"""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


SCREAMING_SNAKE_CASE__   :      Dict							=logging.getLogger(__name__)
SCREAMING_SNAKE_CASE__   :      str							=list(MODEL_FOR_MASKED_LM_MAPPING.keys())
SCREAMING_SNAKE_CASE__   :      Optional[int]							=tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class      _UpperCAmelCase						:






    """simple docstring"""
    __snake_case    =       field(
        default=UpperCamelCase__ ,			metadata={
            """help""": (
                """The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch."""
            )
        } ,			)
    __snake_case    =       field(
        default=UpperCamelCase__ ,			metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(UpperCamelCase__						)} ,			)
    __snake_case    =       field(
        default=UpperCamelCase__ ,			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"""
            )
        } ,			)
    __snake_case    =       field(
        default=UpperCamelCase__ ,			metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}						)
    __snake_case    =       field(
        default=UpperCamelCase__ ,			metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}						)
    __snake_case    =       field(
        default=UpperCamelCase__ ,			metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} ,			)
    __snake_case    =       field(
        default=UpperCamelCase__ ,			metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} ,			)
    __snake_case    =       field(
        default="""main""" ,			metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} ,			)
    __snake_case    =       field(
        default=UpperCamelCase__ ,			metadata={
            """help""": (
                """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                """with private models)."""
            )
        } ,			)







    def 				a__					(			self					)	->	Union[str, Any]:
        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						:






    """simple docstring"""
    __snake_case    =       field(
        default=UpperCamelCase__ ,			metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}						)
    __snake_case    =       field(
        default=UpperCamelCase__ ,			metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}						)
    __snake_case    =       field(default=UpperCamelCase__ ,			metadata={"""help""": """The input training data file (a text file)."""}						)
    __snake_case    =       field(
        default=UpperCamelCase__ ,			metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""} ,			)
    __snake_case    =       field(
        default=UpperCamelCase__ ,			metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""} ,			)
    __snake_case    =       field(
        default=UpperCamelCase__ ,			metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""} ,			)
    __snake_case    =       field(
        default=UpperCamelCase__ ,			metadata={"""help""": """Overwrite the cached training and evaluation sets"""}						)
    __snake_case    =       field(
        default=5 ,			metadata={
            """help""": """The percentage of the train set used as validation set in case there\'s no validation split"""
        } ,			)
    __snake_case    =       field(
        default=UpperCamelCase__ ,			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."""
            )
        } ,			)
    __snake_case    =       field(
        default=UpperCamelCase__ ,			metadata={"""help""": """The number of processes to use for the preprocessing."""} ,			)
    __snake_case    =       field(
        default=0.15 ,			metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}						)
    __snake_case    =       field(
        default=UpperCamelCase__ ,			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 				a__					(			self					)	->	Tuple:
        if self.train_file is not None:
            _lowerCamelCase	: Tuple       =       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	: 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 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->Optional[int]:
    with open(A__						,	'''r'''						,	encoding='''utf-8''' ) as f:
        _lowerCamelCase	: Dict       =       [json.loads(A__ ) for line in f.read().splitlines() if (len(A__ ) > 0 and not line.isspace())]
    assert len(A__ ) == len(A__ )

    _lowerCamelCase	: List[str]       =       {c: dataset[c] for c in dataset.column_names}
    _lowerCamelCase	: Union[str, Any]       =       refs
    return Dataset.from_dict(A__ )



def 					UpperCamelCase					(       )      ->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.

