






"""simple docstring"""






import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()



def       _A    (__a					,       __a					,       __a							)				-> List[str]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						str							=	AlbertConfig.from_json_file(__a							)
    print(f'Building PyTorch model from configuration: {config}'							)
    SCREAMING_SNAKE_CASE_   :						Any							=	AlbertForPreTraining(__a							)

    # Load weights from tf checkpoint
    load_tf_weights_in_albert(__a					,       __a					,       __a							)

    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'							)
    torch.save(model.state_dict()					,       __a							)


if __name__ == "__main__":
  UpperCAmelCase_     :				Any         =				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--albert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained ALBERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  UpperCAmelCase_     :				List[Any]         =				parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)










"""simple docstring"""






from __future__ import annotations

import requests



def       _A    (__a							)				-> dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Any							=	f'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
    return requests.get(__a							).json()



def       _A    (__a = 10							)				-> list[dict]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Any							=	'''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
    SCREAMING_SNAKE_CASE_   :						Dict							=	requests.get(__a							).json()[:max_stories]
    return [get_hackernews_story(__a							) for story_id in story_ids]



def       _A    (__a = 10							)				-> str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Tuple							=	hackernews_top_stories(__a							)
    return "\n".join('''* [{title}]({url})'''.format(**__a							) for story in stories							)


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


