from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available
def 					__lowercase						(    lowerCamelCase__       )					->Dict:

  '''simple docstring'''




  if is_torch_available():
    import torch

    if isinstance(lowerCamelCase__			,					torch.Tensor       ):
      return True

  return isinstance(lowerCamelCase__			,					np.ndarray       )







class        UpperCAmelCase_					(				_a     ):




    def     __lowerCamelCase			(       self	: str )						->			List[str]:
      a__  		=  fields(self )

      # Safety and consistency checks
      if not len(lowerCAmelCase ):
        raise ValueError(f"{self.__class__.__name__} has no fields." )

      a__  		=  getattr(self   ,   class_fields[0].name )
      a__  		=  all(getattr(self   ,   field.name ) is None for field in class_fields[1:] )

      if other_fields_are_none and isinstance(lowerCAmelCase   ,   lowerCAmelCase ):
        for key, value in first_field.items():
          a__  		=  value
      else:
        for field in class_fields:
          a__  		=  getattr(self   ,   field.name )
          if v is not None:
            a__  		=  v




    def __delitem__(       self	: List[str]   ,   *lowerCAmelCase	: Optional[int]   ,   **lowerCAmelCase	: List[str] )						->			Optional[Any]:
      raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance." )




    def     __lowerCamelCase			(       self	: Any   ,   *lowerCAmelCase	: Any   ,   **lowerCAmelCase	: Dict )						->			Any:
      raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance." )




    def     __lowerCamelCase			(       self	: int   ,   *lowerCAmelCase	: Optional[int]   ,   **lowerCAmelCase	: str )						->			Optional[int]:
      raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance." )




    def     __lowerCamelCase			(       self	: Optional[int]   ,   *lowerCAmelCase	: Tuple   ,   **lowerCAmelCase	: str )						->			List[str]:
      raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance." )




    def __getitem__(       self	: Optional[Any]   ,   lowerCAmelCase	: Any )						->			List[Any]:
      if isinstance(lowerCAmelCase   ,   lowerCAmelCase ):
        a__  		=  dict(self.items() )
        return inner_dict[k]
      else:
        return self.to_tuple()[k]




    def __setattr__(       self	: str   ,   lowerCAmelCase	: Dict   ,   lowerCAmelCase	: Any )						->			List[Any]:
      if name in self.keys() and value is not None:
        # Don't call self.__setitem__ to avoid recursion errors
        super().__setitem__(lowerCAmelCase   ,   lowerCAmelCase )
      super().__setattr__(lowerCAmelCase   ,   lowerCAmelCase )




    def __setitem__(       self	: Union[str, Any]   ,   lowerCAmelCase	: Any   ,   lowerCAmelCase	: Optional[Any] )						->			Tuple:
      # Will raise a KeyException if needed
      super().__setitem__(lowerCAmelCase   ,   lowerCAmelCase )
      # Don't call self.__setattr__ to avoid recursion errors
      super().__setattr__(lowerCAmelCase   ,   lowerCAmelCase )




    def     __lowerCamelCase			(       self	: Any )						->			Tuple[Any]:
      return tuple(self[k] for k in self.keys() )


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


_SCREAMING_SNAKE_CASE     =     get_logger(__name__)
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->Optional[int]:

  '''simple docstring'''




  if text.startswith(lowerCamelCase__       ):
    return text[len(lowerCamelCase__       ) :]
  return text  # or whatever
def 					__lowercase						(    lowerCamelCase__       )					->int:

  '''simple docstring'''




  return {remove_prefix(lowerCamelCase__			,					"model."       ): v for k, v in sd.items()}
def 					__lowercase						(    lowerCamelCase__       )					->Any:

  '''simple docstring'''




  a__  		=  {}
  for k in state_dicts[0].keys():
    a__  		=  [sd[k] for sd in state_dicts]
    a__  		=  sum(lowerCamelCase__       ) / len(lowerCamelCase__       )
    assert isinstance(lowerCamelCase__			,					torch.Tensor       )
    a__  		=  new_t
  return new_sd
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->None:

  '''simple docstring'''




  a__  		=  AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__       )
  if os.path.isfile(lowerCamelCase__       ):
    a__  		=  [pl_ckpt_path]
  else:
    assert os.path.isdir(lowerCamelCase__       )
    a__  		=  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}"       )

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

  a__		,a__  		=  hf_model.load_state_dict(lowerCamelCase__			,					strict=lowerCamelCase__       )
  assert not missing, f"missing keys: {missing}"
  hf_model.save_pretrained(lowerCamelCase__       )
  try:
    a__  		=  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)

