
"""simple docstring"""




import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


A__      :	Union[str, Any]          =    logging.getLogger(__name__)
A__      :	Dict          =    list(MODEL_FOR_MASKED_LM_MAPPING.keys())
A__      :	List[Any]          =    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class   _lowercase						:


     '''simple docstring'''





     _A								=							field(
         default=lowerCAmelCase_	,   metadata={
             'help': (
                 'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
             )
         }	,   )
     _A								=							field(
         default=lowerCAmelCase_	,   metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(lowerCAmelCase_				)}	,   )
     _A								=							field(
         default=lowerCAmelCase_	,   metadata={
             'help': (
                 'Override some existing default config settings when a model is trained from scratch. Example: '
                 'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
             )
         }	,   )
     _A								=							field(
         default=lowerCAmelCase_	,   metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
     _A								=							field(
         default=lowerCAmelCase_	,   metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}				)
     _A								=							field(
         default=lowerCAmelCase_	,   metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,   )
     _A								=							field(
         default=lowerCAmelCase_	,   metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}	,   )
     _A								=							field(
         default='main'	,   metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}	,   )
     _A								=							field(
         default=lowerCAmelCase_	,   metadata={
             'help': (
                 'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
                 'with private models).'
             )
         }	,   )



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
               raise ValueError(
                   "--config_overrides can't be used in combination with --config_name or --model_name_or_path"  )







@dataclass
class   _lowercase						:


     '''simple docstring'''





     _A								=							field(
         default=lowerCAmelCase_	,   metadata={'help': 'The name of the dataset to use (via the datasets library).'}				)
     _A								=							field(
         default=lowerCAmelCase_	,   metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}				)
     _A								=							field(default=lowerCAmelCase_	,   metadata={'help': 'The input training data file (a text file).'}				)
     _A								=							field(
         default=lowerCAmelCase_	,   metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}	,   )
     _A								=							field(
         default=lowerCAmelCase_	,   metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}	,   )
     _A								=							field(
         default=lowerCAmelCase_	,   metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}	,   )
     _A								=							field(
         default=lowerCAmelCase_	,   metadata={'help': 'Overwrite the cached training and evaluation sets'}				)
     _A								=							field(
         default=5	,   metadata={
             'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
         }	,   )
     _A								=							field(
         default=lowerCAmelCase_	,   metadata={
             'help': (
                 'The maximum total input sequence length after tokenization. Sequences longer '
                 'than this will be truncated. Default to the max input length of the model.'
             )
         }	,   )
     _A								=							field(
         default=lowerCAmelCase_	,   metadata={'help': 'The number of processes to use for the preprocessing.'}	,   )
     _A								=							field(
         default=0.15	,   metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}				)
     _A								=							field(
         default=lowerCAmelCase_	,   metadata={
             'help': (
                 'Whether to pad all samples to `max_seq_length`. '
                 'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
             )
         }	,   )



     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          if self.train_file is not None:
               UpperCAmelCase__							: Tuple 							=						self.train_file.split("."  )[-1]
               assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
          if self.validation_file is not None:
               UpperCAmelCase__							: Any 							=						self.validation_file.split("."  )[-1]
               assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





def 							a__ (				lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       Any		):






     '''simple docstring'''
     with open(lowerCAmelCase       ,    "r"       ,    encoding="utf-8"		) as f:
          UpperCAmelCase__							: Optional[Any] 							=						[json.loads(lowerCAmelCase		) for line in f.read().splitlines() if (len(lowerCAmelCase		) > 0 and not line.isspace())]
     assert len(lowerCAmelCase		) == len(lowerCAmelCase		)

     UpperCAmelCase__							: int 							=						{c: dataset[c] for c in dataset.column_names}
     UpperCAmelCase__							: Optional[Any] 							=						refs
     return Dataset.from_dict(lowerCAmelCase		)



def 							a__ (				):






     '''simple docstring'''
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
     # We now keep distinct sets of args, for a cleaner separation of concerns.

