




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 						lowercase							(   UpperCamelCase__		):
     _a							 =						["image_processor", "tokenizer"]
     _a							 =						"BlipImageProcessor"
     _a							 =						("BertTokenizer", "BertTokenizerFast")

     def __init__(  self		,     _a		,     _a				)						->							int:
           _A							:     Union[str, Any]			=						False
           super().__init__(_a		,     _a				)
           _A							:     int			=						self.image_processor

     def __call__(  self		,     _a = None		,     _a = None		,     _a = True		,     _a = False		,     _a = None		,     _a = None		,     _a = 0		,     _a = None		,     _a = None		,     _a = False		,     _a = False		,     _a = False		,     _a = False		,     _a = False		,     _a = True		,     _a = None		,     **_a		,     )						->							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:
                 _A							:     Dict			=						self.tokenizer
                 _A							:     Optional[int]			=						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

           # add pixel_values
           _A							:     Any			=						self.image_processor(_a		,     return_tensors=_a				)

           if text is not None:
                 _A							:     List[str]			=						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		,     )
           else:
                 _A							:     Dict			=						None

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

           return encoding_image_processor

     def 					a__	(  self		,     *_a		,     **_a				)						->							int:
           return self.tokenizer.batch_decode(*_a		,     **_a				)

     def 					a__	(  self		,     *_a		,     **_a				)						->							Optional[int]:
           return self.tokenizer.decode(*_a		,     **_a				)



     @property
     def 					a__	(  self				)						->							int:
           _A							:     Dict			=						self.tokenizer.model_input_names
           _A							:     Tuple			=						self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)







def 				lowerCAmelCase_	(     snake_case_     ):
      if n_term == "":
            return []
      _A							:     list			=						[]
      for temp in range(int(snake_case_     )     ):
            series.append(f'''1/{temp + 1}''' if series else """1"""     )
      return series


if __name__ == "__main__":
  _snake_case										=       input("Enter the last number (nth term) of the Harmonic Series")
  print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
  print(harmonic_series(nth_term))

