



'''simple docstring'''





from __future__ import annotations
def   _SCREAMING_SNAKE_CASE    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase     ):



    """simple docstring"""





    if days_between_payments <= 0:
        raise ValueError("""days_between_payments must be > 0"""     )
    if daily_interest_rate < 0:
        raise ValueError("""daily_interest_rate must be >= 0"""     )
    if principal <= 0:
        raise ValueError("""principal must be > 0"""     )
    return principal * daily_interest_rate * days_between_payments
def   _SCREAMING_SNAKE_CASE    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       ):



    """simple docstring"""





    if number_of_compounding_periods <= 0:
        raise ValueError("""number_of_compounding_periods must be > 0"""     )
    if nominal_annual_interest_rate_percentage < 0:
        raise ValueError("""nominal_annual_interest_rate_percentage must be >= 0"""     )
    if principal <= 0:
        raise ValueError("""principal must be > 0"""     )

    return principal * (
        (1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
        - 1
    )
def   _SCREAMING_SNAKE_CASE    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       ):



    """simple docstring"""





    if number_of_years <= 0:
        raise ValueError("""number_of_years must be > 0"""     )
    if nominal_annual_percentage_rate < 0:
        raise ValueError("""nominal_annual_percentage_rate must be >= 0"""     )
    if principal <= 0:
        raise ValueError("""principal must be > 0"""     )

    return compound_interest(
        _snake_case				,       nominal_annual_percentage_rate / 365				,       number_of_years * 365     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()








"""simple docstring"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Any     =      ['''image_processor''', '''tokenizer''']
 UpperCAmelCase       :	Optional[int]     =      '''ViTImageProcessor'''
 UpperCAmelCase       :	int     =      ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

 def __init__(					self			:			Tuple       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			Tuple=None       ,				**_UpperCAmelCase			:			Dict						):
       _A     =  None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'       ,				_UpperCAmelCase       ,				)
             _A     =  kwargs.pop('feature_extractor'						)

       _A     =  image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'						)
       if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'						)

       super().__init__(_UpperCAmelCase       ,				_UpperCAmelCase						)

 def __call__(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			Optional[Any]=None       ,				_UpperCAmelCase			:			Optional[int]=None       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       if text is None and visual_prompt is None and images is None:
             raise ValueError('You have to specify either text, visual prompt or images.'						)

       if text is not None and visual_prompt is not None:
             raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'						)

       if text is not None:
             _A     =  self.tokenizer(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if visual_prompt is not None:
             _A     =  self.image_processor(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if images is not None:
             _A     =  self.image_processor(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if visual_prompt is not None and images is not None:
             _A     =  {
                 'pixel_values': image_features.pixel_values,
                 'conditional_pixel_values': prompt_features.pixel_values,
             }
             return encoding
       elif text is not None and images is not None:
             _A     =  image_features.pixel_values
             return encoding
       elif text is not None:
             return encoding
       elif visual_prompt is not None:
             _A     =  {
                 'conditional_pixel_values': prompt_features.pixel_values,
             }
             return encoding
       else:
             return BatchEncoding(data=dict(**_UpperCAmelCase						)       ,				tensor_type=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			str       ,				*_UpperCAmelCase			:			List[Any]       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       return self.tokenizer.batch_decode(*_UpperCAmelCase       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				*_UpperCAmelCase			:			List[str]       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       return self.tokenizer.decode(*_UpperCAmelCase       ,				**_UpperCAmelCase						)

 @property
 def      lowerCAmelCase_				(					self			:			Dict						):
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,				_UpperCAmelCase       ,				)
       return self.image_processor_class



 @property
 def      lowerCAmelCase_				(					self			:			Tuple						):
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       ,				_UpperCAmelCase       ,				)
       return self.image_processor

