



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			    =				dataclasses.field(
           metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""}  )
       _lowerCamelCase			    =				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			    =				dataclasses.field(metadata={"""help""": """A csv or a json file containing the training data."""}  )
       _lowerCamelCase			    =				dataclasses.field(metadata={"""help""": """A csv or a json file containing the data to predict on."""}  )
       _lowerCamelCase			    =				dataclasses.field(
           default=lowercase__ , metadata={"""help""": """A csv or a json file containing the validation data."""}  )
       _lowerCamelCase			    =				dataclasses.field(
           default=lowercase__ , metadata={"""help""": """The name of the task to train on."""} , )
       _lowerCamelCase			    =				dataclasses.field(
           default=lowercase__ , metadata={"""help""": """The list of labels for the task."""}  )


@dataclasses.dataclass
class   snake_case_ :




       """simple docstring"""






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







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case    )     ->     int:






      '''simple docstring'''







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

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

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

      UpperCAmelCase_  :	Tuple				      =		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    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  **__snake_case    )     ->     Dict:






      '''simple docstring'''







      UpperCAmelCase_  :	List[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()

      UpperCAmelCase_  :	Dict				      =		STModelArguments(model_name_or_path=__snake_case    )
      UpperCAmelCase_  :	List[str]				      =		STDataArguments(train_file=__snake_case					,  infer_file=__snake_case    )
      UpperCAmelCase_  :	List[str]				      =		STTrainingArguments(output_dir=__snake_case    )
      UpperCAmelCase_  :	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
      UpperCAmelCase_  :	Tuple				      =		{}
      UpperCAmelCase_  :	Optional[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
      UpperCAmelCase_  :	Any				      =		args.train_file
      UpperCAmelCase_  :	Optional[int]				      =		args.infer_file

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

      for key in data_files:
            UpperCAmelCase_  :	Union[str, 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:
                  UpperCAmelCase_  :	Any				      =		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..."    )
      UpperCAmelCase_  :	Union[str, Any]				      =		F"""{args.output_dir}/self-train_iter-{{}}""".format
      UpperCAmelCase_  :	Optional[Any]				      =		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()

      UpperCAmelCase_  :	Union[str, Any]				      =		None
      UpperCAmelCase_  :	Dict				      =		None
      UpperCAmelCase_  :	int				      =		0
      UpperCAmelCase_  :	Any				      =		False
      # Show the progress bar
      UpperCAmelCase_  :	Dict				      =		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    )    ):
            UpperCAmelCase_  :	List[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
            UpperCAmelCase_  :	List[str]				      =		os.path.join(__snake_case					,  "stage-1"    )
            UpperCAmelCase_  :	List[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(__snake_case					,  __snake_case    ):
                        arguments_dict.update({key: value}    )

            UpperCAmelCase_  :	List[Any]				      =		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
                  UpperCAmelCase_  :	Union[str, Any]				      =		os.path.join(__snake_case					,  "best-checkpoint"    )
                  UpperCAmelCase_  :	str				      =		os.path.join(__snake_case					,  "stage-2"    )
                  # Update arguments_dict
                  UpperCAmelCase_  :	Tuple				      =		model_path
                  UpperCAmelCase_  :	int				      =		data_files["train"]
                  UpperCAmelCase_  :	Dict				      =		current_output_dir

                  UpperCAmelCase_  :	List[Any]				      =		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    )

            UpperCAmelCase_  :	List[str]				      =		iteration
            UpperCAmelCase_  :	Any				      =		data_dir_format(iteration + 1    )

            UpperCAmelCase_  :	Any				      =		AutoConfig.from_pretrained(os.path.join(__snake_case					,  "best-checkpoint"    )    )
            UpperCAmelCase_  :	Any				      =		config.idalabel
            UpperCAmelCase_  :	Optional[int]				      =		os.path.join(__snake_case					,  "eval_results_best-checkpoint.json"    )
            UpperCAmelCase_  :	Optional[Any]				      =		os.path.join(__snake_case					,  "test_results_best-checkpoint.json"    )
            assert os.path.exists(__snake_case    )

            with open(__snake_case					,  "r"    ) as f:
                  UpperCAmelCase_  :	Tuple				      =		float(json.load(__snake_case    )[args.eval_metric]    )
            UpperCAmelCase_  :	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.
            UpperCAmelCase_  :	str				      =		load_dataset(args.data_file_extension					,  data_files={"data": data_files["infer"]}    )["data"]
            UpperCAmelCase_  :	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()

