


"""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


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







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

       '''simple docstring'''




       if self.train_file is not None:
           snake_case    :      Dict      					=    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    :      Tuple      					=    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						(						lowercase  :  Union[str, Any]						,							lowercase  :  Optional[int] )	->						str:


    """simple docstring"""





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

    snake_case    :      List[Any]      					=    {c: dataset[c] for c in dataset.column_names}
    snake_case    :      List[Any]      					=    refs
    return Dataset.from_dict(SCREAMING_SNAKE_CASE_ )







def   __lowerCAmelCase						(						)	->						Optional[Any]:


    """simple docstring"""





    snake_case    :      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.
        snake_case					,snake_case					,snake_case    :      Optional[Any]      					=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
    else:
        snake_case					,snake_case					,snake_case    :      List[str]      					=    parser.parse_args_into_dataclasses()

    # Detecting last checkpoint.
    snake_case    :      str      					=    None
    if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
        snake_case    :      List[Any]      					=    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"						,							SCREAMING_SNAKE_CASE_ )

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

    model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE_ ) )

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

    snake_case    :      Union[str, Any]      					=    "max_length" if data_args.pad_to_max_length else False

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

    snake_case    :      Union[str, Any]      					=    datasets.map(
        SCREAMING_SNAKE_CASE_						,							batched=SCREAMING_SNAKE_CASE_						,							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    :      int      					=    add_chinese_references(tokenized_datasets["train"]						,							data_args.train_ref_file )
    if data_args.validation_ref_file is not None:
        snake_case    :      str      					=    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    :      Tuple      					=    data_args.train_ref_file or data_args.validation_ref_file
    if has_ref:
        snake_case    :      Any      					=    False

    # Data collator
    # This one will take care of randomly masking the tokens.
    snake_case    :      Dict      					=    DataCollatorForWholeWordMask(tokenizer=SCREAMING_SNAKE_CASE_						,							mlm_probability=data_args.mlm_probability )

    # Initialize our Trainer
    snake_case    :      Optional[int]      					=    Trainer(
        model=SCREAMING_SNAKE_CASE_						,							args=SCREAMING_SNAKE_CASE_						,							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=SCREAMING_SNAKE_CASE_						,							data_collator=SCREAMING_SNAKE_CASE_						,							)

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

        snake_case    :      Tuple      					=    os.path.join(training_args.output_dir						,							"train_results.txt" )
        if trainer.is_world_process_zero():
            with open(SCREAMING_SNAKE_CASE_						,							"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    :      int      					=    {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***" )

        snake_case    :      List[Any]      					=    trainer.evaluate()

        snake_case    :      List[str]      					=    math.exp(eval_output["eval_loss"] )
        snake_case    :      int      					=    perplexity

        snake_case    :      str      					=    os.path.join(training_args.output_dir						,							"eval_results_mlm_wwm.txt" )
        if trainer.is_world_process_zero():
            with open(SCREAMING_SNAKE_CASE_						,							"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						(						lowercase  :  Any )	->						Tuple:


    """simple docstring"""





    main()


if __name__ == "__main__":
  main()











import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    """The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionImg2ImgPipeline` instead."""
)




