





"""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 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


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

 def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Union[str, Any]									=					False
     super().__init__(__lowerCamelCase ,						__lowerCamelCase  )
     __A   :						Dict									=					self.image_processor

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



     '''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:
         __A   :						Union[str, Any]									=					self.tokenizer
         __A   :						List[Any]									=					self.tokenizer(
             text=__lowerCamelCase ,						add_special_tokens=__lowerCamelCase ,						padding=__lowerCamelCase ,						truncation=__lowerCamelCase ,						max_length=__lowerCamelCase ,						stride=__lowerCamelCase ,						pad_to_multiple_of=__lowerCamelCase ,						return_attention_mask=__lowerCamelCase ,						return_overflowing_tokens=__lowerCamelCase ,						return_special_tokens_mask=__lowerCamelCase ,						return_offsets_mapping=__lowerCamelCase ,						return_token_type_ids=__lowerCamelCase ,						return_length=__lowerCamelCase ,						verbose=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase ,						)
         return text_encoding

     # add pixel_values
     __A   :						Any									=					self.image_processor(__lowerCamelCase ,						return_tensors=__lowerCamelCase  )

     if text is not None:
         __A   :						Optional[Any]									=					self.tokenizer(
             text=__lowerCamelCase ,						add_special_tokens=__lowerCamelCase ,						padding=__lowerCamelCase ,						truncation=__lowerCamelCase ,						max_length=__lowerCamelCase ,						stride=__lowerCamelCase ,						pad_to_multiple_of=__lowerCamelCase ,						return_attention_mask=__lowerCamelCase ,						return_overflowing_tokens=__lowerCamelCase ,						return_special_tokens_mask=__lowerCamelCase ,						return_offsets_mapping=__lowerCamelCase ,						return_token_type_ids=__lowerCamelCase ,						return_length=__lowerCamelCase ,						verbose=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase ,						)
     else:
         __A   :						Any									=					None

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

     return encoding_image_processor

 def  UpperCamelCase__(   self ,						*__lowerCamelCase ,						**__lowerCamelCase  ):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						*__lowerCamelCase ,						**__lowerCamelCase  ):



     '''simple docstring'''




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







 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						str									=					self.tokenizer.model_input_names
     __A   :						str									=					self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )











"""simple docstring"""



from decimal import Decimal, getcontext
from math import ceil, factorial







def 		__lowercase					(       snake_case_  :  int						)       ->str:



    '''simple docstring'''







    if not isinstance(snake_case_    ,snake_case_						):
        raise TypeError('''Undefined for non-integers'''						)
    elif precision < 1:
        raise ValueError('''Undefined for non-natural numbers'''						)

    __A   :						int									=					precision
    __A   :						Tuple									=					ceil(precision / 14						)
    __A   :						Dict									=					426880 * Decimal(10005						).sqrt()
    __A   :						Optional[Any]									=					1
    __A   :						int									=					13591409
    __A   :						Optional[int]									=					Decimal(snake_case_						)
    for k in range(1    ,snake_case_						):
        __A   :						int									=					factorial(6 * k						) // (factorial(3 * k						) * factorial(snake_case_						) ** 3)
        linear_term += 545140134
        exponential_term *= -262537412640768000
        partial_sum += Decimal(multinomial_term * linear_term						) / exponential_term
    return str(constant_term / partial_sum						)[:-1]


if __name__ == "__main__":
       a_			=      50
       print(f'''The first {n} digits of pi is: {pi(n)}''')




