

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


lowerCamelCase					: Optional[Any]   					=	logging.get_logger(__name__)

lowerCamelCase					: Union[str, Any]   					=	{
    'Visual-Attention-Network/van-base': (
        'https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'
    ),
}







class 				A__       (			__snake_case    ):
  A__											=							"van"





  def __init__(						self :      List[str]				,   _a :      Tuple=224				,   _a :      Dict=3				,   _a :      Tuple=[7, 3, 3, 3]				,   _a :      List[Any]=[4, 2, 2, 2]				,   _a :      Union[str, Any]=[64, 128, 320, 512]				,   _a :      Any=[3, 3, 12, 3]				,   _a :      Optional[Any]=[8, 8, 4, 4]				,   _a :      int="gelu"				,   _a :      Any=0.02				,   _a :      Union[str, Any]=1e-6				,   _a :      List[str]=1e-2				,   _a :      Union[str, Any]=0.0				,   _a :      List[Any]=0.0				,   **_a :      List[Any]				,   )      ->       Optional[Any]:





       '''simple docstring'''






       super().__init__(**A__					)
       _SCREAMING_SNAKE_CASE				       =image_size
       _SCREAMING_SNAKE_CASE				       =num_channels
       _SCREAMING_SNAKE_CASE				       =patch_sizes
       _SCREAMING_SNAKE_CASE				       =strides
       _SCREAMING_SNAKE_CASE				       =hidden_sizes
       _SCREAMING_SNAKE_CASE				       =depths
       _SCREAMING_SNAKE_CASE				       =mlp_ratios
       _SCREAMING_SNAKE_CASE				       =hidden_act
       _SCREAMING_SNAKE_CASE				       =initializer_range
       _SCREAMING_SNAKE_CASE				       =layer_norm_eps
       _SCREAMING_SNAKE_CASE				       =layer_scale_init_value
       _SCREAMING_SNAKE_CASE				       =drop_path_rate
       _SCREAMING_SNAKE_CASE				       =dropout_rate








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)