     UpperCAmelCase__							: List[str] 							=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
     if len(sys.argv		) == 2 and sys.argv[1].endswith(".json"		):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: List[str] 							=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
     else:
          UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: List[str] 							=						parser.parse_args_into_dataclasses()

     # Detecting last checkpoint.
     UpperCAmelCase__							: str 							=						None
     if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
          UpperCAmelCase__							: List[Any] 							=						get_last_checkpoint(training_args.output_dir		)
          if last_checkpoint is None and len(os.listdir(training_args.output_dir		)		) > 0:
               raise ValueError(
                   F"Output directory ({training_args.output_dir}) already exists and is not empty. "
                   "Use --overwrite_output_dir to overcome."		)
          elif last_checkpoint is not None:
               logger.info(
                   F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                   "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."		)

    # Setup logging
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"       ,    datefmt="%m/%d/%Y %H:%M:%S"       ,    handlers=[logging.StreamHandler(sys.stdout		)]       ,    )
     logger.setLevel(logging.INFO if is_main_process(training_args.local_rank		) else logging.WARN		)

     # Log on each process the small summary:
     logger.warning(
         F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
         + F"distributed training: {bool(training_args.local_rank != -1		)}, 16-bits training: {training_args.fpaa}"		)
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank		):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info("Training/evaluation parameters %s"       ,    lowerCAmelCase		)

     # Set seed before initializing model.
     set_seed(training_args.seed		)

     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
     # (the dataset will be downloaded automatically from the datasets Hub).
     #
     # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
     # 'text' is found. You can easily tweak this behavior (see below).
     #
     # In distributed training, the load_dataset function guarantee that only one local process can concurrently
     # download the dataset.
     if data_args.dataset_name is not None:
          # Downloading and loading a dataset from the hub.
          UpperCAmelCase__							: Optional[int] 							=						load_dataset(data_args.dataset_name       ,    data_args.dataset_config_name		)
          if "validation" not in datasets.keys():
               UpperCAmelCase__							: str 							=						load_dataset(
                   data_args.dataset_name       ,    data_args.dataset_config_name       ,    split=F"train[:{data_args.validation_split_percentage}%]"       ,    )
               UpperCAmelCase__							: Tuple 							=						load_dataset(
                   data_args.dataset_name       ,    data_args.dataset_config_name       ,    split=F"train[{data_args.validation_split_percentage}%:]"       ,    )
     else:
          UpperCAmelCase__							: Dict 							=						{}
          if data_args.train_file is not None:
               UpperCAmelCase__							: Dict 							=						data_args.train_file
          if data_args.validation_file is not None:
               UpperCAmelCase__							: str 							=						data_args.validation_file
          UpperCAmelCase__							: List[str] 							=						data_args.train_file.split("."		)[-1]
          if extension == "txt":
               UpperCAmelCase__							: List[Any] 							=						"text"
          UpperCAmelCase__							: str 							=						load_dataset(lowerCAmelCase       ,    data_files=lowerCAmelCase		)
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.html.

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     UpperCAmelCase__							: List[Any] 							=						{
         "cache_dir": model_args.cache_dir,
         "revision": model_args.model_revision,
         "use_auth_token": True if model_args.use_auth_token else None,
     }
     if model_args.config_name:
          UpperCAmelCase__							: Any 							=						AutoConfig.from_pretrained(model_args.config_name       ,    **lowerCAmelCase		)
     elif model_args.model_name_or_path:
          UpperCAmelCase__							: Tuple 							=						AutoConfig.from_pretrained(model_args.model_name_or_path       ,    **lowerCAmelCase		)
     else:
          UpperCAmelCase__							: Optional[Any] 							=						CONFIG_MAPPING[model_args.model_type]()
          logger.warning("You are instantiating a new config instance from scratch."		)
          if model_args.config_overrides is not None:
               logger.info(F"Overriding config: {model_args.config_overrides}"		)
               config.update_from_string(model_args.config_overrides		)
               logger.info(F"New config: {config}"		)

