


'''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


__magic_name__   :   Union[str, Any]  							=get_logger(__name__)




def      __snake_case       (       lowerCamelCase_ :				str	,  lowerCamelCase_ :				str						):
				'''simple docstring'''




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




def      __snake_case       (       lowerCamelCase_ :				Tuple						):
				'''simple docstring'''




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




def      __snake_case       (       lowerCamelCase_ :				List[Dict[str, torch.Tensor]]						):
				'''simple docstring'''




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




def      __snake_case       (       lowerCamelCase_ :				str	,  lowerCamelCase_ :				str	,  lowerCamelCase_ :				str						):
				'''simple docstring'''




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

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

				__magic_name__  ,      __magic_name__	  = hf_model.load_state_dict(lowerCamelCase_	,  strict=lowerCamelCase_						)
				assert not missing, F'missing keys: {missing}'
				hf_model.save_pretrained(lowerCamelCase_						)
				try:
								__magic_name__	  = 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'''
import numpy as np

__magic_name__   :   str  							=[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]






class 		UpperCamelCase_		:





						"""simple docstring"""




						def __init__( self				:					Union[str, Any]  )							->					None:
										__magic_name__	  = np.array(_lowerCamelCase  )
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					str  )							->					np.ndarray:
										__magic_name__  ,      __magic_name__	  = np.where(letter == self.SQUARE  )
										__magic_name__	  = np.concatenate([indexa + 1, indexa + 1]  )
										return indexes
						def 					__A					( self				:					Any	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					int  )							->					str:
										return self.SQUARE[indexa - 1, indexa - 1]
						def 					__A					( self				:					Union[str, Any]	,				_lowerCamelCase				:					str  )							->					str:
										__magic_name__	  = message.lower()
										__magic_name__	  = message.replace("j"	,				"i"  )

										__magic_name__	  = ""
										for letter_index in range(len(_lowerCamelCase  )  ):
														if message[letter_index] != " ":
																		__magic_name__	  = self.letter_to_numbers(message[letter_index]  )
																		__magic_name__	  = encoded_message + str(numbers[0]  ) + str(numbers[1]  )
														elif message[letter_index] == " ":
																		__magic_name__	  = encoded_message + " "

										return encoded_message

						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					str  )							->					str:
										__magic_name__	  = message.replace(" "	,				"  "  )
										__magic_name__	  = ""
										for numbers_index in range(int(len(_lowerCamelCase  ) / 2  )  ):
														if message[numbers_index * 2] != " ":
																		__magic_name__	  = message[numbers_index * 2]
																		__magic_name__	  = message[numbers_index * 2 + 1]

																		__magic_name__	  = self.numbers_to_letter(int(_lowerCamelCase  )	,				int(_lowerCamelCase  )  )
																		__magic_name__	  = decoded_message + letter
														elif message[numbers_index * 2] == " ":
																		__magic_name__	  = decoded_message + " "

										return decoded_message



