






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_		:    str   							=     logging.getLogger(__name__)
a_		:    Dict   							=     list(MODEL_FOR_MASKED_LM_MAPPING.keys())
a_		:    Tuple   							=     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class 		_snake_case	:
			_lowercase		:     Optional[str]        =				field(
			    default=A__ ,     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=A__ ,     metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(A__       )} ,     )
			_lowercase		:     Optional[str]        =				field(
			    default=A__ ,     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=A__ ,     metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}       )
			_lowercase		:     Optional[str]        =				field(
			    default=A__ ,     metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}       )
			_lowercase		:     Optional[str]        =				field(
			    default=A__ ,     metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,     )
			_lowercase		:     bool        =				field(
			    default=A__ ,     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=A__ ,     metadata={
			        '''help''': (
			            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
			            '''with private models).'''
			        )
			    } ,     )






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[str]:
						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 		_snake_case	:
			_lowercase		:     Optional[str]        =				field(
			    default=A__ ,     metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}       )
			_lowercase		:     Optional[str]        =				field(
			    default=A__ ,     metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}       )
			_lowercase		:     Optional[str]        =				field(default=A__ ,     metadata={'''help''': '''The input training data file (a text file).'''}       )
			_lowercase		:     Optional[str]        =				field(
			    default=A__ ,     metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} ,     )
			_lowercase		:     Optional[str]        =				field(
			    default=A__ ,     metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''} ,     )
			_lowercase		:     Optional[str]        =				field(
			    default=A__ ,     metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''} ,     )
			_lowercase		:     bool        =				field(
			    default=A__ ,     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=A__ ,     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=A__ ,     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=A__ ,     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  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[int]:
						if self.train_file is not None:
									SCREAMING_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:
									SCREAMING_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__					(_UpperCAmelCase				,							_UpperCAmelCase):
			with open(_UpperCAmelCase				,							'r'				,							encoding='utf-8') as f:
						SCREAMING_SNAKE_CASE							=			[json.loads(_UpperCAmelCase) for line in f.read().splitlines() if (len(_UpperCAmelCase) > 0 and not line.isspace())]
			assert len(_UpperCAmelCase) == len(_UpperCAmelCase)

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






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.

			SCREAMING_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.
						SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
			else:
						SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			parser.parse_args_into_dataclasses()

			# Detecting last checkpoint.
			SCREAMING_SNAKE_CASE							=			None
			if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
						SCREAMING_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'				,							_UpperCAmelCase)

			# 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.
						SCREAMING_SNAKE_CASE							=			load_dataset(data_args.dataset_name				,							data_args.dataset_config_name)
						if "validation" not in datasets.keys():
									SCREAMING_SNAKE_CASE							=			load_dataset(
									    data_args.dataset_name				,							data_args.dataset_config_name				,							split=F'''train[:{data_args.validation_split_percentage}%]'''				,							)
									SCREAMING_SNAKE_CASE							=			load_dataset(
									    data_args.dataset_name				,							data_args.dataset_config_name				,							split=F'''train[{data_args.validation_split_percentage}%:]'''				,							)
			else:
						SCREAMING_SNAKE_CASE							=			{}
						if data_args.train_file is not None:
									SCREAMING_SNAKE_CASE							=			data_args.train_file
						if data_args.validation_file is not None:
									SCREAMING_SNAKE_CASE							=			data_args.validation_file
						SCREAMING_SNAKE_CASE							=			data_args.train_file.split('.')[-1]
						if extension == "txt":
									SCREAMING_SNAKE_CASE							=			'text'
						SCREAMING_SNAKE_CASE							=			load_dataset(_UpperCAmelCase				,							data_files=_UpperCAmelCase)
			# 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.
			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,
			}
			if model_args.config_name:
						SCREAMING_SNAKE_CASE							=			AutoConfig.from_pretrained(model_args.config_name				,							**_UpperCAmelCase)
			elif model_args.model_name_or_path:
						SCREAMING_SNAKE_CASE							=			AutoConfig.from_pretrained(model_args.model_name_or_path				,							**_UpperCAmelCase)
			else:
						SCREAMING_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}''')

			SCREAMING_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:
						SCREAMING_SNAKE_CASE							=			AutoTokenizer.from_pretrained(model_args.tokenizer_name				,							**_UpperCAmelCase)
			elif model_args.model_name_or_path:
						SCREAMING_SNAKE_CASE							=			AutoTokenizer.from_pretrained(model_args.model_name_or_path				,							**_UpperCAmelCase)
			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:
						SCREAMING_SNAKE_CASE							=			AutoModelForMaskedLM.from_pretrained(
						    model_args.model_name_or_path				,							from_tf=bool('.ckpt' in model_args.model_name_or_path)				,							config=_UpperCAmelCase				,							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')
						SCREAMING_SNAKE_CASE							=			AutoModelForMaskedLM.from_config(_UpperCAmelCase)

			model.resize_token_embeddings(len(_UpperCAmelCase))

