





"""simple docstring"""





from __future__ import annotations






class    _lowerCAmelCase  :
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				order

         # a_{0} ... a_{k}
         lowerCAmelCase__     :Any											=				[1.0] + [0.0] * order
         # b_{0} ... b_{k}
         lowerCAmelCase__     :int											=				[1.0] + [0.0] * order

         # x[n-1] ... x[n-k]
         lowerCAmelCase__     :str											=				[0.0] * self.order
         # y[n-1] ... y[n-k]
         lowerCAmelCase__     :Optional[int]											=				[0.0] * self.order






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         if len(__UpperCAmelCase ) < self.order:
           lowerCAmelCase__     :Any											=				[1.0, *a_coeffs]

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

         if len(__UpperCAmelCase ) != self.order + 1:
           lowerCAmelCase__     :List[Any]											=				(
               F"Expected b_coeffs to have {self.order + 1} elements "
               F"for {self.order}-order filter, got {len(__UpperCAmelCase )}"
           )
           raise ValueError(__UpperCAmelCase )

         lowerCAmelCase__     :Optional[int]											=				a_coeffs
         lowerCAmelCase__     :Optional[Any]											=				b_coeffs






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				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]
           )

         lowerCAmelCase__     :Union[str, Any]											=				(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

         lowerCAmelCase__     :str											=				self.input_history[:-1]
         lowerCAmelCase__     :List[Any]											=				self.output_history[:-1]

         lowerCAmelCase__     :str											=				sample
         lowerCAmelCase__     :Tuple											=				result

         return result

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      ["""image_processor""", """tokenizer"""]
  _SCREAMING_SNAKE_CASE     					=      """ChineseCLIPImageProcessor"""
  _SCREAMING_SNAKE_CASE     					=      ("""BertTokenizer""", """BertTokenizerFast""")

  def __init__(		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Any=None  ,				__SCREAMING_SNAKE_CASE			:   Tuple=None  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]							):
   __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."  ,				__SCREAMING_SNAKE_CASE  ,				)
    __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__(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	self.image_processor

  def __call__(		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Dict=None  ,				__SCREAMING_SNAKE_CASE			:   Dict=None  ,				__SCREAMING_SNAKE_CASE			:   Tuple=None  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]							):

   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							=	self.tokenizer(__SCREAMING_SNAKE_CASE  ,				return_tensors=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   if images is not None:
    __a							=	self.image_processor(__SCREAMING_SNAKE_CASE  ,				return_tensors=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   if text is not None and images is not None:
    __a							=	image_features.pixel_values
    return encoding
   elif text is not None:
    return encoding
   else:
    return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE							)  ,				tensor_type=__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   int  ,				*__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				**__SCREAMING_SNAKE_CASE			:   str							):
   return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				*__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]							):
   return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  @property
  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __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			:   Any							):
   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,				__SCREAMING_SNAKE_CASE  ,				)
   return self.image_processor_class


