
from argparse import ArgumentParser

from . import BaseTransformersCLICommand




def     UpperCamelCase    (      _a		)					->							List[str]:
     '''simple docstring'''





     return DownloadCommand(args.model       ,		args.cache_dir       ,		args.force       ,		args.trust_remote_code		)






class      UpperCamelCase					(		UpperCamelCase__     ):


   '''simple docstring'''







   @staticmethod
   def 		UpperCamelCase  (     UpperCamelCase_		):
        lowercase_       :Tuple     =       parser.add_parser('''download'''		)
        download_parser.add_argument(
            '''--cache-dir'''   ,							type=UpperCamelCase_   ,							default=UpperCamelCase_   ,							help='''Path to location to store the models'''		)
        download_parser.add_argument(
            '''--force'''   ,							action='''store_true'''   ,							help='''Force the model to be download even if already in cache-dir'''		)
        download_parser.add_argument(
            '''--trust-remote-code'''   ,							action='''store_true'''   ,							help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'''   ,							)
        download_parser.add_argument('''model'''   ,							type=UpperCamelCase_   ,							help='''Name of the model to download'''		)
        download_parser.set_defaults(func=UpperCamelCase_		)
   def __init__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Any     =       model
        lowercase_       :List[Any]     =       cache
        lowercase_       :List[str]     =       force
        lowercase_       :Dict     =       trust_remote_code




   def 		UpperCamelCase  (     self		):
        from ..models.auto import AutoModel, AutoTokenizer

        AutoModel.from_pretrained(
            self._model   ,							cache_dir=self._cache   ,							force_download=self._force   ,							trust_remote_code=self._trust_remote_code		)
        AutoTokenizer.from_pretrained(
            self._model   ,							cache_dir=self._cache   ,							force_download=self._force   ,							trust_remote_code=self._trust_remote_code		)





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)

