

from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	{
    '''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 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   int									=				"xlm-roberta-xl"







			def __init__(     self :				str							,   _UpperCamelCase :				Union[str, Any]=2_5_0_8_8_0							,   _UpperCamelCase :				List[Any]=2_5_6_0							,   _UpperCamelCase :				Any=3_6							,   _UpperCamelCase :				Dict=3_2							,   _UpperCamelCase :				Optional[int]=1_0_2_4_0							,   _UpperCamelCase :				Dict="gelu"							,   _UpperCamelCase :				Union[str, Any]=0.1							,   _UpperCamelCase :				Dict=0.1							,   _UpperCamelCase :				Union[str, Any]=5_1_4							,   _UpperCamelCase :				Dict=1							,   _UpperCamelCase :				int=0.02							,   _UpperCamelCase :				List[str]=1e-05							,   _UpperCamelCase :				Dict=1							,   _UpperCamelCase :				List[str]=0							,   _UpperCamelCase :				str=2							,   _UpperCamelCase :				Dict="absolute"							,   _UpperCamelCase :				Optional[Any]=True							,   _UpperCamelCase :				Dict=None							,   **_UpperCamelCase :				List[Any]							,   )      ->Union[str, Any]:
									super().__init__(pad_token_id=_UpperCamelCase							,   bos_token_id=_UpperCamelCase							,   eos_token_id=_UpperCamelCase							,   **_UpperCamelCase							)
									snake_case_   =	vocab_size
									snake_case_   =	hidden_size
									snake_case_   =	num_hidden_layers
									snake_case_   =	num_attention_heads
									snake_case_   =	hidden_act
									snake_case_   =	intermediate_size
									snake_case_   =	hidden_dropout_prob
									snake_case_   =	attention_probs_dropout_prob
									snake_case_   =	max_position_embeddings
									snake_case_   =	type_vocab_size
									snake_case_   =	initializer_range
									snake_case_   =	layer_norm_eps
									snake_case_   =	position_embedding_type
									snake_case_   =	use_cache
									snake_case_   =	classifier_dropout






class 	snake_case_   (		__A					):







			'''simple docstring'''

			@property
			def 				snake_case__(     self :				List[str]							)      ->Mapping[str, Mapping[int, str]]:
									if self.task == "multiple-choice":
															snake_case_   =	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
															snake_case_   =	{0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									    ]							)


import math


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):

						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5  ,		int(math.sqrt(SCREAMING_SNAKE_CASE__       ) + 1       )  ,		6       ):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__ = 10001       ):

						try:
												snake_case_   =	int(SCREAMING_SNAKE_CASE__       )
						except (TypeError, ValueError):
												raise TypeError('''Parameter nth must be int or castable to int.'''       ) from None
						if nth <= 0:
												raise ValueError('''Parameter nth must be greater than or equal to one.'''       )
						snake_case_   =	[]
						snake_case_   =	2
						while len(SCREAMING_SNAKE_CASE__       ) < nth:
												if is_prime(SCREAMING_SNAKE_CASE__       ):
																		primes.append(SCREAMING_SNAKE_CASE__       )
																		num += 1
												else:
																		num += 1
						return primes[len(SCREAMING_SNAKE_CASE__       ) - 1]


if __name__ == "__main__":
					print(f"""{solution() = }""")