"""simple docstring"""







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


_a  :  List[Any]=				logging.get_logger(__name__)

_a  :  Dict=				{
    "google/vivit-b-16x2-kinetics400": (
        "https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}

class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Dict					=	"""vivit"""



				def __init__(self       :   List[Any]			,						_A       :   str=2_24			,						_A       :   Optional[int]=32			,						_A       :   str=[2, 16, 16]			,						_A       :   Tuple=3			,						_A       :   List[Any]=7_68			,						_A       :   List[str]=12			,						_A       :   List[Any]=12			,						_A       :   Union[str, Any]=30_72			,						_A       :   List[Any]="gelu_fast"			,						_A       :   Optional[int]=0.0			,						_A       :   Dict=0.0			,						_A       :   Tuple=0.02			,						_A       :   Any=1E-06			,						_A       :   int=True			,						**_A       :   List[Any]			,						)					->			int:
					__snake_case    :							Optional[int]   	=				hidden_size
					__snake_case    :							Union[str, Any]   	=				num_hidden_layers
					__snake_case    :							Tuple   	=				num_attention_heads
					__snake_case    :							Union[str, Any]   	=				intermediate_size
					__snake_case    :							Optional[int]   	=				hidden_act
					__snake_case    :							Dict   	=				hidden_dropout_prob
					__snake_case    :							Optional[Any]   	=				attention_probs_dropout_prob
					__snake_case    :							Optional[int]   	=				initializer_range
					__snake_case    :							Union[str, Any]   	=				layer_norm_eps

					__snake_case    :							List[str]   	=				image_size
					__snake_case    :							Tuple   	=				num_frames
					__snake_case    :							Dict   	=				tubelet_size
					__snake_case    :							Dict   	=				num_channels
					__snake_case    :							int   	=				qkv_bias

					super().__init__(**_A)






"""simple docstring"""







from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_a  :  Tuple=				HfArgumentParser(InitializationArguments)
_a  :  str=				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_a  :  int=				AutoTokenizer.from_pretrained(args.tokenizer_name)

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

# Initialize new model with config
_a  :  str=				AutoModelForCausalLM.from_config(config)

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





