




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





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





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


def 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							int				,					a_	:							List[Any]				,					a_	:							List[Any]				,					a_	:							int				,					a_	:							Optional[int]			):
  __a			=					datasets.concatenate_datasets([infer_input, infer_output]				,					axis=1			)

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

  if args.do_filter_by_val_performance:
    assert eval_result >= 0.0 and eval_result <= 1.0
    __a			=					int(eval_result * len(a_			)			)
    print(a_			)
    __a			=					dataset.sort('probability'				,					reverse=a_			)
    __a			=					dataset.select(range(a_			)			)

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

  __a			=					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 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							Dict				,					a_	:							Any				,					a_	:							List[str]				,					**a_	:							Union[str, Any]			):
  __a			=					Accelerator()
  # Make one log on every process with the configuration for debugging.
  logging.basicConfig(
      format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'				,					datefmt='%m/%d/%Y %H:%M:%S'				,					level=logging.INFO				,					)
  logger.info(accelerator.state			)

  # Setup logging, we only want one process per machine to log things on the
  # screen. accelerator.is_local_main_process is only True for one process per
  # machine.
  logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR			)

  if accelerator.is_local_main_process:
    datasets.utils.logging.set_verbosity_warning()
    transformers.utils.logging.set_verbosity_info()
  else:
    datasets.utils.logging.set_verbosity_error()
    transformers.utils.logging.set_verbosity_error()

  __a			=					STModelArguments(model_name_or_path=a_			)
  __a			=					STDataArguments(train_file=a_				,					infer_file=a_			)
  __a			=					STTrainingArguments(output_dir=a_			)
  __a			=					argparse.Namespace()

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

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

    # Sanity checks
  __a			=					{}
  __a			=					None

  # You need to provide the training data and the data to predict on
  assert args.train_file is not None
  assert args.infer_file is not None
  __a			=					args.train_file
  __a			=					args.infer_file

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

  for key in data_files:
    __a			=					data_files[key].split('.'			)[-1]
    assert extension in ["csv", "json"], f"`{key}_file` should be a csv or a json file."
    if args.data_file_extension is None:
      __a			=					extension
    else:
      assert extension == args.data_file_extension, f"`{key}_file` should be a {args.data_file_extension} file`."

  assert (
      args.eval_metric in datasets.list_metrics()
  ), f"{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."

  # If passed along, set the training seed now.
  if args.seed is not None:
    set_seed(args.seed			)

  logger.info('Creating the initial data directory for self-training...'			)
  __a			=					f"{args.output_dir}/self-train_iter-{{}}".format
  __a			=					data_dir_format(0			)

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

  __a			=					None
  __a			=					None
  __a			=					0
  __a			=					False
  # Show the progress bar
  __a			=					tqdm(range(args.max_selftrain_iterations			)				,					disable=not accelerator.is_local_main_process			)

  # Self-train
  for iteration in range(0				,					int(args.max_selftrain_iterations			)			):
    __a			=					data_dir_format(a_			)
    assert os.path.exists(a_			)

    # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
    # iteration > 0
    __a			=					os.path.join(a_				,					'stage-1'			)
    __a			=					{
        'accelerator': accelerator,
        'model_name_or_path': args.model_name_or_path,
        'cache_dir': args.cache_dir,
        'do_train': True,
        'train_file': data_files['train'] if iteration == 0 else data_files['train_pseudo'],
        'do_eval': True if args.eval_file is not None else False,
        'eval_file': data_files['eval'],
        'do_predict': True,
        'infer_file': data_files['infer'],
        'task_name': args.task_name,
        'label_list': args.label_list,
        'output_dir': current_output_dir,
        'eval_metric': args.eval_metric,
        'evaluation_strategy': args.evaluation_strategy,
        'early_stopping_patience': args.early_stopping_patience,
        'early_stopping_threshold': args.early_stopping_threshold,
        'seed': args.seed,
    }
    # Add additional training arguments
    for key, value in kwargs.items():
      if key not in arguments_dict and not hasattr(a_				,					a_			):
        arguments_dict.update({key: value}			)

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

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

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

    __a			=					iteration
    __a			=					data_dir_format(iteration + 1			)

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

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

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

    __a			=					os.path.join(a_				,					f"train_pseudo.{args.data_file_extension}"			)

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

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

        if early_stopping_patience_counter >= args.early_stopping_patience:
          __a			=					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 tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np





class    __lowercase						:







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     torch.manual_seed(0      )
     __a			=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					UNetaDConditionModel(
         sample_size=32      ,							layers_per_block=1      ,							block_out_channels=[32, 64]      ,							down_block_types=[
             'ResnetDownsampleBlock2D',
             'SimpleCrossAttnDownBlock2D',
         ]      ,							mid_block_type='UNetMidBlock2DSimpleCrossAttn'      ,							up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']      ,							in_channels=3      ,							out_channels=6      ,							cross_attention_dim=32      ,							encoder_hid_dim=32      ,							attention_head_dim=8      ,							addition_embed_type='text'      ,							addition_embed_type_num_heads=2      ,							cross_attention_norm='group_norm'      ,							resnet_time_scale_shift='scale_shift'      ,							act_fn='gelu'      ,							)
     unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     torch.manual_seed(0      )
     __a			=					DDPMScheduler(
         num_train_timesteps=1000      ,							beta_schedule='squaredcos_cap_v2'      ,							beta_start=0.0_001      ,							beta_end=0.02      ,							thresholding=UpperCamelCase      ,							dynamic_thresholding_ratio=0.95      ,							sample_max_value=1.0      ,							prediction_type='epsilon'      ,							variance_type='learned_range'      ,							)

