


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 		__magic_name__						(lowercase_				):





     '''simple docstring'''



     __lowercase				:							List[Any]	   =       ['image_processor', 'tokenizer']
     __lowercase				:							Optional[Any]	   =       'CLIPImageProcessor'
     __lowercase				:							Tuple	   =       ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')

     def __init__(					self:str     ,		_a:Optional[Any]=None     ,		_a:List[str]=None     ,		**_a:List[str]   ):
       snake_case__						= 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     ,		)
         snake_case__						= kwargs.pop('''feature_extractor'''   )

       snake_case__						= 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   )

     def __call__(					self:Tuple     ,		_a:Optional[int]=None     ,		_a:Any=None     ,		_a:int=None     ,		**_a:Any   ):

       if text is None and images is None:
         raise ValueError('''You have to specify either text or images. Both cannot be none.'''   )

       if text is not None:
         snake_case__						= self.tokenizer(_lowercase     ,		return_tensors=_lowercase     ,		**_lowercase   )

       if images is not None:
         snake_case__						= self.image_processor(_lowercase     ,		return_tensors=_lowercase     ,		**_lowercase   )

       if text is not None and images is not None:
         snake_case__						= image_features.pixel_values
         return encoding
       elif text is not None:
         return encoding
       else:
         return BatchEncoding(data=dict(**_lowercase   )     ,		tensor_type=_lowercase   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		*_a:Optional[int]     ,		**_a:int   ):
       return self.tokenizer.batch_decode(*_lowercase     ,		**_lowercase   )

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		*_a:List[str]     ,		**_a:Tuple   ):
       return self.tokenizer.decode(*_lowercase     ,		**_lowercase   )







     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.tokenizer.model_input_names
       snake_case__						= self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )




'''simple docstring'''

import os

# Precomputes a list of the 100 first triangular numbers
SCREAMING_SNAKE_CASE							:				List[Any]     	=			[int(0.5 * n * (n + 1)) for n in range(1, 101)]




def 							_UpperCamelCase				(   )      -> List[Any]:
							SCREAMING_SNAKE_CASE_				=				os.path.dirname(os.path.realpath(lowerCAmelCase__   )   )
							SCREAMING_SNAKE_CASE_				=				os.path.join(lowerCAmelCase__		,'words.txt'   )

							SCREAMING_SNAKE_CASE_				=				''
							with open(lowerCAmelCase__   ) as f:
														SCREAMING_SNAKE_CASE_				=				f.readline()

							SCREAMING_SNAKE_CASE_				=				[word.strip('"'   ) for word in words.strip('\r\n'   ).split(','   )]
							SCREAMING_SNAKE_CASE_				=				[
							    word
							    for word in [sum(ord(lowerCAmelCase__   ) - 64 for x in word   ) for word in words]
							    if word in TRIANGULAR_NUMBERS
							]
							return len(lowerCAmelCase__   )


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





