


import requests

SCREAMING_SNAKE_CASE       :		str         =				"https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="




def     UpperCamelCase    (      _a		)					->							None:
     '''simple docstring'''





     lowercase_       :Union[str, Any]     =       requests.get(_NEWS_API + bbc_news_api_key		).json()
     # each article in the list is a dict
     for i, article in enumerate(bbc_news_page['''articles''']       ,		1		):
          print(f"{i}.) {article['title']}"		)


if __name__ == "__main__":
   fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE       :		List[str]         =				HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE       :		List[Any]         =				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE       :		Optional[Any]         =				{
    "vocab_size": len(tokenizer),
    "scale_attn_by_inverse_layer_idx": True,
    "reorder_and_upcast_attn": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE       :		Any         =				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE       :		Dict         =				AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)

