



'''simple docstring'''





from __future__ import annotations

from typing import Any






class 			lowerCAmelCase_:
  '''simple docstring'''




  def __init__(       self    ,__UpperCAmelCase = 6 )       ->						int:
      lowerCAmelCase__					:    List[Any]				 =   None
      lowerCAmelCase__					:    Union[str, Any]				 =   None
      self.create_linked_list(_UpperCAmelCase )




  def 				UpperCAmelCase_      (       self    ,__UpperCAmelCase )       ->						List[Any]:
      lowerCAmelCase__					:    int				 =   Node()
      lowerCAmelCase__					:    Optional[int]				 =   current_node
      lowerCAmelCase__					:    Optional[int]				 =   current_node
      lowerCAmelCase__					:    Dict				 =   current_node
      for _ in range(1    ,_UpperCAmelCase ):
          lowerCAmelCase__					:    List[Any]				 =   Node()
          lowerCAmelCase__					:    Optional[int]				 =   current_node
          lowerCAmelCase__					:    Tuple				 =   previous_node
          lowerCAmelCase__					:    Tuple				 =   current_node
      lowerCAmelCase__					:    List[Any]				 =   self.front
      lowerCAmelCase__					:    Dict				 =   previous_node




  def 				UpperCAmelCase_      (       self )       ->						Tuple:
      return (
          self.front == self.rear
          and self.front is not None
          and self.front.data is None
      )




  def 				UpperCAmelCase_      (       self )       ->						Optional[int]:
      self.check_can_perform_operation()
      return self.front.data if self.front else None




  def 				UpperCAmelCase_      (       self    ,__UpperCAmelCase )       ->						List[Any]:
      if self.rear is None:
          return

      self.check_is_full()
      if not self.is_empty():
          lowerCAmelCase__					:    List[Any]				 =   self.rear.next
      if self.rear:
          lowerCAmelCase__					:    Dict				 =   data




  def 				UpperCAmelCase_      (       self )       ->						Dict:
      self.check_can_perform_operation()
      if self.rear is None or self.front is None:
          return None
      if self.front == self.rear:
          lowerCAmelCase__					:    Dict				 =   self.front.data
          lowerCAmelCase__					:    Dict				 =   None
          return data

      lowerCAmelCase__					:    Optional[int]				 =   self.front
      lowerCAmelCase__					:    Optional[int]				 =   old_front.next
      lowerCAmelCase__					:    str				 =   old_front.data
      lowerCAmelCase__					:    Any				 =   None
      return data




  def 				UpperCAmelCase_      (       self )       ->						Tuple:
      if self.is_empty():
          raise Exception("""Empty Queue""" )






  def 				UpperCAmelCase_      (       self )       ->						Any:
      if self.rear and self.rear.next == self.front:
          raise Exception("""Full Queue""" )







class 			lowerCAmelCase_:
  '''simple docstring'''




  def __init__(       self )       ->						List[str]:
      lowerCAmelCase__					:    str				 =   None
      lowerCAmelCase__					:    List[str]				 =   None
      lowerCAmelCase__					:    Optional[int]				 =   None


if __name__ == "__main__":
 import doctest

 doctest.testmod()








"""simple docstring"""

import warnings

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





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Any     =      ['''image_processor''', '''tokenizer''']
 UpperCAmelCase       :	Optional[int]     =      '''ViTImageProcessor'''
 UpperCAmelCase       :	int     =      ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

 def __init__(					self			:			Tuple       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			Tuple=None       ,				**_UpperCAmelCase			:			Dict						):
       _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.'       ,				_UpperCAmelCase       ,				)
             _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__(_UpperCAmelCase       ,				_UpperCAmelCase						)

 def __call__(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			Optional[Any]=None       ,				_UpperCAmelCase			:			Optional[int]=None       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       if text is None and visual_prompt is None and images is None:
             raise ValueError('You have to specify either text, visual prompt or images.'						)

       if text is not None and visual_prompt is not None:
             raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'						)

       if text is not None:
             _A     =  self.tokenizer(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if visual_prompt is not None:
             _A     =  self.image_processor(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if images is not None:
             _A     =  self.image_processor(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if visual_prompt is not None and images is not None:
             _A     =  {
                 'pixel_values': image_features.pixel_values,
                 'conditional_pixel_values': prompt_features.pixel_values,
             }
             return encoding
       elif text is not None and images is not None:
             _A     =  image_features.pixel_values
             return encoding
       elif text is not None:
             return encoding
       elif visual_prompt is not None:
             _A     =  {
                 'conditional_pixel_values': prompt_features.pixel_values,
             }
             return encoding
       else:
             return BatchEncoding(data=dict(**_UpperCAmelCase						)       ,				tensor_type=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			str       ,				*_UpperCAmelCase			:			List[Any]       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       return self.tokenizer.batch_decode(*_UpperCAmelCase       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				*_UpperCAmelCase			:			List[str]       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       return self.tokenizer.decode(*_UpperCAmelCase       ,				**_UpperCAmelCase						)

 @property
 def      lowerCAmelCase_				(					self			:			Dict						):
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,				_UpperCAmelCase       ,				)
       return self.image_processor_class



 @property
 def      lowerCAmelCase_				(					self			:			Tuple						):
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       ,				_UpperCAmelCase       ,				)
       return self.image_processor

