





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				:	Union[str, Any]      						=  logging.getLogger(__name__)
lowercase				:	Optional[Any]      						=  list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowercase				:	Any      						=  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class 			A__   :




   """simple docstring"""



   __A       :    Optional[str]	  =  field(
       default=__lowercase			,  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=__lowercase			,  metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(__lowercase     )}			,  )
   __A       :    Optional[str]	  =  field(
       default=__lowercase			,  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=__lowercase			,  metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}     )
   __A       :    Optional[str]	  =  field(
       default=__lowercase			,  metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}     )
   __A       :    Optional[str]	  =  field(
       default=__lowercase			,  metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}			,  )
   __A       :    bool	  =  field(
       default=__lowercase			,  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=__lowercase			,  metadata={
           '''help''': (
               '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
               '''with private models).'''
           )
       }			,  )

   def 							__lowercase		( self)      ->						Any:







          '''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 			A__   :




   """simple docstring"""



   __A       :    Optional[str]	  =  field(
       default=__lowercase			,  metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}     )
   __A       :    Optional[str]	  =  field(
       default=__lowercase			,  metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}     )
   __A       :    Optional[str]	  =  field(default=__lowercase			,  metadata={'''help''': '''The input training data file (a text file).'''}     )
   __A       :    Optional[str]	  =  field(
       default=__lowercase			,  metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}			,  )
   __A       :    Optional[str]	  =  field(
       default=__lowercase			,  metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}			,  )
   __A       :    Optional[str]	  =  field(
       default=__lowercase			,  metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}			,  )
   __A       :    bool	  =  field(
       default=__lowercase			,  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=__lowercase			,  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=__lowercase			,  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=__lowercase			,  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:







          '''simple docstring'''

          if self.train_file is not None:
                 a__						:     Optional[int]			=    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__						:     List[Any]			=    self.validation_file.split('.')[-1]
                 assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."



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

       a__						:     Optional[int]			=    {c: dataset[c] for c in dataset.column_names}
       a__						:     Any			=    refs
       return Dataset.from_dict(A__    )



def A_     (		)  ->		List[str]:
       # 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.

       a__						:     Optional[int]			=    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__						:     str			=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
       else:
              a__			,							a__			,							a__						:     Dict			=    parser.parse_args_into_dataclasses()

       # Detecting last checkpoint.
       a__						:     Optional[int]			=    None
       if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
              a__						:     Optional[int]			=    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.
              a__						:     Any			=    load_dataset(data_args.dataset_name		,				data_args.dataset_config_name    )
              if "validation" not in datasets.keys():
                     a__						:     List[str]			=    load_dataset(
                         data_args.dataset_name		,				data_args.dataset_config_name		,				split=F'train[:{data_args.validation_split_percentage}%]'		,				)
                     a__						:     Dict			=    load_dataset(
                         data_args.dataset_name		,				data_args.dataset_config_name		,				split=F'train[{data_args.validation_split_percentage}%:]'		,				)
       else:
              a__						:     Any			=    {}
              if data_args.train_file is not None:
                     a__						:     int			=    data_args.train_file
              if data_args.validation_file is not None:
                     a__						:     Tuple			=    data_args.validation_file
              a__						:     Optional[Any]			=    data_args.train_file.split('.'    )[-1]
              if extension == "txt":
                     a__						:     Optional[Any]			=    'text'
              a__						:     int			=    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.
       a__						:     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:
              a__						:     Union[str, Any]			=    AutoConfig.from_pretrained(model_args.config_name		,				**A__    )
       elif model_args.model_name_or_path:
              a__						:     Any			=    AutoConfig.from_pretrained(model_args.model_name_or_path		,				**A__    )
       else:
              a__						:     Optional[int]			=    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__						:     Optional[Any]			=    {
           '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__						:     int			=    AutoTokenizer.from_pretrained(model_args.tokenizer_name		,				**A__    )
       elif model_args.model_name_or_path:
              a__						:     str			=    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:
              a__						:     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'    )
              a__						:     Optional[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:
              a__						:     Dict			=    datasets['train'].column_names
       else:
              a__						:     Dict			=    datasets['validation'].column_names
       a__						:     List[Any]			=    'text' if 'text' in column_names else column_names[0]

       a__						:     List[str]			=    'max_length' if data_args.pad_to_max_length else False

       def tokenize_function(A__    ):
              # Remove empty lines
              a__						:     Optional[int]			=    [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    )

