

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  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  ['''image_processor''', '''tokenizer''']
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  '''BlipImageProcessor'''
  SCREAMING_SNAKE_CASE    :      List[Any]       =  '''AutoTokenizer'''

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     False
        super().__init__(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.image_processor

  def __call__(				self ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = True ,					_SCREAMING_SNAKE_CASE = False ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = 0 ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = False ,					_SCREAMING_SNAKE_CASE = False ,					_SCREAMING_SNAKE_CASE = False ,					_SCREAMING_SNAKE_CASE = False ,					_SCREAMING_SNAKE_CASE = False ,					_SCREAMING_SNAKE_CASE = True ,					_SCREAMING_SNAKE_CASE = None ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        if images is None and text is None:
              raise ValueError('You have to specify either images or text.'					)

        # Get only text
        if images is None:
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.tokenizer
              SCREAMING_SNAKE_CASE_				:  Any           =     self.tokenizer(
                  text=_SCREAMING_SNAKE_CASE ,					add_special_tokens=_SCREAMING_SNAKE_CASE ,					padding=_SCREAMING_SNAKE_CASE ,					truncation=_SCREAMING_SNAKE_CASE ,					max_length=_SCREAMING_SNAKE_CASE ,					stride=_SCREAMING_SNAKE_CASE ,					pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,					return_attention_mask=_SCREAMING_SNAKE_CASE ,					return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,					return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,					return_offsets_mapping=_SCREAMING_SNAKE_CASE ,					return_token_type_ids=_SCREAMING_SNAKE_CASE ,					return_length=_SCREAMING_SNAKE_CASE ,					verbose=_SCREAMING_SNAKE_CASE ,					return_tensors=_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE ,					)
              return text_encoding

        # add pixel_values
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.image_processor(_SCREAMING_SNAKE_CASE ,					return_tensors=_SCREAMING_SNAKE_CASE					)

        if text is not None:
              SCREAMING_SNAKE_CASE_				:  str           =     self.tokenizer(
                  text=_SCREAMING_SNAKE_CASE ,					add_special_tokens=_SCREAMING_SNAKE_CASE ,					padding=_SCREAMING_SNAKE_CASE ,					truncation=_SCREAMING_SNAKE_CASE ,					max_length=_SCREAMING_SNAKE_CASE ,					stride=_SCREAMING_SNAKE_CASE ,					pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,					return_attention_mask=_SCREAMING_SNAKE_CASE ,					return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,					return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,					return_offsets_mapping=_SCREAMING_SNAKE_CASE ,					return_token_type_ids=_SCREAMING_SNAKE_CASE ,					return_length=_SCREAMING_SNAKE_CASE ,					verbose=_SCREAMING_SNAKE_CASE ,					return_tensors=_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE ,					)
        else:
              SCREAMING_SNAKE_CASE_				:  Dict           =     None

        if text_encoding is not None:
              encoding_image_processor.update(_SCREAMING_SNAKE_CASE					)

        return encoding_image_processor

  def  UpperCAmelCase    (				self ,					*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self ,					*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







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



  @property
  # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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









def    A_  (							a  ):

      """simple docstring"""



      if upper_limit < 0:
            raise ValueError('Limit for the Catalan sequence must be ≥ 0'  )

      SCREAMING_SNAKE_CASE_				:  List[str]           =     [0] * (upper_limit + 1)

      # Base case: C(0) = C(1) = 1
      SCREAMING_SNAKE_CASE_				:  Any           =     1
      if upper_limit > 0:
            SCREAMING_SNAKE_CASE_				:  int           =     1

      # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
      for i in range(2					,   upper_limit + 1  ):
            for j in range(a  ):
                  catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

      return catalan_list


if __name__ == "__main__":
    print('\n********* Catalan Numbers Using Dynamic Programming ************\n')
    print('\n*** Enter -1 at any time to quit ***')
    print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='')
    try:
        while True:
            lowerCAmelCase     :   Union[str, Any]						     =  int(input().strip())
            if N < 0:
                print('\n********* Goodbye!! ************')
                break
            else:
                print(F'The Catalan numbers from 0 through {N} are:')
                print(catalan_numbers(N))
                print('Try another upper limit for the sequence: ', end='')
    except (NameError, ValueError):
        print('\n********* Invalid input, goodbye! ************\n')

    import doctest

    doctest.testmod()






