


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


lowerCamelCase__							:						Dict							     =   logging.getLogger(__name__)
lowerCamelCase__							:						Union[str, Any]							     =   list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowerCamelCase__							:						Any							     =   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class 		__magic_name__						:





     '''simple docstring'''



     __lowercase				:							Optional[str]	   =       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				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(snake_case_				)}   ,)
     __lowercase				:							Optional[str]	   =       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				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}				)
     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}   ,)
     __lowercase				:							bool	   =       field(
         default=snake_case_   ,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=snake_case_   ,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: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 		__magic_name__						:





     '''simple docstring'''



     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'The name of the dataset to use (via the datasets library).'}				)
     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}				)
     __lowercase				:							Optional[str]	   =       field(default=snake_case_   ,metadata={'help': 'The input training data file (a text file).'}				)
     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}   ,)
     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}   ,)
     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}   ,)
     __lowercase				:							bool	   =       field(
         default=snake_case_   ,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=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[int]	   =       field(
         default=snake_case_   ,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=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 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase				)    ->				Dict:
  with open(__lowerCAmelCase							,   '''r'''							,   encoding='''utf-8'''				) as f:
    snake_case__						= [json.loads(__lowerCAmelCase				) for line in f.read().splitlines() if (len(__lowerCAmelCase				) > 0 and not line.isspace())]
  assert len(__lowerCAmelCase				) == len(__lowerCAmelCase				)

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







def SCREAMING_SNAKE_CASE	(			)    ->				List[Any]:
  # 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'''							,   __lowerCAmelCase				)

  # 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(__lowerCAmelCase							,   data_files=__lowerCAmelCase				)
  # 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							,   **__lowerCAmelCase				)
  elif model_args.model_name_or_path:
    snake_case__						= AutoConfig.from_pretrained(model_args.model_name_or_path							,   **__lowerCAmelCase				)
  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							,   **__lowerCAmelCase				)
  elif model_args.model_name_or_path:
    snake_case__						= AutoTokenizer.from_pretrained(model_args.model_name_or_path							,   **__lowerCAmelCase				)
  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=__lowerCAmelCase							,   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(__lowerCAmelCase				)

  model.resize_token_embeddings(len(__lowerCAmelCase				)				)

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

  snake_case__						= datasets.map(
      __lowerCAmelCase							,   batched=__lowerCAmelCase							,   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=__lowerCAmelCase							,   mlm_probability=data_args.mlm_probability				)

  # Initialize our Trainer
  snake_case__						= Trainer(
      model=__lowerCAmelCase							,   args=__lowerCAmelCase							,   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=__lowerCAmelCase							,   data_collator=__lowerCAmelCase							,   )

  # 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=__lowerCAmelCase				)
    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(__lowerCAmelCase							,   '''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(__lowerCAmelCase							,   '''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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				List[str]:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
   main()





import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


lowerCamelCase__							:						Any							     =   """\

"""

lowerCamelCase__							:						List[str]							     =   """
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.

For more information, see https://huggingface.co/docs/transformers/perplexity
"""

lowerCamelCase__							:						Any							     =   """
Args:
    model_id (str): model used for calculating Perplexity
            NOTE: Perplexity can only be calculated for causal language models.
                    This includes models such as gpt2, causal variations of bert,
                    causal versions of t5, and more (the full list can be found
                    in the AutoModelForCausalLM documentation here:
                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )

    input_texts (list of str): input text, each separate text snippet
        is one list entry.
    batch_size (int): the batch size to run texts through the model. Defaults to 16.
    add_start_token (bool): whether to add the start token to the texts,
        so the perplexity can include the probability of the first word. Defaults to True.
    device (str): device to run on, defaults to 'cuda' when available
Returns:
    perplexity: dictionary containing the perplexity scores for the texts
        in the input list, as well as the mean perplexity. If one of the input texts is
        longer than the max input length of the model, then it is truncated to the
        max length for the perplexity computation.
Examples:
    Example 1:
        >>> perplexity = datasets.load_metric(\"perplexity\")
        >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]
        >>> results = perplexity.compute(model_id='gpt2',
        ...                              add_start_token=False,
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        ['perplexities', 'mean_perplexity']
        >>> print(round(results[\"mean_perplexity\"], 2))
        78.22
        >>> print(round(results[\"perplexities\"][0], 2))
        11.11

