




"""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_						=				logging.getLogger(__name__)

A_						=				'''pytorch_model.bin'''






@dataclasses.dataclass
class 					lowercase:







 '''simple docstring'''





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







@dataclasses.dataclass
class 					lowercase:







 '''simple docstring'''





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







@dataclasses.dataclass
class 					lowercase:







 '''simple docstring'''





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







def 					UpperCAmelCase__       (snake_case__							:					Tuple		,   snake_case__							:					Union[str, Any]		,   snake_case__							:					str		,   snake_case__							:					Dict		,   snake_case__							:					List[str]		,   snake_case__							:					Optional[int]    ):
  """simple docstring"""

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

  if args.do_filter_by_confidence:
    _snake_case							:		str								=  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							:		int								=  dataset.select(range(snake_case__    )    )

  _snake_case							:		Dict								=  dataset.remove_columns(["""label""", """probability"""]    )
  _snake_case							:		int								=  dataset.rename_column("""prediction"""		,   """label"""    )
  _snake_case							:		Dict								=  dataset.map(lambda snake_case__    : {"label": idalabel[example["label"]]}    )
  _snake_case							:		Optional[int]								=  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__							:					List[Any]		,   snake_case__							:					Dict		,   snake_case__							:					int		,   snake_case__							:					List[Any]		,   **snake_case__							:					int    ):
  """simple docstring"""

  _snake_case							:		Tuple								=  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							:		List[str]								=  STModelArguments(model_name_or_path=snake_case__    )
  _snake_case							:		Union[str, Any]								=  STDataArguments(train_file=snake_case__		,   infer_file=snake_case__    )
  _snake_case							:		List[Any]								=  STTrainingArguments(output_dir=snake_case__    )
  _snake_case							:		int								=  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							:		str								=  {}
  _snake_case							:		int								=  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							:		Any								=  args.train_file
  _snake_case							:		List[str]								=  args.infer_file

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

  for key in data_files:
    _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:
      _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							:		Any								=  F"{args.output_dir}/self-train_iter-{{}}".format
  _snake_case							:		Optional[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							:		str								=  None
  _snake_case							:		Union[str, Any]								=  None
  _snake_case							:		Optional[int]								=  0
  _snake_case							:		Optional[Any]								=  False
  # Show the progress bar
  _snake_case							:		Optional[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    )    ):
    _snake_case							:		List[str]								=  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							:		Optional[int]								=  os.path.join(snake_case__		,   """stage-1"""    )
    _snake_case							:		Tuple								=  {
        """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							:		List[str]								=  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							:		Any								=  os.path.join(snake_case__		,   """best-checkpoint"""    )
      _snake_case							:		List[str]								=  os.path.join(snake_case__		,   """stage-2"""    )
      # Update arguments_dict
      _snake_case							:		Union[str, Any]								=  model_path
      _snake_case							:		Union[str, Any]								=  data_files["""train"""]
      _snake_case							:		Union[str, Any]								=  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							:		Any								=  data_dir_format(iteration + 1    )

    _snake_case							:		Dict								=  AutoConfig.from_pretrained(os.path.join(snake_case__		,   """best-checkpoint"""    )    )
    _snake_case							:		List[Any]								=  config.idalabel
    _snake_case							:		Optional[Any]								=  os.path.join(snake_case__		,   """eval_results_best-checkpoint.json"""    )
    _snake_case							:		int								=  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							:		Any								=  float(json.load(snake_case__    )[args.eval_metric]    )
    _snake_case							:		List[str]								=  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[str]								=  load_dataset(args.data_file_extension		,   data_files={"""data""": data_files["""infer"""]}    )["""data"""]
    _snake_case							:		Optional[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							:		Any								=  os.path.join(snake_case__		,   F"train_pseudo.{args.data_file_extension}"    )

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

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

        if early_stopping_patience_counter >= args.early_stopping_patience:
          _snake_case							:		str								=  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"""    )		,   )




from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
  import tensorflow as tf

  from ..tf_utils import stable_softmax


if is_torch_available():
  import torch


lowercase  :					List[str]						    =							logging.get_logger(__name__)

@add_end_docstrings(
    lowerCAmelCase					,	R"\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    "					,	)
class __snake_case		(      lowerCAmelCase     ):
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           if self.framework == "tf":
                  lowercase							:     str       	=      tf.where(input_ids == self.tokenizer.mask_token_id      ).numpy()
           elif self.framework == "pt":
                  lowercase							:     Optional[int]       	=      torch.nonzero(input_ids == self.tokenizer.mask_token_id		,as_tuple=snake_case      )
           else:
                  raise ValueError("""Unsupported framework"""      )
           return masked_index
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      self.get_masked_index(snake_case      )
           lowercase							:     Dict       	=      np.prod(masked_index.shape      )
           if numel < 1:
                  raise PipelineException(
                      """fill-mask"""		,self.model.base_model_prefix		,f"No mask_token ({self.tokenizer.mask_token}) found on the input"		,)
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           if isinstance(snake_case		,snake_case      ):
                  for model_input in model_inputs:
                         self._ensure_exactly_one_mask_token(model_input["""input_ids"""][0]      )
           else:
                  for input_ids in model_inputs["input_ids"]:
                         self._ensure_exactly_one_mask_token(snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None		,**snake_case      ):



           '''simple docstring'''







           if return_tensors is None:
                  lowercase							:     int       	=      self.framework
           lowercase							:     Optional[Any]       	=      self.tokenizer(snake_case		,return_tensors=snake_case      )
           self.ensure_exactly_one_mask_token(snake_case      )
           return model_inputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      self.model(**snake_case      )
           lowercase							:     Tuple       	=      model_inputs["""input_ids"""]
           return model_outputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=5		,snake_case=None      ):



