


'''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       							=				logging.getLogger(__name__)

__UpperCAmelCase       							=				'''pytorch_model.bin'''




@dataclasses.dataclass
class 						a__							:





							'''simple docstring'''






							lowercase__     :  str      		= dataclasses.field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}    )
							lowercase__     :  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'''






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




@dataclasses.dataclass
class 						a__							:





							'''simple docstring'''






							lowercase__     :  str      		= dataclasses.field(
							    metadata={"help": "The output directory where the model predictions and checkpoints will be written."}    )
							lowercase__     :  Optional[str]      		= dataclasses.field(
							    default="accuracy"							,							metadata={"help": "The evaluation metric used for the task."}    )
							lowercase__     :  Optional[str]      		= dataclasses.field(
							    default="no"							,							metadata={
							        "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
							    }							,							)
							lowercase__     :  Optional[int]      		= dataclasses.field(
							    default=1_0							,							metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}							,							)
							lowercase__     :  Optional[float]      		= dataclasses.field(
							    default=0.0							,							metadata={
							        "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
							    }							,							)
							lowercase__     :  Optional[bool]      		= dataclasses.field(
							    default=a__							,							metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}							,							)
							lowercase__     :  Optional[bool]      		= dataclasses.field(
							    default=a__							,							metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}							,							)
							lowercase__     :  Optional[bool]      		= dataclasses.field(
							    default=a__							,							metadata={"help": "Whether to fine-tune on labeled data after pseudo training."}							,							)
							lowercase__     :  Optional[float]      		= dataclasses.field(
							    default=0.0							,							metadata={"help": "Confidence threshold for pseudo-labeled data filtering."}							,							)
							lowercase__     :  Optional[int]      		= dataclasses.field(
							    default=1_0_0							,							metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}							,							)
							lowercase__     :  Optional[int]      		= dataclasses.field(
							    default=a__							,							metadata={"help": "Random seed for initialization."}							,							)


def    _snake_case  (	A					,       A					,       A					,       A					,       A					,       A			)		->       Tuple:
							lowerCAmelCase__			  =      datasets.concatenate_datasets([infer_input, infer_output]					,       axis=1			)

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

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

							lowerCAmelCase__			  =      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    _snake_case  (	A					,       A					,       A					,       A					,       **A			)		->       Dict:
							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=A			)
							lowerCAmelCase__			  =      STDataArguments(train_file=A					,       infer_file=A			)
							lowerCAmelCase__			  =      STTrainingArguments(output_dir=A			)
							lowerCAmelCase__			  =      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
							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=A			)
																					os.makedirs(A					,       exist_ok=A			)
							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(A			)
														assert os.path.exists(A			)

														# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
														# iteration > 0
														lowerCAmelCase__			  =      os.path.join(A					,       '''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(A					,       A			):
																												arguments_dict.update({key: value}			)

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

																					lowerCAmelCase__			  =      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			)

														lowerCAmelCase__			  =      iteration
														lowerCAmelCase__			  =      data_dir_format(iteration + 1			)

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

														with open(A					,       '''r'''			) as f:
																					lowerCAmelCase__			  =      float(json.load(A			)[args.eval_metric]			)
														lowerCAmelCase__			  =      os.path.join(A					,       '''infer_output_best-checkpoint.csv'''			)
														assert os.path.exists(A			)
														# 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(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()

														lowerCAmelCase__			  =      os.path.join(A					,       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'''					,       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'''			)					,       )



'''simple docstring'''






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__UpperCAmelCase       							=				False




class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							pass




@nightly
@require_torch_gpu
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''   ,       torch_dtype=torch.floataa  )
														pipe.to(lowerCamelCase_  )
														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''  )

														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.dual_guided(
														    prompt='''first prompt'''   ,       image=lowerCamelCase_   ,       text_to_image_strength=0.75   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=2   ,       output_type='''numpy'''   ,       ).images

														with tempfile.TemporaryDirectory() as tmpdirname:
																					pipe.save_pretrained(lowerCamelCase_  )
																					lowerCAmelCase__			  =      VersatileDiffusionPipeline.from_pretrained(lowerCamelCase_   ,       torch_dtype=torch.floataa  )
														pipe.to(lowerCamelCase_  )
														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      generator.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.dual_guided(
														    prompt='''first prompt'''   ,       image=lowerCamelCase_   ,       text_to_image_strength=0.75   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=2   ,       output_type='''numpy'''   ,       ).images

														assert np.abs(image - new_image  ).sum() < 1e-5, "Models don't have the same forward pass"







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''   ,       torch_dtype=torch.floataa  )
														pipe.to(lowerCamelCase_  )
														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''cyberpunk 2077'''
														lowerCAmelCase__			  =      load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''  )
														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.dual_guided(
														    prompt=lowerCamelCase_   ,       image=lowerCamelCase_   ,       text_to_image_strength=0.75   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=50   ,       output_type='''numpy'''   ,       ).images

														lowerCAmelCase__			  =      image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]  )

														assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-1

														lowerCAmelCase__			  =      '''A painting of a squirrel eating a burger '''
														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.text_to_image(
														    prompt=lowerCamelCase_   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=50   ,       output_type='''numpy'''  ).images

														lowerCAmelCase__			  =      image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]  )

														assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-1

														lowerCAmelCase__			  =      pipe.image_variation(lowerCamelCase_   ,       generator=lowerCamelCase_   ,       output_type='''numpy'''  ).images

														lowerCAmelCase__			  =      image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]  )

														assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-1