


'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_      :		Dict          =		{"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_      :		Dict          =		["XGLMTokenizer"]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_      :		int          =		["XGLMTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_      :		Any          =		[
     "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "XGLMForCausalLM",
     "XGLMModel",
     "XGLMPreTrainedModel",
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_      :		List[str]          =		[
     "FlaxXGLMForCausalLM",
     "FlaxXGLMModel",
     "FlaxXGLMPreTrainedModel",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_      :		str          =		[
     "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFXGLMForCausalLM",
     "TFXGLMModel",
     "TFXGLMPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xglm import XGLMTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xglm_fast import XGLMTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_xglm import (
      TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXGLMForCausalLM,
      TFXGLMModel,
      TFXGLMPreTrainedModel,
  )


else:
 import sys

 UpperCAmelCase_      :		Any          =		_LazyModule(__name__, globals()["__file__"], _import_structure)






'''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


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







@dataclass
class 					a			:
 '''simple docstring'''




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




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    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 					a			:
 '''simple docstring'''




 __lowerCAmelCase						: Optional[str]							  =		field(
     default=snake_case__			,							metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}  )
 __lowerCAmelCase						: Optional[str]							  =		field(
     default=snake_case__			,							metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}  )
 __lowerCAmelCase						: Optional[str]							  =		field(default=snake_case__			,							metadata={"""help""": """The input training data file (a text file)."""}  )
 __lowerCAmelCase						: Optional[str]							  =		field(
     default=snake_case__			,							metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}			,							)
 __lowerCAmelCase						: Optional[str]							  =		field(
     default=snake_case__			,							metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}			,							)
 __lowerCAmelCase						: Optional[str]							  =		field(
     default=snake_case__			,							metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}			,							)
 __lowerCAmelCase						: bool							  =		field(
     default=snake_case__			,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""}  )
 __lowerCAmelCase						: Optional[int]							  =		field(
     default=5			,							metadata={
         """help""": """The percentage of the train set used as validation set in case there's no validation split"""
     }			,							)
 __lowerCAmelCase						: Optional[int]							  =		field(
     default=snake_case__			,							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."""
         )
     }			,							)
 __lowerCAmelCase						: Optional[int]							  =		field(
     default=snake_case__			,							metadata={"""help""": """The number of processes to use for the preprocessing."""}			,							)
 __lowerCAmelCase						: float							  =		field(
     default=0.1_5			,							metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}  )
 __lowerCAmelCase						: bool							  =		field(
     default=snake_case__			,							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   __UpperCamelCase   (    self      )      ->    str:
    if self.train_file is not None:
       _a					:  int						=  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					:  List[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    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





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

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


def    UpperCAmelCase_  (       ):
   '''simple docstring'''





   _a					:  Optional[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					:  List[Any]						=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
   else:
      _a		,						_a		,						_a					:  Dict						=  parser.parse_args_into_dataclasses()

   # Detecting last checkpoint.
   _a					:  Dict						=  None
   if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
      _a					:  List[str]						=  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.
      _a					:  Tuple						=  load_dataset(data_args.dataset_name		,	data_args.dataset_config_name      )
      if "validation" not in datasets.keys():
         _a					:  Tuple						=  load_dataset(
             data_args.dataset_name		,	data_args.dataset_config_name		,	split=f'''train[:{data_args.validation_split_percentage}%]'''		,	)
         _a					:  List[str]						=  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					:  Tuple						=  data_args.train_file
      if data_args.validation_file is not None:
         _a					:  List[Any]						=  data_args.validation_file
      _a					:  Optional[Any]						=  data_args.train_file.split('.'      )[-1]
      if extension == "txt":
         _a					:  Optional[Any]						=  'text'
      _a					:  int						=  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.
   _a					:  List[str]						=  {
       '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					:  List[str]						=  AutoConfig.from_pretrained(model_args.config_name		,	**A      )
   elif model_args.model_name_or_path:
      _a					:  Any						=  AutoConfig.from_pretrained(model_args.model_name_or_path		,	**A      )
   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					:  Any						=  {
       '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					:  Tuple						=  AutoTokenizer.from_pretrained(model_args.tokenizer_name		,	**A      )
   elif model_args.model_name_or_path:
      _a					:  Dict						=  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:
      _a					:  Optional[Any]						=  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'      )
      _a					:  List[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:
      _a					:  str						=  datasets['train'].column_names
   else:
      _a					:  int						=  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(A      ):
      # Remove empty lines
      _a					:  Union[str, Any]						=  [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      )

   _a					:  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:
      _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					:  Optional[int]						=  False

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

   # Initialize our Trainer
   _a					:  Union[str, Any]						=  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:
         _a					:  Any						=  last_checkpoint
      elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path      ):
         _a					:  Optional[int]						=  model_args.model_name_or_path
      else:
         _a					:  int						=  None
      _a					:  Tuple						=  trainer.train(resume_from_checkpoint=A      )
      trainer.save_model()  # Saves the tokenizer too for easy upload

      _a					:  Union[str, Any]						=  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
   _a					:  Optional[int]						=  {}
   if training_args.do_eval:
      logger.info('*** Evaluate ***'      )

      _a					:  Union[str, Any]						=  trainer.evaluate()

      _a					:  Any						=  math.exp(eval_output['eval_loss']      )
      _a					:  List[str]						=  perplexity

      _a					:  Optional[int]						=  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()


