






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


a    =       get_logger(__name__)
def       a__						(  A		,				A					):
   if text.startswith(__snake_case					):
      return text[len(__snake_case					) :]
   return text  # or whatever
def       a__						(  A					):
   return {remove_prefix(__snake_case		,				"""model."""					): v for k, v in sd.items()}
def       a__						(  A					):
   lowercase__						:	str         = {}
   for k in state_dicts[0].keys():
      lowercase__						:	Tuple         = [sd[k] for sd in state_dicts]
      lowercase__						:	int         = sum(__snake_case					) / len(__snake_case					)
      assert isinstance(__snake_case		,				torch.Tensor					)
      lowercase__						:	str         = new_t
   return new_sd
def       a__						(  A		,				A		,				A					):
   lowercase__						:	str         = AutoModelForSeqaSeqLM.from_pretrained(__snake_case					)
   if os.path.isfile(__snake_case					):
      lowercase__						:	int         = [pl_ckpt_path]
   else:
      assert os.path.isdir(__snake_case					)
      lowercase__						:	Tuple         = list(Path(__snake_case					).glob("""*.ckpt"""					)					)
      assert ckpt_files, F'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

   if len(__snake_case					) > 1:
      logger.info(F'''averaging the weights of {ckpt_files}'''					)

   lowercase__						:	Optional[Any]         = [sanitize(torch.load(__snake_case		,				map_location="""cpu"""					)["""state_dict"""]					) for x in ckpt_files]
   lowercase__						:	Optional[Any]         = average_state_dicts(__snake_case					)

   lowercase__     ,					lowercase__						:	Optional[int]         = hf_model.load_state_dict(__snake_case		,				strict=__snake_case					)
   assert not missing, F'''missing keys: {missing}'''
   hf_model.save_pretrained(__snake_case					)
   try:
      lowercase__						:	str         = AutoTokenizer.from_pretrained(__snake_case					)
      tok.save_pretrained(__snake_case					)
   except Exception:
      pass
      # dont copy tokenizer if cant


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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a    =       {'configuration_nat': ['NAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NatConfig']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a    =       [
     'NAT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'NatForImageClassification',
     'NatModel',
     'NatPreTrainedModel',
     'NatBackbone',
 ]

if TYPE_CHECKING:
 from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nat import (
      NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      NatBackbone,
      NatForImageClassification,
      NatModel,
      NatPreTrainedModel,
  )

else:
 import sys

 a    =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


