


'''simple docstring'''



from typing import Callable, Optional, Union

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


_lowercase			:			Union[str, Any]	   =     logging.get_logger(__name__)

_lowercase			:			str	   =     {
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}






class        UpperCamelCase__(   lowerCAmelCase__       ):
				__magic_name__				:      str  		=       "prophetnet"
				__magic_name__				:      Union[str, Any]  		=       ["past_key_values"]
				__magic_name__				:      Optional[Any]  		=       {
				    "num_attention_heads": "num_encoder_attention_heads",
				}




				def __init__(		self			: List[Any]      ,    lowerCAmelCase			: Optional[Any] = 0.1      ,    lowerCAmelCase			: Union[str, Any] = "gelu"      ,    lowerCAmelCase			: Dict = 30522      ,    lowerCAmelCase			: str = 1024      ,    lowerCAmelCase			: str = 4096      ,    lowerCAmelCase			: str = 12      ,    lowerCAmelCase			: List[str] = 16      ,    lowerCAmelCase			: Optional[int] = 4096      ,    lowerCAmelCase			: Optional[int] = 12      ,    lowerCAmelCase			: Tuple = 16      ,    lowerCAmelCase			: List[str] = 0.1      ,    lowerCAmelCase			: Any = 0.1      ,    lowerCAmelCase			: List[str] = 512      ,    lowerCAmelCase			: Optional[int] = 0.02      ,    lowerCAmelCase			: Optional[int] = True      ,    lowerCAmelCase			: Optional[int] = True      ,    lowerCAmelCase			: List[str] = 0      ,    lowerCAmelCase			: Optional[int] = 2      ,    lowerCAmelCase			: Any = 32      ,    lowerCAmelCase			: List[str] = 128      ,    lowerCAmelCase			: List[str] = False      ,    lowerCAmelCase			: Dict = 0.0      ,    lowerCAmelCase			: Optional[Any] = True      ,    lowerCAmelCase			: Optional[int] = 0      ,    lowerCAmelCase			: List[Any] = 1      ,    lowerCAmelCase			: Dict = 2      ,    **lowerCAmelCase			: Optional[Any]      ,    )->     Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase     				=	vocab_size
										UpperCAmelCase     				=	hidden_size
										UpperCAmelCase     				=	encoder_ffn_dim
										UpperCAmelCase     				=	num_encoder_layers
										UpperCAmelCase     				=	num_encoder_attention_heads
										UpperCAmelCase     				=	decoder_ffn_dim
										UpperCAmelCase     				=	num_decoder_layers
										UpperCAmelCase     				=	num_decoder_attention_heads
										UpperCAmelCase     				=	max_position_embeddings
										UpperCAmelCase     				=	init_std  # Normal(0, this parameter)
										UpperCAmelCase     				=	activation_function

										# parameters for prophetnet
										UpperCAmelCase     				=	ngram
										UpperCAmelCase     				=	num_buckets
										UpperCAmelCase     				=	relative_max_distance
										UpperCAmelCase     				=	disable_ngram_loss
										UpperCAmelCase     				=	eps

										# 3 Types of Dropout
										UpperCAmelCase     				=	attention_dropout
										UpperCAmelCase     				=	activation_dropout
										UpperCAmelCase     				=	dropout

										UpperCAmelCase     				=	use_cache

										super().__init__(
										    pad_token_id=a__      ,    bos_token_id=a__      ,    eos_token_id=a__      ,    is_encoder_decoder=a__      ,    add_cross_attention=a__      ,    decoder_start_token_id=a__      ,    **a__      ,    )




				@property
				def      a__(		self			: List[str]      )->     Optional[int]:

										"""simple docstring"""




										return self.num_encoder_layers + self.num_decoder_layers





				@num_hidden_layers.setter
				def      a__(		self			: Dict      ,    lowerCAmelCase			: Optional[Any]      )->     Optional[Any]:

										"""simple docstring"""




										raise NotImplementedError(
										    '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
										    ''' `num_decoder_layers`.'''      )












"""simple docstring"""


from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
A_     :      List[str]								=load_iris()
iris.keys()


print(f'Target names: \n {iris.target_names} ')
print(f'\n Features: \n {iris.feature_names}')

# Train set e Test set
A_							,	A_							,	A_							,	A_     :      List[Any]								=train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

A_     :      str								=KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
A_     :      Any								=[[1, 2, 1, 4], [2, 3, 4, 5]]

A_     :      int								=knn.predict(X_new)

print(
    f'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    f' {iris["target_names"][prediction]}'
)






