
"""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


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





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



      def  __snake_case							(   self      ):
          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			:
      UpperCamelCase							:   Optional[str]           =     field(
          default=lowercase						,		metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}      )
      UpperCamelCase							:   Optional[str]           =     field(
          default=lowercase						,		metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}      )
      UpperCamelCase							:   Optional[str]           =     field(default=lowercase						,		metadata={"""help""": """The input training data file (a text file)."""}      )
      UpperCamelCase							:   Optional[str]           =     field(
          default=lowercase						,		metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}						,		)
      UpperCamelCase							:   Optional[str]           =     field(
          default=lowercase						,		metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}						,		)
      UpperCamelCase							:   Optional[str]           =     field(
          default=lowercase						,		metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}						,		)
      UpperCamelCase							:   bool           =     field(
          default=lowercase						,		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=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."""
              )
          }						,		)
      UpperCamelCase							:   Optional[int]           =     field(
          default=lowercase						,		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=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  __snake_case							(   self      ):
          if self.train_file is not None:
              UpperCAmelCase__	:      str     							=					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:
              UpperCAmelCase__	:      List[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 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    with open(_snake_case    ,'r'    ,encoding='utf-8' ) as f:
        UpperCAmelCase__	:      Optional[Any]     							=					[json.loads(_snake_case ) for line in f.read().splitlines() if (len(_snake_case ) > 0 and not line.isspace())]
    assert len(_snake_case ) == len(_snake_case )

    UpperCAmelCase__	:      List[str]     							=					{c: dataset[c] for c in dataset.column_names}
    UpperCAmelCase__	:      List[str]     							=					refs
    return Dataset.from_dict(_snake_case )
def 					lowerCamelCase   (     ):
    # 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.

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

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

    UpperCAmelCase__	:      int     							=					{
        '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:
        UpperCAmelCase__	:      str     							=					AutoTokenizer.from_pretrained(model_args.tokenizer_name    ,**_snake_case )
    elif model_args.model_name_or_path:
        UpperCAmelCase__	:      List[Any]     							=					AutoTokenizer.from_pretrained(model_args.model_name_or_path    ,**_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:
        UpperCAmelCase__	:      Union[str, Any]     							=					AutoModelForMaskedLM.from_pretrained(
            model_args.model_name_or_path    ,from_tf=bool('.ckpt' in model_args.model_name_or_path )    ,config=_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' )
        UpperCAmelCase__	:      Optional[Any]     							=					AutoModelForMaskedLM.from_config(_snake_case )

    model.resize_token_embeddings(len(_snake_case ) )

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

    UpperCAmelCase__	:      List[Any]     							=					'max_length' if data_args.pad_to_max_length else False

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

    UpperCAmelCase__	:      List[str]     							=					datasets.map(
        _snake_case    ,batched=_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:
        UpperCAmelCase__	:      Any     							=					add_chinese_references(tokenized_datasets['train']    ,data_args.train_ref_file )
    if data_args.validation_ref_file is not None:
        UpperCAmelCase__	:      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
    UpperCAmelCase__	:      int     							=					data_args.train_ref_file or data_args.validation_ref_file
    if has_ref:
        UpperCAmelCase__	:      List[str]     							=					False

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

    # Initialize our Trainer
    UpperCAmelCase__	:      int     							=					Trainer(
        model=_snake_case    ,args=_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=_snake_case    ,data_collator=_snake_case    ,)

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

        UpperCAmelCase__	:      Optional[Any]     							=					os.path.join(training_args.output_dir    ,'train_results.txt' )
        if trainer.is_world_process_zero():
            with open(_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
    UpperCAmelCase__	:      str     							=					{}
    if training_args.do_eval:
        logger.info('*** Evaluate ***' )

        UpperCAmelCase__	:      Dict     							=					trainer.evaluate()

        UpperCAmelCase__	:      Tuple     							=					math.exp(eval_output['eval_loss'] )
        UpperCAmelCase__	:      str     							=					perplexity

        UpperCAmelCase__	:      List[str]     							=					os.path.join(training_args.output_dir    ,'eval_results_mlm_wwm.txt' )
        if trainer.is_world_process_zero():
            with open(_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   (     _snake_case ):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
    main()



"""simple docstring"""



