

"""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


a       :List[str]     =			logging.getLogger(__name__)

a       :Optional[int]     =			'pytorch_model.bin'

@dataclasses.dataclass
class   __a				:






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE    :str							=			dataclasses.field(
         metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""})
     _SCREAMING_SNAKE_CASE    :int							=			dataclasses.field(
         default=UpperCamelCase_ ,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""} ,						)





@dataclasses.dataclass
class   __a				:






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE    :str							=			dataclasses.field(metadata={"""help""": """A csv or a json file containing the training data."""})
     _SCREAMING_SNAKE_CASE    :Optional[int]							=			dataclasses.field(metadata={"""help""": """A csv or a json file containing the data to predict on."""})
     _SCREAMING_SNAKE_CASE    :Dict							=			dataclasses.field(
         default=UpperCamelCase_ ,						metadata={"""help""": """A csv or a json file containing the validation data."""})
     _SCREAMING_SNAKE_CASE    :Dict							=			dataclasses.field(
         default=UpperCamelCase_ ,						metadata={"""help""": """The name of the task to train on."""} ,						)
     _SCREAMING_SNAKE_CASE    :Tuple							=			dataclasses.field(
         default=UpperCamelCase_ ,						metadata={"""help""": """The list of labels for the task."""})





@dataclasses.dataclass
class   __a				:






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





def 			_lowercase       (     __lowerCAmelCase	,       __lowerCAmelCase	,       __lowerCAmelCase	,       __lowerCAmelCase	,       __lowerCAmelCase	,       __lowerCAmelCase				)							->					Tuple:
      SCREAMING_SNAKE_CASE__ :   List[str]   		=						datasets.concatenate_datasets([infer_input, infer_output]	,       axis=1				)

      if args.do_filter_by_confidence:
            SCREAMING_SNAKE_CASE__ :   Tuple   		=						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
            SCREAMING_SNAKE_CASE__ :   Tuple   		=						int(eval_result * len(lowercase__				)				)
            print(lowercase__				)
            SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						dataset.sort("""probability"""	,       reverse=lowercase__				)
            SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						dataset.select(range(lowercase__				)				)

      SCREAMING_SNAKE_CASE__ :   List[str]   		=						dataset.remove_columns(["""label""", """probability"""]				)
      SCREAMING_SNAKE_CASE__ :   Tuple   		=						dataset.rename_column("""prediction"""	,       """label"""				)
      SCREAMING_SNAKE_CASE__ :   Tuple   		=						dataset.map(lambda __lowerCAmelCase				: {"label": idalabel[example["label"]]}				)
      SCREAMING_SNAKE_CASE__ :   List[Any]   		=						dataset.shuffle(seed=args.seed				)

      SCREAMING_SNAKE_CASE__ :   int   		=						os.path.join(lowercase__	,       F'''train_pseudo.{args.data_file_extension}'''				)
      if args.data_file_extension == "csv":
            dataset.to_csv(lowercase__	,       index=lowercase__				)
      else:
            dataset.to_json(lowercase__				)




def 			_lowercase       (     __lowerCAmelCase	,       __lowerCAmelCase	,       __lowerCAmelCase	,       __lowerCAmelCase	,       **__lowerCAmelCase				)							->					Union[str, Any]:
      SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						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()

      SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						STModelArguments(model_name_or_path=lowercase__				)
      SCREAMING_SNAKE_CASE__ :   Tuple   		=						STDataArguments(train_file=lowercase__	,       infer_file=lowercase__				)
      SCREAMING_SNAKE_CASE__ :   str   		=						STTrainingArguments(output_dir=lowercase__				)
      SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						argparse.Namespace()

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

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

    # Sanity checks
      SCREAMING_SNAKE_CASE__ :   Any   		=						{}
      SCREAMING_SNAKE_CASE__ :   List[Any]   		=						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
      SCREAMING_SNAKE_CASE__ :   str   		=						args.train_file
      SCREAMING_SNAKE_CASE__ :   Dict   		=						args.infer_file

      if args.evaluation_strategy != IntervalStrategy.NO.value:
            assert args.eval_file is not None
            SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						args.eval_file

      for key in data_files:
            SCREAMING_SNAKE_CASE__ :   Tuple   		=						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:
                  SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						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..."""				)
      SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						F'''{args.output_dir}/self-train_iter-{{}}'''.format
      SCREAMING_SNAKE_CASE__ :   Any   		=						data_dir_format(0				)

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

      SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						None
      SCREAMING_SNAKE_CASE__ :   Dict   		=						None
      SCREAMING_SNAKE_CASE__ :   Dict   		=						0
      SCREAMING_SNAKE_CASE__ :   Tuple   		=						False
      # Show the progress bar
      SCREAMING_SNAKE_CASE__ :   Any   		=						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				)				):
            SCREAMING_SNAKE_CASE__ :   List[str]   		=						data_dir_format(lowercase__				)
            assert os.path.exists(lowercase__				)

            # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
            # iteration > 0
            SCREAMING_SNAKE_CASE__ :   Dict   		=						os.path.join(lowercase__	,       """stage-1"""				)
            SCREAMING_SNAKE_CASE__ :   Any   		=						{
                """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(lowercase__	,       lowercase__				):
                        arguments_dict.update({key: value}				)

            SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						os.path.join(lowercase__	,       """best-checkpoint"""	,       lowercase__				)
            if os.path.exists(lowercase__				):
                  logger.info(
                      """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1."""	,       lowercase__	,       lowercase__	,       )
            else:
                  logger.info("""***** Running self-training: iteration: %d, stage: 1 *****"""	,       lowercase__				)
                  finetune(**lowercase__				)
                  accelerator.wait_for_everyone()
                  assert os.path.exists(lowercase__				)
                  logger.info("""Self-training job completed: iteration: %d, stage: 1."""	,       lowercase__				)

            if iteration > 0 and args.finetune_on_labeled_data:
                  # Stage 2 (optional): fine-tuning on the original labeled data
                  SCREAMING_SNAKE_CASE__ :   int   		=						os.path.join(lowercase__	,       """best-checkpoint"""				)
                  SCREAMING_SNAKE_CASE__ :   List[Any]   		=						os.path.join(lowercase__	,       """stage-2"""				)
                  # Update arguments_dict
                  SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						model_path
                  SCREAMING_SNAKE_CASE__ :   Any   		=						data_files["""train"""]
                  SCREAMING_SNAKE_CASE__ :   List[Any]   		=						current_output_dir

                  SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						os.path.join(lowercase__	,       """best-checkpoint"""	,       lowercase__				)
                  if os.path.exists(lowercase__				):
                        logger.info(
                            """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2."""	,       lowercase__	,       lowercase__	,       )
                  else:
                        logger.info("""***** Running self-training: iteration: %d, stage: 2 *****"""	,       lowercase__				)
                        finetune(**lowercase__				)
                        accelerator.wait_for_everyone()
                        assert os.path.exists(lowercase__				)
                        logger.info("""Self-training job completed: iteration: %d, stage: 2."""	,       lowercase__				)

            SCREAMING_SNAKE_CASE__ :   Tuple   		=						iteration
            SCREAMING_SNAKE_CASE__ :   str   		=						data_dir_format(iteration + 1				)

            SCREAMING_SNAKE_CASE__ :   Dict   		=						AutoConfig.from_pretrained(os.path.join(lowercase__	,       """best-checkpoint"""				)				)
            SCREAMING_SNAKE_CASE__ :   Tuple   		=						config.idalabel
            SCREAMING_SNAKE_CASE__ :   List[Any]   		=						os.path.join(lowercase__	,       """eval_results_best-checkpoint.json"""				)
            SCREAMING_SNAKE_CASE__ :   str   		=						os.path.join(lowercase__	,       """test_results_best-checkpoint.json"""				)
            assert os.path.exists(lowercase__				)

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

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

            SCREAMING_SNAKE_CASE__ :   str   		=						os.path.join(lowercase__	,       F'''train_pseudo.{args.data_file_extension}'''				)

            if args.evaluation_strategy != IntervalStrategy.NO.value:
                  SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						eval_result

                  if best_iteration is None:
                        SCREAMING_SNAKE_CASE__ :   List[Any]   		=						new_iteration
                        SCREAMING_SNAKE_CASE__ :   str   		=						new_eval_result
                  else:
                        if new_eval_result - best_eval_result > args.early_stopping_threshold:
                              SCREAMING_SNAKE_CASE__ :   int   		=						new_iteration
                              SCREAMING_SNAKE_CASE__ :   Dict   		=						new_eval_result
                              SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						0
                        else:
                              if new_eval_result == best_eval_result:
                                    SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						new_iteration
                                    SCREAMING_SNAKE_CASE__ :   str   		=						new_eval_result
                              early_stopping_patience_counter += 1

                        if early_stopping_patience_counter >= args.early_stopping_patience:
                              SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						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"""	,       lowercase__				)
            logger.info("""Best evaluation result: %s = %f"""	,       args.eval_metric	,       lowercase__				)
            accelerator.wait_for_everyone()
            if accelerator.is_main_process:
                  shutil.copy(
                      os.path.join(lowercase__	,       F'''eval_results_iter-{iteration}.json'''				)	,       os.path.join(lowercase__	,       """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	,       lowercase__				)
            accelerator.wait_for_everyone()
            if accelerator.is_main_process:
                  shutil.copy(
                      os.path.join(lowercase__	,       F'''eval_results_iter-{args.max_selftrain_iterations - 1}.json'''				)	,       os.path.join(lowercase__	,       """eval_results_best-iteration.json"""				)	,       )








"""simple docstring"""




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available




@dataclass
class 			__snake_case   (lowerCamelCase  ):
							__a					 =       42


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .pipeline_text_to_video_synth import TextToVideoSDPipeline
					from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
					from .pipeline_text_to_video_zero import TextToVideoZeroPipeline




