





"""simple docstring"""






from typing import List, Optional, Union

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  =  '''Pix2StructImageProcessor'''
					__lowerCAmelCase  =  ('''T5Tokenizer''', '''T5TokenizerFast''')







					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						List[Any]			       =  False
									super().__init__(_UpperCAmelCase	,			_UpperCAmelCase     )







					def __call__(						self	,			_UpperCAmelCase=None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = True	,			_UpperCAmelCase = False	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = 2048	,			_UpperCAmelCase = 0	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = False	,			_UpperCAmelCase = False	,			_UpperCAmelCase = False	,			_UpperCAmelCase = False	,			_UpperCAmelCase = False	,			_UpperCAmelCase = True	,			_UpperCAmelCase = None	,			**_UpperCAmelCase	,			):
									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 and not self.image_processor.is_vqa:
													__a :						int			       =  self.tokenizer
													__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_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

									if not self.image_processor.is_vqa:
													# add pixel_values
													__a :						Optional[Any]			       =  self.image_processor(
													    _UpperCAmelCase	,			return_tensors=_UpperCAmelCase	,			max_patches=_UpperCAmelCase	,			**_UpperCAmelCase     )
									else:
													# add pixel_values and bbox
													__a :						Dict			       =  self.image_processor(
													    _UpperCAmelCase	,			return_tensors=_UpperCAmelCase	,			max_patches=_UpperCAmelCase	,			header_text=_UpperCAmelCase	,			**_UpperCAmelCase     )

									if text is not None and not self.image_processor.is_vqa:
													__a :						Dict			       =  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	,			)

													if "attention_mask" in text_encoding:
																	__a :						List[str]			       =  text_encoding.pop('''attention_mask'''     )
													if "input_ids" in text_encoding:
																	__a :						Dict			       =  text_encoding.pop('''input_ids'''     )
									else:
													__a :						Optional[Any]			       =  None

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

									return encoding_image_processor







					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 :						Optional[int]			       =  self.tokenizer.model_input_names
									__a :						Dict			       =  self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )






"""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) = }')