import glob
import os
from typing import Dict, List, Union

import torch

from diffusers.utils import is_safetensors_available


if is_safetensors_available():
     import safetensors.torch

from huggingface_hub import snapshot_download

from diffusers import DiffusionPipeline, __version__
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME







class        UpperCAmelCase_					(				_a     ):




    def __init__(       self	: List[str] )						->			Any:
      self.register_to_config()
      super().__init__()




    def     __lowerCamelCase			(       self	: int   ,   lowerCAmelCase	: Optional[int]   ,   lowerCAmelCase	: List[Any] )						->			int:
      if dicta == dicta:
        return True
      else:
        a__		,a__  		=  self._remove_meta_keys(lowerCAmelCase )
        a__		,a__  		=  self._remove_meta_keys(lowerCAmelCase )
        if configa == configa:
          print(f"Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}." )
          return True
      return False




    def     __lowerCamelCase			(       self	: Tuple   ,   lowerCAmelCase	: Dict )						->			str:
      a__  		=  []
      a__  		=  config_dict.copy()
      for key in config_dict.keys():
        if key.startswith("_" ):
          temp_dict.pop(lowerCAmelCase )
          meta_keys.append(lowerCAmelCase )
      return (temp_dict, meta_keys)




    @torch.no_grad()
    def     __lowerCamelCase			(       self	: Dict   ,   lowerCAmelCase	: List[Union[str, os.PathLike]]   ,   **lowerCAmelCase	: Optional[int] )						->			str:
      a__  		=  kwargs.pop("cache_dir"   ,   lowerCAmelCase )
      a__  		=  kwargs.pop("resume_download"   ,   lowerCAmelCase )
      a__  		=  kwargs.pop("force_download"   ,   lowerCAmelCase )
      a__  		=  kwargs.pop("proxies"   ,   lowerCAmelCase )
      a__  		=  kwargs.pop("local_files_only"   ,   lowerCAmelCase )
      a__  		=  kwargs.pop("use_auth_token"   ,   lowerCAmelCase )
      a__  		=  kwargs.pop("revision"   ,   lowerCAmelCase )
      a__  		=  kwargs.pop("torch_dtype"   ,   lowerCAmelCase )
      a__  		=  kwargs.pop("device_map"   ,   lowerCAmelCase )

      a__  		=  kwargs.pop("alpha"   ,   0.5 )
      a__  		=  kwargs.pop("interp"   ,   lowerCAmelCase )

      print("Received list"   ,   lowerCAmelCase )
      print(f"Combining with alpha={alpha}, interpolation mode={interp}" )

      a__  		=  len(lowerCAmelCase )
      # Ignore result from model_index_json comparision of the two checkpoints
      a__  		=  kwargs.pop("force"   ,   lowerCAmelCase )

      # If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
      if checkpoint_count > 3 or checkpoint_count < 2:
        raise ValueError(
            "Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being"
            " passed." )

      print("Received the right number of checkpoints" )
      # chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
      # chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None

      # Validate that the checkpoints can be merged
      # Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
      a__  		=  []
      for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
        a__  		=  DiffusionPipeline.load_config(
            lowerCAmelCase   ,   cache_dir=lowerCAmelCase   ,   resume_download=lowerCAmelCase   ,   force_download=lowerCAmelCase   ,   proxies=lowerCAmelCase   ,   local_files_only=lowerCAmelCase   ,   use_auth_token=lowerCAmelCase   ,   revision=lowerCAmelCase   ,   )
        config_dicts.append(lowerCAmelCase )

      a__  		=  True
      for idx in range(1   ,   len(lowerCAmelCase ) ):
        comparison_result &= self._compare_model_configs(config_dicts[idx - 1]   ,   config_dicts[idx] )
        if not force and comparison_result is False:
          raise ValueError("Incompatible checkpoints. Please check model_index.json for the models." )
          print(config_dicts[0]   ,   config_dicts[1] )
      print("Compatible model_index.json files found" )
      # Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
      a__  		=  []
      for pretrained_model_name_or_path, config_dict in zip(lowerCAmelCase   ,   lowerCAmelCase ):
        a__  		=  [k for k in config_dict.keys() if not k.startswith("_" )]
        a__  		=  [os.path.join(lowerCAmelCase   ,   "*" ) for k in folder_names]
        allow_patterns += [
            WEIGHTS_NAME,
            SCHEDULER_CONFIG_NAME,
            CONFIG_NAME,
            ONNX_WEIGHTS_NAME,
            DiffusionPipeline.config_name,
        ]
        a__  		=  config_dict.get("_class_name" )
        a__  		=  {"diffusers": __version__, "pipeline_class": requested_pipeline_class}

        a__  		=  (
            pretrained_model_name_or_path
            if os.path.isdir(lowerCAmelCase )
            else snapshot_download(
                lowerCAmelCase   ,   cache_dir=lowerCAmelCase   ,   resume_download=lowerCAmelCase   ,   proxies=lowerCAmelCase   ,   local_files_only=lowerCAmelCase   ,   use_auth_token=lowerCAmelCase   ,   revision=lowerCAmelCase   ,   allow_patterns=lowerCAmelCase   ,   user_agent=lowerCAmelCase   ,   )
        )
        print("Cached Folder"   ,   lowerCAmelCase )
        cached_folders.append(lowerCAmelCase )

