

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class  UpperCamelCase_      (	_UpperCAmelCase      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Tuple					    =   ["""image_processor""", """tokenizer"""]
  SCREAMING_SNAKE_CASE__					:			int					    =   """VideoMAEImageProcessor"""
  SCREAMING_SNAKE_CASE__					:			Dict					    =   ("""CLIPTokenizer""", """CLIPTokenizerFast""")



  def __init__(						self     :					Any  ,	A_     :					int=None  ,	A_     :					List[Any]=None  ,	**A_     :					Any						)       ->			Any:
      SCREAMING_SNAKE_CASE__         =    None
      if "feature_extractor" in kwargs:
          warnings.warn(
              "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
              " instead."  ,	lowercase_  ,	)
          SCREAMING_SNAKE_CASE__         =    kwargs.pop("feature_extractor"						)

      SCREAMING_SNAKE_CASE__         =    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__(lowercase_  ,	lowercase_						)
      SCREAMING_SNAKE_CASE__         =    self.image_processor



  def __call__(						self     :					Tuple  ,	A_     :					List[str]=None  ,	A_     :					Tuple=None  ,	A_     :					Any=None  ,	**A_     :					Dict						)       ->			Any:

      if text is None and videos is None:
          raise ValueError("You have to specify either text or videos. Both cannot be none."						)

      if text is not None:
          SCREAMING_SNAKE_CASE__         =    self.tokenizer(lowercase_  ,	return_tensors=lowercase_  ,	**lowercase_						)

      if videos is not None:
          SCREAMING_SNAKE_CASE__         =    self.image_processor(lowercase_  ,	return_tensors=lowercase_  ,	**lowercase_						)

      if text is not None and videos is not None:
          SCREAMING_SNAKE_CASE__         =    image_features.pixel_values
          return encoding
      elif text is not None:
          return encoding
      else:
          return BatchEncoding(data=dict(**lowercase_						)  ,	tensor_type=lowercase_						)



  def   _UpperCamelCase     (						self     :					List[str]  ,	*A_     :					Optional[int]  ,	**A_     :					str						)       ->			Optional[int]:
      return self.tokenizer.batch_decode(*lowercase_  ,	**lowercase_						)



  def   _UpperCamelCase     (						self     :					List[Any]  ,	*A_     :					Optional[int]  ,	**A_     :					Dict						)       ->			List[Any]:
      return self.tokenizer.decode(*lowercase_  ,	**lowercase_						)



  @property
  def   _UpperCamelCase     (						self     :					Dict						)       ->			List[Any]:
      return ["input_ids", "attention_mask", "position_ids", "pixel_values"]



  @property
  def   _UpperCamelCase     (						self     :					Tuple						)       ->			Union[str, Any]:
      warnings.warn(
          "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,	lowercase_  ,	)
      return self.image_processor_class







  @property
  def   _UpperCamelCase     (						self     :					List[str]						)       ->			List[Any]:
      warnings.warn(
          "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,	lowercase_  ,	)
      return self.image_processor



'''simple docstring'''




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		:					str													=		logging.getLogger(__name__)




def 				lowerCamelCase      ( )     -> Tuple:
							lowercase_   :     Any									=			argparse.ArgumentParser(description="""Train a unigram tokenizer on the wikitext dataset."""					)
							parser.add_argument(
							    """--dataset_name""" , type=UpperCAmelCase__ , default="""wikitext""" , help="""Name of the training. Explore datasets at: hf.co/datasets.""" , )
							parser.add_argument(
							    """--dataset_config""" , type=UpperCAmelCase__ , default="""wikitext-103-raw-v1""" , help="""Configuration name of the dataset."""					)
							parser.add_argument(
							    """--batch_size""" , type=UpperCAmelCase__ , default=1000 , help="""Batch size during training.""" , )
							parser.add_argument(
							    """--vocab_size""" , type=UpperCAmelCase__ , default=10048 , help="""Size of the desired vocabulary.""" , )
							parser.add_argument(
							    """--limit""" , default=UpperCAmelCase__ , type=UpperCAmelCase__ , help="""Limit the number of shards (used for debugging).""" , )
							parser.add_argument(
							    """--export_to_hub""" , action="""store_true""" , )

							lowercase_   :     List[Any]									=			parser.parse_args()
							return args




def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[int]					)     -> List[Any]:
							lowercase_   :     Any									=			datasets.load_dataset(args.dataset_name , args.dataset_config , split="""train"""					)

							if args.limit is not None:
														lowercase_   :     Optional[Any]									=			min(len(UpperCAmelCase__					) , args.limit					)
														lowercase_   :     Dict									=			dataset.select(range(UpperCAmelCase__					)					)
														logger.info(F'''Limiting the dataset to {args.limit} entries.'''					)

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

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

							# Prepare the trainer.
							lowercase_   :     str									=			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=UpperCAmelCase__					)
							logger.info("""Tokenizer training complete!"""					)

							lowercase_   :     Dict									=			tokenizer.token_to_id("""[CLS]"""					)
							lowercase_   :     Any									=			tokenizer.token_to_id("""[SEP]"""					)
							lowercase_   :     Optional[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),
							    ] , )
							lowercase_   :     int									=			decoders.Metaspace()

							if args.export_to_hub:
														logger.info("""Exporting the trained tokenzier to Hub."""					)
														lowercase_   :     Any									=			AlbertTokenizerFast(tokenizer_object=UpperCAmelCase__					)
														new_tokenizer.push_to_hub("""unigram-tokenizer-dataset"""					)


if __name__ == "__main__":
						_lowercase		:					Dict													=		parse_args()
						main(args)