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

       # Data collator
       # This one will take care of randomly masking the tokens.
       a__						:     int			=    DataCollatorForWholeWordMask(tokenizer=A__		,				mlm_probability=data_args.mlm_probability    )

       # Initialize our Trainer
       a__						:     Optional[int]			=    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:
                     a__						:     Dict			=    last_checkpoint
              elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path    ):
                     a__						:     Any			=    model_args.model_name_or_path
              else:
                     a__						:     Dict			=    None
              a__						:     Optional[int]			=    trainer.train(resume_from_checkpoint=A__    )
              trainer.save_model()  # Saves the tokenizer too for easy upload

              a__						:     Dict			=    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
       a__						:     Optional[int]			=    {}
       if training_args.do_eval:
              logger.info('*** Evaluate ***'    )

              a__						:     Optional[Any]			=    trainer.evaluate()

              a__						:     int			=    math.exp(eval_output['eval_loss']    )
              a__						:     Any			=    perplexity

              a__						:     Union[str, Any]			=    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 A_     (		A__    )  ->		Any:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
     main()









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowercase				:	List[str]      						=  {
    """configuration_clip""": [
        """CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """CLIPConfig""",
        """CLIPOnnxConfig""",
        """CLIPTextConfig""",
        """CLIPVisionConfig""",
    ],
    """processing_clip""": ["""CLIPProcessor"""],
    """tokenization_clip""": ["""CLIPTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Tuple      						=  ["""CLIPTokenizerFast"""]

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Optional[int]      						=  ["""CLIPFeatureExtractor"""]
    lowercase				:	Union[str, Any]      						=  ["""CLIPImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Dict      						=  [
        """CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """CLIPModel""",
        """CLIPPreTrainedModel""",
        """CLIPTextModel""",
        """CLIPTextModelWithProjection""",
        """CLIPVisionModel""",
        """CLIPVisionModelWithProjection""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	List[str]      						=  [
        """TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFCLIPModel""",
        """TFCLIPPreTrainedModel""",
        """TFCLIPTextModel""",
        """TFCLIPVisionModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Any      						=  [
        """FlaxCLIPModel""",
        """FlaxCLIPPreTrainedModel""",
        """FlaxCLIPTextModel""",
        """FlaxCLIPTextPreTrainedModel""",
        """FlaxCLIPVisionModel""",
        """FlaxCLIPVisionPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_clip import (
        CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
        CLIPConfig,
        CLIPOnnxConfig,
        CLIPTextConfig,
        CLIPVisionConfig,
    )
    from .processing_clip import CLIPProcessor
    from .tokenization_clip import CLIPTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_clip_fast import CLIPTokenizerFast

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_clip import CLIPFeatureExtractor
        from .image_processing_clip import CLIPImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_clip import (
            CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            CLIPModel,
            CLIPPreTrainedModel,
            CLIPTextModel,
            CLIPTextModelWithProjection,
            CLIPVisionModel,
            CLIPVisionModelWithProjection,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_clip import (
            TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCLIPModel,
            TFCLIPPreTrainedModel,
            TFCLIPTextModel,
            TFCLIPVisionModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_clip import (
            FlaxCLIPModel,
            FlaxCLIPPreTrainedModel,
            FlaxCLIPTextModel,
            FlaxCLIPTextPreTrainedModel,
            FlaxCLIPVisionModel,
            FlaxCLIPVisionPreTrainedModel,
        )


else:
    import sys

    lowercase				:	List[Any]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