    Example 2:
        >>> perplexity = datasets.load_metric(\"perplexity\")
        >>> input_texts = datasets.load_dataset(\"wikitext\",
        ...                                     \"wikitext-2-raw-v1\",
        ...                                     split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS
        [...]
        >>> input_texts = [s for s in input_texts if s!='']
        >>> results = perplexity.compute(model_id='gpt2',
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        ['perplexities', 'mean_perplexity']
        >>> print(round(results[\"mean_perplexity\"], 2))
        60.35
        >>> print(round(results[\"perplexities\"][0], 2))
        81.12
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,_KWARGS_DESCRIPTION				)
class 		__magic_name__						(datasets.Metric				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       return datasets.MetricInfo(
           description=_DESCRIPTION     ,		citation=_CITATION     ,		inputs_description=_KWARGS_DESCRIPTION     ,		features=datasets.Features(
               {
                   '''input_texts''': datasets.Value('''string'''   ),
               }   )     ,		reference_urls=['''https://huggingface.co/docs/transformers/perplexity''']     ,		)







     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:int     ,		_a:List[Any]     ,		_a:int = 16     ,		_a:bool = True     ,		_a:Any=None   ):
       if device is not None:
         assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
         if device == "gpu":
           snake_case__						= '''cuda'''
       else:
         snake_case__						= '''cuda''' if torch.cuda.is_available() else '''cpu'''

       snake_case__						= AutoModelForCausalLM.from_pretrained(_a   )
       snake_case__						= model.to(_a   )

       snake_case__						= AutoTokenizer.from_pretrained(_a   )

       # if batch_size > 1 (which generally leads to padding being required), and
       # if there is not an already assigned pad_token, assign an existing
       # special token to also be the padding token
       if tokenizer.pad_token is None and batch_size > 1:
         snake_case__						= list(tokenizer.special_tokens_map_extended.values()   )
         # check that the model already has at least one special token defined
         assert (
             len(_a   ) > 0
         ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
         # assign one of the special tokens to also be the pad token
         tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]}   )

       if add_start_token:
         # leave room for <BOS> token to be added:
         assert (
             tokenizer.bos_token is not None
         ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
         snake_case__						= model.config.max_length - 1
       else:
         snake_case__						= model.config.max_length

       snake_case__						= tokenizer(
           _a     ,		add_special_tokens=_a     ,		padding=_a     ,		truncation=_a     ,		max_length=_a     ,		return_tensors='''pt'''     ,		return_attention_mask=_a     ,		).to(_a   )

       snake_case__						= encodings['''input_ids''']
       snake_case__						= encodings['''attention_mask''']

       # check that each input is long enough:
       if add_start_token:
         assert torch.all(torch.ge(attn_masks.sum(1   )     ,		1   )   ), "Each input text must be at least one token long."
       else:
         assert torch.all(
             torch.ge(attn_masks.sum(1   )     ,		2   )   ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

       snake_case__						= []
       snake_case__						= CrossEntropyLoss(reduction='''none'''   )

       for start_index in logging.tqdm(range(0     ,		len(_a   )     ,		_a   )   ):
         snake_case__						= min(start_index + batch_size     ,		len(_a   )   )
         snake_case__						= encoded_texts[start_index:end_index]
         snake_case__						= attn_masks[start_index:end_index]

         if add_start_token:
           snake_case__						= torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0   )   ).to(_a   )
           snake_case__						= torch.cat([bos_tokens_tensor, encoded_batch]     ,		dim=1   )
           snake_case__						= torch.cat(
               [torch.ones(bos_tokens_tensor.size()     ,		dtype=torch.intaa   ).to(_a   ), attn_mask]     ,		dim=1   )

         snake_case__						= encoded_batch

         with torch.no_grad():
           snake_case__						= model(_a     ,		attention_mask=_a   ).logits

         snake_case__						= out_logits[..., :-1, :].contiguous()
         snake_case__						= labels[..., 1:].contiguous()
         snake_case__						= attn_mask[..., 1:].contiguous()

         snake_case__						= torch.expa(
             (loss_fct(shift_logits.transpose(1     ,		2   )     ,		_a   ) * shift_attention_mask_batch).sum(1   )
             / shift_attention_mask_batch.sum(1   )   )

         ppls += perplexity_batch.tolist()

       return {"perplexities": ppls, "mean_perplexity": np.mean(_a   )}

