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


_A   =			logging.get_logger(__name__)

_A   =			{
    "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 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Optional[int]									=		"vivit"





     def __init__(self				:   List[Any]    ,   _A				:   Tuple=2_2_4    ,   _A				:   Optional[int]=3_2    ,   _A				:   str=[2, 1_6, 1_6]    ,   _A				:   Optional[int]=3    ,   _A				:   Optional[int]=7_6_8    ,   _A				:   int=1_2    ,   _A				:   Any=1_2    ,   _A				:   List[str]=3_0_7_2    ,   _A				:   List[Any]="gelu_fast"    ,   _A				:   Any=0.0    ,   _A				:   Dict=0.0    ,   _A				:   Union[str, Any]=0.02    ,   _A				:   Tuple=1E-06    ,   _A				:   Optional[int]=True    ,   **_A				:   List[Any]    ,   )						->						Optional[Any]:
         snake_case								= hidden_size
         snake_case								= num_hidden_layers
         snake_case								= num_attention_heads
         snake_case								= intermediate_size
         snake_case								= hidden_act
         snake_case								= hidden_dropout_prob
         snake_case								= attention_probs_dropout_prob
         snake_case								= initializer_range
         snake_case								= layer_norm_eps

         snake_case								= image_size
         snake_case								= num_frames
         snake_case								= tubelet_size
         snake_case								= num_channels
         snake_case								= qkv_bias

         super().__init__(**_A       )



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
_A   =			logging.get_logger(__name__)



def 						lowercase_      (       A__			,		A__		)							->							int:
    """simple docstring"""

    snake_case								= RobertaPreLayerNormConfig.from_pretrained(
        A__			,		architectures=["RobertaPreLayerNormForMaskedLM"]		)

    # convert state_dict
    snake_case								= torch.load(hf_hub_download(repo_id=A__			,		filename="pytorch_model.bin"		)		)
    snake_case								= {}
    for tensor_key, tensor_value in original_state_dict.items():
        # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
        if tensor_key.startswith("roberta."		):
            snake_case								= "roberta_prelayernorm." + tensor_key[len("roberta."		) :]

        # The original implementation contains weights which are not used, remove them from the state_dict
        if tensor_key.endswith(".self.LayerNorm.weight"		) or tensor_key.endswith(".self.LayerNorm.bias"		):
            continue

        snake_case								= tensor_value

    snake_case								= RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=A__			,		config=A__			,		state_dict=A__		)
    model.save_pretrained(A__		)

    # convert tokenizer
    snake_case								= AutoTokenizer.from_pretrained(A__		)
    tokenizer.save_pretrained(A__		)


if __name__ == "__main__":
   _A   =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--checkpoint-repo",
       default=None,
       type=str,
       required=True,
       help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   _A   =			parser.parse_args()
   convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