     UpperCAmelCase__							: str 							=						{
         "cache_dir": model_args.cache_dir,
         "use_fast": model_args.use_fast_tokenizer,
         "revision": model_args.model_revision,
         "use_auth_token": True if model_args.use_auth_token else None,
     }
     if model_args.tokenizer_name:
          UpperCAmelCase__							: List[Any] 							=						AutoTokenizer.from_pretrained(model_args.tokenizer_name       ,    **lowerCAmelCase		)
     elif model_args.model_name_or_path:
          UpperCAmelCase__							: Optional[int] 							=						AutoTokenizer.from_pretrained(model_args.model_name_or_path       ,    **lowerCAmelCase		)
     else:
          raise ValueError(
              "You are instantiating a new tokenizer from scratch. This is not supported by this script."
              "You can do it from another script, save it, and load it from here, using --tokenizer_name."		)

     if model_args.model_name_or_path:
          UpperCAmelCase__							: List[Any] 							=						AutoModelForMaskedLM.from_pretrained(
              model_args.model_name_or_path       ,    from_tf=bool(".ckpt" in model_args.model_name_or_path		)       ,    config=lowerCAmelCase       ,    cache_dir=model_args.cache_dir       ,    revision=model_args.model_revision       ,    use_auth_token=True if model_args.use_auth_token else None       ,    )
     else:
          logger.info("Training new model from scratch"		)
          UpperCAmelCase__							: Tuple 							=						AutoModelForMaskedLM.from_config(lowerCAmelCase		)

     model.resize_token_embeddings(len(lowerCAmelCase		)		)

     # Preprocessing the datasets.
     # First we tokenize all the texts.
     if training_args.do_train:
          UpperCAmelCase__							: List[str] 							=						datasets["train"].column_names
     else:
          UpperCAmelCase__							: List[str] 							=						datasets["validation"].column_names
     UpperCAmelCase__							: Dict 							=						"text" if "text" in column_names else column_names[0]

     UpperCAmelCase__							: List[str] 							=						"max_length" if data_args.pad_to_max_length else False

     def tokenize_function(lowerCAmelCase	:       Tuple		):
          # Remove empty lines
          UpperCAmelCase__							: Dict 							=						[line for line in examples["text"] if len(lowerCAmelCase		) > 0 and not line.isspace()]
          return tokenizer(examples["text"]       ,    padding=lowerCAmelCase       ,    truncation=lowerCAmelCase       ,    max_length=data_args.max_seq_length		)

     UpperCAmelCase__							: str 							=						datasets.map(
         lowerCAmelCase       ,    batched=lowerCAmelCase       ,    num_proc=data_args.preprocessing_num_workers       ,    remove_columns=[text_column_name]       ,    load_from_cache_file=not data_args.overwrite_cache       ,    )

     # Add the chinese references if provided
     if data_args.train_ref_file is not None:
          UpperCAmelCase__							: Any 							=						add_chinese_references(tokenized_datasets["train"]       ,    data_args.train_ref_file		)
     if data_args.validation_ref_file is not None:
          UpperCAmelCase__							: List[str] 							=						add_chinese_references(
              tokenized_datasets["validation"]       ,    data_args.validation_ref_file		)
     # If we have ref files, need to avoid it removed by trainer
     UpperCAmelCase__							: Dict 							=						data_args.train_ref_file or data_args.validation_ref_file
     if has_ref:
          UpperCAmelCase__							: Optional[int] 							=						False

     # Data collator
     # This one will take care of randomly masking the tokens.
     UpperCAmelCase__							: Tuple 							=						DataCollatorForWholeWordMask(tokenizer=lowerCAmelCase       ,    mlm_probability=data_args.mlm_probability		)

     # Initialize our Trainer
     UpperCAmelCase__							: Dict 							=						Trainer(
         model=lowerCAmelCase       ,    args=lowerCAmelCase       ,    train_dataset=tokenized_datasets["train"] if training_args.do_train else None       ,    eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None       ,    tokenizer=lowerCAmelCase       ,    data_collator=lowerCAmelCase       ,    )

