






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


lowerCamelCase__       		=  logging.getLogger(__name__)

lowerCamelCase__       		=  """pytorch_model.bin"""




@dataclasses.dataclass
class       snake_case__ :

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




@dataclasses.dataclass
class       snake_case__ :

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




@dataclasses.dataclass
class       snake_case__ :

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






def 	UpperCamelCase	(							snake_case__  : Any						,snake_case__  : Dict						,snake_case__  : Any						,snake_case__  : Any						,snake_case__  : Any						,snake_case__  : Optional[Any]	):




     '''simple docstring'''






     __snake_case      :int 							=    datasets.concatenate_datasets([infer_input, infer_output]						,axis=1	)

     if args.do_filter_by_confidence:
          __snake_case      :Union[str, Any] 							=    dataset.filter(lambda snake_case__	: example["probability"] > args.confidence_threshold	)

     if args.do_filter_by_val_performance:
          assert eval_result >= 0.0 and eval_result <= 1.0
          __snake_case      :Optional[Any] 							=    int(eval_result * len(snake_case__	)	)
          print(snake_case__	)
          __snake_case      :Union[str, Any] 							=    dataset.sort("""probability"""						,reverse=snake_case__	)
          __snake_case      :List[str] 							=    dataset.select(range(snake_case__	)	)

     __snake_case      :Dict 							=    dataset.remove_columns(["""label""", """probability"""]	)
     __snake_case      :int 							=    dataset.rename_column("""prediction"""						,"""label"""	)
     __snake_case      :Optional[int] 							=    dataset.map(lambda snake_case__	: {"label": idalabel[example["label"]]}	)
     __snake_case      :str 							=    dataset.shuffle(seed=args.seed	)

     __snake_case      :List[Any] 							=    os.path.join(snake_case__						,f'''train_pseudo.{args.data_file_extension}'''	)
     if args.data_file_extension == "csv":
          dataset.to_csv(snake_case__						,index=snake_case__	)
     else:
          dataset.to_json(snake_case__	)






def 	UpperCamelCase	(							snake_case__  : Optional[Any]						,snake_case__  : List[Any]						,snake_case__  : Optional[Any]						,snake_case__  : List[str]						,**snake_case__  : Optional[int]	):




     '''simple docstring'''






     __snake_case      :str 							=    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()

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

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

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

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

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

     for key in data_files:
          __snake_case      :Any 							=    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:
               __snake_case      :Tuple 							=    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..."""	)
     __snake_case      :Tuple 							=    f'''{args.output_dir}/self-train_iter-{{}}'''.format
     __snake_case      :int 							=    data_dir_format(0	)

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

     __snake_case      :List[str] 							=    None
     __snake_case      :str 							=    None
     __snake_case      :Dict 							=    0
     __snake_case      :str 							=    False
     # Show the progress bar
     __snake_case      :Optional[int] 							=    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	)	):
          __snake_case      :Union[str, Any] 							=    data_dir_format(snake_case__	)
          assert os.path.exists(snake_case__	)

          # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
          # iteration > 0
          __snake_case      :str 							=    os.path.join(snake_case__						,"""stage-1"""	)
          __snake_case      :int 							=    {
              """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(snake_case__						,snake_case__	):
                    arguments_dict.update({key: value}	)

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

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

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

          __snake_case      :Any 							=    iteration
          __snake_case      :Optional[Any] 							=    data_dir_format(iteration + 1	)

          __snake_case      :Tuple 							=    AutoConfig.from_pretrained(os.path.join(snake_case__						,"""best-checkpoint"""	)	)
          __snake_case      :int 							=    config.idalabel
          __snake_case      :Dict 							=    os.path.join(snake_case__						,"""eval_results_best-checkpoint.json"""	)
          __snake_case      :Tuple 							=    os.path.join(snake_case__						,"""test_results_best-checkpoint.json"""	)
          assert os.path.exists(snake_case__	)

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

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

          __snake_case      :Union[str, Any] 							=    os.path.join(snake_case__						,f'''train_pseudo.{args.data_file_extension}'''	)

          if args.evaluation_strategy != IntervalStrategy.NO.value:
               __snake_case      :int 							=    eval_result

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

                    if early_stopping_patience_counter >= args.early_stopping_patience:
                         __snake_case      :int 							=    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"""						,snake_case__	)
          logger.info("""Best evaluation result: %s = %f"""						,args.eval_metric						,snake_case__	)
          accelerator.wait_for_everyone()
          if accelerator.is_main_process:
               shutil.copy(
                   os.path.join(snake_case__						,f'''eval_results_iter-{iteration}.json'''	)						,os.path.join(snake_case__						,"""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						,snake_case__	)
          accelerator.wait_for_everyone()
          if accelerator.is_main_process:
               shutil.copy(
                   os.path.join(snake_case__						,f'''eval_results_iter-{args.max_selftrain_iterations - 1}.json'''	)						,os.path.join(snake_case__						,"""eval_results_best-iteration.json"""	)						,)










def 	UpperCamelCase	(							snake_case__  : list	):




     '''simple docstring'''






     if not grid or not grid[0]:
          raise TypeError("""The grid does not contain the appropriate information"""	)

     for cell_n in range(1						,len(grid[0]	)	):
          grid[0][cell_n] += grid[0][cell_n - 1]
     __snake_case      :str 							=    grid[0]

     for row_n in range(1						,len(snake_case__	)	):
          __snake_case      :Optional[int] 							=    grid[row_n]
          __snake_case      :Optional[Any] 							=    fill_row(snake_case__						,snake_case__	)
          __snake_case      :Dict 							=    grid[row_n]

     return grid[-1][-1]






def 	UpperCamelCase	(							snake_case__  : list						,snake_case__  : list	):




     '''simple docstring'''






     current_row[0] += row_above[0]
     for cell_n in range(1						,len(snake_case__	)	):
          current_row[cell_n] += min(current_row[cell_n - 1]						,row_above[cell_n]	)

     return current_row


if __name__ == "__main__":
     import doctest

     doctest.testmod()


