import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class 							a_					(		lowercase__		):






 '''simple docstring'''






 UpperCAmelCase_      					=   ['image_processor', 'tokenizer']
 UpperCAmelCase_      					=   'AutoImageProcessor'
 UpperCAmelCase_      					=   'AutoTokenizer'




 def __init__(     self   :    Optional[Any]     ,       lowercase__   :    Dict=None     ,       lowercase__   :    Dict=None     ,       **lowercase__   :    Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   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     ,       )
               lowerCAmelCase__	      =   kwargs.pop('feature_extractor')

        lowerCAmelCase__	      =   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)
        lowerCAmelCase__	      =   self.image_processor
        lowerCAmelCase__	      =   False




 def __call__(     self   :    List[str]     ,       *lowercase__   :    Optional[Any]     ,       **lowercase__   :    Tuple):







        '''simple docstring'''



        if self._in_target_context_manager:
               return self.current_processor(*_lowercase     ,       **_lowercase)

        lowerCAmelCase__	      =   kwargs.pop('images'     ,       _lowercase)
        lowerCAmelCase__	      =   kwargs.pop('text'     ,       _lowercase)
        if len(_lowercase) > 0:
               lowerCAmelCase__	      =   args[0]
               lowerCAmelCase__	      =   args[1:]

        if images is None and text is None:
               raise ValueError('You need to specify either an `images` or `text` input to process.')

        if images is not None:
               lowerCAmelCase__	      =   self.image_processor(_lowercase     ,       *_lowercase     ,       **_lowercase)
        if text is not None:
               lowerCAmelCase__	      =   self.tokenizer(_lowercase     ,       **_lowercase)

        if text is None:
               return inputs
        elif images is None:
               return encodings
        else:
               lowerCAmelCase__	      =   encodings['input_ids']
               return inputs




 def 						__snake_case		(     self   :    List[Any]     ,       *lowercase__   :    Union[str, Any]     ,       **lowercase__   :    Union[str, Any]):







        '''simple docstring'''



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




 def 						__snake_case		(     self   :    Dict     ,       *lowercase__   :    Optional[Any]     ,       **lowercase__   :    Dict):







        '''simple docstring'''



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




 @contextmanager
 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



        warnings.warn(
            '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
            'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
            'your images inputs, or in a separate call.')
        lowerCAmelCase__	      =   True
        lowerCAmelCase__	      =   self.tokenizer
        yield
        lowerCAmelCase__	      =   self.image_processor
        lowerCAmelCase__	      =   False




 @property
 def 						__snake_case		(     self   :    int):







        '''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 						__snake_case		(     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

"""simple docstring"""


from .number_theory.prime_numbers import next_prime
class   UpperCAmelCase_	:
    def __init__(	self			:       str   ,						_lowercase			:       int   ,						_lowercase			:       int | None = None   ,						_lowercase			:       float | None = None   ,						)						->    None:
       _lowercase							     =       size_table
       _lowercase							     =       [None] * self.size_table
       _lowercase							     =       0.75 if lim_charge is None else lim_charge
       _lowercase							     =       1 if charge_factor is None else charge_factor
       _lowercase							     =       []
       _lowercase							     =       {}
    def 		_lowerCamelCase      (	self			:       Any		)						->    Optional[Any]:
       return self._keys
    def 		_lowerCamelCase      (	self			:       Dict		)						->    Dict:
       return sum(1 for slot in self.values if slot is not None		) / (
           self.size_table * self.charge_factor
       )
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       List[str]		)						->    str:
       return key % self.size_table
    def 		_lowerCamelCase      (	self			:       Any   ,						_lowercase			:       Union[str, Any]		)						->    Optional[Any]:
       print(f"""step {step_ord}"""		)
       print(list(range(len(self.values		)		)		)		)
       print(self.values		)
    def 		_lowerCamelCase      (	self			:       str   ,						_lowercase			:       List[Any]		)						->    List[Any]:
       _lowercase							     =       1
       _lowercase							     =       values
       for value in values:
          self.insert_data(_lowercase		)
          self._step_by_step(_lowercase		)
          i += 1
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       Union[str, Any]		)						->    List[Any]:
       _lowercase							     =       data
       _lowercase							     =       data
    def 		_lowerCamelCase      (	self			:       int   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Tuple=None		)						->    Any:
       _lowercase							     =       self.hash_function(key + 1		)

       while self.values[new_key] is not None and self.values[new_key] != key:
          if self.values.count(_lowercase		) > 0:
             _lowercase							     =       self.hash_function(new_key + 1		)
          else:
             _lowercase							     =       None
             break

       return new_key
    def 		_lowerCamelCase      (	self			:       str		)						->    Any:
       _lowercase							     =       [value for value in self.values if value is not None]
       _lowercase							     =       next_prime(self.size_table   ,						factor=2		)
       self._keys.clear()
       _lowercase							     =       [None] * self.size_table  # hell's pointers D: don't DRY ;/
       for value in survivor_values:
          self.insert_data(_lowercase		)




    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Tuple		)						->    Optional[int]:
       _lowercase							     =       self.hash_function(_lowercase		)

       if self.values[key] is None:
          self._set_value(_lowercase   ,						_lowercase		)

       elif self.values[key] == data:
          pass

       else:
          _lowercase							     =       self._collision_resolution(_lowercase   ,						_lowercase		)
          if collision_resolution is not None:
             self._set_value(_lowercase   ,						_lowercase		)
          else:
             self.rehashing()
             self.insert_data(_lowercase		)