
"""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


_snake_case          =						logging.getLogger(__name__)
_snake_case          =						list(MODEL_FOR_MASKED_LM_MAPPING.keys())
_snake_case          =						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



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


				def 				__lowerCamelCase	(					self	:			int  )		->  str:







											"""simple docstring"""




											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        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default=UpperCAmelCase      ,				metadata={"help": "The name of the dataset to use (via the datasets library)."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default=UpperCAmelCase      ,				metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(default=UpperCAmelCase      ,				metadata={"help": "The input training data file (a text file)."}							)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default=UpperCAmelCase      ,				metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}      ,				)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default=UpperCAmelCase      ,				metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}      ,				)
				SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
				    default=UpperCAmelCase      ,				metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}      ,				)
				SCREAMING_SNAKE_CASE_:							bool							=					field(
				    default=UpperCAmelCase      ,				metadata={"help": "Overwrite the cached training and evaluation sets"}							)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					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_:							Optional[int]							=					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."
				        )
				    }      ,				)
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
				    default=UpperCAmelCase      ,				metadata={"help": "The number of processes to use for the preprocessing."}      ,				)
				SCREAMING_SNAKE_CASE_:							float							=					field(
				    default=0.1_5      ,				metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}							)
				SCREAMING_SNAKE_CASE_:							bool							=					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 				__lowerCamelCase	(					self	:			List[Any]  )		->  Any:







											"""simple docstring"""




											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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					List[Any]						):






							"""simple docstring"""



							with open(SCREAMING_SNAKE_CASE     ,  'r'     ,  encoding='utf-8'						) as f:
														_lowerCAmelCase  	=			[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						)

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






def 	__snake_case  (    ):






							"""simple docstring"""



							_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'     ,  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.
														_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(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.
							_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     ,  **SCREAMING_SNAKE_CASE						)
							elif model_args.model_name_or_path:
														_lowerCAmelCase  	=			AutoConfig.from_pretrained(model_args.model_name_or_path     ,  **SCREAMING_SNAKE_CASE						)
							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     ,  **SCREAMING_SNAKE_CASE						)
							elif model_args.model_name_or_path:
														_lowerCAmelCase  	=			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:
														_lowerCAmelCase  	=			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'						)
														_lowerCAmelCase  	=			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:
														_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(SCREAMING_SNAKE_CASE:					str						):
														# Remove empty lines
														_lowerCAmelCase  	=			[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						)

							_lowerCAmelCase  	=			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:
														_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=SCREAMING_SNAKE_CASE     ,  mlm_probability=data_args.mlm_probability						)

							# Initialize our Trainer
							_lowerCAmelCase  	=			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:
																					_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=SCREAMING_SNAKE_CASE						)
														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(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
							_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(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 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							main()


if __name__ == "__main__":
	main()








"""simple docstring"""



import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase							)
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							str							=					field(default="image-classification"      ,				metadata={"include_in_asdict_even_if_is_default": True}							)
				SCREAMING_SNAKE_CASE_:							ClassVar[Features]							=					Features({"image": Image()}							)
				SCREAMING_SNAKE_CASE_:							ClassVar[Features]							=					Features({"labels": ClassLabel}							)
				SCREAMING_SNAKE_CASE_:							str							=					"image"
				SCREAMING_SNAKE_CASE_:							str							=					"labels"


				def 				__lowerCamelCase	(					self	:			str				,     UpperCAmelCase_	:			Optional[Any]  )		->  Dict:







											"""simple docstring"""




											if self.label_column not in features:
																		raise ValueError(F"""Column {self.label_column} is not present in features."""  )
											if not isinstance(features[self.label_column]				,     UpperCAmelCase_  ):
																		raise ValueError(F"""Column {self.label_column} is not a ClassLabel."""  )
											_lowerCAmelCase  	=			copy.deepcopy(self  )
											_lowerCAmelCase  	=			self.label_schema.copy()
											_lowerCAmelCase  	=			features[self.label_column]
											_lowerCAmelCase  	=			label_schema
											return task_template







				@property
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Dict[str, str]:







											"""simple docstring"""




											return {
											    self.image_column: "image",
											    self.label_column: "labels",
											}






