





'''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    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							Optional[int]         =					["image_processor", "tokenizer"]
 __lowerCamelCase						:							List[Any]         =					"BlipImageProcessor"
 __lowerCamelCase						:							Optional[int]         =					("BertTokenizer", "BertTokenizerFast")




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      False
     super().__init__(_lowerCAmelCase			,     _lowerCAmelCase		)
     _lowerCAmelCase				=      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			,     )    ->							BatchEncoding:
     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:
         _lowerCAmelCase				=      self.tokenizer
         _lowerCAmelCase				=      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
     _lowerCAmelCase				=      self.image_processor(_lowerCAmelCase			,     return_tensors=_lowerCAmelCase		)

     if text is not None:
         _lowerCAmelCase				=      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:
         _lowerCAmelCase				=      None

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

     return encoding_image_processor




 def 							_snake_case   (	self			,     *_lowerCAmelCase			,     **_lowerCAmelCase		)    ->							Dict:
     return self.tokenizer.batch_decode(*_lowerCAmelCase			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self			,     *_lowerCAmelCase			,     **_lowerCAmelCase		)    ->							Dict:
     return self.tokenizer.decode(*_lowerCAmelCase			,     **_lowerCAmelCase		)





 @property
 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.tokenizer.model_input_names
     _lowerCAmelCase				=      self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)











'''simple docstring'''


import math


def 	__a(SCREAMING_SNAKE_CASE_  :					int = 100    ):



    '''simple docstring'''





    _lowerCAmelCase				=      sum(i * i for i in range(1		,       n + 1    )    )
    _lowerCAmelCase				=      int(math.pow(sum(range(1		,       n + 1    )    )		,       2    )    )
    return square_of_sum - sum_of_squares


if __name__ == "__main__":
       print(f'''{solution() = }''')


