

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


snake_case_    						=				get_logger(__name__)

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					str    ):



     '''simple docstring'''





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

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Dict    ):



     '''simple docstring'''





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

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[Dict[str, torch.Tensor]]    ):



     '''simple docstring'''





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

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					str    ):



     '''simple docstring'''





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

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

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

     lowercase__   ,				lowercase__					:     Tuple   = hf_model.load_state_dict(SCREAMING_SNAKE_CASE_			,  strict=SCREAMING_SNAKE_CASE_    )
     assert not missing, f"""missing keys: {missing}"""
     hf_model.save_pretrained(SCREAMING_SNAKE_CASE_    )
     try:
          lowercase__					:     Union[str, Any]   = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_    )
          tok.save_pretrained(SCREAMING_SNAKE_CASE_    )
     except Exception:
          pass
          # dont copy tokenizer if cant


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




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available


@dataclass
class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
    __lowerCamelCase		: Union[List[PIL.Image.Image], np.ndarray]


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
       from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
       from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline

