






from typing import List, Optional, Union

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




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Any 				= ["image_processor", "tokenizer"]
       lowerCamelCase							:				List[str] 				= "Pix2StructImageProcessor"
       lowerCamelCase							:				int 				= ("T5Tokenizer", "T5TokenizerFast")



       def __init__(							self			,						a__			,						a__    ) ->      str:






            '''simple docstring'''





            __snake_case			:Dict          =						False
            super().__init__(a__			,						a__    )



       def __call__(							self			,						a__=None			,						a__ = None			,						a__ = True			,						a__ = False			,						a__ = None			,						a__ = None			,						a__ = 20_48			,						a__ = 0			,						a__ = None			,						a__ = None			,						a__ = False			,						a__ = False			,						a__ = False			,						a__ = False			,						a__ = False			,						a__ = True			,						a__ = None			,						**a__			,						) ->      BatchEncoding:






            '''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 and not self.image_processor.is_vqa:
                 __snake_case			:List[Any]          =						self.tokenizer
                 __snake_case			:Optional[Any]          =						self.tokenizer(
                     text=a__			,						add_special_tokens=a__			,						padding=a__			,						truncation=a__			,						max_length=a__			,						stride=a__			,						pad_to_multiple_of=a__			,						return_attention_mask=a__			,						return_overflowing_tokens=a__			,						return_special_tokens_mask=a__			,						return_offsets_mapping=a__			,						return_token_type_ids=a__			,						return_length=a__			,						verbose=a__			,						return_tensors=a__			,						**a__			,						)
                 return text_encoding

            if not self.image_processor.is_vqa:
                 # add pixel_values
                 __snake_case			:Tuple          =						self.image_processor(
                     a__			,						return_tensors=a__			,						max_patches=a__			,						**a__    )
            else:
                 # add pixel_values and bbox
                 __snake_case			:Optional[Any]          =						self.image_processor(
                     a__			,						return_tensors=a__			,						max_patches=a__			,						header_text=a__			,						**a__    )

            if text is not None and not self.image_processor.is_vqa:
                 __snake_case			:Dict          =						self.tokenizer(
                     text=a__			,						add_special_tokens=a__			,						padding=a__			,						truncation=a__			,						max_length=a__			,						stride=a__			,						pad_to_multiple_of=a__			,						return_attention_mask=a__			,						return_overflowing_tokens=a__			,						return_special_tokens_mask=a__			,						return_offsets_mapping=a__			,						return_token_type_ids=a__			,						return_length=a__			,						verbose=a__			,						return_tensors=a__			,						**a__			,						)

                 if "attention_mask" in text_encoding:
                      __snake_case			:List[str]          =						text_encoding.pop("""attention_mask"""    )
                 if "input_ids" in text_encoding:
                      __snake_case			:List[str]          =						text_encoding.pop("""input_ids"""    )
            else:
                 __snake_case			:int          =						None

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

            return encoding_image_processor



       def 					__lowercase						(							self			,						*a__			,						**a__    ) ->      Optional[Any]:






            '''simple docstring'''





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



       def 					__lowercase						(							self			,						*a__			,						**a__    ) ->      Dict:






            '''simple docstring'''





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



       @property
       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





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










import math

lowerCamelCase__							=     10
lowerCamelCase__							=     7
lowerCamelCase__							=     BALLS_PER_COLOUR * NUM_COLOURS






def   UpperCamelCase  (       snake_case__     :   int = 20  ):




     '''simple docstring'''






     __snake_case			:Dict          =						math.comb(snake_case__	,snake_case__  )
     __snake_case			:int          =						math.comb(NUM_BALLS - BALLS_PER_COLOUR	,snake_case__  )

     __snake_case			:List[str]          =						NUM_COLOURS * (1 - missing_colour / total)

     return f'''{result:.9f}'''


if __name__ == "__main__":
     print(solution(20))


