

'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase					: str   					=	logging.get_logger(__name__)

lowerCamelCase					: int   					=	{
    's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}







class 				A__       (			__snake_case    ):
  A__											=							"open-llama"





  def __init__(						self :      Tuple				,   _a :      str=10_0000				,   _a :      Tuple=4096				,   _a :      Optional[int]=1_1008				,   _a :      List[Any]=32				,   _a :      str=32				,   _a :      Union[str, Any]="silu"				,   _a :      Any=2048				,   _a :      Union[str, Any]=0.02				,   _a :      List[str]=1e-6				,   _a :      Union[str, Any]=True				,   _a :      Optional[Any]=0				,   _a :      Any=1				,   _a :      int=2				,   _a :      Optional[Any]=False				,   _a :      Any=True				,   _a :      str=0.1				,   _a :      Tuple=0.1				,   _a :      List[str]=True				,   _a :      List[str]=True				,   _a :      Any=None				,   **_a :      int				,   )      ->       int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE				       =vocab_size
       _SCREAMING_SNAKE_CASE				       =max_position_embeddings
       _SCREAMING_SNAKE_CASE				       =hidden_size
       _SCREAMING_SNAKE_CASE				       =intermediate_size
       _SCREAMING_SNAKE_CASE				       =num_hidden_layers
       _SCREAMING_SNAKE_CASE				       =num_attention_heads
       _SCREAMING_SNAKE_CASE				       =hidden_act
       _SCREAMING_SNAKE_CASE				       =initializer_range
       _SCREAMING_SNAKE_CASE				       =rms_norm_eps
       _SCREAMING_SNAKE_CASE				       =use_cache
       _SCREAMING_SNAKE_CASE				       =kwargs.pop(
           'use_memorry_efficient_attention'				,   A__					)
       _SCREAMING_SNAKE_CASE				       =hidden_dropout_prob
       _SCREAMING_SNAKE_CASE				       =attention_dropout_prob
       _SCREAMING_SNAKE_CASE				       =use_stable_embedding
       _SCREAMING_SNAKE_CASE				       =shared_input_output_embedding
       _SCREAMING_SNAKE_CASE				       =rope_scaling
       self._rope_scaling_validation()

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





  def 		A    (						self :      Any					)      ->       Dict:





       '''simple docstring'''






       if self.rope_scaling is None:
            return

       if not isinstance(self.rope_scaling				,   A__					) 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}"					)
       _SCREAMING_SNAKE_CASE				       =self.rope_scaling.get('type'				,   A__					)
       _SCREAMING_SNAKE_CASE				       =self.rope_scaling.get('factor'				,   A__					)
       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(A__				,   A__					) 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
_UpperCamelCase			:	List[str]								=HfArgumentParser(InitializationArguments)
_UpperCamelCase			:	Dict								=parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_UpperCamelCase			:	List[Any]								=AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_UpperCamelCase			:	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)
_UpperCamelCase			:	int								=AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_UpperCamelCase			:	List[str]								=AutoModelForCausalLM.from_config(config)

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



