
'''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


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






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






					def      _snake_case   (       self		):


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


											"""simple docstring"""





											if self.train_file is not None:
																	lowercase_     :					List[str]													=	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:
																	lowercase_     :					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  snake_case_		(    __SCREAMING_SNAKE_CASE :			Dict				,				__SCREAMING_SNAKE_CASE :			List[str]							):



						"""simple docstring"""





						with open(__SCREAMING_SNAKE_CASE				,				'''r'''				,				encoding='''utf-8'''							) as f:
												lowercase_     :					Dict													=	[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							)

						lowercase_     :					List[str]													=	{c: dataset[c] for c in dataset.column_names}
						lowercase_     :					Union[str, Any]													=	refs
						return Dataset.from_dict(__SCREAMING_SNAKE_CASE							)
def  snake_case_		(    ):



						"""simple docstring"""





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

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

						lowercase_     :					int													=	{
						    '''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:
												lowercase_     :					Optional[int]													=	AutoTokenizer.from_pretrained(model_args.tokenizer_name				,				**__SCREAMING_SNAKE_CASE							)
						elif model_args.model_name_or_path:
												lowercase_     :					Tuple													=	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:
												lowercase_     :					int													=	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'''							)
												lowercase_     :					Tuple													=	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:
												lowercase_     :					Dict													=	datasets['''train'''].column_names
						else:
												lowercase_     :					Union[str, Any]													=	datasets['''validation'''].column_names
						lowercase_     :					Dict													=	'''text''' if '''text''' in column_names else column_names[0]

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

						def tokenize_function(__SCREAMING_SNAKE_CASE :			Tuple							):
												# Remove empty lines
												lowercase_     :					List[str]													=	[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							)

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

						# Data collator
						# This one will take care of randomly masking the tokens.
						lowercase_     :					List[str]													=	DataCollatorForWholeWordMask(tokenizer=__SCREAMING_SNAKE_CASE				,				mlm_probability=data_args.mlm_probability							)

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

												lowercase_     :					int													=	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
						lowercase_     :					List[str]													=	{}
						if training_args.do_eval:
												logger.info('''*** Evaluate ***'''							)

												lowercase_     :					Any													=	trainer.evaluate()

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

												lowercase_     :					str													=	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 :			Union[str, Any]							):



						"""simple docstring"""





						main()


if __name__ == "__main__":
							main()





'''simple docstring'''







import sys

_lowercase			:  Union[str, Any]   							=	(
    "73167176531330624919225119674426574742355349194934"
    "96983520312774506326239578318016984801869478851843"
    "85861560789112949495459501737958331952853208805511"
    "12540698747158523863050715693290963295227443043557"
    "66896648950445244523161731856403098711121722383113"
    "62229893423380308135336276614282806444486645238749"
    "30358907296290491560440772390713810515859307960866"
    "70172427121883998797908792274921901699720888093776"
    "65727333001053367881220235421809751254540594752243"
    "52584907711670556013604839586446706324415722155397"
    "53697817977846174064955149290862569321978468622482"
    "83972241375657056057490261407972968652414535100474"
    "82166370484403199890008895243450658541227588666881"
    "16427171479924442928230863465674813919123162824586"
    "17866458359124566529476545682848912883142607690042"
    "24219022671055626321111109370544217506941658960408"
    "07198403850962455444362981230987879927244284909188"
    "84580156166097919133875499200524063689912560717606"
    "05886116467109405077541002256983155200055935729725"
    "71636269561882670428252483600823257530420752963450"
)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						lowercase_     :					Any													=	1
						for digit in s:
												product *= int(__SCREAMING_SNAKE_CASE							)
						return product
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str = N							):



						"""simple docstring"""





						lowercase_     :					Any													=	-sys.maxsize - 1
						lowercase_     :					List[Any]													=	n[:13]
						lowercase_     :					List[str]													=	13
						while cur_index < len(__SCREAMING_SNAKE_CASE							) - 13:
												if int(n[cur_index]							) >= int(substr[0]							):
																		lowercase_     :					Tuple													=	substr[1:] + n[cur_index]
																		cur_index += 1
												else:
																		lowercase_     :					str													=	max(__SCREAMING_SNAKE_CASE				,				str_eval(__SCREAMING_SNAKE_CASE							)							)
																		lowercase_     :					List[Any]													=	n[cur_index : cur_index + 13]
																		cur_index += 13
						return largest_product


if __name__ == "__main__":
							print(f"""{solution() = }""")