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class 				a			(					lowercase      ):
      UpperCamelCase							:   Tuple           =     ["""image_processor""", """tokenizer"""]
      UpperCamelCase							:   List[str]           =     """LayoutLMv2ImageProcessor"""
      UpperCamelCase							:   Optional[int]           =     ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")



      def __init__(   self     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	**UpperCamelCase_      ):
          if "feature_extractor" in kwargs:
              warnings.warn(
                  'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                  ' instead.'     ,	UpperCamelCase_     ,	)
              UpperCAmelCase__	:      Tuple     							=					kwargs.pop('feature_extractor'      )

          UpperCAmelCase__	:      Optional[Any]     							=					image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
              raise ValueError('You need to specify an `image_processor`.'      )
          if tokenizer is None:
              raise ValueError('You need to specify a `tokenizer`.'      )

          super().__init__(UpperCamelCase_     ,	UpperCamelCase_      )



      def __call__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = 0     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = None     ,	**UpperCamelCase_     ,	):
          # verify input
          if self.image_processor.apply_ocr and (boxes is not None):
              raise ValueError(
                  'You cannot provide bounding boxes '
                  'if you initialized the image processor with apply_ocr set to True.'      )

          if self.image_processor.apply_ocr and (word_labels is not None):
              raise ValueError(
                  'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'      )

          if return_overflowing_tokens is True and return_offsets_mapping is False:
              raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'      )

          # first, apply the image processor
          UpperCAmelCase__	:      List[Any]     							=					self.image_processor(images=UpperCamelCase_     ,	return_tensors=UpperCamelCase_      )

          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
              if isinstance(UpperCamelCase_     ,	UpperCamelCase_      ):
                  UpperCAmelCase__	:      Any     							=					[text]  # add batch dimension (as the image processor always adds a batch dimension)
              UpperCAmelCase__	:      Union[str, Any]     							=					features['words']

          UpperCAmelCase__	:      List[Any]     							=					self.tokenizer(
              text=text if text is not None else features['words']     ,	text_pair=text_pair if text_pair is not None else None     ,	boxes=boxes if boxes is not None else features['boxes']     ,	word_labels=UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_     ,	padding=UpperCamelCase_     ,	truncation=UpperCamelCase_     ,	max_length=UpperCamelCase_     ,	stride=UpperCamelCase_     ,	pad_to_multiple_of=UpperCamelCase_     ,	return_token_type_ids=UpperCamelCase_     ,	return_attention_mask=UpperCamelCase_     ,	return_overflowing_tokens=UpperCamelCase_     ,	return_special_tokens_mask=UpperCamelCase_     ,	return_offsets_mapping=UpperCamelCase_     ,	return_length=UpperCamelCase_     ,	verbose=UpperCamelCase_     ,	return_tensors=UpperCamelCase_     ,	**UpperCamelCase_     ,	)

          # add pixel values
          UpperCAmelCase__	:      List[Any]     							=					features.pop('pixel_values'      )
          if return_overflowing_tokens is True:
              UpperCAmelCase__	:      int     							=					self.get_overflowing_images(UpperCamelCase_     ,	encoded_inputs['overflow_to_sample_mapping']      )
          UpperCAmelCase__	:      Dict     							=					images

          return encoded_inputs



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          UpperCAmelCase__	:      Dict     							=					[]
          for sample_idx in overflow_to_sample_mapping:
              images_with_overflow.append(images[sample_idx]      )

          if len(UpperCamelCase_      ) != len(UpperCamelCase_      ):
              raise ValueError(
                  'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
                  F''' {len(UpperCamelCase_      )} and {len(UpperCamelCase_      )}'''      )

          return images_with_overflow



      def  __snake_case							(   self     ,	*UpperCamelCase_     ,	**UpperCamelCase_      ):
          return self.tokenizer.batch_decode(*UpperCamelCase_     ,	**UpperCamelCase_      )



      def  __snake_case							(   self     ,	*UpperCamelCase_     ,	**UpperCamelCase_      ):
          return self.tokenizer.decode(*UpperCamelCase_     ,	**UpperCamelCase_      )



      @property
      def  __snake_case							(   self      ):
          return ["input_ids", "bbox", "attention_mask", "image"]



      @property
      def  __snake_case							(   self      ):
          warnings.warn(
              '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,	UpperCamelCase_     ,	)
          return self.image_processor_class



      @property
      def  __snake_case							(   self      ):
          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,	UpperCamelCase_     ,	)
          return self.image_processor

