

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     					=  get_logger(__name__)







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







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







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Union[str, Any]:

     """simple docstring"""







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







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   List[Any]:

     """simple docstring"""







     _lowerCAmelCase :       str	   =     {}
     for k in state_dicts[0].keys():
          _lowerCAmelCase :       List[str]	   =     [sd[k] for sd in state_dicts]
          _lowerCAmelCase :       Any	   =     sum(_lowerCamelCase					) / len(_lowerCamelCase					)
          assert isinstance(_lowerCamelCase		,				torch.Tensor					)
          _lowerCAmelCase :       Tuple	   =     new_t
     return new_sd







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







     _lowerCAmelCase :       List[Any]	   =     AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase					)
     if os.path.isfile(_lowerCamelCase					):
          _lowerCAmelCase :       Optional[int]	   =     [pl_ckpt_path]
     else:
          assert os.path.isdir(_lowerCamelCase					)
          _lowerCAmelCase :       str	   =     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}'''					)

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

     _lowerCAmelCase :       List[str]	   =     hf_model.load_state_dict(_lowerCamelCase		,				strict=_lowerCamelCase					)
     assert not missing, f'''missing keys: {missing}'''
     hf_model.save_pretrained(_lowerCamelCase					)
     try:
          _lowerCAmelCase :       Optional[int]	   =     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)













from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  Dict				,						a					:  str				,						a					:  Optional[Any]     )						->Any:
													super().__init__()
													self.register_modules(unet=a				,						scheduler=a     )






							@torch.no_grad()
							def __call__(   self					:  int				,						a					:  int = 1				,						a					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None				,						a					:  int = 10_00				,						a					:  Optional[str] = "pil"				,						a					:  bool = True				,						)						->Union[ImagePipelineOutput, Tuple]:
													# Sample gaussian noise to begin loop
													if isinstance(self.unet.config.sample_size				,						a     ):
																			SCREAMING_SNAKE_CASE__  :					int =       (
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
													else:
																			SCREAMING_SNAKE_CASE__  :					Dict =       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

													if self.device.type == "mps":
																			# randn does not work reproducibly on mps
																			SCREAMING_SNAKE_CASE__  :					List[str] =       randn_tensor(a				,						generator=a     )
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       image.to(self.device     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       randn_tensor(a				,						generator=a				,						device=self.device     )

													# set step values
													self.scheduler.set_timesteps(a     )

													for t in self.progress_bar(self.scheduler.timesteps     ):
																			# 1. predict noise model_output
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.unet(a				,						a     ).sample

																			# 2. compute previous image: x_t -> x_t-1
																			SCREAMING_SNAKE_CASE__  :					Tuple =       self.scheduler.step(a				,						a				,						a				,						generator=a     ).prev_sample

													SCREAMING_SNAKE_CASE__  :					List[str] =       (image / 2 + 0.5).clamp(0				,						1     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       image.cpu().permute(0				,						2				,						3				,						1     ).numpy()
													if output_type == "pil":
																			SCREAMING_SNAKE_CASE__  :					Dict =       self.numpy_to_pil(a     )

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=a     )