


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


lowerCAmelCase__      		= logging.getLogger(__name__)
lowerCAmelCase__      		= list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowerCAmelCase__      		= tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class      lowerCAmelCase__       :




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


     def  _lowerCamelCase (						self)			->				Optional[Any]:
            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      lowerCAmelCase__       :




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "The name of the dataset to use (via the datasets library)."})
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "The configuration name of the dataset to use (via the datasets library)."})
     __SCREAMING_SNAKE_CASE  			= field(default=a     ,    metadata={"help": "The input training data file (a text file)."})
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}     ,    )
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}     ,    )
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}     ,    )
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "Overwrite the cached training and evaluation sets"})
     __SCREAMING_SNAKE_CASE  			= field(
         default=5     ,    metadata={
             "help": "The percentage of the train set used as validation set in case there's no validation split"
         }     ,    )
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    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."
             )
         }     ,    )
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "The number of processes to use for the preprocessing."}     ,    )
     __SCREAMING_SNAKE_CASE  			= field(
         default=0.15     ,    metadata={"help": "Ratio of tokens to mask for masked language modeling loss"})
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    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:
                   _A      :       Any				    = 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:
                   _A      :       int				    = self.validation_file.split(".")[-1]
                   assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."




def 							_UpperCAmelCase				(UpperCamelCase__     :  str				,							UpperCamelCase__     :  Dict						):
       with open(UpperCamelCase__				,							"r"				,							encoding="utf-8"						) as f:
              _A      :       Union[str, Any]				    = [json.loads(UpperCamelCase__						) for line in f.read().splitlines() if (len(UpperCamelCase__						) > 0 and not line.isspace())]
       assert len(UpperCamelCase__						) == len(UpperCamelCase__						)

       _A      :       Dict				    = {c: dataset[c] for c in dataset.column_names}
       _A      :       str				    = refs
       return Dataset.from_dict(UpperCamelCase__						)
def 							_UpperCAmelCase				():
       # 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.

       _A      :       Union[str, Any]				    = 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.
              _A    ,			_A    ,			_A      :       str				    = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
       else:
              _A    ,			_A    ,			_A      :       Optional[int]				    = parser.parse_args_into_dataclasses()

       # Detecting last checkpoint.
       _A      :       Any				    = None
       if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
              _A      :       int				    = 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"				,							UpperCamelCase__						)

       # 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.
              _A      :       List[Any]				    = load_dataset(data_args.dataset_name				,							data_args.dataset_config_name						)
              if "validation" not in datasets.keys():
                     _A      :       Dict				    = load_dataset(
                         data_args.dataset_name				,							data_args.dataset_config_name				,							split=f"train[:{data_args.validation_split_percentage}%]"				,							)
                     _A      :       Optional[Any]				    = load_dataset(
                         data_args.dataset_name				,							data_args.dataset_config_name				,							split=f"train[{data_args.validation_split_percentage}%:]"				,							)
       else:
              _A      :       int				    = {}
              if data_args.train_file is not None:
                     _A      :       Any				    = data_args.train_file
              if data_args.validation_file is not None:
                     _A      :       List[str]				    = data_args.validation_file
              _A      :       int				    = data_args.train_file.split("."						)[-1]
              if extension == "txt":
                     _A      :       Tuple				    = "text"
              _A      :       Dict				    = load_dataset(UpperCamelCase__				,							data_files=UpperCamelCase__						)
       # 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.
       _A      :       Dict				    = {
           "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:
              _A      :       Optional[Any]				    = AutoConfig.from_pretrained(model_args.config_name				,							**UpperCamelCase__						)
       elif model_args.model_name_or_path:
              _A      :       List[str]				    = AutoConfig.from_pretrained(model_args.model_name_or_path				,							**UpperCamelCase__						)
       else:
              _A      :       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}"						)

       _A      :       Optional[int]				    = {
           "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:
              _A      :       List[Any]				    = AutoTokenizer.from_pretrained(model_args.tokenizer_name				,							**UpperCamelCase__						)
       elif model_args.model_name_or_path:
              _A      :       Any				    = AutoTokenizer.from_pretrained(model_args.model_name_or_path				,							**UpperCamelCase__						)
       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:
              _A      :       Dict				    = AutoModelForMaskedLM.from_pretrained(
                  model_args.model_name_or_path				,							from_tf=bool(".ckpt" in model_args.model_name_or_path						)				,							config=UpperCamelCase__				,							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"						)
              _A      :       Tuple				    = AutoModelForMaskedLM.from_config(UpperCamelCase__						)

       model.resize_token_embeddings(len(UpperCamelCase__						)						)

       # Preprocessing the datasets.
       # First we tokenize all the texts.
       if training_args.do_train:
              _A      :       str				    = datasets["train"].column_names
       else:
              _A      :       Tuple				    = datasets["validation"].column_names
       _A      :       Union[str, Any]				    = "text" if "text" in column_names else column_names[0]

