





import warnings

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





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						str							=   ["""image_processor""", """tokenizer"""]
  __lowerCAmelCase   :						Dict							=   """ViTImageProcessor"""
  __lowerCAmelCase   :						Tuple							=   ("""CLIPTokenizer""", """CLIPTokenizerFast""")


  def __init__(							self ,__snake_case=None ,__snake_case=None ,**__snake_case ):



       """simple docstring"""





       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.''' ,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__(snake_case_ ,snake_case_ )


  def __call__(							self ,__snake_case=None ,__snake_case=None ,__snake_case=None ,__snake_case=None ,**__snake_case ):



       """simple docstring"""





       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(snake_case_ ,return_tensors=snake_case_ ,**snake_case_ )

       if visual_prompt is not None:
            A_          =       self.image_processor(snake_case_ ,return_tensors=snake_case_ ,**snake_case_ )

       if images is not None:
            A_          =       self.image_processor(snake_case_ ,return_tensors=snake_case_ ,**snake_case_ )

       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(**snake_case_ ) ,tensor_type=snake_case_ )


  def 						__UpperCAmelCase     (							self ,*__snake_case ,**__snake_case ):



       """simple docstring"""





       return self.tokenizer.batch_decode(*snake_case_ ,**snake_case_ )


  def 						__UpperCAmelCase     (							self ,*__snake_case ,**__snake_case ):



       """simple docstring"""





       return self.tokenizer.decode(*snake_case_ ,**snake_case_ )


  @property
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,snake_case_ ,)
       return self.image_processor_class




  @property
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,snake_case_ ,)
       return self.image_processor








from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

lowercase_													=      datasets.load_iris()

lowercase_													=      np.array(data['''data'''])
lowercase_													=      np.array(data['''target'''])
lowercase_													=      data['''target_names''']

lowercase_							,							lowercase_							,							lowercase_							,							lowercase_													=      train_test_split(X, y)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Optional[Any]						)	->    Optional[int]:
					return np.linalg.norm(np.array(__lowerCamelCase						) - np.array(__lowerCamelCase						)						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     List[str]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Union[str, Any]					, __lowerCamelCase						:     int=5						)	->    str:
					__lowerCAmelCase						 =zip(__lowerCamelCase					, __lowerCamelCase						)
					# List of distances of all points from the point to be classified
					__lowerCAmelCase						 =[]
					for data_point in data:
										__lowerCAmelCase						 =euclidean_distance(data_point[0]					, __lowerCamelCase						)
										distances.append((distance, data_point[1])						)
					# Choosing 'k' points with the least distances.
					__lowerCAmelCase						 =[i[1] for i in sorted(__lowerCamelCase						)[:k]]
					# Most commonly occurring class among them
					# is the class into which the point is classified
					__lowerCAmelCase						 =Counter(__lowerCamelCase						).most_common(1						)[0][0]
					return classes[result]


if __name__ == "__main__":
				print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))




