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


__lowercase											=      logging.get_logger(__name__)

__lowercase											=      {
    '''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		Dict      			=   """transfo-xl"""
    a__			:		Optional[int]      			=   ["""mems"""]
    a__			:		Tuple      			=   {
        """n_token""": """vocab_size""",
        """hidden_size""": """d_model""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__(			self				,				__lowercase=267_735				,				__lowercase=[20_000, 40_000, 200_000]				,				__lowercase=1_024				,				__lowercase=1_024				,				__lowercase=16				,				__lowercase=64				,				__lowercase=4_096				,				__lowercase=4				,				__lowercase=False				,				__lowercase=18				,				__lowercase=1_600				,				__lowercase=1_000				,				__lowercase=True				,				__lowercase=True				,				__lowercase=0				,				__lowercase=-1				,				__lowercase=True				,				__lowercase=0.1				,				__lowercase=0.0				,				__lowercase=True				,				__lowercase="normal"				,				__lowercase=0.01				,				__lowercase=0.01				,				__lowercase=0.02				,				__lowercase=1E-5				,				__lowercase=0				,				**__lowercase				,				) ->			Dict:
        __UpperCamelCase  :List[Any]    						=	vocab_size
        __UpperCamelCase  :List[str]    						=	[]
        self.cutoffs.extend(__lowercase)
        if proj_share_all_but_first:
            __UpperCamelCase  :Dict    						=	[False] + [True] * len(self.cutoffs)
        else:
            __UpperCamelCase  :List[str]    						=	[False] + [False] * len(self.cutoffs)
        __UpperCamelCase  :Optional[int]    						=	d_model
        __UpperCamelCase  :Optional[int]    						=	d_embed
        __UpperCamelCase  :Dict    						=	d_head
        __UpperCamelCase  :List[Any]    						=	d_inner
        __UpperCamelCase  :Optional[int]    						=	div_val
        __UpperCamelCase  :Dict    						=	pre_lnorm
        __UpperCamelCase  :Any    						=	n_layer
        __UpperCamelCase  :Any    						=	n_head
        __UpperCamelCase  :List[str]    						=	mem_len
        __UpperCamelCase  :Optional[Any]    						=	same_length
        __UpperCamelCase  :Union[str, Any]    						=	attn_type
        __UpperCamelCase  :str    						=	clamp_len
        __UpperCamelCase  :Union[str, Any]    						=	sample_softmax
        __UpperCamelCase  :int    						=	adaptive
        __UpperCamelCase  :List[Any]    						=	dropout
        __UpperCamelCase  :Dict    						=	dropatt
        __UpperCamelCase  :List[Any]    						=	untie_r
        __UpperCamelCase  :Any    						=	init
        __UpperCamelCase  :str    						=	init_range
        __UpperCamelCase  :Tuple    						=	proj_init_std
        __UpperCamelCase  :List[Any]    						=	init_std
        __UpperCamelCase  :Any    						=	layer_norm_epsilon
        super().__init__(eos_token_id=__lowercase				,				**__lowercase)







    @property
    def     UpperCamelCase__     (			self) ->			Dict:
        # Message copied from Transformer-XL documentation
        logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit.""")
        return -1







    @max_position_embeddings.setter
    def     UpperCamelCase__     (			self				,				__lowercase) ->			str:
        # Message copied from Transformer-XL documentation
        raise NotImplementedError(
            f"""The model {self.model_type} is one of the few models that has no sequence length limit.""")






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						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=5			):
    '''simple docstring'''







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