       _A      :       List[Any]				    = "max_length" if data_args.pad_to_max_length else False

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

       _A      :       Optional[int]				    = datasets.map(
           UpperCamelCase__				,							batched=UpperCamelCase__				,							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:
              _A      :       str				    = add_chinese_references(tokenized_datasets["train"]				,							data_args.train_ref_file						)
       if data_args.validation_ref_file is not None:
              _A      :       Tuple				    = add_chinese_references(
                  tokenized_datasets["validation"]				,							data_args.validation_ref_file						)
       # If we have ref files, need to avoid it removed by trainer
       _A      :       Union[str, Any]				    = data_args.train_ref_file or data_args.validation_ref_file
       if has_ref:
              _A      :       Any				    = False

       # Data collator
       # This one will take care of randomly masking the tokens.
       _A      :       Union[str, Any]				    = DataCollatorForWholeWordMask(tokenizer=UpperCamelCase__				,							mlm_probability=data_args.mlm_probability						)

       # Initialize our Trainer
       _A      :       Dict				    = Trainer(
           model=UpperCamelCase__				,							args=UpperCamelCase__				,							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=UpperCamelCase__				,							data_collator=UpperCamelCase__				,							)

       # Training
       if training_args.do_train:
              if last_checkpoint is not None:
                     _A      :       Dict				    = last_checkpoint
              elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path						):
                     _A      :       Tuple				    = model_args.model_name_or_path
              else:
                     _A      :       Tuple				    = None
              _A      :       str				    = trainer.train(resume_from_checkpoint=UpperCamelCase__						)
              trainer.save_model()  # Saves the tokenizer too for easy upload

              _A      :       Optional[Any]				    = os.path.join(training_args.output_dir				,							"train_results.txt"						)
              if trainer.is_world_process_zero():
                     with open(UpperCamelCase__				,							"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
       _A      :       str				    = {}
       if training_args.do_eval:
              logger.info("*** Evaluate ***"						)

              _A      :       List[Any]				    = trainer.evaluate()

              _A      :       Optional[Any]				    = math.exp(eval_output["eval_loss"]						)
              _A      :       Dict				    = perplexity

              _A      :       Dict				    = os.path.join(training_args.output_dir				,							"eval_results_mlm_wwm.txt"						)
              if trainer.is_world_process_zero():
                     with open(UpperCamelCase__				,							"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 							_UpperCAmelCase				(UpperCamelCase__     :  Tuple						):
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
    main()




import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union

import datasets
import numpy as np
import torch
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

lowerCAmelCase__      		= logging.getLogger(__name__)







@dataclass
class      lowerCAmelCase__       :




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"})
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "Pretrained config name or path if not the same as model_name"})
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"})
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}     ,    )
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}     ,    )
     __SCREAMING_SNAKE_CASE  			= field(
         default="main"     ,    metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}     ,    )
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={
             "help": (
                 "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                 "with private models)."
             )
         }     ,    )







@dataclass
class      lowerCAmelCase__       :




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= field(default=a     ,    metadata={"help": "The input training data file (a text file)."})
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}     ,    )
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "Overwrite the cached training and evaluation sets"})
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "The number of processes to use for the preprocessing."}     ,    )
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={
             "help": (
                 "The maximum total input sequence length after tokenization. If passed, sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }     ,    )
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={
             "help": (
                 "Whether to pad all samples to the maximum sentence length. "
                 "If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
                 "efficient on GPU but very bad for TPU."
             )
         }     ,    )
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of training examples to this "
                 "value if set."
             )
         }     ,    )
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                 "value if set."
             )
         }     ,    )


     def  _lowerCamelCase (						self)			->				int:
            if self.train_file is not None:
                   _A      :       Optional[int]				    = self.train_file.split(".")[-1]
                   assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
            if self.validation_file is not None:
                   _A      :       Dict				    = self.validation_file.split(".")[-1]
                   assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."







@dataclass
class      lowerCAmelCase__       :




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= 42
     __SCREAMING_SNAKE_CASE  			= True
     __SCREAMING_SNAKE_CASE  			= None
     __SCREAMING_SNAKE_CASE  			= None


     def __call__(						self ,						__lowerCamelCase)			->				str:
            _A      :       List[Any]				    = "label" if "label" in features[0].keys() else "labels"
            _A      :       Any				    = [feature.pop(__lowerCamelCase) for feature in features]
            _A      :       Optional[int]				    = len(__lowerCamelCase)
            _A      :       int				    = len(features[0]["input_ids"])
            _A      :       Tuple				    = [
                [{k: v[i] for k, v in feature.items()} for i in range(__lowerCamelCase)] for feature in features
            ]
            _A      :       str				    = list(chain(*__lowerCamelCase))

            _A      :       Tuple				    = self.tokenizer.pad(
                __lowerCamelCase ,						padding=self.padding ,						max_length=self.max_length ,						pad_to_multiple_of=self.pad_to_multiple_of ,						return_tensors="pt" ,						)

            # Un-flatten
            _A      :       Optional[int]				    = {k: v.view(__lowerCamelCase ,						__lowerCamelCase ,						-1) for k, v in batch.items()}
            # Add back labels
            _A      :       Optional[int]				    = torch.tensor(__lowerCamelCase ,						dtype=torch.intaa)
            return batch




def 							_UpperCAmelCase				():
       # 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.

