





import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


_UpperCAmelCase										=					get_logger(__name__)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Dict:
       if text.startswith(UpperCamelCase_	):
              return text[len(UpperCamelCase_	) :]
       return text  # or whatever




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		int:
       return {remove_prefix(UpperCamelCase_       ,		"model."	): v for k, v in sd.items()}




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		Tuple:
       UpperCamelCase_ 							=	{}
       for k in state_dicts[0].keys():
              UpperCamelCase_ 							=	[sd[k] for sd in state_dicts]
              UpperCamelCase_ 							=	sum(UpperCamelCase_	) / len(UpperCamelCase_	)
              assert isinstance(UpperCamelCase_       ,		torch.Tensor	)
              UpperCamelCase_ 							=	new_t
       return new_sd




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		None:
       UpperCamelCase_ 							=	AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_	)
       if os.path.isfile(UpperCamelCase_	):
              UpperCamelCase_ 							=	[pl_ckpt_path]
       else:
              assert os.path.isdir(UpperCamelCase_	)
              UpperCamelCase_ 							=	list(Path(UpperCamelCase_	).glob("*.ckpt"	)	)
              assert ckpt_files, F'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

       if len(UpperCamelCase_	) > 1:
              logger.info(F'''averaging the weights of {ckpt_files}'''	)

       UpperCamelCase_ 							=	[sanitize(torch.load(UpperCamelCase_       ,		map_location="cpu"	)["state_dict"]	) for x in ckpt_files]
       UpperCamelCase_ 							=	average_state_dicts(UpperCamelCase_	)

       UpperCamelCase_							,			UpperCamelCase_ 							=	hf_model.load_state_dict(UpperCamelCase_       ,		strict=UpperCamelCase_	)
       assert not missing, F'''missing keys: {missing}'''
       hf_model.save_pretrained(UpperCamelCase_	)
       try:
              UpperCamelCase_ 							=	AutoTokenizer.from_pretrained(UpperCamelCase_	)
              tok.save_pretrained(UpperCamelCase_	)
       except Exception:
              pass
              # dont copy tokenizer if cant


if __name__ == "__main__":
     fire.Fire(convert_pl_to_hf)






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


A__       =			logging.get_logger(__name__)

A__       =			{
    """MCG-NJU/videomae-base""": """https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json""",
}





class    __lowerCAmelCase					(	lowerCamelCase__			):
				__lowerCamelCase					=							'''videomae'''




				def __init__(				self		,							_snake_case=224		,							_snake_case=16		,							_snake_case=3		,							_snake_case=16		,							_snake_case=2		,							_snake_case=768		,							_snake_case=12		,							_snake_case=12		,							_snake_case=3072		,							_snake_case="gelu"		,							_snake_case=0.0		,							_snake_case=0.0		,							_snake_case=0.02		,							_snake_case=1e-12		,							_snake_case=True		,							_snake_case=True		,							_snake_case=6		,							_snake_case=384		,							_snake_case=4		,							_snake_case=1536		,							_snake_case=True		,							**_snake_case		,							):



									"""simple docstring"""





									super().__init__(**_snake_case						)

									_lowerCAmelCase         =       image_size
									_lowerCAmelCase         =       patch_size
									_lowerCAmelCase         =       num_channels
									_lowerCAmelCase         =       num_frames
									_lowerCAmelCase         =       tubelet_size

									_lowerCAmelCase         =       hidden_size
									_lowerCAmelCase         =       num_hidden_layers
									_lowerCAmelCase         =       num_attention_heads
									_lowerCAmelCase         =       intermediate_size
									_lowerCAmelCase         =       hidden_act
									_lowerCAmelCase         =       hidden_dropout_prob
									_lowerCAmelCase         =       attention_probs_dropout_prob
									_lowerCAmelCase         =       initializer_range
									_lowerCAmelCase         =       layer_norm_eps
									_lowerCAmelCase         =       qkv_bias
									_lowerCAmelCase         =       use_mean_pooling

									_lowerCAmelCase         =       decoder_num_attention_heads
									_lowerCAmelCase         =       decoder_hidden_size
									_lowerCAmelCase         =       decoder_num_hidden_layers
									_lowerCAmelCase         =       decoder_intermediate_size
									_lowerCAmelCase         =       norm_pix_loss



