

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


_SCREAMING_SNAKE_CASE  :						str         =    logging.getLogger(__name__)
_SCREAMING_SNAKE_CASE  :						int         =    list(MODEL_FOR_MASKED_LM_MAPPING.keys())
_SCREAMING_SNAKE_CASE  :						Tuple         =    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default=A__		,  metadata={
          "help": (
              "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
          )
      }		,  )
  a     =      field(
      default=A__		,  metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(A__ )}		,  )
  a     =      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"
          )
      }		,  )
  a     =      field(
      default=A__		,  metadata={"help": "Pretrained config name or path if not the same as model_name"} )
  a     =      field(
      default=A__		,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
  a     =      field(
      default=A__		,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}		,  )
  a     =      field(
      default=A__		,  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=A__		,  metadata={
          "help": (
              "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
              "with private models)."
          )
      }		,  )






  def    lowercase_	(							self		:	Any      )	->							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  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default=A__		,  metadata={"help": "The name of the dataset to use (via the datasets library)."} )
  a     =      field(
      default=A__		,  metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
  a     =      field(default=A__		,  metadata={"help": "The input training data file (a text file)."} )
  a     =      field(
      default=A__		,  metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}		,  )
  a     =      field(
      default=A__		,  metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}		,  )
  a     =      field(
      default=A__		,  metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}		,  )
  a     =      field(
      default=A__		,  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=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."
          )
      }		,  )
  a     =      field(
      default=A__		,  metadata={"help": "The number of processes to use for the preprocessing."}		,  )
  a     =      field(
      default=0.1_5		,  metadata={"help": "Ratio of tokens to mask for masked language modeling loss"} )
  a     =      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    lowercase_	(							self		:	Optional[Any]      )	->							str:
       if self.train_file is not None:
            SCREAMING_SNAKE_CASE__     :   List[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:
            SCREAMING_SNAKE_CASE__     :   Optional[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_     (		_A   ,	_A       ):





     '''simple docstring'''




     with open(_A   ,	'''r'''   ,	encoding='''utf-8'''       ) as f:
          SCREAMING_SNAKE_CASE__     :   List[str]					=					[json.loads(_A       ) for line in f.read().splitlines() if (len(_A       ) > 0 and not line.isspace())]
     assert len(_A       ) == len(_A       )

     SCREAMING_SNAKE_CASE__     :   Tuple					=					{c: dataset[c] for c in dataset.column_names}
     SCREAMING_SNAKE_CASE__     :   str					=					refs
     return Dataset.from_dict(_A       )
def        UpperCAmelCase_     (		):





     '''simple docstring'''




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

     # Detecting last checkpoint.
     SCREAMING_SNAKE_CASE__     :   str					=					None
     if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
          SCREAMING_SNAKE_CASE__     :   Dict					=					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'''   ,	_A       )

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

     SCREAMING_SNAKE_CASE__     :   List[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:
          SCREAMING_SNAKE_CASE__     :   str					=					AutoTokenizer.from_pretrained(model_args.tokenizer_name   ,	**_A       )
     elif model_args.model_name_or_path:
          SCREAMING_SNAKE_CASE__     :   Union[str, Any]					=					AutoTokenizer.from_pretrained(model_args.model_name_or_path   ,	**_A       )
     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:
          SCREAMING_SNAKE_CASE__     :   Dict					=					AutoModelForMaskedLM.from_pretrained(
              model_args.model_name_or_path   ,	from_tf=bool('''.ckpt''' in model_args.model_name_or_path       )   ,	config=_A   ,	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'''       )
          SCREAMING_SNAKE_CASE__     :   Union[str, Any]					=					AutoModelForMaskedLM.from_config(_A       )

     model.resize_token_embeddings(len(_A       )       )

     # Preprocessing the datasets.
     # First we tokenize all the texts.
     if training_args.do_train:
          SCREAMING_SNAKE_CASE__     :   Optional[int]					=					datasets['''train'''].column_names
     else:
          SCREAMING_SNAKE_CASE__     :   List[Any]					=					datasets['''validation'''].column_names
     SCREAMING_SNAKE_CASE__     :   Union[str, Any]					=					'''text''' if '''text''' in column_names else column_names[0]

     SCREAMING_SNAKE_CASE__     :   List[Any]					=					'''max_length''' if data_args.pad_to_max_length else False

     def tokenize_function(_A       ):
          # Remove empty lines
          SCREAMING_SNAKE_CASE__     :   Optional[int]					=					[line for line in examples['''text'''] if len(_A       ) > 0 and not line.isspace()]
          return tokenizer(examples['''text''']   ,	padding=_A   ,	truncation=_A   ,	max_length=data_args.max_seq_length       )

     SCREAMING_SNAKE_CASE__     :   Union[str, Any]					=					datasets.map(
         _A   ,	batched=_A   ,	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:
          SCREAMING_SNAKE_CASE__     :   Union[str, Any]					=					add_chinese_references(tokenized_datasets['''train''']   ,	data_args.train_ref_file       )
     if data_args.validation_ref_file is not None:
          SCREAMING_SNAKE_CASE__     :   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
     SCREAMING_SNAKE_CASE__     :   str					=					data_args.train_ref_file or data_args.validation_ref_file
     if has_ref:
          SCREAMING_SNAKE_CASE__     :   str					=					False

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

     # Initialize our Trainer
     SCREAMING_SNAKE_CASE__     :   int					=					Trainer(
         model=_A   ,	args=_A   ,	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=_A   ,	data_collator=_A   ,	)