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

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

    # 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	: List[str]       =       load_dataset(data_args.dataset_name						,	data_args.dataset_config_name )
        if "validation" not in datasets.keys():
            _lowerCamelCase	: List[Any]       =       load_dataset(
                data_args.dataset_name						,	data_args.dataset_config_name						,	split=F'''train[:{data_args.validation_split_percentage}%]'''						,	)
            _lowerCamelCase	: Any       =       load_dataset(
                data_args.dataset_name						,	data_args.dataset_config_name						,	split=F'''train[{data_args.validation_split_percentage}%:]'''						,	)
    else:
        _lowerCamelCase	: str       =       {}
        if data_args.train_file is not None:
            _lowerCamelCase	: str       =       data_args.train_file
        if data_args.validation_file is not None:
            _lowerCamelCase	: Optional[Any]       =       data_args.validation_file
        _lowerCamelCase	: Dict       =       data_args.train_file.split('''.''' )[-1]
        if extension == "txt":
            _lowerCamelCase	: Optional[int]       =       '''text'''
        _lowerCamelCase	: List[Any]       =       load_dataset(A__						,	data_files=A__ )
    # 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	: 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:
        _lowerCamelCase	: Tuple       =       AutoConfig.from_pretrained(model_args.config_name						,	**A__ )
    elif model_args.model_name_or_path:
        _lowerCamelCase	: Tuple       =       AutoConfig.from_pretrained(model_args.model_name_or_path						,	**A__ )
    else:
        _lowerCamelCase	: 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}''' )

    _lowerCamelCase	: 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:
        _lowerCamelCase	: Union[str, Any]       =       AutoTokenizer.from_pretrained(model_args.tokenizer_name						,	**A__ )
    elif model_args.model_name_or_path:
        _lowerCamelCase	: int       =       AutoTokenizer.from_pretrained(model_args.model_name_or_path						,	**A__ )
    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	: Union[str, Any]       =       AutoModelForMaskedLM.from_pretrained(
            model_args.model_name_or_path						,	from_tf=bool('''.ckpt''' in model_args.model_name_or_path )						,	config=A__						,	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	: Any       =       AutoModelForMaskedLM.from_config(A__ )

    model.resize_token_embeddings(len(A__ ) )

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

    _lowerCamelCase	: Union[str, Any]       =       '''max_length''' if data_args.pad_to_max_length else False

    def tokenize_function(SCREAMING_SNAKE_CASE_ ):
        # Remove empty lines
        _lowerCamelCase	: List[Any]       =       [line for line in examples['''text'''] if len(A__ ) > 0 and not line.isspace()]
        return tokenizer(examples['''text''']						,	padding=A__						,	truncation=A__						,	max_length=data_args.max_seq_length )

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

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

    # Initialize our Trainer
    _lowerCamelCase	: str       =       Trainer(
        model=A__						,	args=A__						,	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=A__						,	data_collator=A__						,	)

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

        _lowerCamelCase	: List[str]       =       os.path.join(training_args.output_dir						,	'''train_results.txt''' )
        if trainer.is_world_process_zero():
            with open(A__						,	'''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	: int       =       {}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***''' )

        _lowerCamelCase	: Tuple       =       trainer.evaluate()

        _lowerCamelCase	: Union[str, Any]       =       math.exp(eval_output['''eval_loss'''] )
        _lowerCamelCase	: Optional[Any]       =       perplexity

        _lowerCamelCase	: Tuple       =       os.path.join(training_args.output_dir						,	'''eval_results_mlm_wwm.txt''' )
        if trainer.is_world_process_zero():
            with open(A__						,	'''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					(       SCREAMING_SNAKE_CASE_ )      ->Tuple:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
  main()




'''simple docstring'''
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		lowercase__											=argparse.ArgumentParser()

		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

		parser.add_argument(
		    '--txt2img_unclip',
		    default='kakaobrain/karlo-v1-alpha',
		    type=str,
		    required=False,
		    help='The pretrained txt2img unclip.',
		)

		lowercase__											=parser.parse_args()

		lowercase__											=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		lowercase__											=CLIPImageProcessor()
		lowercase__											=CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

		lowercase__											=UnCLIPImageVariationPipeline(
		    decoder=txtaimg.decoder,
		    text_encoder=txtaimg.text_encoder,
		    tokenizer=txtaimg.tokenizer,
		    text_proj=txtaimg.text_proj,
		    feature_extractor=feature_extractor,
		    image_encoder=image_encoder,
		    super_res_first=txtaimg.super_res_first,
		    super_res_last=txtaimg.super_res_last,
		    decoder_scheduler=txtaimg.decoder_scheduler,
		    super_res_scheduler=txtaimg.super_res_scheduler,
		)

		imgaimg.save_pretrained(args.dump_path)
