



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			):






   """simple docstring"""





   lowercase__      :  Any      =			["""image_processor""", """tokenizer"""]
   lowercase__      :  int      =			"""BlipImageProcessor"""
   lowercase__      :  Optional[Any]      =			"""AutoTokenizer"""





   def __init__(				self	:       Tuple			,     lowercase	:       Any			,     lowercase	:       Any   )						->					Any:
         """simple docstring"""







         __lowercase					=				False
         super().__init__(lowercase			,     lowercase   )
         __lowercase					=				self.image_processor





   def __call__(				self	:       int			,     lowercase	:       ImageInput = None			,     lowercase	:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,     lowercase	:       bool = True			,     lowercase	:       Union[bool, str, PaddingStrategy] = False			,     lowercase	:       Union[bool, str, TruncationStrategy] = None			,     lowercase	:       Optional[int] = None			,     lowercase	:       int = 0			,     lowercase	:       Optional[int] = None			,     lowercase	:       Optional[bool] = None			,     lowercase	:       bool = False			,     lowercase	:       bool = False			,     lowercase	:       bool = False			,     lowercase	:       bool = False			,     lowercase	:       bool = False			,     lowercase	:       bool = True			,     lowercase	:       Optional[Union[str, TensorType]] = None			,     **lowercase	:       Dict			,     )						->					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:
               __lowercase					=				self.tokenizer
               __lowercase					=				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
         __lowercase					=				self.image_processor(lowercase			,     return_tensors=lowercase   )

         if text is not None:
               __lowercase					=				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:
               __lowercase					=				None

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

         return encoding_image_processor





   def 		snake_case__			(				self	:       List[Any]			,     *lowercase	:       Optional[int]			,     **lowercase	:       List[Any]   )						->					str:
         """simple docstring"""







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





   def 		snake_case__			(				self	:       Optional[int]			,     *lowercase	:       List[Any]			,     **lowercase	:       str   )						->					str:
         """simple docstring"""







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







   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def 		snake_case__			(				self	:       Optional[int]   )						->					Tuple:
         """simple docstring"""







         __lowercase					=				self.tokenizer.model_input_names
         __lowercase					=				self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )










def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__   ) ->      float:
						__lowercase					=				(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
						# formula for sum of series
						return total






def  UpperCAmelCase__    (    ) ->      List[str]:
						print(sum_of_series(1						,       1						,       10   )   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





