


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE :   Any							      =				{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE :   str							      =				[
          "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TapasForMaskedLM",
          "TapasForQuestionAnswering",
          "TapasForSequenceClassification",
          "TapasModel",
          "TapasPreTrainedModel",
          "load_tf_weights_in_tapas",
      ]
try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE :   str							      =				[
          "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFTapasForMaskedLM",
          "TFTapasForQuestionAnswering",
          "TFTapasForSequenceClassification",
          "TFTapasModel",
          "TFTapasPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
      from .tokenization_tapas import TapasTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tapas import (
                TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TapasForMaskedLM,
                TapasForQuestionAnswering,
                TapasForSequenceClassification,
                TapasModel,
                TapasPreTrainedModel,
                load_tf_weights_in_tapas,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_tapas import (
                TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFTapasForMaskedLM,
                TFTapasForQuestionAnswering,
                TFTapasForSequenceClassification,
                TFTapasModel,
                TFTapasPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE :   Any							      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






from __future__ import annotations

import requests



def        UpperCamelCase_(   lowerCamelCase_				)					->		dict:
    _lowercase :							Dict   	=	F'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
    return requests.get(lowerCamelCase_				).json()



def        UpperCamelCase_(   lowerCamelCase_ = 10				)					->		list[dict]:
    _lowercase :							Dict   	=	'https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'
    _lowercase :							Dict   	=	requests.get(lowerCamelCase_				).json()[:max_stories]
    return [get_hackernews_story(lowerCamelCase_				) for story_id in story_ids]



def        UpperCamelCase_(   lowerCamelCase_ = 10				)					->		str:
    _lowercase :							Optional[int]   	=	hackernews_top_stories(lowerCamelCase_				)
    return "\n".join('* [{title}]({url})'.format(**lowerCamelCase_				) for story in stories				)


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


