

'''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


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





@dataclass
class 		__SCREAMING_SNAKE_CASE       :
				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    lowerCamelCase_  (					self    :		List[str]      ):
										'''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 		__SCREAMING_SNAKE_CASE       :
				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=0.1_5	,       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    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''







										if self.train_file is not None:
																lowercase   :       Union[str, Any]								=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[str]								=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 (      __magic_name__	:    List[Any]      ,   __magic_name__	:    List[Any]	)							->     Union[str, Any]:







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

						lowercase   :       Any								={c: dataset[c] for c in dataset.column_names}
						lowercase   :       Dict								=refs
						return Dataset.from_dict(__magic_name__	)






def 							_lowerCAmelCase (      )							->     int:
						lowercase   :       Tuple								=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   :       int								=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
						else:
												lowercase		,  lowercase		,  lowercase   :       List[Any]								=parser.parse_args_into_dataclasses()

						# Detecting last checkpoint.
						lowercase   :       List[Any]								=None
						if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
												lowercase   :       int								=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'''      ,   __magic_name__	)

						# 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   :       Tuple								=load_dataset(data_args.dataset_name      ,   data_args.dataset_config_name	)
												if "validation" not in datasets.keys():
																		lowercase   :       Optional[Any]								=load_dataset(
																		    data_args.dataset_name      ,   data_args.dataset_config_name      ,   split=f'''train[:{data_args.validation_split_percentage}%]'''      ,   )
																		lowercase   :       Optional[Any]								=load_dataset(
																		    data_args.dataset_name      ,   data_args.dataset_config_name      ,   split=f'''train[{data_args.validation_split_percentage}%:]'''      ,   )
						else:
												lowercase   :       int								={}
												if data_args.train_file is not None:
																		lowercase   :       Tuple								=data_args.train_file
												if data_args.validation_file is not None:
																		lowercase   :       int								=data_args.validation_file
												lowercase   :       str								=data_args.train_file.split('''.'''	)[-1]
												if extension == "txt":
																		lowercase   :       Tuple								='''text'''
												lowercase   :       Tuple								=load_dataset(__magic_name__      ,   data_files=__magic_name__	)
						# 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      ,   **__magic_name__	)
						elif model_args.model_name_or_path:
												lowercase   :       Union[str, Any]								=AutoConfig.from_pretrained(model_args.model_name_or_path      ,   **__magic_name__	)
						else:
												lowercase   :       Optional[Any]								=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   :       Tuple								={
						    '''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   :       Dict								=AutoTokenizer.from_pretrained(model_args.tokenizer_name      ,   **__magic_name__	)
						elif model_args.model_name_or_path:
												lowercase   :       List[Any]								=AutoTokenizer.from_pretrained(model_args.model_name_or_path      ,   **__magic_name__	)
						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   :       Dict								=AutoModelForMaskedLM.from_pretrained(
												    model_args.model_name_or_path      ,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path	)      ,   config=__magic_name__      ,   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   :       List[str]								=AutoModelForMaskedLM.from_config(__magic_name__	)

						model.resize_token_embeddings(len(__magic_name__	)	)

						# Preprocessing the datasets.
						# First we tokenize all the texts.
						if training_args.do_train:
												lowercase   :       str								=datasets['''train'''].column_names
						else:
												lowercase   :       Union[str, Any]								=datasets['''validation'''].column_names
						lowercase   :       Optional[Any]								='''text''' if '''text''' in column_names else column_names[0]

						lowercase   :       List[str]								='''max_length''' if data_args.pad_to_max_length else False

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

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

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

						# Initialize our Trainer
						lowercase   :       List[Any]								=Trainer(
						    model=__magic_name__      ,   args=__magic_name__      ,   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=__magic_name__      ,   data_collator=__magic_name__      ,   )

						# Training
						if training_args.do_train:
												if last_checkpoint is not None:
																		lowercase   :       Any								=last_checkpoint
												elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path	):
																		lowercase   :       Dict								=model_args.model_name_or_path
												else:
																		lowercase   :       str								=None
												lowercase   :       Optional[Any]								=trainer.train(resume_from_checkpoint=__magic_name__	)
												trainer.save_model()  # Saves the tokenizer too for easy upload

												lowercase   :       List[str]								=os.path.join(training_args.output_dir      ,   '''train_results.txt'''	)
												if trainer.is_world_process_zero():
																		with open(__magic_name__      ,   '''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   :       int								=trainer.evaluate()

												lowercase   :       Dict								=math.exp(eval_output['''eval_loss''']	)
												lowercase   :       List[Any]								=perplexity

												lowercase   :       Optional[int]								=os.path.join(training_args.output_dir      ,   '''eval_results_mlm_wwm.txt'''	)
												if trainer.is_world_process_zero():
																		with open(__magic_name__      ,   '''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 (      __magic_name__	:    Tuple	)							->     List[Any]:
						main()


if __name__ == "__main__":
							main()








'''simple docstring'''





from __future__ import annotations






def 							_lowerCAmelCase (      __magic_name__	:    list[list[int]]	)							->     bool:
						lowercase   :       str								=len(__magic_name__	)
						# We need to create solution object to save path.
						lowercase   :       int								=[[0 for _ in range(__magic_name__	)] for _ in range(__magic_name__	)]
						lowercase   :       List[Any]								=run_maze(__magic_name__      ,   0      ,   0      ,   __magic_name__	)
						if solved:
												print('''\n'''.join(str(__magic_name__	) for row in solutions	)	)
						else:
												print('''No solution exists!'''	)
						return solved






def 							_lowerCAmelCase (      __magic_name__	:    list[list[int]]      ,   __magic_name__	:    int      ,   __magic_name__	:    int      ,   __magic_name__	:    list[list[int]]	)							->     bool:
						lowercase   :       Optional[int]								=len(__magic_name__	)
						# Final check point.
						if i == j == (size - 1):
												lowercase   :       Optional[int]								=1
												return True

						lowercase   :       Optional[int]								=(not i < 0) and (not j < 0)  # Check lower bounds
						lowercase   :       Tuple								=(i < size) and (j < size)  # Check upper bounds

						if lower_flag and upper_flag:
												# check for already visited and block points.
												lowercase   :       Union[str, Any]								=(not solutions[i][j]) and (not maze[i][j])
												if block_flag:
																		# check visited
																		lowercase   :       Union[str, Any]								=1

																		# check for directions
																		if (
																		    run_maze(__magic_name__      ,   i + 1      ,   __magic_name__      ,   __magic_name__	)
																		    or run_maze(__magic_name__      ,   __magic_name__      ,   j + 1      ,   __magic_name__	)
																		    or run_maze(__magic_name__      ,   i - 1      ,   __magic_name__      ,   __magic_name__	)
																		    or run_maze(__magic_name__      ,   __magic_name__      ,   j - 1      ,   __magic_name__	)
																		):
																								return True

																		lowercase   :       str								=0
																		return False
						return False


if __name__ == "__main__":
							import doctest

							doctest.testmod()





