


from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE       :		List[str]         =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :		List[Any]         =				{
    "s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   lowercase					:	Optional[int]									="""open-llama"""
   def __init__(     self   ,							UpperCamelCase_=10_0000   ,							UpperCamelCase_=4096   ,							UpperCamelCase_=1_1008   ,							UpperCamelCase_=32   ,							UpperCamelCase_=32   ,							UpperCamelCase_="silu"   ,							UpperCamelCase_=2048   ,							UpperCamelCase_=0.02   ,							UpperCamelCase_=1E-6   ,							UpperCamelCase_=True   ,							UpperCamelCase_=0   ,							UpperCamelCase_=1   ,							UpperCamelCase_=2   ,							UpperCamelCase_=False   ,							UpperCamelCase_=True   ,							UpperCamelCase_=0.1   ,							UpperCamelCase_=0.1   ,							UpperCamelCase_=True   ,							UpperCamelCase_=True   ,							UpperCamelCase_=None   ,							**UpperCamelCase_   ,							):
        lowercase_       :Dict     =       vocab_size
        lowercase_       :List[Any]     =       max_position_embeddings
        lowercase_       :int     =       hidden_size
        lowercase_       :Any     =       intermediate_size
        lowercase_       :List[str]     =       num_hidden_layers
        lowercase_       :Optional[int]     =       num_attention_heads
        lowercase_       :Any     =       hidden_act
        lowercase_       :List[str]     =       initializer_range
        lowercase_       :List[Any]     =       rms_norm_eps
        lowercase_       :Union[str, Any]     =       use_cache
        lowercase_       :Any     =       kwargs.pop(
            '''use_memorry_efficient_attention'''   ,							UpperCamelCase_		)
        lowercase_       :Tuple     =       hidden_dropout_prob
        lowercase_       :List[str]     =       attention_dropout_prob
        lowercase_       :Optional[Any]     =       use_stable_embedding
        lowercase_       :List[str]     =       shared_input_output_embedding
        lowercase_       :Optional[Any]     =       rope_scaling
        self._rope_scaling_validation()

        super().__init__(
            pad_token_id=UpperCamelCase_   ,							bos_token_id=UpperCamelCase_   ,							eos_token_id=UpperCamelCase_   ,							tie_word_embeddings=UpperCamelCase_   ,							**UpperCamelCase_   ,							)




   def 		UpperCamelCase  (     self		):
        if self.rope_scaling is None:
             return

        if not isinstance(self.rope_scaling   ,							UpperCamelCase_		) or len(self.rope_scaling		) != 2:
             raise ValueError(
                 '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
                 f"got {self.rope_scaling}"		)
        lowercase_       :Optional[Any]     =       self.rope_scaling.get('''type'''   ,							UpperCamelCase_		)
        lowercase_       :Dict     =       self.rope_scaling.get('''factor'''   ,							UpperCamelCase_		)
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
             raise ValueError(
                 f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"		)
        if rope_scaling_factor is None or not isinstance(UpperCamelCase_   ,							UpperCamelCase_		) or rope_scaling_factor <= 1.0:
             raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"		)





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)

