
from collections import OrderedDict
from typing import Mapping

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


A     :	Union[str, Any]    = logging.get_logger(__name__)

A     :	Union[str, Any]    = {
    'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/config.json',
    'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/config.json',
    'xlm-roberta-large-finetuned-conll02-dutch': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll02-spanish': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-english': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-german': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'
    ),
}







class 			__A( a			):
				snake_case_						   =    '''xlm-roberta'''




				def __init__(     self		,     _snake_case=30_522		,     _snake_case=768		,     _snake_case=12		,     _snake_case=12		,     _snake_case=3_072		,     _snake_case="gelu"		,     _snake_case=0.1		,     _snake_case=0.1		,     _snake_case=512		,     _snake_case=2		,     _snake_case=0.02		,     _snake_case=1E-12		,     _snake_case=1		,     _snake_case=0		,     _snake_case=2		,     _snake_case="absolute"		,     _snake_case=True		,     _snake_case=None		,     **_snake_case		,     )    ->      Dict:


								'''simple docstring'''




								super().__init__(pad_token_id=_snake_case		,     bos_token_id=_snake_case		,     eos_token_id=_snake_case		,     **_snake_case				)

								__a										=    vocab_size
								__a										=    hidden_size
								__a										=    num_hidden_layers
								__a										=    num_attention_heads
								__a										=    hidden_act
								__a										=    intermediate_size
								__a										=    hidden_dropout_prob
								__a										=    attention_probs_dropout_prob
								__a										=    max_position_embeddings
								__a										=    type_vocab_size
								__a										=    initializer_range
								__a										=    layer_norm_eps
								__a										=    position_embedding_type
								__a										=    use_cache
								__a										=    classifier_dropout







class 			__A( a			):




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


								'''simple docstring'''




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

def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  list:
				__a										=    len(a__  )
				__a										=    [[0] * n for i in range(a__  )]
				for i in range(a__  ):
								__a										=    y_points[i]

				for i in range(2						,    a__  ):
								for j in range(a__						,    a__  ):
												__a										=    (
												    (xa - x_points[j - i + 1]) * q[j][i - 1]
												    - (xa - x_points[j]) * q[j - 1][i - 1]
												) / (x_points[j] - x_points[j - i + 1])

				return [q[n - 1][n - 1], q]


if __name__ == "__main__":
					import doctest

					doctest.testmod()