





'''simple docstring'''




def 	UpperCAmelCase_							(					__lowercase							:      str			,     __lowercase							:      Any			,     __lowercase							:      int       )		->       Dict:


     '''simple docstring'''



     if exponent == 1:
          return base
     if exponent % 2 == 0:
          _UpperCAmelCase     							=   _modexpt(lowerCamelCase__			,     exponent // 2			,     lowerCamelCase__       ) % modulo_value
          return (x * x) % modulo_value
     else:
          return (base * _modexpt(lowerCamelCase__			,     exponent - 1			,     lowerCamelCase__       )) % modulo_value
def 	UpperCAmelCase_							(					__lowercase							:      Optional[Any] = 1777			,     __lowercase							:      Dict = 1855			,     __lowercase							:      str = 8       )		->       Any:


     '''simple docstring'''



     _UpperCAmelCase     							=   base
     for _ in range(1			,     lowerCamelCase__       ):
          _UpperCAmelCase     							=   _modexpt(lowerCamelCase__			,     lowerCamelCase__			,     10**digits       )

     return result


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









'''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							(			__UpperCAmelCase ):
	__SCREAMING_SNAKE_CASE   :		Any					=					['image_processor', 'tokenizer']
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					'BlipImageProcessor'
	__SCREAMING_SNAKE_CASE   :		List[Any]					=					('BertTokenizer', 'BertTokenizerFast')


	def __init__(self       ,      lowercase       ,      lowercase							):
				A_	:						List[Any]	=     False
				super().__init__(lowercase       ,      lowercase							)
				A_	:						Tuple	=     self.image_processor


	def __call__(self       ,      lowercase = None       ,      lowercase = None       ,      lowercase = True       ,      lowercase = False       ,      lowercase = None       ,      lowercase = None       ,      lowercase = 0       ,      lowercase = None       ,      lowercase = None       ,      lowercase = False       ,      lowercase = False       ,      lowercase = False       ,      lowercase = False       ,      lowercase = False       ,      lowercase = True       ,      lowercase = None       ,      **lowercase       ,      ):
				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_	:						Optional[Any]	=     self.tokenizer
							A_	:						Tuple	=     self.tokenizer(
							    text=lowercase       ,      add_special_tokens=lowercase       ,      padding=lowercase       ,      truncation=lowercase       ,      max_length=lowercase       ,      stride=lowercase       ,      pad_to_multiple_of=lowercase       ,      return_attention_mask=lowercase       ,      return_overflowing_tokens=lowercase       ,      return_special_tokens_mask=lowercase       ,      return_offsets_mapping=lowercase       ,      return_token_type_ids=lowercase       ,      return_length=lowercase       ,      verbose=lowercase       ,      return_tensors=lowercase       ,      **lowercase       ,      )
							return text_encoding

				# add pixel_values
				A_	:						int	=     self.image_processor(lowercase       ,      return_tensors=lowercase							)

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

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

				return encoding_image_processor


	def     _a	(self       ,      *lowercase       ,      **lowercase							):
				return self.tokenizer.batch_decode(*lowercase       ,      **lowercase							)


	def     _a	(self       ,      *lowercase       ,      **lowercase							):
				return self.tokenizer.decode(*lowercase       ,      **lowercase							)


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