






"""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 					snake_case		:


   '''simple docstring'''

   A_						:	Optional[str]						 = field(
       default=_lowerCAmelCase   ,	metadata={
           "help": (
               "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
           )
       }   ,	)
   A_						:	Optional[str]						 = field(
       default=_lowerCAmelCase   ,	metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(_lowerCAmelCase						)}   ,	)
   A_						:	Optional[str]						 = 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"
           )
       }   ,	)
   A_						:	Optional[str]						 = field(
       default=_lowerCAmelCase   ,	metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
   A_						:	Optional[str]						 = field(
       default=_lowerCAmelCase   ,	metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
   A_						:	Optional[str]						 = field(
       default=_lowerCAmelCase   ,	metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,	)
   A_						:	bool						 = field(
       default=_lowerCAmelCase   ,	metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}   ,	)
   A_						:	str						 = field(
       default="main"   ,	metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}   ,	)
   A_						:	bool						 = field(
       default=_lowerCAmelCase   ,	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 :     Dict  ):

     '''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 					snake_case		:


   '''simple docstring'''

   A_						:	Optional[str]						 = field(
       default=_lowerCAmelCase   ,	metadata={"help": "The name of the dataset to use (via the datasets library)."}						)
   A_						:	Optional[str]						 = field(
       default=_lowerCAmelCase   ,	metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}						)
   A_						:	Optional[str]						 = field(default=_lowerCAmelCase   ,	metadata={"help": "The input training data file (a text file)."}						)
   A_						:	Optional[str]						 = field(
       default=_lowerCAmelCase   ,	metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}   ,	)
   A_						:	Optional[str]						 = field(
       default=_lowerCAmelCase   ,	metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}   ,	)
   A_						:	Optional[str]						 = field(
       default=_lowerCAmelCase   ,	metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}   ,	)
   A_						:	bool						 = field(
       default=_lowerCAmelCase   ,	metadata={"help": "Overwrite the cached training and evaluation sets"}						)
   A_						:	Optional[int]						 = field(
       default=5   ,	metadata={
           "help": "The percentage of the train set used as validation set in case there's no validation split"
       }   ,	)
   A_						:	Optional[int]						 = 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."
           )
       }   ,	)
   A_						:	Optional[int]						 = field(
       default=_lowerCAmelCase   ,	metadata={"help": "The number of processes to use for the preprocessing."}   ,	)
   A_						:	float						 = field(
       default=0.15   ,	metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}						)
   A_						:	bool						 = 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 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     if self.train_file is not None:
       __A									= 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:
       __A									= 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 ):




  """simple docstring"""







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

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



def 					lowerCAmelCase				(    ):




  """simple docstring"""







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

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

  __A									= {
      '''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:
    __A									= AutoTokenizer.from_pretrained(model_args.tokenizer_name		,       **__UpperCamelCase )
  elif model_args.model_name_or_path:
    __A									= 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:
    __A									= 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''' )
    __A									= AutoModelForMaskedLM.from_config(__UpperCamelCase )

  model.resize_token_embeddings(len(__UpperCamelCase ) )

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

  __A									= '''max_length''' if data_args.pad_to_max_length else False

  def tokenize_function(__UpperCamelCase ):
    # Remove empty lines
    __A									= [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 )

  __A									= 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:
    __A									= add_chinese_references(tokenized_datasets['''train''']		,       data_args.train_ref_file )
  if data_args.validation_ref_file is not None:
    __A									= add_chinese_references(
        tokenized_datasets['''validation''']		,       data_args.validation_ref_file )
  # If we have ref files, need to avoid it removed by trainer
  __A									= data_args.train_ref_file or data_args.validation_ref_file
  if has_ref:
    __A									= False

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

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

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

    __A									= trainer.evaluate()

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

    __A									= 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 ):




  """simple docstring"""







  main()


if __name__ == "__main__":
       main()












"""simple docstring"""


def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  __A									= current_set.copy()
  for row_index, row in enumerate(__UpperCamelCase ):
    __A									= row[0]
    for column_index, column in enumerate(__UpperCamelCase ):
      if magnitude == 0:
        __A									= column
        continue
      __A									= column / magnitude
    # Subtract to cancel term
  __A									= current_set[0]
  __A									= [first_row]
  __A									= current_set[1::]
  for row in current_set:
    __A									= []
    # If first term is 0, it is already in form we want, so we preserve it
    if row[0] == 0:
      final_set.append(__UpperCamelCase )
      continue
    for column_index in range(len(__UpperCamelCase ) ):
      temp_row.append(first_row[column_index] - row[column_index] )
    final_set.append(__UpperCamelCase )
  # Create next recursion iteration set
  if len(final_set[0] ) != 3:
    __A									= final_set[0]
    __A									= []
    __A									= []
    for row in final_set[1::]:
      current_first_column.append(row[0] )
      next_iteration.append(row[1::] )
    __A									= simplify(__UpperCamelCase )
    for i in range(len(__UpperCamelCase ) ):
      resultant[i].insert(0		,       current_first_column[i] )
    resultant.insert(0		,       __UpperCamelCase )
    __A									= resultant
  return final_set



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if len(__UpperCamelCase ) == 0:
    raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
  __A									= len(__UpperCamelCase ) + 1
  if any(len(__UpperCamelCase ) != _length for item in equations ):
    raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
  for row in equations:
    if any(not isinstance(__UpperCamelCase		,       (int, float) ) for column in row ):
      raise ValueError('''solve_simultaneous() requires lists of integers''' )
  if len(__UpperCamelCase ) == 1:
    return [equations[0][-1] / equations[0][0]]
  __A									= equations.copy()
  if any(0 in row for row in data_set ):
    __A									= data_set.copy()
    __A									= []
    for row_index, row in enumerate(__UpperCamelCase ):
      if 0 not in row:
        __A									= data_set.pop(__UpperCamelCase )
        break
    if not full_row:
      raise ValueError('''solve_simultaneous() requires at least 1 full equation''' )
    data_set.insert(0		,       __UpperCamelCase )
  __A									= data_set.copy()
  __A									= simplify(__UpperCamelCase )
  __A									= simplified[::-1]
  __A									= []
  for row in simplified:
    __A									= row[-1]
    if not solutions:
      if row[-2] == 0:
        solutions.append(0 )
        continue
      solutions.append(current_solution / row[-2] )
      continue
    __A									= row.copy()[: len(__UpperCamelCase ) - 1 :]
    while temp_row[0] == 0:
      temp_row.pop(0 )
    if len(__UpperCamelCase ) == 0:
      solutions.append(0 )
      continue
    __A									= temp_row[1::]
    __A									= temp_row[::-1]
    for column_index, column in enumerate(__UpperCamelCase ):
      current_solution -= column * solutions[column_index]
    solutions.append(__UpperCamelCase )
  __A									= []
  for item in solutions:
    final.append(float(round(__UpperCamelCase		,       5 ) ) )
  return final[::-1]


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       lowercase_				     =     [
           [2, 1, 1, 1, 1, 4],
           [1, 2, 1, 1, 1, 5],
           [1, 1, 2, 1, 1, 6],
           [1, 1, 1, 2, 1, 7],
           [1, 1, 1, 1, 2, 8],
       ]
       print(solve_simultaneous(eq))
       print(solve_simultaneous([[4, 2]]))




