


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 							lowercase__(						snake_case__			):






      '''simple docstring'''







      def __init__(		self)					->						str:







         """simple docstring"""







         self.register_to_config()
         super().__init__()




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Tuple:







         """simple docstring"""







         if dicta == dicta:
            return True
         else:
            UpperCamelCase__ :			int 						=self._remove_meta_keys(A_)
            UpperCamelCase__ :			Tuple 						=self._remove_meta_keys(A_)
            if configa == configa:
               print(F'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.''')
               return True
         return False




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						Any:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=[]
         UpperCamelCase__ :			Union[str, Any] 						=config_dict.copy()
         for key in config_dict.keys():
            if key.startswith("_"):
               temp_dict.pop(A_)
               meta_keys.append(A_)
         return (temp_dict, meta_keys)




      @torch.no_grad()
      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE)					->						List[Any]:







         """simple docstring"""







         UpperCamelCase__ :			Dict 						=kwargs.pop("cache_dir"   ,			A_)
         UpperCamelCase__ :			Optional[int] 						=kwargs.pop("resume_download"   ,			A_)
         UpperCamelCase__ :			int 						=kwargs.pop("force_download"   ,			A_)
         UpperCamelCase__ :			Any 						=kwargs.pop("proxies"   ,			A_)
         UpperCamelCase__ :			int 						=kwargs.pop("local_files_only"   ,			A_)
         UpperCamelCase__ :			Any 						=kwargs.pop("use_auth_token"   ,			A_)
         UpperCamelCase__ :			Tuple 						=kwargs.pop("revision"   ,			A_)
         UpperCamelCase__ :			Any 						=kwargs.pop("torch_dtype"   ,			A_)
         UpperCamelCase__ :			List[Any] 						=kwargs.pop("device_map"   ,			A_)

         UpperCamelCase__ :			Optional[int] 						=kwargs.pop("alpha"   ,			0.5)
         UpperCamelCase__ :			Union[str, Any] 						=kwargs.pop("interp"   ,			A_)

         print("Received list"   ,			A_)
         print(F'''Combining with alpha={alpha}, interpolation mode={interp}''')

         UpperCamelCase__ :			List[Any] 						=len(A_)
         # Ignore result from model_index_json comparision of the two checkpoints
         UpperCamelCase__ :			Tuple 						=kwargs.pop("force"   ,			A_)

         # 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 '_'
         UpperCamelCase__ :			Dict 						=[]
         for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
            UpperCamelCase__ :			List[str] 						=DiffusionPipeline.load_config(
                A_   ,			cache_dir=A_   ,			resume_download=A_   ,			force_download=A_   ,			proxies=A_   ,			local_files_only=A_   ,			use_auth_token=A_   ,			revision=A_   ,			)
            config_dicts.append(A_)

         UpperCamelCase__ :			List[Any] 						=True
         for idx in range(1   ,			len(A_)):
            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.
         UpperCamelCase__ :			Any 						=[]
         for pretrained_model_name_or_path, config_dict in zip(A_   ,			A_):
            UpperCamelCase__ :			Dict 						=[k for k in config_dict.keys() if not k.startswith("_")]
            UpperCamelCase__ :			List[Any] 						=[os.path.join(A_   ,			"*") for k in folder_names]
            allow_patterns += [
                WEIGHTS_NAME,
                SCHEDULER_CONFIG_NAME,
                CONFIG_NAME,
                ONNX_WEIGHTS_NAME,
                DiffusionPipeline.config_name,
            ]
            UpperCamelCase__ :			List[Any] 						=config_dict.get("_class_name")
            UpperCamelCase__ :			Any 						={"diffusers": __version__, "pipeline_class": requested_pipeline_class}

            UpperCamelCase__ :			Optional[Any] 						=(
                pretrained_model_name_or_path
                if os.path.isdir(A_)
                else snapshot_download(
                    A_   ,			cache_dir=A_   ,			resume_download=A_   ,			proxies=A_   ,			local_files_only=A_   ,			use_auth_token=A_   ,			revision=A_   ,			allow_patterns=A_   ,			user_agent=A_   ,			)
            )
            print("Cached Folder"   ,			A_)
            cached_folders.append(A_)

