




"""simple docstring"""






from typing import Callable, Optional, Union

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


lowercase__     						=						logging.get_logger(__name__)

lowercase__     						=						{
    """microsoft/xprophetnet-large-wiki100-cased""": (
        """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"""
    ),
}






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			"""xlm-prophetnet"""
			lowerCamelCase__      =			["""past_key_values"""]
			lowerCamelCase__      =			{
			    """num_attention_heads""": """num_encoder_attention_heads""",
			}
			def __init__(  self  ,      lowercase = 0.1  ,      lowercase = "gelu"  ,      lowercase = 30522  ,      lowercase = 1024  ,      lowercase = 4096  ,      lowercase = 12  ,      lowercase = 16  ,      lowercase = 4096  ,      lowercase = 12  ,      lowercase = 16  ,      lowercase = 0.1  ,      lowercase = 0.1  ,      lowercase = 512  ,      lowercase = 0.02  ,      lowercase = True  ,      lowercase = True  ,      lowercase = 0  ,      lowercase = 2  ,      lowercase = 32  ,      lowercase = 128  ,      lowercase = False  ,      lowercase = 0.0  ,      lowercase = True  ,      lowercase = 0  ,      lowercase = 1  ,      lowercase = 2  ,      **lowercase  ,      ):
							_lowerCamelCase     :			Tuple    =    vocab_size
							_lowerCamelCase     :			Dict    =    hidden_size
							_lowerCamelCase     :			List[str]    =    encoder_ffn_dim
							_lowerCamelCase     :			Optional[int]    =    num_encoder_layers
							_lowerCamelCase     :			Tuple    =    num_encoder_attention_heads
							_lowerCamelCase     :			Dict    =    decoder_ffn_dim
							_lowerCamelCase     :			Optional[Any]    =    num_decoder_layers
							_lowerCamelCase     :			Any    =    num_decoder_attention_heads
							_lowerCamelCase     :			Optional[int]    =    max_position_embeddings
							_lowerCamelCase     :			int    =    init_std  # Normal(0, this parameter)
							_lowerCamelCase     :			int    =    activation_function

							# parameters for xlmprophetnet
							_lowerCamelCase     :			List[str]    =    ngram
							_lowerCamelCase     :			Tuple    =    num_buckets
							_lowerCamelCase     :			int    =    relative_max_distance
							_lowerCamelCase     :			str    =    disable_ngram_loss
							_lowerCamelCase     :			Union[str, Any]    =    eps

							# 3 Types of Dropout
							_lowerCamelCase     :			str    =    attention_dropout
							_lowerCamelCase     :			List[Any]    =    activation_dropout
							_lowerCamelCase     :			Any    =    dropout

							_lowerCamelCase     :			List[Any]    =    use_cache

							super().__init__(
							    pad_token_id=lowercase  ,      bos_token_id=lowercase  ,      eos_token_id=lowercase  ,      is_encoder_decoder=lowercase  ,      add_cross_attention=lowercase  ,      decoder_start_token_id=lowercase  ,      **lowercase  ,      )
			@property
			def 					A_    (  self  ):
							return self.num_encoder_layers + self.num_decoder_layers






			@num_hidden_layers.setter
			def 					A_    (  self  ,      lowercase  ):
							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 __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm







def      _snake_case			(				lowercase__							,    lowercase__		):
				return math.sqrt(sum(pow(a - b							,    2		) for a, b in zip(lowercase__							,    lowercase__		)		)		)







def      _snake_case			(				lowercase__							,    lowercase__		):

				if dataset.ndim != value_array.ndim:
								_lowerCamelCase     :			Tuple    =    (
								    'Wrong input data\'s dimensions... '
								    f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
								)
								raise ValueError(lowercase__		)

				try:
								if dataset.shape[1] != value_array.shape[1]:
												_lowerCamelCase     :			Optional[int]    =    (
												    'Wrong input data\'s shape... '
												    f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
												)
												raise ValueError(lowercase__		)
				except IndexError:
								if dataset.ndim != value_array.ndim:
												raise TypeError('Wrong shape'		)

				if dataset.dtype != value_array.dtype:
								_lowerCamelCase     :			int    =    (
								    'Input data have different datatype... '
								    f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
								)
								raise TypeError(lowercase__		)

				_lowerCamelCase     :			Optional[int]    =    []

				for value in value_array:
								_lowerCamelCase     :			Tuple    =    euclidean(lowercase__							,    dataset[0]		)
								_lowerCamelCase     :			Union[str, Any]    =    dataset[0].tolist()

								for dataset_value in dataset[1:]:
												_lowerCamelCase     :			Optional[Any]    =    euclidean(lowercase__							,    lowercase__		)

												if dist > temp_dist:
																_lowerCamelCase     :			List[Any]    =    temp_dist
																_lowerCamelCase     :			List[str]    =    dataset_value.tolist()

								answer.append([vector, dist]		)

				return answer







def      _snake_case			(				lowercase__							,    lowercase__		):
				return np.dot(lowercase__							,    lowercase__		) / (norm(lowercase__		) * norm(lowercase__		))


if __name__ == "__main__":
			import doctest

			doctest.testmod()