






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




@dataclass
class  UpperCamelCase_      :



	'''simple docstring'''




	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={
	        '''help''': (
	            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
	        )
	    }       ,					)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(snake_case_				)}       ,					)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					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=snake_case_       ,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,					)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					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=snake_case_       ,					metadata={
	        '''help''': (
	            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
	            '''with private models).'''
	        )
	    }       ,					)




	def 							_UpperCamelCase			(       self					)       ->    Tuple:






								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  UpperCamelCase_      :



	'''simple docstring'''




	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}				)
	lowerCAmelCase													= field(default=snake_case_       ,					metadata={'''help''': '''The input training data file (a text file).'''}				)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}       ,					)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}       ,					)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}       ,					)
	lowerCAmelCase													= field(
	    default=snake_case_       ,					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=snake_case_       ,					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=snake_case_       ,					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=snake_case_       ,					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					)       ->    Optional[int]:






								if self.train_file is not None:
															snake_case_				=							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_				=							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_							,     a_):

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

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





def 						__UpperCAmelCase			(     ):
							snake_case_				=							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_			,		snake_case_			,		snake_case_				=							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
							else:
														snake_case_			,		snake_case_			,		snake_case_				=							parser.parse_args_into_dataclasses()

							# Detecting last checkpoint.
							snake_case_				=							None
							if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
														snake_case_				=							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.
														snake_case_				=							load_dataset(data_args.dataset_name							,     data_args.dataset_config_name)
														if "validation" not in datasets.keys():
																					snake_case_				=							load_dataset(
																					    data_args.dataset_name							,     data_args.dataset_config_name							,     split=f'''train[:{data_args.validation_split_percentage}%]'''							,     )
																					snake_case_				=							load_dataset(
																					    data_args.dataset_name							,     data_args.dataset_config_name							,     split=f'''train[{data_args.validation_split_percentage}%:]'''							,     )
							else:
														snake_case_				=							{}
														if data_args.train_file is not None:
																					snake_case_				=							data_args.train_file
														if data_args.validation_file is not None:
																					snake_case_				=							data_args.validation_file
														snake_case_				=							data_args.train_file.split('.')[-1]
														if extension == "txt":
																					snake_case_				=							'text'
														snake_case_				=							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.
							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,
							}
							if model_args.config_name:
														snake_case_				=							AutoConfig.from_pretrained(model_args.config_name							,     **__a)
							elif model_args.model_name_or_path:
														snake_case_				=							AutoConfig.from_pretrained(model_args.model_name_or_path							,     **__a)
							else:
														snake_case_				=							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_				=							{
							    '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_				=							AutoTokenizer.from_pretrained(model_args.tokenizer_name							,     **__a)
							elif model_args.model_name_or_path:
														snake_case_				=							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:
														snake_case_				=							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')
														snake_case_				=							AutoModelForMaskedLM.from_config(__a)

							model.resize_token_embeddings(len(__a))

							# Preprocessing the datasets.
							# First we tokenize all the texts.
							if training_args.do_train:
														snake_case_				=							datasets['train'].column_names
							else:
														snake_case_				=							datasets['validation'].column_names
							snake_case_				=							'text' if 'text' in column_names else column_names[0]

							snake_case_				=							'max_length' if data_args.pad_to_max_length else False

							def tokenize_function(a_):
														# Remove empty lines
														snake_case_				=							[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)

							snake_case_				=							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:
														snake_case_				=							add_chinese_references(tokenized_datasets['train']							,     data_args.train_ref_file)
							if data_args.validation_ref_file is not None:
														snake_case_				=							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_				=							data_args.train_ref_file or data_args.validation_ref_file
							if has_ref:
														snake_case_				=							False

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

							# Initialize our Trainer
							snake_case_				=							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:
																					snake_case_				=							last_checkpoint
														elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path):
																					snake_case_				=							model_args.model_name_or_path
														else:
																					snake_case_				=							None
														snake_case_				=							trainer.train(resume_from_checkpoint=__a)
														trainer.save_model()  # Saves the tokenizer too for easy upload

														snake_case_				=							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
							snake_case_				=							{}
							if training_args.do_eval:
														logger.info('*** Evaluate ***')

														snake_case_				=							trainer.evaluate()

														snake_case_				=							math.exp(eval_output['eval_loss'])
														snake_case_				=							perplexity

														snake_case_				=							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_):
							main()


if __name__ == "__main__":
	main()












def    __lowerCamelCase					(							__a   :int = 1_0_0_0_0_0_0    )		->				int:






		"""simple docstring"""

		A__        =   limit + 1
		A__        =   [0] * limit
		for first_term in range(1    ,     __a    ):
				for n in range(__a    ,     __a    ,     __a    ):
						A__        =   first_term + n / first_term
						if common_difference % 4:  # d must be divisble by 4
								continue
						else:
								common_difference /= 4
								if (
								    first_term > common_difference
								    and first_term < 4 * common_difference
								):  # since x,y,z are positive integers
										frequency[n] += 1  # so z>0 and a>d ,also 4d<a

		A__        =   sum(1 for x in frequency[1:limit] if x == 1_0    )

		return count


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



