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


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

SCREAMING_SNAKE_CASE       :		int         =				{
    "sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   lowercase					:	List[Any]									="""vit_msn"""
   def __init__(     self   ,							UpperCamelCase_=768   ,							UpperCamelCase_=12   ,							UpperCamelCase_=12   ,							UpperCamelCase_=3072   ,							UpperCamelCase_="gelu"   ,							UpperCamelCase_=0.0   ,							UpperCamelCase_=0.0   ,							UpperCamelCase_=0.02   ,							UpperCamelCase_=1E-06   ,							UpperCamelCase_=224   ,							UpperCamelCase_=16   ,							UpperCamelCase_=3   ,							UpperCamelCase_=True   ,							**UpperCamelCase_   ,							):
        super().__init__(**UpperCamelCase_		)

        lowercase_       :Tuple     =       hidden_size
        lowercase_       :str     =       num_hidden_layers
        lowercase_       :List[Any]     =       num_attention_heads
        lowercase_       :List[Any]     =       intermediate_size
        lowercase_       :Dict     =       hidden_act
        lowercase_       :str     =       hidden_dropout_prob
        lowercase_       :int     =       attention_probs_dropout_prob
        lowercase_       :str     =       initializer_range
        lowercase_       :List[Any]     =       layer_norm_eps
        lowercase_       :str     =       image_size
        lowercase_       :Tuple     =       patch_size
        lowercase_       :List[Any]     =       num_channels
        lowercase_       :Optional[Any]     =       qkv_bias





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)

