
"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            =    {
    '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				Optional[Any]				   =						"pegasus"
      __SCREAMING_SNAKE_CASE :				Tuple				   =						["past_key_values"]
      __SCREAMING_SNAKE_CASE :				Tuple				   =						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




      def __init__(				self ,							lowercase_=5_0_2_6_5 ,							lowercase_=1_0_2_4 ,							lowercase_=1_2 ,							lowercase_=4_0_9_6 ,							lowercase_=1_6 ,							lowercase_=1_2 ,							lowercase_=4_0_9_6 ,							lowercase_=1_6 ,							lowercase_=0.0 ,							lowercase_=0.0 ,							lowercase_=True ,							lowercase_=True ,							lowercase_="gelu" ,							lowercase_=1_0_2_4 ,							lowercase_=0.1 ,							lowercase_=0.0 ,							lowercase_=0.0 ,							lowercase_=0.0_2 ,							lowercase_=0 ,							lowercase_=False ,							lowercase_=0 ,							lowercase_=1 ,							lowercase_=1 ,							**lowercase_ ,							)    ->       Tuple:
          UpperCAmelCase          =       vocab_size
          UpperCAmelCase          =       max_position_embeddings
          UpperCAmelCase          =       d_model
          UpperCAmelCase          =       encoder_ffn_dim
          UpperCAmelCase          =       encoder_layers
          UpperCAmelCase          =       encoder_attention_heads
          UpperCAmelCase          =       decoder_ffn_dim
          UpperCAmelCase          =       decoder_layers
          UpperCAmelCase          =       decoder_attention_heads
          UpperCAmelCase          =       dropout
          UpperCAmelCase          =       attention_dropout
          UpperCAmelCase          =       activation_dropout
          UpperCAmelCase          =       activation_function
          UpperCAmelCase          =       init_std
          UpperCAmelCase          =       encoder_layerdrop
          UpperCAmelCase          =       decoder_layerdrop
          UpperCAmelCase          =       use_cache
          UpperCAmelCase          =       encoder_layers
          UpperCAmelCase          =       scale_embedding  # scale factor will be sqrt(d_model) if True
          super().__init__(
              pad_token_id=lowercase_ ,							eos_token_id=lowercase_ ,							is_encoder_decoder=lowercase_ ,							decoder_start_token_id=lowercase_ ,							forced_eos_token_id=lowercase_ ,							**lowercase_ ,							)




      @property
      def      a_   (				self				)    ->       int:
          return self.encoder_attention_heads




      @property
      def      a_   (				self				)    ->       int:
          return self.d_model





"""simple docstring"""

from collections import Counter

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

SCREAMING_SNAKE_CASE_            =    datasets.load_iris()

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

SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_            =    train_test_split(X, y)




def      lowercase__    (       lowerCAmelCase					:       Optional[int]			,					lowerCAmelCase					:       Any			)			->   Optional[Any]:






    """simple docstring"""




    return np.linalg.norm(np.array(lowerCAmelCase			) - np.array(lowerCAmelCase			)			)




def      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       Any			,					lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       Tuple			,					lowerCAmelCase					:       Dict=5			)			->   str:






    """simple docstring"""




    UpperCAmelCase          =       zip(lowerCAmelCase			,					lowerCAmelCase			)
    # List of distances of all points from the point to be classified
    UpperCAmelCase          =       []
    for data_point in data:
        UpperCAmelCase          =       euclidean_distance(data_point[0]			,					lowerCAmelCase			)
        distances.append((distance, data_point[1])			)
    # Choosing 'k' points with the least distances.
    UpperCAmelCase          =       [i[1] for i in sorted(lowerCAmelCase			)[:k]]
    # Most commonly occurring class among them
    # is the class into which the point is classified
    UpperCAmelCase          =       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]))



