



from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info






def      lowerCAmelCase__		(   lowerCamelCase_		:		List[str]):





				'''simple docstring'''

				return {key.lstrip('''-'''): value for key, value in zip(unknown_args[::2]	,unknown_args[1::2])}






def      lowerCAmelCase__		(   ):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[int]												=					ArgumentParser(
				    '''HuggingFace Datasets CLI tool'''	,usage='''datasets-cli <command> [<args>]'''	,allow_abbrev=lowerCamelCase_)
				lowerCAmelCase__					:    Union[str, Any]												=					parser.add_subparsers(help='''datasets-cli command helpers''')
				set_verbosity_info()

				# Register commands
				ConvertCommand.register_subcommand(lowerCamelCase_)
				EnvironmentCommand.register_subcommand(lowerCamelCase_)
				TestCommand.register_subcommand(lowerCamelCase_)
				RunBeamCommand.register_subcommand(lowerCamelCase_)
				DummyDataCommand.register_subcommand(lowerCamelCase_)

				# Parse args
				lowerCAmelCase__						,		lowerCAmelCase__					:    int												=					parser.parse_known_args()
				if not hasattr(lowerCamelCase_	,'''func'''):
								parser.print_help()
								exit(1)
				lowerCAmelCase__					:    str												=					parse_unknown_args(lowerCamelCase_)

				# Run
				lowerCAmelCase__					:    str												=					args.func(lowerCamelCase_	,**lowerCamelCase_)
				service.run()


if __name__ == "__main__":
							main()






from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
__snake_case    :		Dict  =HfArgumentParser(InitializationArguments)
__snake_case    :		Tuple  =parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
__snake_case    :		Optional[int]  =AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
__snake_case    :		List[str]  ={
    '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)
__snake_case    :		List[Any]  =AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
__snake_case    :		int  =AutoModelForCausalLM.from_config(config)

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

