



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 				A			(		__UpperCAmelCase							,					__UpperCAmelCase	)       ->       Union[str, Any]:



						'''simple docstring'''






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







def 				A			(		__UpperCAmelCase	)       ->       Tuple:



						'''simple docstring'''






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







def 				A			(		__UpperCAmelCase	)       ->       str:



						'''simple docstring'''






						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 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       None:



						'''simple docstring'''






						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 .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims





