





from __future__ import annotations

import requests


def 	UpperCamelCase__(       UpperCamelCase__			:       str  )->dict:
       A__											=						f"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
       return requests.get(UpperCamelCase__  ).json()


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


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


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












import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()


def 	UpperCamelCase__(       UpperCamelCase__			:       Any ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       int  )->int:
       # Initialise PyTorch model
       A__											=						BertConfig.from_json_file(UpperCamelCase__  )
       print(f"Building PyTorch model from configuration: {config}"  )
       A__											=						BertForPreTraining(UpperCamelCase__  )

       # Load weights from tf checkpoint
       load_tf_weights_in_bert(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

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


if __name__ == "__main__":
       a__:      str									=		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(
           '--bert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained BERT 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.'
       )
       a__:      Any									=		parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





