"""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


__SCREAMING_SNAKE_CASE								=logging.getLogger(__name__)

__SCREAMING_SNAKE_CASE								="pytorch_model.bin"







@dataclasses.dataclass
class  UpperCamelCase	:
   lowercase							=						dataclasses.field(
       metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'}						)
   lowercase							=						dataclasses.field(
       default=lowercase_		,   metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'}		,   )







@dataclasses.dataclass
class  UpperCamelCase	:
   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=lowercase_		,   metadata={'help': 'A csv or a json file containing the validation data.'}						)
   lowercase							=						dataclasses.field(
       default=lowercase_		,   metadata={'help': 'The name of the task to train on.'}		,   )
   lowercase							=						dataclasses.field(
       default=lowercase_		,   metadata={'help': 'The list of labels for the task.'}						)







@dataclasses.dataclass
class  UpperCamelCase	:
   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=1_0		,   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=lowercase_		,   metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'}		,   )
   lowercase							=						dataclasses.field(
       default=lowercase_		,   metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'}		,   )
   lowercase							=						dataclasses.field(
       default=lowercase_		,   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_0_0		,   metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}		,   )
   lowercase							=						dataclasses.field(
       default=lowercase_		,   metadata={'help': 'Random seed for initialization.'}		,   )






def     lowercase__( __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  Tuple  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  Tuple  ,    __SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,    __SCREAMING_SNAKE_CASE							:  Any   ):
     lowercase_			:       Optional[int]									=			datasets.concatenate_datasets([infer_input, infer_output]  ,    axis=1   )

     if args.do_filter_by_confidence:
          lowercase_			:       Tuple									=			dataset.filter(lambda __SCREAMING_SNAKE_CASE   : example["probability"] > args.confidence_threshold   )

     if args.do_filter_by_val_performance:
          assert eval_result >= 0.0 and eval_result <= 1.0
          lowercase_			:       str									=			int(eval_result * len(__SCREAMING_SNAKE_CASE   )   )
          print(__SCREAMING_SNAKE_CASE   )
          lowercase_			:       List[Any]									=			dataset.sort('probability'  ,    reverse=__SCREAMING_SNAKE_CASE   )
          lowercase_			:       List[Any]									=			dataset.select(range(__SCREAMING_SNAKE_CASE   )   )

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

     lowercase_			:       Union[str, Any]									=			os.path.join(__SCREAMING_SNAKE_CASE  ,    F'''train_pseudo.{args.data_file_extension}'''   )
     if args.data_file_extension == "csv":
          dataset.to_csv(__SCREAMING_SNAKE_CASE  ,    index=__SCREAMING_SNAKE_CASE   )
     else:
          dataset.to_json(__SCREAMING_SNAKE_CASE   )







def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[int]  ,    __SCREAMING_SNAKE_CASE							:  Any  ,    __SCREAMING_SNAKE_CASE							:  Dict  ,    __SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,    **__SCREAMING_SNAKE_CASE							:  List[str]   ):
     lowercase_			:       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()

     lowercase_			:       Optional[Any]									=			STModelArguments(model_name_or_path=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       List[str]									=			STDataArguments(train_file=__SCREAMING_SNAKE_CASE  ,    infer_file=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Dict									=			STTrainingArguments(output_dir=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       List[Any]									=			argparse.Namespace()

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

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

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

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

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

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

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

          # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
          # iteration > 0
          lowercase_			:       Optional[Any]									=			os.path.join(__SCREAMING_SNAKE_CASE  ,    'stage-1'   )
          lowercase_			:       Optional[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(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
                    arguments_dict.update({key: value}   )

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

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

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

          lowercase_			:       str									=			iteration
          lowercase_			:       int									=			data_dir_format(iteration + 1   )

          lowercase_			:       List[str]									=			AutoConfig.from_pretrained(os.path.join(__SCREAMING_SNAKE_CASE  ,    'best-checkpoint'   )   )
          lowercase_			:       int									=			config.idalabel
          lowercase_			:       int									=			os.path.join(__SCREAMING_SNAKE_CASE  ,    'eval_results_best-checkpoint.json'   )
          lowercase_			:       Optional[int]									=			os.path.join(__SCREAMING_SNAKE_CASE  ,    'test_results_best-checkpoint.json'   )
          assert os.path.exists(__SCREAMING_SNAKE_CASE   )