            UpperCAmelCase_  :	Union[str, Any]				      =		os.path.join(__snake_case					,  F"""train_pseudo.{args.data_file_extension}"""    )

            if args.evaluation_strategy != IntervalStrategy.NO.value:
                  UpperCAmelCase_  :	Any				      =		eval_result

                  if best_iteration is None:
                        UpperCAmelCase_  :	str				      =		new_iteration
                        UpperCAmelCase_  :	str				      =		new_eval_result
                  else:
                        if new_eval_result - best_eval_result > args.early_stopping_threshold:
                              UpperCAmelCase_  :	List[str]				      =		new_iteration
                              UpperCAmelCase_  :	int				      =		new_eval_result
                              UpperCAmelCase_  :	Tuple				      =		0
                        else:
                              if new_eval_result == best_eval_result:
                                    UpperCAmelCase_  :	Optional[int]				      =		new_iteration
                                    UpperCAmelCase_  :	int				      =		new_eval_result
                              early_stopping_patience_counter += 1

                        if early_stopping_patience_counter >= args.early_stopping_patience:
                              UpperCAmelCase_  :	Tuple				      =		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 Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""umt5"""
       _lowerCamelCase			    =				["""past_key_values"""]



       def __init__(     self     ,lowercase=250112     ,lowercase=512     ,lowercase=64     ,lowercase=1024     ,lowercase=8     ,lowercase=None     ,lowercase=6     ,lowercase=32     ,lowercase=128     ,lowercase=0.1     ,lowercase=1E-6     ,lowercase=1.0     ,lowercase="gated-gelu"     ,lowercase=True     ,lowercase=True     ,lowercase="T5Tokenizer"     ,lowercase=True     ,lowercase=0     ,lowercase=1     ,lowercase=0     ,**lowercase     ,):

             """simple docstring"""





             super().__init__(
                 is_encoder_decoder=lowercase     ,tokenizer_class=lowercase     ,tie_word_embeddings=lowercase     ,pad_token_id=lowercase     ,eos_token_id=lowercase     ,decoder_start_token_id=lowercase     ,**lowercase     ,)
             UpperCAmelCase_  :	Optional[int]				      =		vocab_size
             UpperCAmelCase_  :	Any				      =		d_model
             UpperCAmelCase_  :	Any				      =		d_kv
             UpperCAmelCase_  :	int				      =		d_ff
             UpperCAmelCase_  :	Tuple				      =		num_layers
             UpperCAmelCase_  :	int				      =		(
                 num_decoder_layers if num_decoder_layers is not None else self.num_layers
             )  # default = symmetry
             UpperCAmelCase_  :	Optional[int]				      =		num_heads
             UpperCAmelCase_  :	str				      =		relative_attention_num_buckets
             UpperCAmelCase_  :	Any				      =		relative_attention_max_distance
             UpperCAmelCase_  :	Optional[Any]				      =		dropout_rate
             UpperCAmelCase_  :	Union[str, Any]				      =		layer_norm_epsilon
             UpperCAmelCase_  :	Optional[Any]				      =		initializer_factor
             UpperCAmelCase_  :	int				      =		feed_forward_proj
             UpperCAmelCase_  :	str				      =		use_cache

             UpperCAmelCase_  :	List[str]				      =		self.feed_forward_proj.split("-")
             UpperCAmelCase_  :	Any				      =		act_info[-1]
             UpperCAmelCase_  :	Optional[int]				      =		act_info[0] == "gated"

             if len(lowercase) > 1 and act_info[0] != "gated" or len(lowercase) > 2:
                   raise ValueError(
                       F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                       "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                       "'gated-gelu' or 'relu'")

             if feed_forward_proj == "gated-gelu":
                   UpperCAmelCase_  :	Tuple				      =		"gelu_new"



       @property
       def  A_   (     self):

             """simple docstring"""





             return self.d_model



       @property
       def  A_   (     self):

             """simple docstring"""





             return self.num_heads



       @property
       def  A_   (     self):

             """simple docstring"""





             return self.num_layers


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		{
                 "input_ids": {0: "batch", 1: "encoder_sequence"},
                 "attention_mask": {0: "batch", 1: "encoder_sequence"},
             }
             if self.use_past:
                   UpperCAmelCase_  :	Union[str, Any]				      =		"past_encoder_sequence + sequence"
                   UpperCAmelCase_  :	Optional[int]				      =		{0: "batch"}
                   UpperCAmelCase_  :	Union[str, Any]				      =		{0: "batch", 1: "past_decoder_sequence + sequence"}
             else:
                   UpperCAmelCase_  :	Optional[int]				      =		{0: "batch", 1: "decoder_sequence"}
                   UpperCAmelCase_  :	Dict				      =		{0: "batch", 1: "decoder_sequence"}

             if self.use_past:
                   self.fill_with_past_key_values_(lowercase     ,direction="inputs")

             return common_inputs



       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
       def  A_   (     self):

             """simple docstring"""





             return 13



       @property
       def  A_   (     self):

             """simple docstring"""





             return 5E-4
