
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowercase  :					Any						    =							logging.get_logger(__name__)

lowercase  :					Optional[int]						    =							{
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}

class __snake_case		(      lowerCAmelCase     ):
    _a		:						Any=				"deberta-v2"
    def __init__(  self		,snake_case=128100		,snake_case=1536		,snake_case=24		,snake_case=24		,snake_case=6144		,snake_case="gelu"		,snake_case=0.1		,snake_case=0.1		,snake_case=512		,snake_case=0		,snake_case=0.02		,snake_case=1e-7		,snake_case=False		,snake_case=-1		,snake_case=0		,snake_case=True		,snake_case=None		,snake_case=0		,snake_case="gelu"		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case      )

           lowercase							:     Union[str, Any]       	=      hidden_size
           lowercase							:     int       	=      num_hidden_layers
           lowercase							:     str       	=      num_attention_heads
           lowercase							:     Any       	=      intermediate_size
           lowercase							:     Any       	=      hidden_act
           lowercase							:     Optional[Any]       	=      hidden_dropout_prob
           lowercase							:     List[Any]       	=      attention_probs_dropout_prob
           lowercase							:     Optional[Any]       	=      max_position_embeddings
           lowercase							:     Optional[int]       	=      type_vocab_size
           lowercase							:     Optional[int]       	=      initializer_range
           lowercase							:     Tuple       	=      relative_attention
           lowercase							:     Any       	=      max_relative_positions
           lowercase							:     Any       	=      pad_token_id
           lowercase							:     Optional[int]       	=      position_biased_input

           # Backwards compatibility
           if type(snake_case      ) == str:
                  lowercase							:     Tuple       	=      [x.strip() for x in pos_att_type.lower().split("""|"""      )]

           lowercase							:     List[str]       	=      pos_att_type
           lowercase							:     Dict       	=      vocab_size
           lowercase							:     int       	=      layer_norm_eps

           lowercase							:     Union[str, Any]       	=      kwargs.get("""pooler_hidden_size"""		,snake_case      )
           lowercase							:     Any       	=      pooler_dropout
           lowercase							:     Dict       	=      pooler_hidden_act






class __snake_case		(      lowerCAmelCase     ):
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.task == "multiple-choice":
                  lowercase							:     Union[str, Any]       	=      {0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
                  lowercase							:     List[str]       	=      {0: """batch""", 1: """sequence"""}
           if self._config.type_vocab_size > 0:
                  return OrderedDict(
                      [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)]      )
           else:
                  return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)]      )
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return 12
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = -1		,snake_case = -1		,snake_case = -1		,snake_case = False		,snake_case = None		,snake_case = 3		,snake_case = 40		,snake_case = 40		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      super().generate_dummy_inputs(preprocessor=snake_case		,framework=snake_case      )
           if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                  del dummy_inputs["token_type_ids"]
           return dummy_inputs




'''simple docstring'''







from __future__ import annotations

import requests


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str  )  ->			dict:
       _a    : Any     					=F"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
       return requests.get(_UpperCAmelCase  ).json()


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int = 10  )  ->			list[dict]:
       _a    : Union[str, Any]     					="""https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"""
       _a    : int     					=requests.get(_UpperCAmelCase  ).json()[:max_stories]
       return [get_hackernews_story(_UpperCAmelCase  ) for story_id in story_ids]


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int = 10  )  ->			str:
       _a    : Union[str, Any]     					=hackernews_top_stories(_UpperCAmelCase  )
       return "\n".join("""* [{title}]({url})""".format(**_UpperCAmelCase  ) for story in stories  )


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


