch.split(__lowercase       ,			__lowercase       ,			dim=1		)
																else:
																						__lowerCAmelCase							       =				noise_pred

																# compute previous image: x_t -> x_t-1
																__lowerCAmelCase							       =				self.scheduler.step(__lowercase       ,			__lowercase       ,			__lowercase		).prev_sample

										if guidance_scale > 1:
																__lowerCAmelCase       ,					__lowerCAmelCase							       =				latent_model_input.chunk(2       ,			dim=0		)
										else:
																__lowerCAmelCase							       =				latent_model_input

										__lowerCAmelCase							       =				1 / self.vae.config.scaling_factor * latents
										__lowerCAmelCase							       =				self.vae.decode(__lowercase		).sample

										__lowerCAmelCase							       =				(samples / 2 + 0.5).clamp(0       ,			1		)

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										__lowerCAmelCase							       =				samples.cpu().permute(0       ,			2       ,			3       ,			1		).float().numpy()

										if output_type == "pil":
																__lowerCAmelCase							       =				self.numpy_to_pil(__lowercase		)

										if not return_dict:
																return (samples,)

										return ImagePipelineOutput(images=__lowercase		)

