




from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

__A								=       typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
__A								=       typing.Union[np.floataa, int, float]  # noqa: UP007







def      lowerCamelCase_						(			UpperCamelCase__							: Vector					,	UpperCamelCase__							: Vector )	->			VectorOut:





				"""simple docstring"""


				return np.sqrt(np.sum((np.asarray(UpperCamelCase__ ) - np.asarray(UpperCamelCase__ )) ** 2 ) )







def      lowerCamelCase_						(			UpperCamelCase__							: Vector					,	UpperCamelCase__							: Vector )	->			VectorOut:





				"""simple docstring"""


				return sum((va - va) ** 2 for va, va in zip(UpperCamelCase__					,	UpperCamelCase__ ) ) ** (1 / 2)


if __name__ == "__main__":







					def      lowerCamelCase_						(			)	->			None:





									"""simple docstring"""


									from timeit import timeit

									print('Without Numpy' )
									print(
									    timeit(
									        'euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'					,	number=1_0000					,	globals=globals()					,	) )
									print('With Numpy' )
									print(
									    timeit(
									        'euclidean_distance([1, 2, 3], [4, 5, 6])'					,	number=1_0000					,	globals=globals()					,	) )


					benchmark()








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A								=       logging.get_logger(__name__)

__A								=       {
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			'''xlm-roberta-xl'''


			def __init__( self     ,		lowerCamelCase__=250_880     ,		lowerCamelCase__=2_560     ,		lowerCamelCase__=36     ,		lowerCamelCase__=32     ,		lowerCamelCase__=10_240     ,		lowerCamelCase__="gelu"     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=514     ,		lowerCamelCase__=1     ,		lowerCamelCase__=0.02     ,		lowerCamelCase__=1e-05     ,		lowerCamelCase__=1     ,		lowerCamelCase__=0     ,		lowerCamelCase__=2     ,		lowerCamelCase__="absolute"     ,		lowerCamelCase__=True     ,		lowerCamelCase__=None     ,		**lowerCamelCase__     ,		)       -> str:



							'''simple docstring'''
							super().__init__(pad_token_id=lowerCamelCase__     ,		bos_token_id=lowerCamelCase__     ,		eos_token_id=lowerCamelCase__     ,		**lowerCamelCase__				)
							__lowerCamelCase									=       vocab_size
							__lowerCamelCase									=       hidden_size
							__lowerCamelCase									=       num_hidden_layers
							__lowerCamelCase									=       num_attention_heads
							__lowerCamelCase									=       hidden_act
							__lowerCamelCase									=       intermediate_size
							__lowerCamelCase									=       hidden_dropout_prob
							__lowerCamelCase									=       attention_probs_dropout_prob
							__lowerCamelCase									=       max_position_embeddings
							__lowerCamelCase									=       type_vocab_size
							__lowerCamelCase									=       initializer_range
							__lowerCamelCase									=       layer_norm_eps
							__lowerCamelCase									=       position_embedding_type
							__lowerCamelCase									=       use_cache
							__lowerCamelCase									=       classifier_dropout







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			@property
			def  lowercase_       ( self				)       -> Mapping[str, Mapping[int, str]]:



							'''simple docstring'''
							if self.task == "multiple-choice":
											__lowerCamelCase									=       {0: 'batch', 1: 'choice', 2: 'sequence'}
							else:
											__lowerCamelCase									=       {0: 'batch', 1: 'sequence'}
							return OrderedDict(
							    [
							        ('input_ids', dynamic_axis),
							        ('attention_mask', dynamic_axis),
							    ]				)


