





"""simple docstring"""






import warnings
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							):







					'''simple docstring'''




					__lowerCAmelCase  =  ['''image_processor''', '''tokenizer''']
					__lowerCAmelCase  =  '''FlavaImageProcessor'''
					__lowerCAmelCase  =  ('''BertTokenizer''', '''BertTokenizerFast''')







					def __init__(						self	,			_UpperCAmelCase=None	,			_UpperCAmelCase=None	,			**_UpperCAmelCase     ):
									__a :						str			       =  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 :						Optional[Any]			       =  kwargs.pop('''feature_extractor'''     )

									__a :						Tuple			       =  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     )
									__a :						Optional[Any]			       =  self.image_processor







					def __call__(						self	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = True	,			_UpperCAmelCase = False	,			_UpperCAmelCase = False	,			_UpperCAmelCase = None	,			_UpperCAmelCase = 0	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = False	,			_UpperCAmelCase = False	,			_UpperCAmelCase = False	,			_UpperCAmelCase = False	,			_UpperCAmelCase = True	,			_UpperCAmelCase = None	,			**_UpperCAmelCase	,			):

									if text is None and images is None:
													raise ValueError('''You have to specify either text or images. Both cannot be none.'''     )

									if text is not None:
													__a :						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_token_type_ids=_UpperCAmelCase	,			return_attention_mask=_UpperCAmelCase	,			return_overflowing_tokens=_UpperCAmelCase	,			return_special_tokens_mask=_UpperCAmelCase	,			return_offsets_mapping=_UpperCAmelCase	,			return_length=_UpperCAmelCase	,			verbose=_UpperCAmelCase	,			return_tensors=_UpperCAmelCase	,			**_UpperCAmelCase	,			)
									if images is not None:
													__a :						Any			       =  self.image_processor(
													    _UpperCAmelCase	,			return_image_mask=_UpperCAmelCase	,			return_codebook_pixels=_UpperCAmelCase	,			return_tensors=_UpperCAmelCase	,			**_UpperCAmelCase	,			)

									if text is not None and images is not None:
													encoding.update(_UpperCAmelCase     )
													return encoding
									elif text is not None:
													return encoding
									else:
													return BatchEncoding(data=dict(**_UpperCAmelCase     )	,			tensor_type=_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			*_UpperCAmelCase	,			**_UpperCAmelCase     ):
									return self.tokenizer.batch_decode(*_UpperCAmelCase	,			**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			*_UpperCAmelCase	,			**_UpperCAmelCase     ):
									return self.tokenizer.decode(*_UpperCAmelCase	,			**_UpperCAmelCase     )







					@property
					def  _lowerCamelCase		(						self     ):
									__a :						List[str]			       =  self.tokenizer.model_input_names
									__a :						int			       =  self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )







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






"""simple docstring"""






def     __A      (			a_						:float   ,       a_						:float)		->		float:
				return price * (1 + tax_rate)


if __name__ == "__main__":
		print(F'{price_plus_tax(100, 0.25) = }')
		print(F'{price_plus_tax(125.50, 0.05) = }')