



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__			:   Any      			=    get_logger(__name__)


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


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


def 	_A	(   _UpperCamelCase			):
				_UpperCAmelCase						:  Tuple     					=	{}
				for k in state_dicts[0].keys():
								_UpperCAmelCase						:  int     					=	[sd[k] for sd in state_dicts]
								_UpperCAmelCase						:  Tuple     					=	sum(_UpperCamelCase			) / len(_UpperCamelCase			)
								assert isinstance(_UpperCamelCase      ,					torch.Tensor			)
								_UpperCAmelCase						:  List[Any]     					=	new_t
				return new_sd


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			):
				_UpperCAmelCase						:  Tuple     					=	AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)
				if os.path.isfile(_UpperCamelCase			):
								_UpperCAmelCase						:  Optional[int]     					=	[pl_ckpt_path]
				else:
								assert os.path.isdir(_UpperCamelCase			)
								_UpperCAmelCase						:  Dict     					=	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						:  Optional[Any]     					=	[sanitize(torch.load(_UpperCamelCase      ,					map_location='''cpu'''			)['''state_dict''']			) for x in ckpt_files]
				_UpperCAmelCase						:  Optional[int]     					=	average_state_dicts(_UpperCamelCase			)

				_UpperCAmelCase					, _UpperCAmelCase						:  Dict     					=	hf_model.load_state_dict(_UpperCamelCase      ,					strict=_UpperCamelCase			)
				assert not missing, F'''missing keys: {missing}'''
				hf_model.save_pretrained(_UpperCamelCase			)
				try:
								_UpperCAmelCase						:  Any     					=	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)





def 	_A	(   _UpperCamelCase			):
				if not isinstance(_UpperCamelCase      ,					_UpperCamelCase			):
								_UpperCAmelCase						:  List[Any]     					=	(
								    '''operation can not be conducted on a object of type '''
								    F'''{type(_UpperCamelCase			).__name__}'''
								)
								raise TypeError(_UpperCamelCase			)
				_UpperCAmelCase						:  List[str]     					=	''''''
				for _ in range(0      ,					32			):
								bit_string += str(number % 2			)
								_UpperCAmelCase						:  Union[str, Any]     					=	number >> 1
				return bit_string


def 	_A	(   _UpperCamelCase			):
				if number < 0:
								raise ValueError('''the value of input must be positive'''			)
				elif isinstance(_UpperCamelCase      ,					_UpperCamelCase			):
								raise TypeError('''Input value must be a \'int\' type'''			)
				elif isinstance(_UpperCamelCase      ,					_UpperCamelCase			):
								raise TypeError('''\'<\' not supported between instances of \'str\' and \'int\''''			)
				_UpperCAmelCase						:  Tuple     					=	0
				# iterator over [1 to 32],since we are dealing with 32 bit integer
				for _ in range(1      ,					33			):
								# left shift the bits by unity
								_UpperCAmelCase						:  List[str]     					=	result << 1
								# get the end bit
								_UpperCAmelCase						:  List[Any]     					=	number % 2
								# right shift the bits by unity
								_UpperCAmelCase						:  Optional[int]     					=	number >> 1
								# add that bit to our ans
								_UpperCAmelCase						:  Any     					=	result | end_bit
				return get_reverse_bit_string(_UpperCamelCase			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
