import warnings
from typing import List, Optional, Union

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




class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    Union[str, Any]  			=   ['''image_processor''', '''tokenizer''']
   lowerCAmelCase      :    Optional[int]  			=   '''FlavaImageProcessor'''
   lowerCAmelCase      :    Any  			=   ('''BertTokenizer''', '''BertTokenizerFast''')



   def __init__(	self					:  Tuple		,  _lowercase					:  str=None		,  _lowercase					:  Optional[Any]=None		,  **_lowercase					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     List[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.'''		,  _lowercase		,  )
     _UpperCamelCase:     List[str]							= kwargs.pop('''feature_extractor'''							)

    _UpperCamelCase:     int							= 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							)
    _UpperCamelCase:     Tuple							= self.image_processor



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



    """simple docstring"""



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

    if text is not None:
     _UpperCamelCase:     Union[str, Any]							= self.tokenizer(
         text=_lowercase		,  add_special_tokens=_lowercase		,  padding=_lowercase		,  truncation=_lowercase		,  max_length=_lowercase		,  stride=_lowercase		,  pad_to_multiple_of=_lowercase		,  return_token_type_ids=_lowercase		,  return_attention_mask=_lowercase		,  return_overflowing_tokens=_lowercase		,  return_special_tokens_mask=_lowercase		,  return_offsets_mapping=_lowercase		,  return_length=_lowercase		,  verbose=_lowercase		,  return_tensors=_lowercase		,  **_lowercase		,  )
    if images is not None:
     _UpperCamelCase:     Optional[Any]							= self.image_processor(
         _lowercase		,  return_image_mask=_lowercase		,  return_codebook_pixels=_lowercase		,  return_tensors=_lowercase		,  **_lowercase		,  )

    if text is not None and images is not None:
     encoding.update(_lowercase							)
     return encoding
    elif text is not None:
     return encoding
    else:
     return BatchEncoding(data=dict(**_lowercase							)		,  tensor_type=_lowercase							)



   def   lowerCAmelCase						(	self					:  Tuple		,  *_lowercase					:  List[str]		,  **_lowercase					:  Optional[int]							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  Tuple		,  *_lowercase					:  Optional[Any]		,  **_lowercase					:  Optional[Any]							):



    """simple docstring"""



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



   @property
   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= self.tokenizer.model_input_names
    _UpperCamelCase:     str							= self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



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



    """simple docstring"""



    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   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''		,  _lowercase		,  )
    return self.image_processor
def       lowerCAmelCase_     (		lowercase:							int = 600_851_475_143		)    ->				int:







 '''simple docstring'''

 try:
  _UpperCamelCase:     Optional[int]							= int(lowercase		)
 except (TypeError, ValueError):
  raise TypeError('''Parameter n must be int or castable to int.'''		)
 if n <= 0:
  raise ValueError('''Parameter n must be greater than or equal to one.'''		)
 _UpperCamelCase:     Dict							= 1
 _UpperCamelCase:     Tuple							= 2
 while i * i <= n:
  while n % i == 0:
   _UpperCamelCase:     List[Any]							= i
   n //= i
  i += 1
 if n > 1:
  _UpperCamelCase:     Any							= n
 return int(lowercase		)


if __name__ == "__main__":
  print(f"""{solution() = }""")