






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 							_UpperCamelCase			(a_			):
      snake_case_  =							["""image_processor""", """tokenizer"""]
      snake_case_  =							"""BlipImageProcessor"""
      snake_case_  =							("""BertTokenizer""", """BertTokenizerFast""")


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase	)->	int:
           __lowerCAmelCase								=					False
           super().__init__(__UpperCamelCase					,						__UpperCamelCase	)
           __lowerCAmelCase								=					self.image_processor


      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					,						)->	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=__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					,						)
                return text_encoding

           # add pixel_values
           __lowerCAmelCase								=					self.image_processor(__UpperCamelCase					,						return_tensors=__UpperCamelCase	)

           if text is not None:
                __lowerCAmelCase								=					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					,						)
           else:
                __lowerCAmelCase								=					None

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

           return encoding_image_processor


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


      def  __UpperCAmelCase		(	self					,						*__UpperCamelCase					,						**__UpperCamelCase	)->	Tuple:
           return self.tokenizer.decode(*__UpperCamelCase					,						**__UpperCamelCase	)


      @property
      def  __UpperCAmelCase		(	self	)->	Tuple:
           __lowerCAmelCase								=					self.tokenizer.model_input_names
           __lowerCAmelCase								=					self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)














from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
lowerCamelCase			:					int         =	1.0_5457_1817E-34  # unit of ℏ : J * s

lowerCamelCase			:					Union[str, Any]         =	3E8  # unit of c : m * s^-1





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):

     if (force, area, distance).count(0       ) != 1:
          raise ValueError("One and only one argument must be 0"       )
     if force < 0:
          raise ValueError("Magnitude of force can not be negative"       )
     if distance < 0:
          raise ValueError("Distance can not be negative"       )
     if area < 0:
          raise ValueError("Area can not be negative"       )
     if force == 0:
          __lowerCAmelCase								=					(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
              240 * (distance) ** 4
          )
          return {"force": force}
     elif area == 0:
          __lowerCAmelCase								=					(240 * force * (distance) ** 4) / (
              REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
          )
          return {"area": area}
     elif distance == 0:
          __lowerCAmelCase								=					(
              (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
          ) ** (1 / 4)
          return {"distance": distance}
     raise ValueError("One and only one argument must be 0"       )


# Run doctest
if __name__ == "__main__":
 import doctest

 doctest.testmod()