       _A      :       int				    = 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.
              _A    ,			_A    ,			_A      :       int				    = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
       else:
              _A    ,			_A    ,			_A      :       Union[str, Any]				    = parser.parse_args_into_dataclasses()

       # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
       # information sent is the one passed as arguments along with your Python/PyTorch versions.
       send_example_telemetry("run_swag"				,							UpperCamelCase__				,							UpperCamelCase__						)

       # 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						)]				,							)

       if training_args.should_log:
              # The default of training_args.log_level is passive, so we set log level at info here to have that default.
              transformers.utils.logging.set_verbosity_info()

       _A      :       int				    = training_args.get_process_log_level()
       logger.setLevel(UpperCamelCase__						)
       datasets.utils.logging.set_verbosity(UpperCamelCase__						)
       transformers.utils.logging.set_verbosity(UpperCamelCase__						)
       transformers.utils.logging.enable_default_handler()
       transformers.utils.logging.enable_explicit_format()

       # 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}"						)
       logger.info(f"Training/evaluation parameters {training_args}"						)

       # Detecting last checkpoint.
       _A      :       List[Any]				    = None
       if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
              _A      :       Optional[int]				    = 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 and training_args.resume_from_checkpoint is 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."						)

    # 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.train_file is not None or data_args.validation_file is not None:
              _A      :       List[str]				    = {}
              if data_args.train_file is not None:
                     _A      :       Optional[int]				    = data_args.train_file
              if data_args.validation_file is not None:
                     _A      :       Tuple				    = data_args.validation_file
              _A      :       Union[str, Any]				    = data_args.train_file.split("."						)[-1]
              _A      :       List[str]				    = load_dataset(
                  UpperCamelCase__				,							data_files=UpperCamelCase__				,							cache_dir=model_args.cache_dir				,							use_auth_token=True if model_args.use_auth_token else None				,							)
       else:
              # Downloading and loading the swag dataset from the hub.
              _A      :       Union[str, Any]				    = load_dataset(
                  "swag"				,							"regular"				,							cache_dir=model_args.cache_dir				,							use_auth_token=True if model_args.use_auth_token else None				,							)
       # 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.
       _A      :       Optional[Any]				    = AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path				,							cache_dir=model_args.cache_dir				,							revision=model_args.model_revision				,							use_auth_token=True if model_args.use_auth_token else None				,							)
       _A      :       Optional[Any]				    = AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,							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				,							)
       _A      :       List[Any]				    = AutoModelForMultipleChoice.from_pretrained(
           model_args.model_name_or_path				,							from_tf=bool(".ckpt" in model_args.model_name_or_path						)				,							config=UpperCamelCase__				,							cache_dir=model_args.cache_dir				,							revision=model_args.model_revision				,							use_auth_token=True if model_args.use_auth_token else None				,							)

       # When using your own dataset or a different dataset from swag, you will probably need to change this.
       _A      :       str				    = [f"ending{i}" for i in range(4						)]
       _A      :       Union[str, Any]				    = "sent1"
       _A      :       str				    = "sent2"

       if data_args.max_seq_length is None:
              _A      :       Any				    = tokenizer.model_max_length
              if max_seq_length > 1024:
                     logger.warning(
                         "The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
                         " of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
                         " override this default with `--block_size xxx`."						)
                     _A      :       Optional[Any]				    = 1024
       else:
              if data_args.max_seq_length > tokenizer.model_max_length:
                     logger.warning(
                         f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
                         f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."						)
              _A      :       int				    = min(data_args.max_seq_length				,							tokenizer.model_max_length						)

       # Preprocessing the datasets.
       def preprocess_function(UpperCamelCase__     :  List[Any]						):
              _A      :       List[Any]				    = [[context] * 4 for context in examples[context_name]]
              _A      :       Any				    = examples[question_header_name]
              _A      :       Union[str, Any]				    = [
                  [f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(UpperCamelCase__						)
              ]

              # Flatten out
              _A      :       Dict				    = list(chain(*UpperCamelCase__						)						)
              _A      :       List[Any]				    = list(chain(*UpperCamelCase__						)						)

