




'''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  =			logging.getLogger(__name__)
_lowercase  =			list(MODEL_FOR_MASKED_LM_MAPPING.keys())
_lowercase  =			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class 						a_				:
	lowercase_       :     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.'''
	        )
	    }     ,						)
	lowercase_       :     Optional[str]			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(UpperCAmelCase__						)}     ,						)
	lowercase_       :     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'''
	        )
	    }     ,						)
	lowercase_       :     Optional[str]			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}						)
	lowercase_       :     Optional[str]			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}						)
	lowercase_       :     Optional[str]			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,						)
	lowercase_       :     bool			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}     ,						)
	lowercase_       :     str			  =      field(
	    default='''main'''     ,						metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}     ,						)
	lowercase_       :     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   lowercase__   (       self     :  int      ):
							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 						a_				:
	lowercase_       :     Optional[str]			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}						)
	lowercase_       :     Optional[str]			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}						)
	lowercase_       :     Optional[str]			  =      field(default=UpperCAmelCase__     ,						metadata={'''help''': '''The input training data file (a text file).'''}						)
	lowercase_       :     Optional[str]			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}     ,						)
	lowercase_       :     Optional[str]			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}     ,						)
	lowercase_       :     Optional[str]			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}     ,						)
	lowercase_       :     bool			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}						)
	lowercase_       :     Optional[int]			  =      field(
	    default=5     ,						metadata={
	        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
	    }     ,						)
	lowercase_       :     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.'''
	        )
	    }     ,						)
	lowercase_       :     Optional[int]			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''The number of processes to use for the preprocessing.'''}     ,						)
	lowercase_       :     float			  =      field(
	    default=0.15     ,						metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}						)
	lowercase_       :     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   lowercase__   (       self     :  Tuple      ):
							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 						lowerCamelCase__   (				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 						lowerCamelCase__   (				):
						# 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.

						__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 						lowerCamelCase__   (				a							):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
	main()







'''simple docstring'''


from collections import defaultdict
def 						lowerCamelCase__   (				a    ,	a							):
						__snake_case						 =   first_str.lower().strip()
						__snake_case						 =   second_str.lower().strip()

						# Remove whitespace
						__snake_case						 =   first_str.replace(' '    ,	''							)
						__snake_case						 =   second_str.replace(' '    ,	''							)

						# Strings of different lengths are not anagrams
						if len(a							) != len(a							):
												return False

						# Default values for count should be 0
						__snake_case						 =   defaultdict(a							)

						# For each character in input strings,
						# increment count in the corresponding
						for i in range(len(a							)							):
												count[first_str[i]] += 1
												count[second_str[i]] -= 1

						return all(_count == 0 for _count in count.values()							)


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	_lowercase  =			input("""Enter the first string """).strip()
	_lowercase  =			input("""Enter the second string """).strip()

	_lowercase  =			check_anagrams(input_a, input_b)
	print(f'''{input_a} and {input_b} are {'' if status else 'not '}anagrams.''')

