





'''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


A__				:   str      =logging.getLogger(__name__)
A__				:   List[str]      =list(MODEL_FOR_MASKED_LM_MAPPING.keys())
A__				:   Any      =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




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





      """simple docstring"""


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

      _lowerCAmelCase							     =							{c: dataset[c] for c in dataset.column_names}
      _lowerCAmelCase							     =							refs
      return Dataset.from_dict(UpperCAmelCase_  )
def    UpperCamelCase__      (		):





      """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     ,							_lowerCAmelCase     ,							_lowerCAmelCase							     =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
      else:
            _lowerCAmelCase     ,							_lowerCAmelCase     ,							_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"""							,	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.
            _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(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.
      _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							,	**UpperCAmelCase_  )
      elif model_args.model_name_or_path:
            _lowerCAmelCase							     =							AutoConfig.from_pretrained(model_args.model_name_or_path							,	**UpperCAmelCase_  )
      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							,	**UpperCAmelCase_  )
      elif model_args.model_name_or_path:
            _lowerCAmelCase							     =							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:
            _lowerCAmelCase							     =							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"""  )
            _lowerCAmelCase							     =							AutoModelForMaskedLM.from_config(UpperCAmelCase_  )

      model.resize_token_embeddings(len(UpperCAmelCase_  )  )

      # 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(lowerCAmelCase  ):
            # Remove empty lines
            _lowerCAmelCase							     =							[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  )

      _lowerCAmelCase							     =							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:
            _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=UpperCAmelCase_							,	mlm_probability=data_args.mlm_probability  )

      # Initialize our Trainer
      _lowerCAmelCase							     =							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:
                  _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=UpperCAmelCase_  )
            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(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
      _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(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    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      main()


if __name__ == "__main__":
      main()










'''simple docstring'''

import os





def __snake_case					(      UpperCAmelCase_						:						str = "matrix.txt"		):
 with open(os.path.join(os.path.dirname(UpperCAmelCase_		)					,				UpperCAmelCase_		)		) as in_file:
  lowerCamelCase_      =  in_file.read()

 lowerCamelCase_      =  [[int(UpperCAmelCase_		) for cell in row.split(","		)] for row in data.strip().splitlines()]
 lowerCamelCase_      =  [[0 for cell in row] for row in grid]
 lowerCamelCase_      =  len(grid[0]		)

 lowerCamelCase_      =  [[0 for i in range(UpperCAmelCase_		)] for j in range(UpperCAmelCase_		)]
 lowerCamelCase_      =  grid[0][0]
 for i in range(1					,				UpperCAmelCase_		):
  lowerCamelCase_      =  grid[0][i] + dp[0][i - 1]
 for i in range(1					,				UpperCAmelCase_		):
  lowerCamelCase_      =  grid[i][0] + dp[i - 1][0]

 for i in range(1					,				UpperCAmelCase_		):
  for j in range(1					,				UpperCAmelCase_		):
   lowerCamelCase_      =  grid[i][j] + min(dp[i - 1][j]					,				dp[i][j - 1]		)

 return dp[-1][-1]


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



