




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        __lowerCAmelCase					:

			"""simple docstring"""







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


			def  lowercase_       ( self				)       -> List[Any]:



							'''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					:

			"""simple docstring"""







			snake_case_  						=			field(
			    default=_lowerCamelCase     ,							metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}					)
			snake_case_  						=			field(
			    default=_lowerCamelCase     ,							metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}					)
			snake_case_  						=			field(default=_lowerCamelCase     ,							metadata={'''help''': '''The input training data file (a text file).'''}					)
			snake_case_  						=			field(
			    default=_lowerCamelCase     ,							metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}     ,							)
			snake_case_  						=			field(
			    default=_lowerCamelCase     ,							metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}     ,							)
			snake_case_  						=			field(
			    default=_lowerCamelCase     ,							metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}     ,							)
			snake_case_  						=			field(
			    default=_lowerCamelCase     ,							metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}					)
			snake_case_  						=			field(
			    default=5     ,							metadata={
			        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
			    }     ,							)
			snake_case_  						=			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.'''
			        )
			    }     ,							)
			snake_case_  						=			field(
			    default=_lowerCamelCase     ,							metadata={'''help''': '''The number of processes to use for the preprocessing.'''}     ,							)
			snake_case_  						=			field(
			    default=0.1_5     ,							metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}					)
			snake_case_  						=			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  lowercase_       ( self				)       -> List[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      lowerCamelCase_						(			UpperCamelCase__							: Optional[int]					,	UpperCamelCase__							: int )	->			Optional[Any]:





				"""simple docstring"""


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

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







def      lowerCamelCase_						(			)	->			Any:





				"""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									=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
				else:
								__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'					,	__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									=       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(__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									=       {
				    '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					,	**__A )
				elif model_args.model_name_or_path:
								__lowerCamelCase									=       AutoConfig.from_pretrained(model_args.model_name_or_path					,	**__A )
				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					,	**__A )
				elif model_args.model_name_or_path:
								__lowerCamelCase									=       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									=       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									=       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									=       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(UpperCamelCase__							: int ):
								# Remove empty lines
								__lowerCamelCase									=       [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									=       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									=       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=__A					,	mlm_probability=data_args.mlm_probability )

				# Initialize our Trainer
				__lowerCamelCase									=       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									=       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=__A )
								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(__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									=       {}
				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(__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      lowerCamelCase_						(			UpperCamelCase__							: Any )	->			Tuple:





				"""simple docstring"""


				main()


if __name__ == "__main__":
					main()







"""simple docstring"""

def  __snake_case							(	__A      :						int							,		__A      :						int     )     ->       float:






				'''simple docstring'''
				return base * power(__A							,		(exponent - 1)     ) if exponent else 1


if __name__ == "__main__":
							print('Raise base to the power of exponent using recursion...')
							A_						:    str				=      int(input('Enter the base: ').strip())
							A_						:    Dict				=      int(input('Enter the exponent: ').strip())
							A_						:    List[str]				=      power(base, abs(exponent))
							if exponent < 0:  # power() does not properly deal w/ negative exponents
														A_						:    Any				=      1 / result
							print(f'''{base} to the power of {exponent} is {result}''')





