



'''simple docstring'''






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__:       List[Any]							     =      get_logger(__name__)


def 			snake_case_					( _lowerCAmelCase  :						str       ,				_lowerCAmelCase  :						str  )				->       Tuple:
							if text.startswith(_lowerCAmelCase  ):
														return text[len(_lowerCAmelCase  ) :]
							return text  # or whatever


def 			snake_case_					( _lowerCAmelCase  :						List[Any]  )				->       str:
							return {remove_prefix(_lowerCAmelCase       ,				'''model.'''  ): v for k, v in sd.items()}


def 			snake_case_					( _lowerCAmelCase  :						List[Dict[str, torch.Tensor]]  )				->       List[str]:
							UpperCAmelCase			:	str										=		{}
							for k in state_dicts[0].keys():
														UpperCAmelCase			:	List[Any]										=		[sd[k] for sd in state_dicts]
														UpperCAmelCase			:	str										=		sum(_lowerCAmelCase  ) / len(_lowerCAmelCase  )
														assert isinstance(_lowerCAmelCase       ,				torch.Tensor  )
														UpperCAmelCase			:	int										=		new_t
							return new_sd


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

							if len(_lowerCAmelCase  ) > 1:
														logger.info(f"""averaging the weights of {ckpt_files}"""  )

							UpperCAmelCase			:	List[str]										=		[sanitize(torch.load(_lowerCAmelCase       ,				map_location='''cpu'''  )['''state_dict''']  ) for x in ckpt_files]
							UpperCAmelCase			:	Dict										=		average_state_dicts(_lowerCAmelCase  )

							UpperCAmelCase			:	Union[str, Any]										=		hf_model.load_state_dict(_lowerCAmelCase       ,				strict=_lowerCAmelCase  )
							assert not missing, f"""missing keys: {missing}"""
							hf_model.save_pretrained(_lowerCAmelCase  )
							try:
														UpperCAmelCase			:	int										=		AutoTokenizer.from_pretrained(_lowerCAmelCase  )
														tok.save_pretrained(_lowerCAmelCase  )
							except Exception:
														pass
														# dont copy tokenizer if cant


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










'''simple docstring'''






UpperCamelCase__:       Any							     =      [[0, 1, 1], [0, 0, 1], [1, 0, 0]]

class     SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(						self      :					List[str]		,      __snake_case      :					int		)      ->		int:
								UpperCAmelCase			:	Any										=		name
								UpperCAmelCase			:	Optional[Any]										=		[]
								UpperCAmelCase			:	Any										=		[]







	def 					A     (						self      :					Dict		,      __snake_case      :					str		)      ->		Tuple:
								self.inbound.append(__snake_case		)







	def 					A     (						self      :					Union[str, Any]		,      __snake_case      :					str		)      ->		Optional[int]:
								self.outbound.append(__snake_case		)







	def __repr__(						self      :					Optional[int]		)      ->		Optional[int]:
								return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""


def 			snake_case_					( _lowerCAmelCase  :						Tuple       ,				_lowerCAmelCase  :						Dict=3       ,				_lowerCAmelCase  :						Tuple=0.8_5  )				->       Optional[Any]:
							UpperCAmelCase			:	Tuple										=		{}
							for node in nodes:
														UpperCAmelCase			:	List[str]										=		1

							UpperCAmelCase			:	List[Any]										=		{}
							for node in nodes:
														UpperCAmelCase			:	str										=		len(node.outbound  )

							for i in range(_lowerCAmelCase  ):
														print(f"""======= Iteration {i + 1} ======="""  )
														for _, node in enumerate(_lowerCAmelCase  ):
																					UpperCAmelCase			:	str										=		(1 - d) + d * sum(
																					    ranks[ib] / outbounds[ib] for ib in node.inbound  )
														print(_lowerCAmelCase  )


def 			snake_case_					( )				->       List[str]:
							UpperCAmelCase			:	str										=		list(input('''Enter Names of the Nodes: '''  ).split()  )

							UpperCAmelCase			:	Dict										=		[Node(_lowerCAmelCase  ) for name in names]

							for ri, row in enumerate(_lowerCAmelCase  ):
														for ci, col in enumerate(_lowerCAmelCase  ):
																					if col == 1:
																												nodes[ci].add_inbound(names[ri]  )
																												nodes[ri].add_outbound(names[ci]  )

							print('''======= Nodes ======='''  )
							for node in nodes:
														print(_lowerCAmelCase  )

							page_rank(_lowerCAmelCase  )


if __name__ == "__main__":
						main()





