


'''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[str]				     =					get_logger(__name__)

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase):
						if text.startswith(lowerCamelCase):
												return text[len(lowerCamelCase) :]
						return text  # or whatever

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

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

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						__lowerCAmelCase											=					AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase)
						if os.path.isfile(lowerCamelCase):
												__lowerCAmelCase											=					[pl_ckpt_path]
						else:
												assert os.path.isdir(lowerCamelCase)
												__lowerCAmelCase											=					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}""")

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

						__lowerCAmelCase							,       __lowerCAmelCase											=					hf_model.load_state_dict(lowerCamelCase,					strict=lowerCamelCase)
						assert not missing, F"""missing keys: {missing}"""
						hf_model.save_pretrained(lowerCamelCase)
						try:
												__lowerCAmelCase											=					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 math

def 				__magic_name__(    lowerCamelCase):
						__lowerCAmelCase											=					math.log(2,					1_0) * number
						__lowerCAmelCase											=					round((log_number - int(lowerCamelCase)),					1_5)

						return difference

def 				__magic_name__(    lowerCamelCase = 6_7_8_9_1_0):
						__lowerCAmelCase											=					9_0
						__lowerCAmelCase											=					0

						__lowerCAmelCase											=					math.log(1.23,					1_0)
						__lowerCAmelCase											=					math.log(1.24,					1_0)
						__lowerCAmelCase											=					0

						while position < number:
												__lowerCAmelCase											=					log_difference(lowerCamelCase)

												if difference >= upper_limit:
																		power_iterator += 9_3

												elif difference < lower_limit:
																		power_iterator += 1_9_6

												else:
																		__lowerCAmelCase											=					power_iterator
																		power_iterator += 1_9_6
																		position += 1

						return previous_power


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	print(f"""{solution() = }""")

