




"""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_				:    Tuple										=     logging.getLogger(__name__)
a_				:    List[Any]										=     list(MODEL_FOR_MASKED_LM_MAPPING.keys())
a_				:    Dict										=     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class __lowercase:




					'''simple docstring'''


					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={
					        'help': (
					            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
					        )
					    }	,	)
					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(lowercase__				)}	,	)
					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	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						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}				)
					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,	)
					__a						:						bool     		= field(
					    default=lowercase__	,	metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}	,	)
					__a						:						str     		= field(
					    default='main'	,	metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}	,	)
					__a						:						bool     		= field(
					    default=lowercase__	,	metadata={
					        'help': (
					            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
					            'with private models).'
					        )
					    }	,	)
					def     snake_case_				(    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						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'The name of the dataset to use (via the datasets library).'}				)
					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}				)
					__a						:						Optional[str]     		= field(default=lowercase__	,	metadata={'help': 'The input training data file (a text file).'}				)
					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}	,	)
					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}	,	)
					__a						:						Optional[str]     		= field(
					    default=lowercase__	,	metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}	,	)
					__a						:						bool     		= field(
					    default=lowercase__	,	metadata={'help': 'Overwrite the cached training and evaluation sets'}				)
					__a						:						Optional[int]     		= field(
					    default=5	,	metadata={
					        'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
					    }	,	)
					__a						:						Optional[int]     		= field(
					    default=lowercase__	,	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[int]     		= field(
					    default=lowercase__	,	metadata={'help': 'The number of processes to use for the preprocessing.'}	,	)
					__a						:						float     		= field(
					    default=0.1_5	,	metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}				)
					__a						:						bool     		= field(
					    default=lowercase__	,	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     snake_case_				(    self     ):
								if self.train_file is not None:
											__lowerCamelCase					:	Tuple	       =				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					:	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__:    List[str]							,				A__:    Any     )  ->     Optional[int]:
			with open(A__							,				'r'							,				encoding='utf-8'     ) as f:
						__lowerCamelCase					:	Dict	       =				[json.loads(A__     ) for line in f.read().splitlines() if (len(A__     ) > 0 and not line.isspace())]
			assert len(A__     ) == len(A__     )

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





def 						UpperCAmelCase				(			)  ->     List[Any]:
			# 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					:	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.
						__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Tuple	       =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
			else:
						__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	int	       =				parser.parse_args_into_dataclasses()

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

			__lowerCamelCase					:	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:
						__lowerCamelCase					:	Union[str, Any]	       =				AutoTokenizer.from_pretrained(model_args.tokenizer_name							,				**A__     )
			elif model_args.model_name_or_path:
						__lowerCamelCase					:	Tuple	       =				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:
						__lowerCamelCase					:	str	       =				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'     )
						__lowerCamelCase					:	int	       =				AutoModelForMaskedLM.from_config(A__     )

			model.resize_token_embeddings(len(A__     )     )

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

			__lowerCamelCase					:	Dict	       =				'max_length' if data_args.pad_to_max_length else False

			def tokenize_function(A__:    List[Any]     ):
						# Remove empty lines
						__lowerCamelCase					:	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     )

			__lowerCamelCase					:	str	       =				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:
						__lowerCamelCase					:	Optional[Any]	       =				add_chinese_references(tokenized_datasets['train']							,				data_args.train_ref_file     )
			if data_args.validation_ref_file is not None:
						__lowerCamelCase					:	Any	       =				add_chinese_references(
						    tokenized_datasets['validation']							,				data_args.validation_ref_file     )
			# If we have ref files, need to avoid it removed by trainer
			__lowerCamelCase					:	Union[str, Any]	       =				data_args.train_ref_file or data_args.validation_ref_file
			if has_ref:
						__lowerCamelCase					:	Tuple	       =				False

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

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

						__lowerCamelCase					:	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
			__lowerCamelCase					:	Optional[int]	       =				{}
			if training_args.do_eval:
						logger.info('*** Evaluate ***'     )

						__lowerCamelCase					:	int	       =				trainer.evaluate()

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

						__lowerCamelCase					:	Union[str, 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__:    List[Any]     )  ->     Tuple:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
					main()









