



import warnings

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






class     __a			(  SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							["image_processor", "tokenizer"]
	SCREAMING_SNAKE_CASE  	=							"ViTImageProcessor"
	SCREAMING_SNAKE_CASE  	=							("CLIPTokenizer", "CLIPTokenizerFast")



	def __init__(    self				:     int					,						snake_case_				:     int=None					,						snake_case_				:     Union[str, Any]=None					,						**snake_case_				:     Optional[Any])->				Tuple:
						__lowerCAmelCase						 =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_					,						)
											__lowerCAmelCase						 =kwargs.pop("""feature_extractor""")

						__lowerCAmelCase						 =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				:     Any					,						snake_case_				:     List[Any]=None					,						snake_case_				:     Any=None					,						snake_case_				:     List[Any]=None					,						snake_case_				:     Union[str, Any]=None					,						**snake_case_				:     Union[str, Any])->				List[str]:
						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:
											__lowerCAmelCase						 =self.tokenizer(snake_case_					,						return_tensors=snake_case_					,						**snake_case_)

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

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

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



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						*snake_case_				:     Dict					,						**snake_case_				:     Union[str, Any])->				Optional[int]:
						return self.tokenizer.batch_decode(*snake_case_					,						**snake_case_)



	def 		UpperCamelCase				(    self				:     Dict					,						*snake_case_				:     int					,						**snake_case_				:     List[str])->				Tuple:
						return self.tokenizer.decode(*snake_case_					,						**snake_case_)



	@property
	def 		UpperCamelCase				(    self				:     Union[str, Any])->				Union[str, Any]:
						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				:     List[Any])->				Tuple:
						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]))




