


'''simple docstring'''


from argparse import ArgumentParser

from . import BaseTransformersCLICommand




def      lowerCamelCase					(      lowerCamelCase  :      Tuple):
      return DownloadCommand(args.model							,						args.cache_dir							,						args.force							,						args.trust_remote_code)






class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  @staticmethod
  def        _a							(							_a :       ArgumentParser ):

        '''simple docstring'''




        A_						:  Any          =				parser.add_parser("""download""" )
        download_parser.add_argument(
            """--cache-dir"""	,type=_a	,default=_a	,help="""Path to location to store the models""" )
        download_parser.add_argument(
            """--force"""	,action="""store_true"""	,help="""Force the model to be download even if already in cache-dir""" )
        download_parser.add_argument(
            """--trust-remote-code"""	,action="""store_true"""	,help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""	,)
        download_parser.add_argument("""model"""	,type=_a	,help="""Name of the model to download""" )
        download_parser.set_defaults(func=_a )






  def __init__(							self :       List[str]	,_a :       str	,_a :       str	,_a :       bool	,_a :       bool ):

        '''simple docstring'''




        A_						:  int          =				model
        A_						:  Tuple          =				cache
        A_						:  Tuple          =				force
        A_						:  str          =				trust_remote_code






  def        _a							(							self :       str ):

        '''simple docstring'''




        from ..models.auto import AutoModel, AutoTokenizer

        AutoModel.from_pretrained(
            self._model	,cache_dir=self._cache	,force_download=self._force	,trust_remote_code=self._trust_remote_code )
        AutoTokenizer.from_pretrained(
            self._model	,cache_dir=self._cache	,force_download=self._force	,trust_remote_code=self._trust_remote_code )




'''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 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""image_processor""", """tokenizer"""]
  a_  					=							"""ViltImageProcessor"""
  a_  					=							("""BertTokenizer""", """BertTokenizerFast""")






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

        '''simple docstring'''




        A_						:  Any          =				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	,)
              A_						:  List[str]          =				kwargs.pop("""feature_extractor""" )

        A_						:  List[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__(_a	,_a )
        A_						:  Optional[Any]          =				self.image_processor






  def __call__(							self :       Any	,_a :       Tuple	,_a :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,_a :       bool = True	,_a :       Union[bool, str, PaddingStrategy] = False	,_a :       Union[bool, str, TruncationStrategy] = None	,_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'''




        A_						:  int          =				self.tokenizer(
            text=_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 + pixel_mask
        A_						:  Optional[int]          =				self.image_processor(_a	,return_tensors=_a )
        encoding.update(_a )

        return encoding






  def        _a							(							self :       List[Any]	,*_a :       Any	,**_a :       Any ):

        '''simple docstring'''




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






  def        _a							(							self :       int	,*_a :       int	,**_a :       Optional[int] ):

        '''simple docstring'''




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






  @property
  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  Optional[int]          =				self.tokenizer.model_input_names
        A_						:  str          =				self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )






  @property
  def        _a							(							self :       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        _a							(							self :       int ):

        '''simple docstring'''




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