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

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

          lowercase_			:       Optional[int]									=			os.path.join(__SCREAMING_SNAKE_CASE  ,    F'''train_pseudo.{args.data_file_extension}'''   )

          if args.evaluation_strategy != IntervalStrategy.NO.value:
               lowercase_			:       List[Any]									=			eval_result

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

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


"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE								={
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						'switch_transformers'
   lowercase							=						['past_key_values']
   lowercase							=						{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
   def __init__(    self  ,__UpperCamelCase=3_2128  ,__UpperCamelCase=768  ,__UpperCamelCase=64  ,__UpperCamelCase=2048  ,__UpperCamelCase=64  ,__UpperCamelCase=12  ,__UpperCamelCase=3  ,__UpperCamelCase=12  ,__UpperCamelCase=3  ,__UpperCamelCase=12  ,__UpperCamelCase=8  ,__UpperCamelCase=False  ,__UpperCamelCase=0.01  ,__UpperCamelCase="float32"  ,__UpperCamelCase=False  ,__UpperCamelCase=32  ,__UpperCamelCase=128  ,__UpperCamelCase=0.1  ,__UpperCamelCase=1e-6  ,__UpperCamelCase=0.001  ,__UpperCamelCase=0.001  ,__UpperCamelCase=1.0  ,__UpperCamelCase="relu"  ,__UpperCamelCase=True  ,__UpperCamelCase=False  ,__UpperCamelCase=True  ,__UpperCamelCase=0  ,__UpperCamelCase=1  ,**__UpperCamelCase  ,)			->   str:


        '''simple docstring'''


        lowercase_			:       List[str]									=			vocab_size
        lowercase_			:       Optional[Any]									=			d_model
        lowercase_			:       Dict									=			d_kv
        lowercase_			:       Dict									=			d_ff

        lowercase_			:       str									=			num_sparse_encoder_layers

        lowercase_			:       List[str]									=			num_layers
        lowercase_			:       int									=			(
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        lowercase_			:       Optional[Any]									=			num_sparse_decoder_layers

        # This tells us, each how many encoder layer we'll have to set a sparse layer.
        if self.num_sparse_encoder_layers > 0:
             lowercase_			:       Tuple									=			self.num_layers // self.num_sparse_encoder_layers
        else:
             lowercase_			:       Union[str, Any]									=			self.num_layers  # HACK: this will create 0 sparse layers

        # This tells us, each how many encoder layer we'll have to set a sparse layer.
        if self.num_sparse_decoder_layers > 0:
             lowercase_			:       Union[str, Any]									=			self.num_decoder_layers // self.num_sparse_decoder_layers
        else:
             lowercase_			:       List[Any]									=			self.num_decoder_layers  # HACK: this will create 0 sparse layers

        lowercase_			:       List[Any]									=			num_heads
        lowercase_			:       Dict									=			num_experts
        lowercase_			:       List[str]									=			expert_capacity
        lowercase_			:       Optional[Any]									=			router_bias
        lowercase_			:       Optional[Any]									=			router_jitter_noise
        if router_dtype not in ["float32", "float16", "bfloat16"]:
             raise ValueError(f'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''    )
        lowercase_			:       Optional[Any]									=			router_dtype

        lowercase_			:       Union[str, Any]									=			router_ignore_padding_tokens
        lowercase_			:       Any									=			relative_attention_num_buckets
        lowercase_			:       List[Any]									=			relative_attention_max_distance

        lowercase_			:       str									=			dropout_rate
        lowercase_			:       Any									=			layer_norm_epsilon
        lowercase_			:       Tuple									=			initializer_factor
        lowercase_			:       str									=			feed_forward_proj
        lowercase_			:       List[Any]									=			use_cache
        lowercase_			:       str									=			add_router_probs

        lowercase_			:       Tuple									=			router_z_loss_coef
        lowercase_			:       int									=			router_aux_loss_coef

        lowercase_			:       List[str]									=			self.feed_forward_proj.split('-'    )
        lowercase_			:       List[str]									=			act_info[-1]
        lowercase_			:       Optional[Any]									=			act_info[0] == 'gated'

        if len(__UpperCamelCase    ) > 1 and act_info[0] != "gated" or len(__UpperCamelCase    ) > 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\''    )

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
             lowercase_			:       Union[str, Any]									=			'gelu_new'

        super().__init__(
            pad_token_id=__UpperCamelCase  ,eos_token_id=__UpperCamelCase  ,is_encoder_decoder=__UpperCamelCase  ,**__UpperCamelCase  ,)

