
"""simple docstring"""




import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							['image_processor', 'tokenizer']
     _A								=							'BlipImageProcessor'
     _A								=							'AutoTokenizer'



     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Any:
          super().__init__(__UpperCamelCase ,			__UpperCamelCase  )

          # add QFormer tokenizer
          UpperCAmelCase__							: Optional[Any] 							=						qformer_tokenizer



     def __call__(      self ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = True ,			__UpperCamelCase = False ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = 0 ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = False ,			__UpperCamelCase = False ,			__UpperCamelCase = False ,			__UpperCamelCase = False ,			__UpperCamelCase = False ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					BatchFeature:
          if images is None and text is None:
               raise ValueError("You have to specify at least images or text."  )

          UpperCAmelCase__							: Dict 							=						BatchFeature()

          if text is not None:
               UpperCAmelCase__							: List[str] 							=						self.tokenizer(
                   text=__UpperCamelCase ,			add_special_tokens=__UpperCamelCase ,			padding=__UpperCamelCase ,			truncation=__UpperCamelCase ,			max_length=__UpperCamelCase ,			stride=__UpperCamelCase ,			pad_to_multiple_of=__UpperCamelCase ,			return_attention_mask=__UpperCamelCase ,			return_overflowing_tokens=__UpperCamelCase ,			return_special_tokens_mask=__UpperCamelCase ,			return_offsets_mapping=__UpperCamelCase ,			return_token_type_ids=__UpperCamelCase ,			return_length=__UpperCamelCase ,			verbose=__UpperCamelCase ,			return_tensors=__UpperCamelCase ,			**__UpperCamelCase ,			)
               encoding.update(__UpperCamelCase  )
               UpperCAmelCase__							: Union[str, Any] 							=						self.qformer_tokenizer(
                   text=__UpperCamelCase ,			add_special_tokens=__UpperCamelCase ,			padding=__UpperCamelCase ,			truncation=__UpperCamelCase ,			max_length=__UpperCamelCase ,			stride=__UpperCamelCase ,			pad_to_multiple_of=__UpperCamelCase ,			return_attention_mask=__UpperCamelCase ,			return_overflowing_tokens=__UpperCamelCase ,			return_special_tokens_mask=__UpperCamelCase ,			return_offsets_mapping=__UpperCamelCase ,			return_token_type_ids=__UpperCamelCase ,			return_length=__UpperCamelCase ,			verbose=__UpperCamelCase ,			return_tensors=__UpperCamelCase ,			**__UpperCamelCase ,			)
               UpperCAmelCase__							: Any 							=						qformer_text_encoding.pop("input_ids"  )
               UpperCAmelCase__							: Optional[int] 							=						qformer_text_encoding.pop("attention_mask"  )

          if images is not None:
               UpperCAmelCase__							: Tuple 							=						self.image_processor(__UpperCamelCase ,			return_tensors=__UpperCamelCase  )
               encoding.update(__UpperCamelCase  )

          return encoding



     def  lowerCAmelCase__       (      self ,			*__UpperCamelCase ,			**__UpperCamelCase  )->					List[Any]:
          return self.tokenizer.batch_decode(*__UpperCamelCase ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			*__UpperCamelCase ,			**__UpperCamelCase  )->					List[str]:
          return self.tokenizer.decode(*__UpperCamelCase ,			**__UpperCamelCase  )



     @property
     # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          UpperCAmelCase__							: List[str] 							=						self.tokenizer.model_input_names
          UpperCAmelCase__							: List[Any] 							=						self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			**__UpperCamelCase  )->					Optional[int]:
          if os.path.isfile(__UpperCamelCase  ):
               raise ValueError(F"Provided path ({save_directory}) should be a directory, not a file"  )
          os.makedirs(__UpperCamelCase ,			exist_ok=__UpperCamelCase  )
          UpperCAmelCase__							: int 							=						os.path.join(__UpperCamelCase ,			"qformer_tokenizer"  )
          self.qformer_tokenizer.save_pretrained(__UpperCamelCase  )
          return super().save_pretrained(__UpperCamelCase ,			**__UpperCamelCase  )



     @classmethod
     def  lowerCAmelCase__       (      cls ,			__UpperCamelCase ,			**__UpperCamelCase  )->					Optional[Any]:
          UpperCAmelCase__							: Any 							=						AutoTokenizer.from_pretrained(__UpperCamelCase ,			subfolder="qformer_tokenizer"  )
          UpperCAmelCase__							: Tuple 							=						cls._get_arguments_from_pretrained(__UpperCamelCase ,			**__UpperCamelCase  )
          args.append(__UpperCamelCase  )
          return cls(*__UpperCamelCase  )


"""simple docstring"""




from timeit import timeit



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     if number < 0:
          raise ValueError("the value of input must not be negative"		)
     UpperCAmelCase__							: Tuple 							=						0
     while number:
          number &= number - 1
          result += 1
     return result



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     if number < 0:
          raise ValueError("the value of input must not be negative"		)
     UpperCAmelCase__							: Union[str, Any] 							=						0
     while number:
          if number % 2 == 1:
               result += 1
          number >>= 1
     return result



def 							a__ (				):






     '''simple docstring'''

     def do_benchmark(lowerCAmelCase	:       int		) -> None:
          UpperCAmelCase__							: Dict 							=						"import __main__ as z"
          print(F"Benchmark when {number = }:"		)
          print(F"{get_set_bits_count_using_modulo_operator(lowerCAmelCase		) = }"		)
          UpperCAmelCase__							: Tuple 							=						timeit("z.get_set_bits_count_using_modulo_operator(25)"       ,    setup=lowerCAmelCase		)
          print(F"timeit() runs in {timing} seconds"		)
          print(F"{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase		) = }"		)
          UpperCAmelCase__							: Any 							=						timeit(
              "z.get_set_bits_count_using_brian_kernighans_algorithm(25)"       ,    setup=lowerCAmelCase       ,    )
          print(F"timeit() runs in {timing} seconds"		)

     for number in (25, 37, 58, 0):
          do_benchmark(lowerCAmelCase		)
          print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()