           '''simple docstring'''







           if target_ids is not None and target_ids.shape[0] < top_k:
                  lowercase							:     str       	=      target_ids.shape[0]
           lowercase							:     Optional[Any]       	=      model_outputs["""input_ids"""][0]
           lowercase							:     List[str]       	=      model_outputs["""logits"""]

           if self.framework == "tf":
                  lowercase							:     List[str]       	=      tf.where(input_ids == self.tokenizer.mask_token_id      ).numpy()[:, 0]

                  lowercase							:     Tuple       	=      outputs.numpy()

                  lowercase							:     Tuple       	=      outputs[0, masked_index, :]
                  lowercase							:     Any       	=      stable_softmax(snake_case		,axis=-1      )
                  if target_ids is not None:
                         lowercase							:     Union[str, Any]       	=      tf.gather_nd(tf.squeeze(snake_case		,0      )		,target_ids.reshape(-1		,1      )      )
                         lowercase							:     int       	=      tf.expand_dims(snake_case		,0      )

                  lowercase							:     Tuple       	=      tf.math.top_k(snake_case		,k=snake_case      )
                  lowercase	,			lowercase							:     int       	=      topk.values.numpy(), topk.indices.numpy()
           else:
                  lowercase							:     Optional[Any]       	=      torch.nonzero(input_ids == self.tokenizer.mask_token_id		,as_tuple=snake_case      ).squeeze(-1      )
                  # Fill mask pipeline supports only one ${mask_token} per sample

                  lowercase							:     Union[str, Any]       	=      outputs[0, masked_index, :]
                  lowercase							:     Tuple       	=      logits.softmax(dim=-1      )
                  if target_ids is not None:
                         lowercase							:     List[str]       	=      probs[..., target_ids]

                  lowercase	,			lowercase							:     Union[str, Any]       	=      probs.topk(snake_case      )

           lowercase							:     Any       	=      []
           lowercase							:     List[Any]       	=      values.shape[0] == 1
           for i, (_values, _predictions) in enumerate(zip(values.tolist()		,predictions.tolist()      )      ):
                  lowercase							:     Dict       	=      []
                  for v, p in zip(_values		,_predictions      ):
                         # Copy is important since we're going to modify this array in place
                         lowercase							:     Dict       	=      input_ids.numpy().copy()
                         if target_ids is not None:
                                lowercase							:     Union[str, Any]       	=      target_ids[p].tolist()

                         lowercase							:     Tuple       	=      p
                         # Filter padding out:
                         lowercase							:     List[str]       	=      tokens[np.where(tokens != self.tokenizer.pad_token_id      )]
                         # Originally we skip special tokens to give readable output.
                         # For multi masks though, the other [MASK] would be removed otherwise
                         # making the output look odd, so we add them back
                         lowercase							:     Tuple       	=      self.tokenizer.decode(snake_case		,skip_special_tokens=snake_case      )
                         lowercase							:     Optional[Any]       	=      {"""score""": v, """token""": p, """token_str""": self.tokenizer.decode([p]      ), """sequence""": sequence}
                         row.append(snake_case      )
                  result.append(snake_case      )
           if single_mask:
                  return result[0]
           return result
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           if isinstance(snake_case		,snake_case      ):
                  lowercase							:     List[Any]       	=      [targets]
           try:
                  lowercase							:     List[str]       	=      self.tokenizer.get_vocab()
           except Exception:
                  lowercase							:     Any       	=      {}
           lowercase							:     Dict       	=      []
           for target in targets:
                  lowercase							:     Dict       	=      vocab.get(snake_case		,snake_case      )
                  if id_ is None:
                         lowercase							:     Optional[int]       	=      self.tokenizer(
                             snake_case		,add_special_tokens=snake_case		,return_attention_mask=snake_case		,return_token_type_ids=snake_case		,max_length=1		,truncation=snake_case		,)["""input_ids"""]
                         if len(snake_case      ) == 0:
                                logger.warning(
                                    f"The specified target token `{target}` does not exist in the model vocabulary. "
                                    """We cannot replace it with anything meaningful, ignoring it"""      )
                                continue
                         lowercase							:     Union[str, Any]       	=      input_ids[0]
                         # XXX: If users encounter this pass
                         # it becomes pretty slow, so let's make sure
                         # The warning enables them to fix the input to
                         # get faster performance.
                         logger.warning(
                             f"The specified target token `{target}` does not exist in the model vocabulary. "
                             f"Replacing with `{self.tokenizer.convert_ids_to_tokens(id_      )}`."      )
                  target_ids.append(id_      )
           lowercase							:     Optional[Any]       	=      list(set(snake_case      )      )
           if len(snake_case      ) == 0:
                  raise ValueError("""At least one target must be provided when passed."""      )
           lowercase							:     Optional[Any]       	=      np.array(snake_case      )
           return target_ids
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case=None		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      {}

           if targets is not None:
                  lowercase							:     str       	=      self.get_target_ids(snake_case		,snake_case      )
                  lowercase							:     List[Any]       	=      target_ids

           if top_k is not None:
                  lowercase							:     List[str]       	=      top_k

           if self.tokenizer.mask_token_id is None:
                  raise PipelineException(
                      """fill-mask"""		,self.model.base_model_prefix		,"""The tokenizer does not define a `mask_token`."""      )
           return {}, {}, postprocess_params
    def __call__(  self		,snake_case		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      super().__call__(snake_case		,**snake_case      )
           if isinstance(snake_case		,snake_case      ) and len(snake_case      ) == 1:
                  return outputs[0]
           return outputs