      # Step 3:-
      # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
      a__  		=  DiffusionPipeline.from_pretrained(
          cached_folders[0]   ,   torch_dtype=lowerCAmelCase   ,   device_map=lowerCAmelCase )
      final_pipe.to(self.device )

      a__  		=  None
      if len(lowerCAmelCase ) > 2:
        a__  		=  os.path.join(cached_folders[2] )

      if interp == "sigmoid":
        a__  		=  CheckpointMergerPipeline.sigmoid
      elif interp == "inv_sigmoid":
        a__  		=  CheckpointMergerPipeline.inv_sigmoid
      elif interp == "add_diff":
        a__  		=  CheckpointMergerPipeline.add_difference
      else:
        a__  		=  CheckpointMergerPipeline.weighted_sum

      # Find each module's state dict.
      for attr in final_pipe.config.keys():
        if not attr.startswith("_" ):
          a__  		=  os.path.join(cached_folders[1]   ,   lowerCAmelCase )
          if os.path.exists(lowerCAmelCase ):
            a__  		=  [
                *glob.glob(os.path.join(lowerCAmelCase   ,   "*.safetensors" ) ),
                *glob.glob(os.path.join(lowerCAmelCase   ,   "*.bin" ) ),
            ]
            a__  		=  files[0] if len(lowerCAmelCase ) > 0 else None
          if len(lowerCAmelCase ) < 3:
            a__  		=  None
          else:
            a__  		=  os.path.join(cached_folders[2]   ,   lowerCAmelCase )
            if os.path.exists(lowerCAmelCase ):
              a__  		=  [
                  *glob.glob(os.path.join(lowerCAmelCase   ,   "*.safetensors" ) ),
                  *glob.glob(os.path.join(lowerCAmelCase   ,   "*.bin" ) ),
              ]
              a__  		=  files[0] if len(lowerCAmelCase ) > 0 else None
                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
          if checkpoint_path_a is None and checkpoint_path_a is None:
            print(f"Skipping {attr}: not present in 2nd or 3d model" )
            continue
          try:
            a__  		=  getattr(lowerCAmelCase   ,   lowerCAmelCase )
            if isinstance(lowerCAmelCase   ,   lowerCAmelCase ):  # ignore requires_safety_checker boolean
              continue
            a__  		=  getattr(lowerCAmelCase   ,   "state_dict" )
            a__  		=  theta_a()

            a__  		=  getattr(lowerCAmelCase   ,   "load_state_dict" )
            a__  		=  (
                safetensors.torch.load_file(lowerCAmelCase )
                if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors" ))
                else torch.load(lowerCAmelCase   ,   map_location="cpu" )
            )
            a__  		=  None
            if checkpoint_path_a:
              a__  		=  (
                  safetensors.torch.load_file(lowerCAmelCase )
                  if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors" ))
                  else torch.load(lowerCAmelCase   ,   map_location="cpu" )
              )

            if not theta_a.keys() == theta_a.keys():
              print(f"Skipping {attr}: key mismatch" )
              continue
            if theta_a and not theta_a.keys() == theta_a.keys():
              print(f"Skipping {attr}:y mismatch" )
          except Exception as e:
            print(f"Skipping {attr} do to an unexpected error: {str(lowerCAmelCase )}" )
            continue
          print(f"MERGING {attr}" )

          for key in theta_a.keys():
            if theta_a:
              a__  		=  theta_func(theta_a[key]   ,   theta_a[key]   ,   theta_a[key]   ,   lowerCAmelCase )
            else:
              a__  		=  theta_func(theta_a[key]   ,   theta_a[key]   ,   lowerCAmelCase   ,   lowerCAmelCase )

          del theta_a
          del theta_a
          update_theta_a(lowerCAmelCase )

          del theta_a
      return final_pipe




    @staticmethod
    def     __lowerCamelCase			(       lowerCAmelCase	: Any   ,   lowerCAmelCase	: int   ,   lowerCAmelCase	: Optional[int]   ,   lowerCAmelCase	: int )						->			Dict:
      return ((1 - alpha) * thetaa) + (alpha * thetaa)




    @staticmethod
    def     __lowerCamelCase			(       lowerCAmelCase	: Tuple   ,   lowerCAmelCase	: str   ,   lowerCAmelCase	: Optional[Any]   ,   lowerCAmelCase	: str )						->			Optional[int]:
      a__  		=  alpha * alpha * (3 - (2 * alpha))
      return thetaa + ((thetaa - thetaa) * alpha)




    @staticmethod
    def     __lowerCamelCase			(       lowerCAmelCase	: Tuple   ,   lowerCAmelCase	: List[Any]   ,   lowerCAmelCase	: List[str]   ,   lowerCAmelCase	: List[Any] )						->			List[Any]:
      import math

      a__  		=  0.5 - math.sin(math.asin(1.0 - 2.0 * alpha ) / 3.0 )
      return thetaa + ((thetaa - thetaa) * alpha)




    @staticmethod
    def     __lowerCamelCase			(       lowerCAmelCase	: Optional[int]   ,   lowerCAmelCase	: Union[str, Any]   ,   lowerCAmelCase	: List[str]   ,   lowerCAmelCase	: int )						->			Dict:
      return thetaa + (thetaa - thetaa) * (1.0 - alpha)


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)

