
import warnings
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 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     ["image_processor", "tokenizer"]
	snake_case_					    =     "ViltImageProcessor"
	snake_case_					    =     ("BertTokenizer", "BertTokenizerFast")






	def __init__(       self					: Optional[int]							,A					: Optional[int]=None							,A					: Dict=None							,**A					: List[str] ):
					__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."							,A							,)
									__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__(A							,A )
					__A   			= self.image_processor






	def __call__(       self					: Optional[Any]							,A					: Dict							,A					: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,A					: bool = True							,A					: Union[bool, str, PaddingStrategy] = False							,A					: Union[bool, str, TruncationStrategy] = None							,A					: Optional[int] = None							,A					: int = 0							,A					: Optional[int] = None							,A					: Optional[bool] = None							,A					: Optional[bool] = None							,A					: bool = False							,A					: bool = False							,A					: bool = False							,A					: bool = False							,A					: bool = True							,A					: Optional[Union[str, TensorType]] = None							,**A					: Tuple							,):
					__A   			= self.tokenizer(
					    text=A							,add_special_tokens=A							,padding=A							,truncation=A							,max_length=A							,stride=A							,pad_to_multiple_of=A							,return_token_type_ids=A							,return_attention_mask=A							,return_overflowing_tokens=A							,return_special_tokens_mask=A							,return_offsets_mapping=A							,return_length=A							,verbose=A							,return_tensors=A							,**A							,)
					# add pixel_values + pixel_mask
					__A   			= self.image_processor(A							,return_tensors=A )
					encoding.update(A )

					return encoding






	def 				UpperCamelCase_  (       self					: Tuple							,*A					: Optional[Any]							,**A					: Tuple ):
					return self.tokenizer.batch_decode(*A							,**A )






	def 				UpperCamelCase_  (       self					: List[Any]							,*A					: int							,**A					: Dict ):
					return self.tokenizer.decode(*A							,**A )






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






	@property
	def 				UpperCamelCase_  (       self					: str ):
					warnings.warn(
					    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							,A							,)
					return self.image_processor_class







	@property
	def 				UpperCamelCase_  (       self					: Optional[int] ):
					warnings.warn(
					    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,A							,)
					return self.image_processor





'''simple docstring'''






from numpy import exp, pi, sqrt




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_ = 0.0       ,   lowerCAmelCase_ = 1.0		)->		int:





       '''simple docstring'''


       return 1 / sqrt(2 * pi * sigma**2		) * exp(-((x - mu) ** 2) / (2 * sigma**2)		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



