




'''simple docstring'''


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     SCREAMING_SNAKE_CASE						(	snake_case_							):






      def __init__(   self      :     List[Any]					):


           '''simple docstring'''

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






      def        lowercase_	(   self      :     int	,	lowercase__      :     Dict	,	lowercase__      :     List[Any]					):


           '''simple docstring'''

           if dicta == dicta:
                return True
           else:
                a_    :       str    	=     self._remove_meta_keys(lowercase__					)
                a_    :       int    	=     self._remove_meta_keys(lowercase__					)
                if configa == configa:
                     print(F"Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}."					)
                     return True
           return False






      def        lowercase_	(   self      :     List[str]	,	lowercase__      :     Dict					):


           '''simple docstring'''

           a_    :       Optional[int]    	=     []
           a_    :       Dict    	=     config_dict.copy()
           for key in config_dict.keys():
                if key.startswith("""_"""					):
                     temp_dict.pop(lowercase__					)
                     meta_keys.append(lowercase__					)
           return (temp_dict, meta_keys)






      @torch.no_grad()
      def        lowercase_	(   self      :     List[str]	,	lowercase__      :     List[Union[str, os.PathLike]]	,	**lowercase__      :     Union[str, Any]					):


           '''simple docstring'''

           a_    :       Tuple    	=     kwargs.pop("""cache_dir"""	,	lowercase__					)
           a_    :       Any    	=     kwargs.pop("""resume_download"""	,	lowercase__					)
           a_    :       Optional[Any]    	=     kwargs.pop("""force_download"""	,	lowercase__					)
           a_    :       Tuple    	=     kwargs.pop("""proxies"""	,	lowercase__					)
           a_    :       Optional[int]    	=     kwargs.pop("""local_files_only"""	,	lowercase__					)
           a_    :       Union[str, Any]    	=     kwargs.pop("""use_auth_token"""	,	lowercase__					)
           a_    :       Optional[Any]    	=     kwargs.pop("""revision"""	,	lowercase__					)
           a_    :       Optional[Any]    	=     kwargs.pop("""torch_dtype"""	,	lowercase__					)
           a_    :       List[str]    	=     kwargs.pop("""device_map"""	,	lowercase__					)

           a_    :       List[str]    	=     kwargs.pop("""alpha"""	,	0.5					)
           a_    :       str    	=     kwargs.pop("""interp"""	,	lowercase__					)

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

           a_    :       Union[str, Any]    	=     len(lowercase__					)
           # Ignore result from model_index_json comparision of the two checkpoints
           a_    :       Tuple    	=     kwargs.pop("""force"""	,	lowercase__					)

           # 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_    :       int    	=     []
           for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
                a_    :       Tuple    	=     DiffusionPipeline.load_config(
                    lowercase__	,	cache_dir=lowercase__	,	resume_download=lowercase__	,	force_download=lowercase__	,	proxies=lowercase__	,	local_files_only=lowercase__	,	use_auth_token=lowercase__	,	revision=lowercase__	,	)
                config_dicts.append(lowercase__					)

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

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

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

           a_    :       int    	=     None
           if len(lowercase__					) > 2:
                a_    :       Optional[Any]    	=     os.path.join(cached_folders[2]					)

           if interp == "sigmoid":
                a_    :       Tuple    	=     CheckpointMergerPipeline.sigmoid
           elif interp == "inv_sigmoid":
                a_    :       Dict    	=     CheckpointMergerPipeline.inv_sigmoid
           elif interp == "add_diff":
                a_    :       Tuple    	=     CheckpointMergerPipeline.add_difference
           else:
                a_    :       Union[str, Any]    	=     CheckpointMergerPipeline.weighted_sum

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

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

                     for key in theta_a.keys():
                          if theta_a:
                               a_    :       Union[str, Any]    	=     theta_func(theta_a[key]	,	theta_a[key]	,	theta_a[key]	,	lowercase__					)
                          else:
                               a_    :       Optional[int]    	=     theta_func(theta_a[key]	,	theta_a[key]	,	lowercase__	,	lowercase__					)

                     del theta_a
                     del theta_a
                     update_theta_a(lowercase__					)

                     del theta_a
           return final_pipe






      @staticmethod
      def        lowercase_	(   lowercase__      :     Optional[Any]	,	lowercase__      :     Optional[int]	,	lowercase__      :     Dict	,	lowercase__      :     Dict					):


           '''simple docstring'''

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






      @staticmethod
      def        lowercase_	(   lowercase__      :     Tuple	,	lowercase__      :     Tuple	,	lowercase__      :     List[Any]	,	lowercase__      :     List[str]					):


           '''simple docstring'''

           a_    :       Optional[int]    	=     alpha * alpha * (3 - (2 * alpha))
           return thetaa + ((thetaa - thetaa) * alpha)






      @staticmethod
      def        lowercase_	(   lowercase__      :     Optional[Any]	,	lowercase__      :     Union[str, Any]	,	lowercase__      :     List[str]	,	lowercase__      :     Dict					):


           '''simple docstring'''

           import math

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






      @staticmethod
      def        lowercase_	(   lowercase__      :     List[Any]	,	lowercase__      :     str	,	lowercase__      :     Optional[Any]	,	lowercase__      :     int					):


           '''simple docstring'''

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






'''simple docstring'''


def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				int = 1000							):




     """simple docstring"""


     return sum(i for i in range(UpperCamelCase__							) if i % 3 == 0 or i % 5 == 0							)


if __name__ == "__main__":
 print(f"{solution() = }")
