


"""simple docstring"""







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


a__    :							Tuple            =					logging.get_logger(__name__)

a__    :							Dict            =					{
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}




class 			UpperCamelCase_     (			_SCREAMING_SNAKE_CASE):

	"""simple docstring"""





	snake_case__    :  Any    =      "xglm"
	snake_case__    :  Optional[Any]    =      ["past_key_values"]

	snake_case__    :  Any    =      {
	    "num_attention_heads": "attention_heads",
	    "hidden_size": "d_model",
	    "num_hidden_layers": "num_layers",
	}




	def __init__(			self      :    List[str]      ,      UpperCAmelCase__      :    Optional[Any]=2_5_6_0_0_8      ,      UpperCAmelCase__      :    Optional[int]=2_0_4_8      ,      UpperCAmelCase__      :    Optional[int]=1_0_2_4      ,      UpperCAmelCase__      :    Any=4_0_9_6      ,      UpperCAmelCase__      :    Optional[int]=2_4      ,      UpperCAmelCase__      :    int=1_6      ,      UpperCAmelCase__      :    List[str]="gelu"      ,      UpperCAmelCase__      :    List[Any]=0.1      ,      UpperCAmelCase__      :    str=0.1      ,      UpperCAmelCase__      :    Tuple=0.0      ,      UpperCAmelCase__      :    Optional[int]=0.0      ,      UpperCAmelCase__      :    List[Any]=0.02      ,      UpperCAmelCase__      :    List[str]=True      ,      UpperCAmelCase__      :    Optional[Any]=True      ,      UpperCAmelCase__      :    Dict=2      ,      UpperCAmelCase__      :    int=1      ,      UpperCAmelCase__      :    Any=0      ,      UpperCAmelCase__      :    Union[str, Any]=2      ,      **UpperCAmelCase__      :    Dict      ,      )	->     Dict:
						__SCREAMING_SNAKE_CASE						       =    vocab_size
						__SCREAMING_SNAKE_CASE						       =    max_position_embeddings
						__SCREAMING_SNAKE_CASE						       =    d_model
						__SCREAMING_SNAKE_CASE						       =    ffn_dim
						__SCREAMING_SNAKE_CASE						       =    num_layers
						__SCREAMING_SNAKE_CASE						       =    attention_heads
						__SCREAMING_SNAKE_CASE						       =    activation_function
						__SCREAMING_SNAKE_CASE						       =    dropout
						__SCREAMING_SNAKE_CASE						       =    attention_dropout
						__SCREAMING_SNAKE_CASE						       =    activation_dropout
						__SCREAMING_SNAKE_CASE						       =    layerdrop
						__SCREAMING_SNAKE_CASE						       =    init_std
						__SCREAMING_SNAKE_CASE						       =    scale_embedding  # scale factor will be sqrt(d_model) if True
						__SCREAMING_SNAKE_CASE						       =    use_cache

						super().__init__(
						    pad_token_id=UpperCAmelCase__      ,      bos_token_id=UpperCAmelCase__      ,      eos_token_id=UpperCAmelCase__      ,      decoder_start_token_id=UpperCAmelCase__      ,      **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()
