






'''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	(				_lowercase		):
							a				      =       ["""image_processor""", """tokenizer"""]
							a				      =       """BridgeTowerImageProcessor"""
							a				      =       ("""RobertaTokenizer""", """RobertaTokenizerFast""")




							def __init__(							self:				Optional[Any]			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Tuple     ):
														super().__init__(UpperCamelCase__			,							UpperCamelCase__     )




							def __call__(							self:				Union[str, Any]			,							UpperCamelCase__:				Any			,							UpperCamelCase__:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,							UpperCamelCase__:				bool = True			,							UpperCamelCase__:				Union[bool, str, PaddingStrategy] = False			,							UpperCamelCase__:				Union[bool, str, TruncationStrategy] = None			,							UpperCamelCase__:				Optional[int] = None			,							UpperCamelCase__:				int = 0			,							UpperCamelCase__:				Optional[int] = None			,							UpperCamelCase__:				Optional[bool] = None			,							UpperCamelCase__:				Optional[bool] = None			,							UpperCamelCase__:				bool = False			,							UpperCamelCase__:				bool = False			,							UpperCamelCase__:				bool = False			,							UpperCamelCase__:				bool = False			,							UpperCamelCase__:				bool = True			,							UpperCamelCase__:				Optional[Union[str, TensorType]] = None			,							**UpperCamelCase__:				Any			,							):
														lowerCamelCase__					:	int         =       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__			,							)
														# add pixel_values + pixel_mask
														lowerCamelCase__					:	List[str]         =       self.image_processor(
														    UpperCamelCase__			,							return_tensors=UpperCamelCase__			,							do_normalize=UpperCamelCase__			,							do_center_crop=UpperCamelCase__			,							**UpperCamelCase__     )
														encoding.update(UpperCamelCase__     )

														return encoding




							def 		lowerCamelCase_				(							self:				List[Any]			,							*UpperCamelCase__:				Dict			,							**UpperCamelCase__:				Optional[int]     ):
														return self.tokenizer.batch_decode(*UpperCamelCase__			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Tuple			,							*UpperCamelCase__:				Tuple			,							**UpperCamelCase__:				int     ):
														return self.tokenizer.decode(*UpperCamelCase__			,							**UpperCamelCase__     )




							@property
							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	Dict         =       self.tokenizer.model_input_names
														lowerCamelCase__					:	Optional[int]         =       self.image_processor.model_input_names
														return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )









'''simple docstring'''







from collections.abc import Sequence





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       float:
							return sum(c * (x**i) for i, c in enumerate(UpperCamelCase      )      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       float:
							lowerCamelCase__					:	str         =       0.0
							for coeff in reversed(UpperCamelCase      ):
														lowerCamelCase__					:	Optional[int]         =       result * x + coeff
							return result


if __name__ == "__main__":
			_A	:			Any         =(0.0, 0.0, 5.0, 9.3, 7.0)
			_A	:			Optional[Any]         =10.0
			print(evaluate_poly(poly, x))
			print(horner(poly, x))