              # Tokenize
              _A      :       str				    = tokenizer(
                  UpperCamelCase__				,							UpperCamelCase__				,							truncation=UpperCamelCase__				,							max_length=UpperCamelCase__				,							padding="max_length" if data_args.pad_to_max_length else False				,							)
              # Un-flatten
              return {k: [v[i : i + 4] for i in range(0				,							len(UpperCamelCase__						)				,							4						)] for k, v in tokenized_examples.items()}

       if training_args.do_train:
              if "train" not in raw_datasets:
                     raise ValueError("--do_train requires a train dataset"						)
              _A      :       Optional[int]				    = raw_datasets["train"]
              if data_args.max_train_samples is not None:
                     _A      :       Union[str, Any]				    = min(len(UpperCamelCase__						)				,							data_args.max_train_samples						)
                     _A      :       Any				    = train_dataset.select(range(UpperCamelCase__						)						)
              with training_args.main_process_first(desc="train dataset map pre-processing"						):
                     _A      :       Optional[int]				    = train_dataset.map(
                         UpperCamelCase__				,							batched=UpperCamelCase__				,							num_proc=data_args.preprocessing_num_workers				,							load_from_cache_file=not data_args.overwrite_cache				,							)

       if training_args.do_eval:
              if "validation" not in raw_datasets:
                     raise ValueError("--do_eval requires a validation dataset"						)
              _A      :       Optional[int]				    = raw_datasets["validation"]
              if data_args.max_eval_samples is not None:
                     _A      :       str				    = min(len(UpperCamelCase__						)				,							data_args.max_eval_samples						)
                     _A      :       Dict				    = eval_dataset.select(range(UpperCamelCase__						)						)
              with training_args.main_process_first(desc="validation dataset map pre-processing"						):
                     _A      :       List[str]				    = eval_dataset.map(
                         UpperCamelCase__				,							batched=UpperCamelCase__				,							num_proc=data_args.preprocessing_num_workers				,							load_from_cache_file=not data_args.overwrite_cache				,							)

    # Data collator
       _A      :       str				    = (
           default_data_collator
           if data_args.pad_to_max_length
           else DataCollatorForMultipleChoice(tokenizer=UpperCamelCase__				,							pad_to_multiple_of=8 if training_args.fpaa else None						)
       )

       # Metric
       def compute_metrics(UpperCamelCase__     :  Tuple						):
              _A    ,			_A      :       List[str]				    = eval_predictions
              _A      :       Optional[int]				    = np.argmax(UpperCamelCase__				,							axis=1						)
              return {"accuracy": (preds == label_ids).astype(np.floataa						).mean().item()}

       # Initialize our Trainer
       _A      :       List[str]				    = Trainer(
           model=UpperCamelCase__				,							args=UpperCamelCase__				,							train_dataset=train_dataset if training_args.do_train else None				,							eval_dataset=eval_dataset if training_args.do_eval else None				,							tokenizer=UpperCamelCase__				,							data_collator=UpperCamelCase__				,							compute_metrics=UpperCamelCase__				,							)

       # Training
       if training_args.do_train:
              _A      :       Any				    = None
              if training_args.resume_from_checkpoint is not None:
                     _A      :       Optional[int]				    = training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     _A      :       int				    = last_checkpoint
              _A      :       Any				    = trainer.train(resume_from_checkpoint=UpperCamelCase__						)
              trainer.save_model()  # Saves the tokenizer too for easy upload
              _A      :       Optional[int]				    = train_result.metrics

              _A      :       Tuple				    = (
                  data_args.max_train_samples if data_args.max_train_samples is not None else len(UpperCamelCase__						)
              )
              _A      :       Tuple				    = min(UpperCamelCase__				,							len(UpperCamelCase__						)						)

              trainer.log_metrics("train"				,							UpperCamelCase__						)
              trainer.save_metrics("train"				,							UpperCamelCase__						)
              trainer.save_state()

       # Evaluation
       if training_args.do_eval:
              logger.info("*** Evaluate ***"						)

              _A      :       List[Any]				    = trainer.evaluate()
              _A      :       int				    = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(UpperCamelCase__						)
              _A      :       Optional[Any]				    = min(UpperCamelCase__				,							len(UpperCamelCase__						)						)

              trainer.log_metrics("eval"				,							UpperCamelCase__						)
              trainer.save_metrics("eval"				,							UpperCamelCase__						)

       _A      :       Tuple				    = {
           "finetuned_from": model_args.model_name_or_path,
           "tasks": "multiple-choice",
           "dataset_tags": "swag",
           "dataset_args": "regular",
           "dataset": "SWAG",
           "language": "en",
       }

       if training_args.push_to_hub:
              trainer.push_to_hub(**UpperCamelCase__						)
       else:
              trainer.create_model_card(**UpperCamelCase__						)
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[int]						):
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
    main()