     torch.manual_seed(0      )
     __a			=					IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     torch.manual_seed(0      )
     __a			=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					UNetaDConditionModel(
         sample_size=32      ,							layers_per_block=[1, 2]      ,							block_out_channels=[32, 64]      ,							down_block_types=[
             'ResnetDownsampleBlock2D',
             'SimpleCrossAttnDownBlock2D',
         ]      ,							mid_block_type='UNetMidBlock2DSimpleCrossAttn'      ,							up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']      ,							in_channels=6      ,							out_channels=6      ,							cross_attention_dim=32      ,							encoder_hid_dim=32      ,							attention_head_dim=8      ,							addition_embed_type='text'      ,							addition_embed_type_num_heads=2      ,							cross_attention_norm='group_norm'      ,							resnet_time_scale_shift='scale_shift'      ,							act_fn='gelu'      ,							class_embed_type='timestep'      ,							mid_block_scale_factor=1.414      ,							time_embedding_act_fn='gelu'      ,							time_embedding_dim=32      ,							)
     unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     torch.manual_seed(0      )
     __a			=					DDPMScheduler(
         num_train_timesteps=1000      ,							beta_schedule='squaredcos_cap_v2'      ,							beta_start=0.0_001      ,							beta_end=0.02      ,							thresholding=UpperCamelCase      ,							dynamic_thresholding_ratio=0.95      ,							sample_max_value=1.0      ,							prediction_type='epsilon'      ,							variance_type='learned_range'      ,							)

     torch.manual_seed(0      )
     __a			=					DDPMScheduler(
         num_train_timesteps=1000      ,							beta_schedule='squaredcos_cap_v2'      ,							beta_start=0.0_001      ,							beta_end=0.02      ,							)

     torch.manual_seed(0      )
     __a			=					IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "image_noising_scheduler": image_noising_scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					self.get_dummy_components()
     __a			=					self.pipeline_class(**UpperCamelCase      )
     pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					self.get_dummy_inputs(UpperCamelCase      )

     __a			=					inputs['prompt']
     __a			=					inputs['generator']
     __a			=					inputs['num_inference_steps']
     __a			=					inputs['output_type']

     if "image" in inputs:
       __a			=					inputs['image']
     else:
       __a			=					None

     if "mask_image" in inputs:
       __a			=					inputs['mask_image']
     else:
       __a			=					None

     if "original_image" in inputs:
       __a			=					inputs['original_image']
     else:
       __a			=					None

     __a	,       __a			=					pipe.encode_prompt(UpperCamelCase      )

     # inputs with prompt converted to embeddings
     __a			=					{
         'prompt_embeds': prompt_embeds,
         'negative_prompt_embeds': negative_prompt_embeds,
         'generator': generator,
         'num_inference_steps': num_inference_steps,
         'output_type': output_type,
     }

     if image is not None:
       __a			=					image

     if mask_image is not None:
       __a			=					mask_image

     if original_image is not None:
       __a			=					original_image

     # set all optional components to None
     for optional_component in pipe._optional_components:
       setattr(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )

     __a			=					pipe(**UpperCamelCase      )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
       pipe.save_pretrained(UpperCamelCase      )
       __a			=					self.pipeline_class.from_pretrained(UpperCamelCase      )
       pipe_loaded.to(UpperCamelCase      )
       pipe_loaded.set_progress_bar_config(disable=UpperCamelCase      )

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     for optional_component in pipe._optional_components:
       self.assertTrue(
           getattr(UpperCamelCase      ,							UpperCamelCase      ) is None      ,							f"`{optional_component}` did not stay set to None after loading."      ,							)

     __a			=					self.get_dummy_inputs(UpperCamelCase      )

     __a			=					inputs['generator']
     __a			=					inputs['num_inference_steps']
     __a			=					inputs['output_type']

     # inputs with prompt converted to embeddings
     __a			=					{
         'prompt_embeds': prompt_embeds,
         'negative_prompt_embeds': negative_prompt_embeds,
         'generator': generator,
         'num_inference_steps': num_inference_steps,
         'output_type': output_type,
     }

     if image is not None:
       __a			=					image

     if mask_image is not None:
       __a			=					mask_image

     if original_image is not None:
       __a			=					original_image

     __a			=					pipe_loaded(**UpperCamelCase      )[0]

     __a			=					np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
     self.assertLess(UpperCamelCase      ,							1e-4      )







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					self.get_dummy_components()
     __a			=					self.pipeline_class(**UpperCamelCase      )
     pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					self.get_dummy_inputs(UpperCamelCase      )
     __a			=					pipe(**UpperCamelCase      )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
       pipe.save_pretrained(UpperCamelCase      )
       __a			=					self.pipeline_class.from_pretrained(UpperCamelCase      )
       pipe_loaded.to(UpperCamelCase      )
       pipe_loaded.set_progress_bar_config(disable=UpperCamelCase      )

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     __a			=					self.get_dummy_inputs(UpperCamelCase      )
     __a			=					pipe_loaded(**UpperCamelCase      )[0]

     __a			=					np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
     self.assertLess(UpperCamelCase      ,							1e-4      )



