



"""simple docstring"""

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

from datasets import Dataset, load_dataset

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


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







@dataclass
class  __lowercase  :







						"""simple docstring"""







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







						def     __UpperCamelCase				(self							):

											if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
																raise ValueError(
																    """--config_overrides can't be used in combination with --config_name or --model_name_or_path"""							)







@dataclass
class  __lowercase  :







						"""simple docstring"""







						_A		:				Any											=		field(
						    default=UpperCamelCase__       ,     metadata={"""help""": """The name of the dataset to use (via the datasets library)."""})
						_A		:				Optional[Any]											=		field(
						    default=UpperCamelCase__       ,     metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
						_A		:				Dict											=		field(default=UpperCamelCase__       ,     metadata={"""help""": """The input training data file (a text file)."""})
						_A		:				Any											=		field(
						    default=UpperCamelCase__       ,     metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}       ,     )
						_A		:				int											=		field(
						    default=UpperCamelCase__       ,     metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}       ,     )
						_A		:				Optional[int]											=		field(
						    default=UpperCamelCase__       ,     metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}       ,     )
						_A		:				str											=		field(
						    default=UpperCamelCase__       ,     metadata={"""help""": """Overwrite the cached training and evaluation sets"""})
						_A		:				Tuple											=		field(
						    default=5       ,     metadata={
						        """help""": """The percentage of the train set used as validation set in case there's no validation split"""
						    }       ,     )
						_A		:				List[Any]											=		field(
						    default=UpperCamelCase__       ,     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		:				Optional[Any]											=		field(
						    default=UpperCamelCase__       ,     metadata={"""help""": """The number of processes to use for the preprocessing."""}       ,     )
						_A		:				Dict											=		field(
						    default=0.15       ,     metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""})
						_A		:				Any											=		field(
						    default=UpperCamelCase__       ,     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							):

											if self.train_file is not None:
																snake_case_       :		Dict			       =       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:
																snake_case_       :		Union[str, 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 				SCREAMING_SNAKE_CASE__   (		SCREAMING_SNAKE_CASE__			:	List[str]					,     SCREAMING_SNAKE_CASE__			:	int      ):



					"""simple docstring"""





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

					snake_case_       :		Optional[Any]			       =       {c: dataset[c] for c in dataset.column_names}
					snake_case_       :		List[str]			       =       refs
					return Dataset.from_dict(SCREAMING_SNAKE_CASE__      )







def 				SCREAMING_SNAKE_CASE__   (		):



					"""simple docstring"""





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

					# Detecting last checkpoint.
					snake_case_       :		List[str]			       =       None
					if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
										snake_case_       :		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:
															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"""					,     SCREAMING_SNAKE_CASE__      )

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

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

					model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE__      )      )

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

					snake_case_       :		Union[str, Any]			       =       """max_length""" if data_args.pad_to_max_length else False

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

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

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

					# Initialize our Trainer
					snake_case_       :		Tuple			       =       Trainer(
					    model=SCREAMING_SNAKE_CASE__					,     args=SCREAMING_SNAKE_CASE__					,     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=SCREAMING_SNAKE_CASE__					,     data_collator=SCREAMING_SNAKE_CASE__					,     )

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

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

										snake_case_       :		Tuple			       =       trainer.evaluate()

										snake_case_       :		Optional[int]			       =       math.exp(eval_output["""eval_loss"""]      )
										snake_case_       :		List[Any]			       =       perplexity

										snake_case_       :		Optional[int]			       =       os.path.join(training_args.output_dir					,     """eval_results_mlm_wwm.txt"""      )
										if trainer.is_world_process_zero():
															with open(SCREAMING_SNAKE_CASE__					,     """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 				SCREAMING_SNAKE_CASE__   (		SCREAMING_SNAKE_CASE__			:	Optional[int]      ):



					"""simple docstring"""





					main()


if __name__ == "__main__":
			main()










import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script




@require_cpu
class 		__A   (      unittest.TestCase      ):

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										debug_launcher(test_script.main			)


						def      A__    (		self :Dict			):
										'''simple docstring'''

										debug_launcher(test_ops.main			)




