



'''simple docstring'''


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  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  ["""image_processor""", """tokenizer"""]
    _SCREAMING_SNAKE_CASE	  =  """BlipImageProcessor"""
    _SCREAMING_SNAKE_CASE	  =  ("""BertTokenizer""", """BertTokenizerFast""")






    def __init__(  self      :					Optional[int]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  False
       super().__init__(UpperCamelCase__  ,						UpperCamelCase__ )
       UpperCamelCase					=  self.image_processor






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


       """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:
          UpperCamelCase					=  self.tokenizer
          UpperCamelCase					=  self.tokenizer(
              text=UpperCamelCase__  ,						add_special_tokens=UpperCamelCase__  ,						padding=UpperCamelCase__  ,						truncation=UpperCamelCase__  ,						max_length=UpperCamelCase__  ,						stride=UpperCamelCase__  ,						pad_to_multiple_of=UpperCamelCase__  ,						return_attention_mask=UpperCamelCase__  ,						return_overflowing_tokens=UpperCamelCase__  ,						return_special_tokens_mask=UpperCamelCase__  ,						return_offsets_mapping=UpperCamelCase__  ,						return_token_type_ids=UpperCamelCase__  ,						return_length=UpperCamelCase__  ,						verbose=UpperCamelCase__  ,						return_tensors=UpperCamelCase__  ,						**UpperCamelCase__  ,						)
          return text_encoding

       # add pixel_values
       UpperCamelCase					=  self.image_processor(UpperCamelCase__  ,						return_tensors=UpperCamelCase__ )

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

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

       return encoding_image_processor






    def  A       (  self      :					Tuple  ,						*UpperCamelCase__      :					Tuple  ,						**UpperCamelCase__      :					Any ):


       """simple docstring"""







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






    def  A       (  self      :					Union[str, Any]  ,						*UpperCamelCase__      :					Tuple  ,						**UpperCamelCase__      :					Dict ):


       """simple docstring"""







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






    @property
    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  self.tokenizer.model_input_names
       UpperCamelCase					=  self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )




from math import factorial

def 						UpperCamelCase					(lowercase_: int = 20					)     ->      int:
	A__							:    Union[str, Any]        =		2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
	# 2, 3,...
	A__							:    Tuple        =		n // 2

	return int(factorial(lowercase_					) / (factorial(lowercase_					) * factorial(n - k					))					)


if __name__ == "__main__":
	import sys

	if len(sys.argv) == 1:
		print(solution(20))
	else:
		try:
			A_       : List[str]         =			int(sys.argv[1])
			print(solution(n))
		except ValueError:
			print('Invalid entry - please enter a number.')



