


"""simple docstring"""







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


a__    :							int            =					logging.get_logger(__name__)

a__    :							Tuple            =					{'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  Dict    =      "ctrl"
	snake_case__    :  int    =      ["past_key_values"]
	snake_case__    :  Tuple    =      {
	    "max_position_embeddings": "n_positions",
	    "hidden_size": "n_embd",
	    "num_attention_heads": "n_head",
	    "num_hidden_layers": "n_layer",
	}




	def __init__(			self      :    List[Any]      ,      UpperCAmelCase__      :    Any=2_4_6_5_3_4      ,      UpperCAmelCase__      :    List[Any]=2_5_6      ,      UpperCAmelCase__      :    Optional[int]=1_2_8_0      ,      UpperCAmelCase__      :    Optional[Any]=8_1_9_2      ,      UpperCAmelCase__      :    int=4_8      ,      UpperCAmelCase__      :    Optional[int]=1_6      ,      UpperCAmelCase__      :    Tuple=0.1      ,      UpperCAmelCase__      :    Optional[Any]=0.1      ,      UpperCAmelCase__      :    Any=1E-6      ,      UpperCAmelCase__      :    List[str]=0.02      ,      UpperCAmelCase__      :    Union[str, Any]=True      ,      **UpperCAmelCase__      :    Tuple      ,      )	->     str:
						__SCREAMING_SNAKE_CASE						       =    vocab_size
						__SCREAMING_SNAKE_CASE						       =    n_positions
						__SCREAMING_SNAKE_CASE						       =    n_embd
						__SCREAMING_SNAKE_CASE						       =    n_layer
						__SCREAMING_SNAKE_CASE						       =    n_head
						__SCREAMING_SNAKE_CASE						       =    dff
						__SCREAMING_SNAKE_CASE						       =    resid_pdrop
						__SCREAMING_SNAKE_CASE						       =    embd_pdrop
						__SCREAMING_SNAKE_CASE						       =    layer_norm_epsilon
						__SCREAMING_SNAKE_CASE						       =    initializer_range

						__SCREAMING_SNAKE_CASE						       =    use_cache

						super().__init__(**UpperCAmelCase__       )




"""simple docstring"""







from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return math.sqrt(sum(pow(a - b  ,						2       ) for a, b in zip(lowerCAmelCase_  ,						lowerCAmelCase_       )       )       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					if dataset.ndim != value_array.ndim:
										__SCREAMING_SNAKE_CASE						       =    (
										    "Wrong input data's dimensions... "
										    f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
										)
										raise ValueError(lowerCAmelCase_       )

					try:
										if dataset.shape[1] != value_array.shape[1]:
															__SCREAMING_SNAKE_CASE						       =    (
															    "Wrong input data's shape... "
															    f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
															)
															raise ValueError(lowerCAmelCase_       )
					except IndexError:
										if dataset.ndim != value_array.ndim:
															raise TypeError("Wrong shape"       )

					if dataset.dtype != value_array.dtype:
										__SCREAMING_SNAKE_CASE						       =    (
										    "Input data have different datatype... "
										    f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
										)
										raise TypeError(lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    []

					for value in value_array:
										__SCREAMING_SNAKE_CASE						       =    euclidean(lowerCAmelCase_  ,						dataset[0]       )
										__SCREAMING_SNAKE_CASE						       =    dataset[0].tolist()

										for dataset_value in dataset[1:]:
															__SCREAMING_SNAKE_CASE						       =    euclidean(lowerCAmelCase_  ,						lowerCAmelCase_       )

															if dist > temp_dist:
																				__SCREAMING_SNAKE_CASE						       =    temp_dist
																				__SCREAMING_SNAKE_CASE						       =    dataset_value.tolist()

										answer.append([vector, dist]       )

					return answer




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return np.dot(lowerCAmelCase_  ,						lowerCAmelCase_       ) / (norm(lowerCAmelCase_       ) * norm(lowerCAmelCase_       ))


if __name__ == "__main__":
		import doctest

		doctest.testmod()
