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




				barcode //= 10  # exclude the last digit
				__magic_name__	  = False
				__magic_name__	  = 0

				# extract and check each digit
				while barcode != 0:
								__magic_name__	  = 1 if checker else 3
								s += mult * (barcode % 10)
								barcode //= 10
								__magic_name__	  = not checker

				return (10 - (s % 10)) % 10




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




				return len(str(lowerCamelCase_						)						) == 13 and get_check_digit(lowerCamelCase_						) == barcode % 10




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




				if str(lowerCamelCase_						).isalpha():
								__magic_name__	  = F'Barcode \'{barcode}\' has alphabetic characters.'
								raise ValueError(lowerCamelCase_						)
				elif int(lowerCamelCase_						) < 0:
								raise ValueError("The entered barcode has a negative value. Try again."						)
				else:
								return int(lowerCamelCase_						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__magic_name__   :   Optional[int]  							=get_barcode(input('Barcode: ').strip())

						if is_valid(barcode):
												print(F'''\'{barcode}\' is a valid barcode.''')
						else:
												print(F'''\'{barcode}\' is NOT a valid barcode.''')



