



'''simple docstring'''






from collections import Counter

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

lowerCAmelCase: Optional[int]							=						datasets.load_iris()

lowerCAmelCase: List[Any]							=						np.array(data['data'])
lowerCAmelCase: Union[str, Any]							=						np.array(data['target'])
lowerCAmelCase: Optional[int]							=						data['target_names']

lowerCAmelCase	,		lowerCAmelCase	,		lowerCAmelCase	,		lowerCAmelCase: Dict							=						train_test_split(X, y)
def 				lowerCamelCase__	(						_A			,      _A    ):
  return np.linalg.norm(np.array(_A    ) - np.array(_A    )    )
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A			,      _A=5    ):
  a						: Union[str, Any]					       =					zip(_A			,      _A    )
  # List of distances of all points from the point to be classified
  a						: Union[str, Any]					       =					[]
  for data_point in data:
    a						: Dict					       =					euclidean_distance(data_point[0]			,      _A    )
    distances.append((distance, data_point[1])    )
  # Choosing 'k' points with the least distances.
  a						: int					       =					[i[1] for i in sorted(_A    )[:k]]
  # Most commonly occurring class among them
  # is the class into which the point is classified
  a						: Dict					       =					Counter(_A    ).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]))




'''simple docstring'''






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
     lowerCAmelCase: Any							=						argparse.ArgumentParser()

     parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

     parser.add_argument(
         '--txt2img_unclip',
         default='kakaobrain/karlo-v1-alpha',
         type=str,
         required=False,
         help='The pretrained txt2img unclip.',
     )

     lowerCAmelCase: Optional[int]							=						parser.parse_args()

     lowerCAmelCase: List[Any]							=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

     lowerCAmelCase: Optional[Any]							=						CLIPImageProcessor()
     lowerCAmelCase: Tuple							=						CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

     lowerCAmelCase: List[str]							=						UnCLIPImageVariationPipeline(
         decoder=txtaimg.decoder,
         text_encoder=txtaimg.text_encoder,
         tokenizer=txtaimg.tokenizer,
         text_proj=txtaimg.text_proj,
         feature_extractor=feature_extractor,
         image_encoder=image_encoder,
         super_res_first=txtaimg.super_res_first,
         super_res_last=txtaimg.super_res_last,
         decoder_scheduler=txtaimg.decoder_scheduler,
         super_res_scheduler=txtaimg.super_res_scheduler,
     )

     imgaimg.save_pretrained(args.dump_path)