


"""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


__magic_name__						: Optional[int]    =				logging.getLogger(__name__)
__magic_name__						: int    =				list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__magic_name__						: str    =				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class 			__snake_case   :
							__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   __a   (     self:    List[str]   ):
										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 			__snake_case   :
							__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   __a   (     self:    Tuple   ):
										if self.train_file is not None:
													__lowerCamelCase          =      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:
													__lowerCamelCase          =      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_ (	lowercase__ :str,			lowercase__ :Optional[int]     ):
			with open(lowercase__,			"""r""",			encoding="""utf-8"""     ) as f:
						__lowerCamelCase          =      [json.loads(lowercase__     ) for line in f.read().splitlines() if (len(lowercase__     ) > 0 and not line.isspace())]
			assert len(lowercase__     ) == len(lowercase__     )

			__lowerCamelCase          =      {c: dataset[c] for c in dataset.column_names}
			__lowerCamelCase          =      refs
			return Dataset.from_dict(lowercase__     )





def       a_ (	):
			# 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.

			__lowerCamelCase          =      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.
						__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase          =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
			else:
						__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase          =      parser.parse_args_into_dataclasses()

			# Detecting last checkpoint.
			__lowerCamelCase          =      None
			if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
						__lowerCamelCase          =      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""",			lowercase__     )

			# 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.
						__lowerCamelCase          =      load_dataset(data_args.dataset_name,			data_args.dataset_config_name     )
						if "validation" not in datasets.keys():
									__lowerCamelCase          =      load_dataset(
									    data_args.dataset_name,			data_args.dataset_config_name,			split=f'train[:{data_args.validation_split_percentage}%]',			)
									__lowerCamelCase          =      load_dataset(
									    data_args.dataset_name,			data_args.dataset_config_name,			split=f'train[{data_args.validation_split_percentage}%:]',			)
			else:
						__lowerCamelCase          =      {}
						if data_args.train_file is not None:
									__lowerCamelCase          =      data_args.train_file
						if data_args.validation_file is not None:
									__lowerCamelCase          =      data_args.validation_file
						__lowerCamelCase          =      data_args.train_file.split("""."""     )[-1]
						if extension == "txt":
									__lowerCamelCase          =      """text"""
						__lowerCamelCase          =      load_dataset(lowercase__,			data_files=lowercase__     )
			# 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.
			__lowerCamelCase          =      {
			    """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:
						__lowerCamelCase          =      AutoConfig.from_pretrained(model_args.config_name,			**lowercase__     )
			elif model_args.model_name_or_path:
						__lowerCamelCase          =      AutoConfig.from_pretrained(model_args.model_name_or_path,			**lowercase__     )
			else:
						__lowerCamelCase          =      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}'     )

			__lowerCamelCase          =      {
			    """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:
						__lowerCamelCase          =      AutoTokenizer.from_pretrained(model_args.tokenizer_name,			**lowercase__     )
			elif model_args.model_name_or_path:
						__lowerCamelCase          =      AutoTokenizer.from_pretrained(model_args.model_name_or_path,			**lowercase__     )
			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:
						__lowerCamelCase          =      AutoModelForMaskedLM.from_pretrained(
						    model_args.model_name_or_path,			from_tf=bool(""".ckpt""" in model_args.model_name_or_path     ),			config=lowercase__,			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"""     )
						__lowerCamelCase          =      AutoModelForMaskedLM.from_config(lowercase__     )

			model.resize_token_embeddings(len(lowercase__     )     )

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

			__lowerCamelCase          =      """max_length""" if data_args.pad_to_max_length else False

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

			__lowerCamelCase          =      datasets.map(
			    lowercase__,			batched=lowercase__,			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:
						__lowerCamelCase          =      add_chinese_references(tokenized_datasets["""train"""],			data_args.train_ref_file     )
			if data_args.validation_ref_file is not None:
						__lowerCamelCase          =      add_chinese_references(
						    tokenized_datasets["""validation"""],			data_args.validation_ref_file     )
			# If we have ref files, need to avoid it removed by trainer
			__lowerCamelCase          =      data_args.train_ref_file or data_args.validation_ref_file
			if has_ref:
						__lowerCamelCase          =      False

			# Data collator
			# This one will take care of randomly masking the tokens.
			__lowerCamelCase          =      DataCollatorForWholeWordMask(tokenizer=lowercase__,			mlm_probability=data_args.mlm_probability     )

			# Initialize our Trainer
			__lowerCamelCase          =      Trainer(
			    model=lowercase__,			args=lowercase__,			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=lowercase__,			data_collator=lowercase__,			)

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

						__lowerCamelCase          =      os.path.join(training_args.output_dir,			"""train_results.txt"""     )
						if trainer.is_world_process_zero():
									with open(lowercase__,			"""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
			__lowerCamelCase          =      {}
			if training_args.do_eval:
						logger.info("""*** Evaluate ***"""     )

						__lowerCamelCase          =      trainer.evaluate()

						__lowerCamelCase          =      math.exp(eval_output["""eval_loss"""]     )
						__lowerCamelCase          =      perplexity

						__lowerCamelCase          =      os.path.join(training_args.output_dir,			"""eval_results_mlm_wwm.txt"""     )
						if trainer.is_world_process_zero():
									with open(lowercase__,			"""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_ (	lowercase__ :int     ):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
					main()








"""simple docstring"""




def       a_ (	lowercase__ :str,			lowercase__ :int     ):
			return [sentence[i : i + ngram_size] for i in range(len(lowercase__     ) - ngram_size + 1     )]


if __name__ == "__main__":
					from doctest import testmod

					testmod()