     # Training
     if training_args.do_train:
          if last_checkpoint is not None:
               UpperCAmelCase__							: List[str] 							=						last_checkpoint
          elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path		):
               UpperCAmelCase__							: Union[str, Any] 							=						model_args.model_name_or_path
          else:
               UpperCAmelCase__							: List[Any] 							=						None
          UpperCAmelCase__							: List[Any] 							=						trainer.train(resume_from_checkpoint=lowerCAmelCase		)
          trainer.save_model()  # Saves the tokenizer too for easy upload

          UpperCAmelCase__							: int 							=						os.path.join(training_args.output_dir       ,    "train_results.txt"		)
          if trainer.is_world_process_zero():
               with open(lowerCAmelCase       ,    "w"		) as writer:
                    logger.info("***** Train results *****"		)
                    for key, value in sorted(train_result.metrics.items()		):
                         logger.info(F"  {key} = {value}"		)
                         writer.write(F"{key} = {value}\n"		)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
               trainer.state.save_to_json(os.path.join(training_args.output_dir       ,    "trainer_state.json"		)		)

    # Evaluation
     UpperCAmelCase__							: Optional[Any] 							=						{}
     if training_args.do_eval:
          logger.info("*** Evaluate ***"		)

          UpperCAmelCase__							: Optional[Any] 							=						trainer.evaluate()

          UpperCAmelCase__							: Union[str, Any] 							=						math.exp(eval_output["eval_loss"]		)
          UpperCAmelCase__							: int 							=						perplexity

          UpperCAmelCase__							: List[Any] 							=						os.path.join(training_args.output_dir       ,    "eval_results_mlm_wwm.txt"		)
          if trainer.is_world_process_zero():
               with open(lowerCAmelCase       ,    "w"		) as writer:
                    logger.info("***** Eval results *****"		)
                    for key, value in sorted(results.items()		):
                         logger.info(F"  {key} = {value}"		)
                         writer.write(F"{key} = {value}\n"		)

     return results



def 							a__ (				lowerCAmelCase	:       Tuple		):






     '''simple docstring'''
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
    main()





"""simple docstring"""




from manim import *







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: str 							=						Rectangle(height=0.5 ,			width=0.5  )
          UpperCAmelCase__							: List[str] 							=						Rectangle(height=0.46 ,			width=0.46  ).set_stroke(width=0  )

          UpperCAmelCase__							: List[Any] 							=						[mem.copy() for i in range(6  )]
          UpperCAmelCase__							: Union[str, Any] 							=						[mem.copy() for i in range(6  )]
          UpperCAmelCase__							: List[Any] 							=						VGroup(*__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0  )
          UpperCAmelCase__							: int 							=						VGroup(*__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0  )
          UpperCAmelCase__							: Tuple 							=						VGroup(__UpperCamelCase ,			__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0  )
          UpperCAmelCase__							: Tuple 							=						Text("CPU" ,			font_size=24  )
          UpperCAmelCase__							: Any 							=						Group(__UpperCamelCase ,			__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0.5 ,			aligned_edge=__UpperCamelCase  )
          cpu.move_to([-2.5, -0.5, 0]  )
          self.add(__UpperCamelCase  )

          UpperCAmelCase__							: List[Any] 							=						[mem.copy() for i in range(4  )]
          UpperCAmelCase__							: List[str] 							=						VGroup(*__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0  )
          UpperCAmelCase__							: Union[str, Any] 							=						Text("GPU" ,			font_size=24  )
          UpperCAmelCase__							: Dict 							=						Group(__UpperCamelCase ,			__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0.5 ,			aligned_edge=__UpperCamelCase  )
          gpu.move_to([-1, -1, 0]  )
          self.add(__UpperCamelCase  )

          UpperCAmelCase__							: Optional[int] 							=						[mem.copy() for i in range(6  )]
          UpperCAmelCase__							: List[str] 							=						VGroup(*__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0  )

