


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE :   List[Any]							      =				{
    "configuration_albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig", "AlbertOnnxConfig"],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE :   int							      =				["AlbertTokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE :   List[str]							      =				["AlbertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE :   List[str]							      =				[
          "ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "AlbertForMaskedLM",
          "AlbertForMultipleChoice",
          "AlbertForPreTraining",
          "AlbertForQuestionAnswering",
          "AlbertForSequenceClassification",
          "AlbertForTokenClassification",
          "AlbertModel",
          "AlbertPreTrainedModel",
          "load_tf_weights_in_albert",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE :   Optional[Any]							      =				[
          "TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFAlbertForMaskedLM",
          "TFAlbertForMultipleChoice",
          "TFAlbertForPreTraining",
          "TFAlbertForQuestionAnswering",
          "TFAlbertForSequenceClassification",
          "TFAlbertForTokenClassification",
          "TFAlbertMainLayer",
          "TFAlbertModel",
          "TFAlbertPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				[
          "FlaxAlbertForMaskedLM",
          "FlaxAlbertForMultipleChoice",
          "FlaxAlbertForPreTraining",
          "FlaxAlbertForQuestionAnswering",
          "FlaxAlbertForSequenceClassification",
          "FlaxAlbertForTokenClassification",
          "FlaxAlbertModel",
          "FlaxAlbertPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_albert import AlbertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_albert_fast import AlbertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_albert import (
                ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                AlbertForMaskedLM,
                AlbertForMultipleChoice,
                AlbertForPreTraining,
                AlbertForQuestionAnswering,
                AlbertForSequenceClassification,
                AlbertForTokenClassification,
                AlbertModel,
                AlbertPreTrainedModel,
                load_tf_weights_in_albert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_albert import (
                TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFAlbertForMaskedLM,
                TFAlbertForMultipleChoice,
                TFAlbertForPreTraining,
                TFAlbertForQuestionAnswering,
                TFAlbertForSequenceClassification,
                TFAlbertForTokenClassification,
                TFAlbertMainLayer,
                TFAlbertModel,
                TFAlbertPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_albert import (
                FlaxAlbertForMaskedLM,
                FlaxAlbertForMultipleChoice,
                FlaxAlbertForPreTraining,
                FlaxAlbertForQuestionAnswering,
                FlaxAlbertForSequenceClassification,
                FlaxAlbertForTokenClassification,
                FlaxAlbertModel,
                FlaxAlbertPreTrainedModel,
            )
else:
      import sys

      SCREAMING_SNAKE_CASE :   str							      =				_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())


