



'''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


lowercase											=      get_logger(__name__)






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							):


     '''simple docstring'''







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






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







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






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







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






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							,						lowercase__							):


     '''simple docstring'''







     a_									=AutoModelForSeqaSeqLM.from_pretrained(lowercase__							)
     if os.path.isfile(lowercase__							):
          a_									=[pl_ckpt_path]
     else:
          assert os.path.isdir(lowercase__							)
          a_									=list(Path(lowercase__							).glob("*.ckpt"							)							)
          assert ckpt_files, F"""could not find any ckpt files inside the {pl_ckpt_path} directory"""

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

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

     a_		,    a_									=hf_model.load_state_dict(lowercase__							,						strict=lowercase__							)
     assert not missing, F"""missing keys: {missing}"""
     hf_model.save_pretrained(lowercase__							)
     try:
          a_									=AutoTokenizer.from_pretrained(lowercase__							)
          tok.save_pretrained(lowercase__							)
     except Exception:
          pass
          # dont copy tokenizer if cant


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







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowercase											=      {}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase											=      ['''BarthezTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase											=      ['''BarthezTokenizerFast''']


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_barthez import BarthezTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_barthez_fast import BarthezTokenizerFast

else:
  import sys

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


