





from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase__      =logging.get_logger(__name__)

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





class 			lowerCAmelCase__( UpperCamelCase_    ):






       '''simple docstring'''


       __snake_case 			=    'data2vec-text'




       def __init__(      self    ,						__lowerCamelCase=3_0_5_2_2    ,						__lowerCamelCase=7_6_8    ,						__lowerCamelCase=1_2    ,						__lowerCamelCase=1_2    ,						__lowerCamelCase=3_0_7_2    ,						__lowerCamelCase="gelu"    ,						__lowerCamelCase=0.1    ,						__lowerCamelCase=0.1    ,						__lowerCamelCase=5_1_2    ,						__lowerCamelCase=2    ,						__lowerCamelCase=0.02    ,						__lowerCamelCase=1E-12    ,						__lowerCamelCase=1    ,						__lowerCamelCase=0    ,						__lowerCamelCase=2    ,						__lowerCamelCase="absolute"    ,						__lowerCamelCase=True    ,						__lowerCamelCase=None    ,						**__lowerCamelCase    ,						)			->     List[Any]:
            super().__init__(pad_token_id=UpperCamelCase__    ,						bos_token_id=UpperCamelCase__    ,						eos_token_id=UpperCamelCase__    ,						**UpperCamelCase__							)

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							vocab_size
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							hidden_size
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							num_hidden_layers
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							num_attention_heads
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							hidden_act
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							intermediate_size
            _SCREAMING_SNAKE_CASE       :	str						    =							hidden_dropout_prob
            _SCREAMING_SNAKE_CASE       :	Any						    =							attention_probs_dropout_prob
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							max_position_embeddings
            _SCREAMING_SNAKE_CASE       :	str						    =							type_vocab_size
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							initializer_range
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							layer_norm_eps
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							position_embedding_type
            _SCREAMING_SNAKE_CASE       :	Any						    =							use_cache
            _SCREAMING_SNAKE_CASE       :	str						    =							classifier_dropout





class 			lowerCAmelCase__( UpperCamelCase_    ):






       '''simple docstring'''


       @property
       def     UpperCamelCase_			(      self							)			->     List[Any]:





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











from __future__ import annotations

import requests






def     lowerCamelCase__			(__lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Dict						    =							f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
     return requests.get(__lowerCamelCase	).json()






def     lowerCamelCase__			(__lowerCamelCase = 10	):
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							"https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							requests.get(__lowerCamelCase	).json()[:max_stories]
     return [get_hackernews_story(__lowerCamelCase	) for story_id in story_ids]






def     lowerCamelCase__			(__lowerCamelCase = 10	):
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							hackernews_top_stories(__lowerCamelCase	)
     return "\n".join("* [{title}]({url})".format(**__lowerCamelCase	) for story in stories	)


if __name__ == "__main__":
       print(hackernews_top_stories_as_markdown())