





import warnings

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

class    lowerCAmelCase							(					UpperCamelCase__			):
 UpperCAmelCase__													=		["""image_processor""", """tokenizer"""]
 UpperCAmelCase__													=		"""CLIPImageProcessor"""
 UpperCAmelCase__													=		("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")
 def __init__(      self  :      Any			,     UpperCAmelCase  :      Any=None			,     UpperCAmelCase  :      Dict=None			,     **UpperCAmelCase  :      Dict	)			->	Optional[int]:
     lowerCamelCase__    :      List[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.'			,     lowercase_			,     )
         lowerCamelCase__    :      List[Any]					 =		kwargs.pop('feature_extractor'	)

     lowerCamelCase__    :      Dict					 =		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__(lowercase_			,     lowercase_	)
 def __call__(      self  :      Optional[int]			,     UpperCAmelCase  :      str=None			,     UpperCAmelCase  :      Any=None			,     UpperCAmelCase  :      Dict=None			,     **UpperCAmelCase  :      Optional[Any]	)			->	Tuple:

     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:
         lowerCamelCase__    :      Union[str, Any]					 =		self.tokenizer(lowercase_			,     return_tensors=lowercase_			,     **lowercase_	)

     if images is not None:
         lowerCamelCase__    :      List[str]					 =		self.image_processor(lowercase_			,     return_tensors=lowercase_			,     **lowercase_	)

     if text is not None and images is not None:
         lowerCamelCase__    :      List[str]					 =		image_features.pixel_values
         return encoding
     elif text is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**lowercase_	)			,     tensor_type=lowercase_	)
 def      A_	(      self  :      Tuple			,     *UpperCAmelCase  :      str			,     **UpperCAmelCase  :      Optional[int]	)			->	Any:
     return self.tokenizer.batch_decode(*lowercase_			,     **lowercase_	)
 def      A_	(      self  :      Dict			,     *UpperCAmelCase  :      Union[str, Any]			,     **UpperCAmelCase  :      Optional[Any]	)			->	Tuple:
     return self.tokenizer.decode(*lowercase_			,     **lowercase_	)







 @property
 def      A_	(      self  :      Any	)			->	List[Any]:
     lowerCamelCase__    :      Union[str, Any]					 =		self.tokenizer.model_input_names
     lowerCamelCase__    :      str					 =		self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)












'''simple docstring'''

from __future__ import annotations






class    UpperCAmelCase	:
 def __init__(				self      :Optional[int]						,       lowercase_      :int							)->							None:
     A__				=			order

     # a_{0} ... a_{k}
     A__				=			[1.0] + [0.0] * order
     # b_{0} ... b_{k}
     A__				=			[1.0] + [0.0] * order

     # x[n-1] ... x[n-k]
     A__				=			[0.0] * self.order
     # y[n-1] ... y[n-k]
     A__				=			[0.0] * self.order
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :list[float]						,       lowercase_      :list[float]							)->							None:
     if len(lowercase_							) < self.order:
         A__				=			[1.0, *a_coeffs]

     if len(lowercase_							) != self.order + 1:
         A__				=			(
             F"Expected a_coeffs to have {self.order + 1} elements "
             F"for {self.order}-order filter, got {len(lowercase_							)}"
         )
         raise ValueError(lowercase_							)

     if len(lowercase_							) != self.order + 1:
         A__				=			(
             F"Expected b_coeffs to have {self.order + 1} elements "
             F"for {self.order}-order filter, got {len(lowercase_							)}"
         )
         raise ValueError(lowercase_							)

     A__				=			a_coeffs
     A__				=			b_coeffs



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :float							)->							float:
     A__				=			0.0

     # Start at index 1 and do index 0 at the end.
     for i in range(1						,       self.order + 1							):
         result += (
             self.b_coeffs[i] * self.input_history[i - 1]
             - self.a_coeffs[i] * self.output_history[i - 1]
         )

     A__				=			(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

     A__				=			self.input_history[:-1]
     A__				=			self.output_history[:-1]

     A__				=			sample
     A__				=			result

     return result