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

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

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

          SCREAMING_SNAKE_CASE__     :   str					=					math.exp(eval_output['''eval_loss''']       )
          SCREAMING_SNAKE_CASE__     :   int					=					perplexity

          SCREAMING_SNAKE_CASE__     :   Any					=					os.path.join(training_args.output_dir   ,	'''eval_results_mlm_wwm.txt'''       )
          if trainer.is_world_process_zero():
               with open(_A   ,	'''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_     (		_A       ):





     '''simple docstring'''




     main()


if __name__ == "__main__":
       main()





import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm
       import torch_xla.debug.metrics as met






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  def __init__(							self		:	Tuple			,     *__lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Any=None			,     __lowerCamelCase		:	Optional[int]=None			,     **__lowerCamelCase		:	Optional[int]      )	->							List[str]:
       super().__init__(*__lowerCamelCase			,     **__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					eval_examples
       SCREAMING_SNAKE_CASE__					=					post_process_function






  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	Dict=None			,     __lowerCamelCase		:	int=None			,     __lowerCamelCase		:	str=None			,     __lowerCamelCase		:	str = "eval"      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					self.eval_dataset if eval_dataset is None else eval_dataset
       SCREAMING_SNAKE_CASE__					=					self.get_eval_dataloader(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					self.eval_examples if eval_examples is None else eval_examples

       # Temporarily disable metric computation, we will do it in the loop here.
       SCREAMING_SNAKE_CASE__					=					self.compute_metrics
       SCREAMING_SNAKE_CASE__					=					None
       SCREAMING_SNAKE_CASE__					=					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       SCREAMING_SNAKE_CASE__					=					time.time()
       try:
            SCREAMING_SNAKE_CASE__					=					eval_loop(
                __lowerCamelCase			,     description='''Evaluation'''			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=__lowerCamelCase			,     metric_key_prefix=__lowerCamelCase			,     )
       finally:
            SCREAMING_SNAKE_CASE__					=					compute_metrics
       SCREAMING_SNAKE_CASE__					=					self.args.eval_batch_size * self.args.world_size
       if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
            start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
       output.metrics.update(
           speed_metrics(
               __lowerCamelCase			,     __lowerCamelCase			,     num_samples=output.num_samples			,     num_steps=math.ceil(output.num_samples / total_batch_size      )			,     )      )
       if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
            # Only the main node write the results by default
            SCREAMING_SNAKE_CASE__					=					self.post_process_function(__lowerCamelCase			,     __lowerCamelCase			,     output.predictions      )
            SCREAMING_SNAKE_CASE__					=					self.compute_metrics(__lowerCamelCase      )

            # Prefix all keys with metric_key_prefix + '_'
            for key in list(metrics.keys()      ):
                 if not key.startswith(f'''{metric_key_prefix}_'''      ):
                      SCREAMING_SNAKE_CASE__					=					metrics.pop(__lowerCamelCase      )
            metrics.update(output.metrics      )
       else:
            SCREAMING_SNAKE_CASE__					=					output.metrics

       if self.args.should_log:
            # Only the main node log the results by default
            self.log(__lowerCamelCase      )

       if self.args.tpu_metrics_debug or self.args.debug:
            # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
            xm.master_print(met.metrics_report()      )

       SCREAMING_SNAKE_CASE__					=					self.callback_handler.on_evaluate(self.args			,     self.state			,     self.control			,     __lowerCamelCase      )
       return metrics







  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	str=None			,     __lowerCamelCase		:	str = "test"      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					self.get_test_dataloader(__lowerCamelCase      )

       # Temporarily disable metric computation, we will do it in the loop here.
       SCREAMING_SNAKE_CASE__					=					self.compute_metrics
       SCREAMING_SNAKE_CASE__					=					None
       SCREAMING_SNAKE_CASE__					=					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       SCREAMING_SNAKE_CASE__					=					time.time()
       try:
            SCREAMING_SNAKE_CASE__					=					eval_loop(
                __lowerCamelCase			,     description='''Prediction'''			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=__lowerCamelCase			,     metric_key_prefix=__lowerCamelCase			,     )
       finally:
            SCREAMING_SNAKE_CASE__					=					compute_metrics
       SCREAMING_SNAKE_CASE__					=					self.args.eval_batch_size * self.args.world_size
       if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
            start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
       output.metrics.update(
           speed_metrics(
               __lowerCamelCase			,     __lowerCamelCase			,     num_samples=output.num_samples			,     num_steps=math.ceil(output.num_samples / total_batch_size      )			,     )      )

       if self.post_process_function is None or self.compute_metrics is None:
            return output

       SCREAMING_SNAKE_CASE__					=					self.post_process_function(__lowerCamelCase			,     __lowerCamelCase			,     output.predictions			,     '''predict'''      )
       SCREAMING_SNAKE_CASE__					=					self.compute_metrics(__lowerCamelCase      )

       # Prefix all keys with metric_key_prefix + '_'
       for key in list(metrics.keys()      ):
            if not key.startswith(f'''{metric_key_prefix}_'''      ):
                 SCREAMING_SNAKE_CASE__					=					metrics.pop(__lowerCamelCase      )
       metrics.update(output.metrics      )
       return PredictionOutput(predictions=predictions.predictions			,     label_ids=predictions.label_ids			,     metrics=__lowerCamelCase      )


