




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


__A						:		int          =						get_logger(__name__)





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     List[Any]:







				'''simple docstring'''


				if text.startswith(UpperCamelCase__   ):
								return text[len(UpperCamelCase__   ) :]
				return text  # or whatever





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Tuple:







				'''simple docstring'''


				return {remove_prefix(UpperCamelCase__						,			"""model."""   ): v for k, v in sd.items()}





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Optional[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  {}
				for k in state_dicts[0].keys():
								__lowerCAmelCase					=  [sd[k] for sd in state_dicts]
								__lowerCAmelCase					=  sum(UpperCamelCase__   ) / len(UpperCamelCase__   )
								assert isinstance(UpperCamelCase__						,			torch.Tensor   )
								__lowerCAmelCase					=  new_t
				return new_sd





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     None:







				'''simple docstring'''


				__lowerCAmelCase					=  AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__   )
				if os.path.isfile(UpperCamelCase__   ):
								__lowerCAmelCase					=  [pl_ckpt_path]
				else:
								assert os.path.isdir(UpperCamelCase__   )
								__lowerCAmelCase					=  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}'''   )

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

				__lowerCAmelCase	,				__lowerCAmelCase					=  hf_model.load_state_dict(UpperCamelCase__						,			strict=UpperCamelCase__   )
				assert not missing, F'''missing keys: {missing}'''
				hf_model.save_pretrained(UpperCamelCase__   )
				try:
								__lowerCAmelCase					=  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


lowerCamelCase 	=   logging.get_logger(__name__)

lowerCamelCase 	=   {
    '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      Tuple        =  'yoso'



   def __init__(							self,       lowercase_=50265,       lowercase_=768,       lowercase_=12,       lowercase_=12,       lowercase_=3072,       lowercase_="gelu",       lowercase_=0.1,       lowercase_=0.1,       lowercase_=4096,       lowercase_=1,       lowercase_=0.02,       lowercase_=1E-12,       lowercase_="absolute",       lowercase_=True,       lowercase_=9,       lowercase_=64,       lowercase_=None,       lowercase_=True,       lowercase_=True,       lowercase_=1,       lowercase_=0,       lowercase_=2,       **lowercase_,       )				->       List[str]:






     """simple docstring"""






     super().__init__(pad_token_id=lowercase_,       bos_token_id=lowercase_,       eos_token_id=lowercase_,       **lowercase_			)

     a__    =vocab_size
     a__    =max_position_embeddings
     a__    =hidden_size
     a__    =num_hidden_layers
     a__    =num_attention_heads
     a__    =intermediate_size
     a__    =hidden_act
     a__    =hidden_dropout_prob
     a__    =attention_probs_dropout_prob
     a__    =initializer_range
     a__    =type_vocab_size
     a__    =layer_norm_eps
     a__    =position_embedding_type
     a__    =use_expectation
     a__    =hash_code_len
     a__    =num_hash
     a__    =conv_window
     a__    =use_fast_hash
     a__    =lsh_backward