"""simple docstring"""
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class __lowercase(      lowercase__				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a = None						,	__a = None						,	__a = None						,	__a = False						,	__a = False						,	__a = None						,	__a = None						,	**__a						,	):
								super().__init__(
								    __a						,	split=__a						,	features=__a						,	cache_dir=__a						,	keep_in_memory=__a						,	streaming=__a						,	num_proc=__a						,	**__a						,	)
								__lowerCamelCase					:	List[Any]	       =				field
								__lowerCamelCase					:	Dict	       =				path_or_paths if isinstance(__a						,	__a     ) else {self.split: path_or_paths}
								__lowerCamelCase					:	str	       =				Json(
								    cache_dir=__a						,	data_files=__a						,	features=__a						,	field=__a						,	**__a						,	)





					def     snake_case_				(    self     ):
								# Build iterable dataset
								if self.streaming:
											__lowerCamelCase					:	str	       =				self.builder.as_streaming_dataset(split=self.split     )
								# Build regular (map-style) dataset
								else:
											__lowerCamelCase					:	Any	       =				None
											__lowerCamelCase					:	Any	       =				None
											__lowerCamelCase					:	str	       =				None
											__lowerCamelCase					:	int	       =				None

											self.builder.download_and_prepare(
											    download_config=__a						,	download_mode=__a						,	verification_mode=__a						,	base_path=__a						,	num_proc=self.num_proc						,	)
											__lowerCamelCase					:	List[Any]	       =				self.builder.as_dataset(
											    split=self.split						,	verification_mode=__a						,	in_memory=self.keep_in_memory     )
								return dataset



class __lowercase:




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a						,	__a = None						,	__a = None						,	**__a						,	):
								if num_proc is not None and num_proc <= 0:
											raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''     )

								__lowerCamelCase					:	Optional[int]	       =				dataset
								__lowerCamelCase					:	Optional[int]	       =				path_or_buf
								__lowerCamelCase					:	List[str]	       =				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
								__lowerCamelCase					:	str	       =				num_proc
								__lowerCamelCase					:	Optional[Any]	       =				'utf-8'
								__lowerCamelCase					:	Optional[int]	       =				to_json_kwargs
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				self.to_json_kwargs.pop('path_or_buf'						,	__a     )
								__lowerCamelCase					:	Any	       =				self.to_json_kwargs.pop('orient'						,	'records'     )
								__lowerCamelCase					:	List[Any]	       =				self.to_json_kwargs.pop('lines'						,	True if orient == 'records' else False     )
								__lowerCamelCase					:	Tuple	       =				self.to_json_kwargs.pop('index'						,	False if orient in ['split', 'table'] else True     )
								__lowerCamelCase					:	int	       =				self.to_json_kwargs.pop('compression'						,	__a     )

								if compression not in [None, "infer", "gzip", "bz2", "xz"]:
											raise NotImplementedError(f'''`datasets` currently does not support {compression} compression'''     )

								if isinstance(self.path_or_buf						,	(str, bytes, os.PathLike)     ):
											with fsspec.open(self.path_or_buf						,	'wb'						,	compression=__a     ) as buffer:
														__lowerCamelCase					:	int	       =				self._write(file_obj=__a						,	orient=__a						,	lines=__a						,	index=__a						,	**self.to_json_kwargs     )
								else:
											if compression:
														raise NotImplementedError(
														    f'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
														    ' was passed. Please provide a local path instead.'     )
											__lowerCamelCase					:	Dict	       =				self._write(
											    file_obj=self.path_or_buf						,	orient=__a						,	lines=__a						,	index=__a						,	**self.to_json_kwargs     )
								return written
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Union[str, Any]	       =				args

								__lowerCamelCase					:	int	       =				query_table(
								    table=self.dataset.data						,	key=slice(__a						,	offset + self.batch_size     )						,	indices=self.dataset._indices						,	)
								__lowerCamelCase					:	List[str]	       =				batch.to_pandas().to_json(
								    path_or_buf=__a						,	orient=__a						,	lines=__a						,	index=__a						,	**__a     )
								if not json_str.endswith('\n'     ):
											json_str += "\n"
								return json_str.encode(self.encoding     )





					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	**__a						,	):
								__lowerCamelCase					:	Any	       =				0

								if self.num_proc is None or self.num_proc == 1:
											for offset in logging.tqdm(
											    range(0						,	len(self.dataset     )						,	self.batch_size     )						,	unit='ba'						,	disable=not logging.is_progress_bar_enabled()						,	desc='Creating json from Arrow format'						,	):
														__lowerCamelCase					:	List[str]	       =				self._batch_json((offset, orient, lines, index, to_json_kwargs)     )
														written += file_obj.write(__a     )
								else:
											__lowerCamelCase       ,      __lowerCamelCase					:	Optional[int]	       =				len(self.dataset     ), self.batch_size
											with multiprocessing.Pool(self.num_proc     ) as pool:
														for json_str in logging.tqdm(
														    pool.imap(
														        self._batch_json						,	[(offset, orient, lines, index, to_json_kwargs) for offset in range(0						,	__a						,	__a     )]						,	)						,	total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,	unit='ba'						,	disable=not logging.is_progress_bar_enabled()						,	desc='Creating json from Arrow format'						,	):
																	written += file_obj.write(__a     )

								return written