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

         UpperCamelCase__ :			Union[str, Any] 						=None
         if len(A_) > 2:
            UpperCamelCase__ :			Optional[Any] 						=os.path.join(cached_folders[2])

         if interp == "sigmoid":
            UpperCamelCase__ :			Union[str, Any] 						=CheckpointMergerPipeline.sigmoid
         elif interp == "inv_sigmoid":
            UpperCamelCase__ :			List[str] 						=CheckpointMergerPipeline.inv_sigmoid
         elif interp == "add_diff":
            UpperCamelCase__ :			Optional[Any] 						=CheckpointMergerPipeline.add_difference
         else:
            UpperCamelCase__ :			List[str] 						=CheckpointMergerPipeline.weighted_sum

         # Find each module's state dict.
         for attr in final_pipe.config.keys():
            if not attr.startswith("_"):
               UpperCamelCase__ :			List[Any] 						=os.path.join(cached_folders[1]   ,			A_)
               if os.path.exists(A_):
                  UpperCamelCase__ :			Tuple 						=[
                      *glob.glob(os.path.join(A_   ,			"*.safetensors")),
                      *glob.glob(os.path.join(A_   ,			"*.bin")),
                  ]
                  UpperCamelCase__ :			List[str] 						=files[0] if len(A_) > 0 else None
               if len(A_) < 3:
                  UpperCamelCase__ :			str 						=None
               else:
                  UpperCamelCase__ :			Union[str, Any] 						=os.path.join(cached_folders[2]   ,			A_)
                  if os.path.exists(A_):
                     UpperCamelCase__ :			Dict 						=[
                         *glob.glob(os.path.join(A_   ,			"*.safetensors")),
                         *glob.glob(os.path.join(A_   ,			"*.bin")),
                     ]
                     UpperCamelCase__ :			int 						=files[0] if len(A_) > 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:
                  UpperCamelCase__ :			Optional[Any] 						=getattr(A_   ,			A_)
                  if isinstance(A_   ,			A_):  # ignore requires_safety_checker boolean
                     continue
                  UpperCamelCase__ :			int 						=getattr(A_   ,			"state_dict")
                  UpperCamelCase__ :			List[Any] 						=theta_a()

                  UpperCamelCase__ :			Tuple 						=getattr(A_   ,			"load_state_dict")
                  UpperCamelCase__ :			str 						=(
                      safetensors.torch.load_file(A_)
                      if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"))
                      else torch.load(A_   ,			map_location="cpu")
                  )
                  UpperCamelCase__ :			Tuple 						=None
                  if checkpoint_path_a:
                     UpperCamelCase__ :			Optional[int] 						=(
                         safetensors.torch.load_file(A_)
                         if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"))
                         else torch.load(A_   ,			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(A_)}''')
                  continue
               print(F'''MERGING {attr}''')

               for key in theta_a.keys():
                  if theta_a:
                     UpperCamelCase__ :			str 						=theta_func(theta_a[key]   ,			theta_a[key]   ,			theta_a[key]   ,			A_)
                  else:
                     UpperCamelCase__ :			Union[str, Any] 						=theta_func(theta_a[key]   ,			theta_a[key]   ,			A_   ,			A_)

               del theta_a
               del theta_a
               update_theta_a(A_)

               del theta_a
         return final_pipe




      @staticmethod
      def    UpperCAmelCase							(		__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Dict:







         """simple docstring"""







         return ((1 - alpha) * thetaa) + (alpha * thetaa)




      @staticmethod
      def    UpperCAmelCase							(		__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Optional[int]:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=alpha * alpha * (3 - (2 * alpha))
         return thetaa + ((thetaa - thetaa) * alpha)




      @staticmethod
      def    UpperCAmelCase							(		__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Any:







         """simple docstring"""







         import math

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




      @staticmethod
      def    UpperCAmelCase							(		__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Dict:







         """simple docstring"""







         return thetaa + (thetaa - thetaa) * (1.0 - alpha)




def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Optional[int]:

     """simple docstring"""







     if power < 0:
          return -1
     base %= mod
     _lowerCAmelCase :       Tuple	   =     1

     while power > 0:
          if power & 1:
               _lowerCAmelCase :       Optional[Any]	   =     (result * base) % mod
          _lowerCAmelCase :       int	   =     power >> 1
          _lowerCAmelCase :       Optional[int]	   =     (base * base) % mod

     return result







def 						_snake_case		(     )							->   Optional[int]:

     """simple docstring"""







     print(modular_exponential(3		,				200		,				13					)					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  main()






