
import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


lowercase_											=	logging.getLogger(__name__)


def     a__			(    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					int			=						argparse.ArgumentParser(description='''Train a unigram tokenizer on the wikitext dataset.'''    )
  parser.add_argument(
      '''--dataset_name'''   ,	type=snake_case   ,	default='''wikitext'''   ,	help='''Name of the training. Explore datasets at: hf.co/datasets.'''   ,	)
  parser.add_argument(
      '''--dataset_config'''   ,	type=snake_case   ,	default='''wikitext-103-raw-v1'''   ,	help='''Configuration name of the dataset.'''    )
  parser.add_argument(
      '''--batch_size'''   ,	type=snake_case   ,	default=1_000   ,	help='''Batch size during training.'''   ,	)
  parser.add_argument(
      '''--vocab_size'''   ,	type=snake_case   ,	default=10_048   ,	help='''Size of the desired vocabulary.'''   ,	)
  parser.add_argument(
      '''--limit'''   ,	default=snake_case   ,	type=snake_case   ,	help='''Limit the number of shards (used for debugging).'''   ,	)
  parser.add_argument(
      '''--export_to_hub'''   ,	action='''store_true'''   ,	)

  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						parser.parse_args()
  return args


def     a__			(    snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					Any			=						datasets.load_dataset(args.dataset_name   ,	args.dataset_config   ,	split='''train'''    )

  if args.limit is not None:
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						min(len(snake_case    )   ,	args.limit    )
    __SCREAMING_SNAKE_CASE     :					str			=						dataset.select(range(snake_case    )    )
    logger.info(F'''Limiting the dataset to {args.limit} entries.'''    )

  def batch_iterator():
    for i in range(0   ,	len(snake_case    )   ,	args.batch_size    ):
      yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
  __SCREAMING_SNAKE_CASE     :					Any			=						Tokenizer(Unigram()    )
  __SCREAMING_SNAKE_CASE     :					Any			=						normalizers.Sequence([normalizers.Replace('''``'''   ,	'''"'''    ), normalizers.Replace('''\'\''''   ,	'''"'''    )]    )
  __SCREAMING_SNAKE_CASE     :					str			=						pre_tokenizers.Metaspace()

  # Prepare the trainer.
  __SCREAMING_SNAKE_CASE     :					Any			=						UnigramTrainer(
      unk_token='''<unk>'''   ,	special_tokens=['''[CLS]''', '''[SEP]''', '''<unk>''', '''<pad>''', '''[MASK]''']   ,	vocab_size=args.vocab_size   ,	)

  logger.info('''Training the tokenizer.'''    )
  tokenizer.train_from_iterator(batch_iterator()   ,	trainer=snake_case    )
  logger.info('''Tokenizer training complete!'''    )

  __SCREAMING_SNAKE_CASE     :					Dict			=						tokenizer.token_to_id('''[CLS]'''    )
  __SCREAMING_SNAKE_CASE     :					List[Any]			=						tokenizer.token_to_id('''[SEP]'''    )
  __SCREAMING_SNAKE_CASE     :					Any			=						processors.TemplateProcessing(
      single='''[CLS]:0 $A:0 [SEP]:0'''   ,	pair='''[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1'''   ,	special_tokens=[
          ('''[CLS]''', cls_token_id),
          ('''[SEP]''', sep_token_id),
      ]   ,	)
  __SCREAMING_SNAKE_CASE     :					Tuple			=						decoders.Metaspace()

  if args.export_to_hub:
    logger.info('''Exporting the trained tokenzier to Hub.'''    )
    __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						AlbertTokenizerFast(tokenizer_object=snake_case    )
    new_tokenizer.push_to_hub('''unigram-tokenizer-dataset'''    )


if __name__ == "__main__":
   lowercase_											=	parse_args()
   main(args)


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   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	['''image_processor''', '''tokenizer''']
     lowerCAmelCase_     =	'''LayoutLMv2ImageProcessor'''
     lowerCAmelCase_     =	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')



     def __init__(							self    :    Optional[Any]      ,		_A    :    List[Any]=None      ,		_A    :    List[str]=None      ,		**_A    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						None
       if "feature_extractor" in kwargs:
         warnings.warn(
             '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
             ''' instead.'''      ,		_A      ,		)
         __SCREAMING_SNAKE_CASE     :					Tuple			=						kwargs.pop('''feature_extractor'''				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						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__(_A      ,		_A				)



     def __call__(							self    :    Optional[int]      ,		_A    :    Tuple      ,		_A    :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,		_A    :    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,		_A    :    Union[List[List[int]], List[List[List[int]]]] = None      ,		_A    :    Optional[Union[List[int], List[List[int]]]] = None      ,		_A    :    bool = True      ,		_A    :    Union[bool, str, PaddingStrategy] = False      ,		_A    :    Union[bool, str, TruncationStrategy] = False      ,		_A    :    Optional[int] = None      ,		_A    :    int = 0      ,		_A    :    Optional[int] = None      ,		_A    :    Optional[bool] = None      ,		_A    :    Optional[bool] = None      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = True      ,		_A    :    Optional[Union[str, TensorType]] = None      ,		**_A    :    Tuple      ,		):







       """simple docstring"""




       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
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.image_processor(images=_A      ,		return_tensors=_A				)

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

       __SCREAMING_SNAKE_CASE     :					str			=						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=_A      ,		add_special_tokens=_A      ,		padding=_A      ,		truncation=_A      ,		max_length=_A      ,		stride=_A      ,		pad_to_multiple_of=_A      ,		return_token_type_ids=_A      ,		return_attention_mask=_A      ,		return_overflowing_tokens=_A      ,		return_special_tokens_mask=_A      ,		return_offsets_mapping=_A      ,		return_length=_A      ,		verbose=_A      ,		return_tensors=_A      ,		**_A      ,		)

       # add pixel values
       __SCREAMING_SNAKE_CASE     :					str			=						features.pop('''pixel_values'''				)
       if return_overflowing_tokens is True:
         __SCREAMING_SNAKE_CASE     :					str			=						self.get_overflowing_images(_A      ,		encoded_inputs['''overflow_to_sample_mapping''']				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						images

       return encoded_inputs



     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    List[Any]      ,		_A    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						[]
       for sample_idx in overflow_to_sample_mapping:
         images_with_overflow.append(images[sample_idx]				)

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

       return images_with_overflow



     def 				UpperCAmelCase__    (							self    :    int      ,		*_A    :    Dict      ,		**_A    :    Optional[Any]				):







       """simple docstring"""




       return self.tokenizer.batch_decode(*_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]      ,		*_A    :    Union[str, Any]      ,		**_A    :    List[str]				):







       """simple docstring"""




       return self.tokenizer.decode(*_A      ,		**_A				)



     @property
     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]



     @property
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''      ,		_A      ,		)
       return self.image_processor_class




     @property
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''      ,		_A      ,		)
       return self.image_processor