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

			SCREAMING_SNAKE_CASE							=			'max_length' if data_args.pad_to_max_length else False

			def tokenize_function(_UpperCAmelCase):
						# Remove empty lines
						SCREAMING_SNAKE_CASE							=			[line for line in examples['text'] if len(_UpperCAmelCase) > 0 and not line.isspace()]
						return tokenizer(examples['text']				,							padding=_UpperCAmelCase				,							truncation=_UpperCAmelCase				,							max_length=data_args.max_seq_length)

			SCREAMING_SNAKE_CASE							=			datasets.map(
			    _UpperCAmelCase				,							batched=_UpperCAmelCase				,							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:
						SCREAMING_SNAKE_CASE							=			add_chinese_references(tokenized_datasets['train']				,							data_args.train_ref_file)
			if data_args.validation_ref_file is not None:
						SCREAMING_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
			SCREAMING_SNAKE_CASE							=			data_args.train_ref_file or data_args.validation_ref_file
			if has_ref:
						SCREAMING_SNAKE_CASE							=			False

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

			# Initialize our Trainer
			SCREAMING_SNAKE_CASE							=			Trainer(
			    model=_UpperCAmelCase				,							args=_UpperCAmelCase				,							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=_UpperCAmelCase				,							data_collator=_UpperCAmelCase				,							)

			# Training
			if training_args.do_train:
						if last_checkpoint is not None:
									SCREAMING_SNAKE_CASE							=			last_checkpoint
						elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path):
									SCREAMING_SNAKE_CASE							=			model_args.model_name_or_path
						else:
									SCREAMING_SNAKE_CASE							=			None
						SCREAMING_SNAKE_CASE							=			trainer.train(resume_from_checkpoint=_UpperCAmelCase)
						trainer.save_model()  # Saves the tokenizer too for easy upload

						SCREAMING_SNAKE_CASE							=			os.path.join(training_args.output_dir				,							'train_results.txt')
						if trainer.is_world_process_zero():
									with open(_UpperCAmelCase				,							'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
			SCREAMING_SNAKE_CASE							=			{}
			if training_args.do_eval:
						logger.info('*** Evaluate ***')

						SCREAMING_SNAKE_CASE							=			trainer.evaluate()

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

						SCREAMING_SNAKE_CASE							=			os.path.join(training_args.output_dir				,							'eval_results_mlm_wwm.txt')
						if trainer.is_world_process_zero():
									with open(_UpperCAmelCase				,							'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):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
				main()










'''simple docstring'''



from collections import deque







def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, Any]						)-> Optional[int]:




     '''simple docstring'''





     __snake_case            =	len(_lowerCamelCase						)
     __snake_case            =	deque()
     __snake_case            =	[False for _ in range(_lowerCamelCase						)]
     __snake_case            =	[-1 for _ in range(_lowerCamelCase						)]
     __snake_case            =	index_of[:]

     def strong_connect(_lowerCamelCase				:  Any ,						_lowerCamelCase				:  Dict ,						_lowerCamelCase				:  List[str]						):
          __snake_case            =	index  # the number when this node is seen
          __snake_case            =	index  # lowest rank node reachable from here
          index += 1
          stack.append(_lowerCamelCase						)
          __snake_case            =	True

          for w in g[v]:
               if index_of[w] == -1:
                    __snake_case            =	strong_connect(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)
                    __snake_case            =	(
                        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                    )
               elif on_stack[w]:
                    __snake_case            =	(
                        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                    )

          if lowlink_of[v] == index_of[v]:
               __snake_case            =	[]
               __snake_case            =	stack.pop()
               __snake_case            =	False
               component.append(_lowerCamelCase						)
               while w != v:
                    __snake_case            =	stack.pop()
                    __snake_case            =	False
                    component.append(_lowerCamelCase						)
               components.append(_lowerCamelCase						)
          return index

     __snake_case            =	[]
     for v in range(_lowerCamelCase						):
          if index_of[v] == -1:
               strong_connect(_lowerCamelCase ,						0 ,						_lowerCamelCase						)

     return components







def       _UpperCamelCase			(_lowerCamelCase				:  Tuple ,						_lowerCamelCase				:  Optional[Any]						)-> Dict:




     '''simple docstring'''





     __snake_case            =	[[] for _ in range(_lowerCamelCase						)]
     for u, v in edges:
          g[u].append(_lowerCamelCase						)
     return g


if __name__ == "__main__":
     # Test
     UpperCAmelCase_							:	List[str]	     =		7
     UpperCAmelCase_							:	int	     =		[0, 0, 1, 2, 3, 3, 4, 4, 6]
     UpperCAmelCase_							:	Dict	     =		[1, 3, 2, 0, 1, 4, 5, 6, 5]
     UpperCAmelCase_							:	List[str]	     =		[(u, v) for u, v in zip(source, target)]
     UpperCAmelCase_							:	Tuple	     =		create_graph(n_vertices, edges)

     assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)