          UpperCAmelCase__							: Tuple 							=						Text("Model" ,			font_size=24  )
          UpperCAmelCase__							: Dict 							=						Group(__UpperCamelCase ,			__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0.5 ,			aligned_edge=__UpperCamelCase  )
          model.move_to([3, -1.0, 0]  )
          self.add(__UpperCamelCase  )

          UpperCAmelCase__							: List[str] 							=						[]
          for i, rect in enumerate(__UpperCamelCase  ):
               rect.set_stroke(__UpperCamelCase  )
               # target = fill.copy().set_fill(YELLOW, opacity=0.7)
               # target.move_to(rect)
               # self.add(target)

               UpperCAmelCase__							: int 							=						Rectangle(height=0.46 / 4 ,			width=0.46 / 3  ).set_stroke(width=0.0  ).set_fill(__UpperCamelCase ,			opacity=0.7  )

               if i == 0:
                    cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT  ) ,			buff=0.02 ,			direction=__UpperCamelCase  )
                    cpu_target.set_x(cpu_target.get_x() + 0.1  )
               elif i == 3:
                    cpu_target.next_to(cpu_targs[0] ,			direction=__UpperCamelCase ,			buff=0.0  )
               else:
                    cpu_target.next_to(cpu_targs[i - 1] ,			direction=__UpperCamelCase ,			buff=0.0  )
               self.add(__UpperCamelCase  )
               cpu_targs.append(__UpperCamelCase  )

          UpperCAmelCase__							: List[Any] 							=						[mem.copy() for i in range(6  )]
          UpperCAmelCase__							: Any 							=						VGroup(*__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0  )

          UpperCAmelCase__							: Tuple 							=						Text("Loaded Checkpoint" ,			font_size=24  )
          UpperCAmelCase__							: Any 							=						Group(__UpperCamelCase ,			__UpperCamelCase  ).arrange(__UpperCamelCase ,			aligned_edge=__UpperCamelCase ,			buff=0.4  )
          checkpoint.move_to([3, 0.5, 0]  )

          UpperCAmelCase__							: Optional[Any] 							=						Square(side_length=2.2  )
          key.move_to([-5, 2, 0]  )

          UpperCAmelCase__							: Any 							=						MarkupText(
              F"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model" ,			font_size=18 ,			)

          key_text.move_to([-5, 2.4, 0]  )

          self.add(__UpperCamelCase ,			__UpperCamelCase  )

          UpperCAmelCase__							: str 							=						MarkupText(
              F"<span fgcolor='{BLUE}'>●</span> Checkpoint" ,			font_size=18 ,			)

          blue_text.next_to(__UpperCamelCase ,			DOWN * 2.4 ,			aligned_edge=key_text.get_left()  )

          UpperCAmelCase__							: Optional[Any] 							=						MarkupText(
              F"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>." ,			font_size=24 ,			)
          step_a.move_to([2, 2, 0]  )
          self.play(Write(__UpperCamelCase  ) ,			Write(__UpperCamelCase  )  )

          self.play(Write(__UpperCamelCase ,			run_time=1  ) ,			Create(__UpperCamelCase ,			run_time=1  )  )

          UpperCAmelCase__							: Union[str, Any] 							=						[]
          UpperCAmelCase__							: List[str] 							=						[]
          for i, rect in enumerate(__UpperCamelCase  ):
               UpperCAmelCase__							: Optional[Any] 							=						fill.copy().set_fill(__UpperCamelCase ,			opacity=0.7  )
               target.move_to(__UpperCamelCase  )
               first_animations.append(GrowFromCenter(__UpperCamelCase ,			run_time=1  )  )

               UpperCAmelCase__							: List[str] 							=						target.copy()
               cpu_target.generate_target()
               if i < 5:
                    cpu_target.target.move_to(cpu_left_col_base[i + 1]  )
               else:
                    cpu_target.target.move_to(cpu_right_col_base[i - 5]  )
               second_animations.append(MoveToTarget(__UpperCamelCase ,			run_time=1.5  )  )

          self.play(*__UpperCamelCase  )
          self.play(*__UpperCamelCase  )
          self.wait()



