





'''simple docstring'''

def lowerCamelCase						(     __lowerCamelCase :    int    )						->int:
    _SCREAMING_SNAKE_CASE									=			[1]

    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			0, 0, 0
    _SCREAMING_SNAKE_CASE									=			ugly_nums[ia] * 2
    _SCREAMING_SNAKE_CASE									=			ugly_nums[ia] * 3
    _SCREAMING_SNAKE_CASE									=			ugly_nums[ia] * 5

    for _ in range(1					,       __lowerCamelCase    ):
        _SCREAMING_SNAKE_CASE									=			min(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
        ugly_nums.append(__lowerCamelCase    )
        if next_num == next_a:
            ia += 1
            _SCREAMING_SNAKE_CASE									=			ugly_nums[ia] * 2
        if next_num == next_a:
            ia += 1
            _SCREAMING_SNAKE_CASE									=			ugly_nums[ia] * 3
        if next_num == next_a:
            ia += 1
            _SCREAMING_SNAKE_CASE									=			ugly_nums[ia] * 5
    return ugly_nums[-1]


if __name__ == "__main__":
      from doctest import testmod

      testmod(verbose=True)
      print(f"""{ugly_numbers(200) = }""")










'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''data2vec-text'''


    def __init__(		self ,		A=3_0522 ,		A=768 ,		A=12 ,		A=12 ,		A=3072 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=512 ,		A=2 ,		A=0.02 ,		A=1e-12 ,		A=1 ,		A=0 ,		A=2 ,		A="absolute" ,		A=True ,		A=None ,		**A ,		)		->   int:
        super().__init__(pad_token_id=A ,		bos_token_id=A ,		eos_token_id=A ,		**A       )

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




class 					a_     (				snake_case_       ):

    '''simple docstring'''


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



