



'''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 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 def __init__(				self				)						->  Tuple:
        self.register_to_config()
        super().__init__()







 def _snake_case					(				self    ,a_    ,a_				)						->  List[Any]:
        if dicta == dicta:
               return True
        else:
               _UpperCAmelCase	,_UpperCAmelCase    :		Any     	=					self._remove_meta_keys(a_				)
               _UpperCAmelCase	,_UpperCAmelCase    :		Union[str, Any]     	=					self._remove_meta_keys(a_				)
               if configa == configa:
                      print(f'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'''				)
                      return True
        return False







 def _snake_case					(				self    ,a_				)						->  Optional[int]:
        _UpperCAmelCase    :		Dict     	=					[]
        _UpperCAmelCase    :		int     	=					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 _snake_case					(				self    ,a_    ,**a_				)						->  Tuple:
        _UpperCAmelCase    :		str     	=					kwargs.pop("""cache_dir"""    ,a_				)
        _UpperCAmelCase    :		List[Any]     	=					kwargs.pop("""resume_download"""    ,a_				)
        _UpperCAmelCase    :		Tuple     	=					kwargs.pop("""force_download"""    ,a_				)
        _UpperCAmelCase    :		str     	=					kwargs.pop("""proxies"""    ,a_				)
        _UpperCAmelCase    :		Optional[Any]     	=					kwargs.pop("""local_files_only"""    ,a_				)
        _UpperCAmelCase    :		Optional[int]     	=					kwargs.pop("""use_auth_token"""    ,a_				)
        _UpperCAmelCase    :		Optional[Any]     	=					kwargs.pop("""revision"""    ,a_				)
        _UpperCAmelCase    :		Any     	=					kwargs.pop("""torch_dtype"""    ,a_				)
        _UpperCAmelCase    :		Dict     	=					kwargs.pop("""device_map"""    ,a_				)

        _UpperCAmelCase    :		Union[str, Any]     	=					kwargs.pop("""alpha"""    ,0.5				)
        _UpperCAmelCase    :		Tuple     	=					kwargs.pop("""interp"""    ,a_				)

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

        _UpperCAmelCase    :		str     	=					len(a_				)
        # Ignore result from model_index_json comparision of the two checkpoints
        _UpperCAmelCase    :		str     	=					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    :		List[Any]     	=					[]
        for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
               _UpperCAmelCase    :		Union[str, Any]     	=					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    :		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    :		Dict     	=					[]
        for pretrained_model_name_or_path, config_dict in zip(a_    ,a_				):
               _UpperCAmelCase    :		List[Any]     	=					[k for k in config_dict.keys() if not k.startswith("""_"""				)]
               _UpperCAmelCase    :		str     	=					[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    :		Dict     	=					config_dict.get("""_class_name"""				)
               _UpperCAmelCase    :		Any     	=					{"""diffusers""": __version__, """pipeline_class""": requested_pipeline_class}

               _UpperCAmelCase    :		List[str]     	=					(
                   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    :		int     	=					DiffusionPipeline.from_pretrained(
            cached_folders[0]    ,torch_dtype=a_    ,device_map=a_				)
        final_pipe.to(self.device				)

        _UpperCAmelCase    :		List[Any]     	=					None
        if len(a_				) > 2:
               _UpperCAmelCase    :		Tuple     	=					os.path.join(cached_folders[2]				)

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

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

                             _UpperCAmelCase    :		List[str]     	=					getattr(a_    ,"""load_state_dict"""				)
                             _UpperCAmelCase    :		List[Any]     	=					(
                                 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    :		Dict     	=					(
                                        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    :		Any     	=					theta_func(theta_a[key]    ,theta_a[key]    ,theta_a[key]    ,a_				)
                             else:
                                    _UpperCAmelCase    :		Optional[int]     	=					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 _snake_case					(				a_    ,a_    ,a_    ,a_				)						->  int:
        return ((1 - alpha) * thetaa) + (alpha * thetaa)







 @staticmethod
 def _snake_case					(				a_    ,a_    ,a_    ,a_				)						->  Optional[Any]:
        _UpperCAmelCase    :		Dict     	=					alpha * alpha * (3 - (2 * alpha))
        return thetaa + ((thetaa - thetaa) * alpha)







 @staticmethod
 def _snake_case					(				a_    ,a_    ,a_    ,a_				)						->  int:
        import math

        _UpperCAmelCase    :		Union[str, Any]     	=					0.5 - math.sin(math.asin(1.0 - 2.0 * alpha				) / 3.0				)
        return thetaa + ((thetaa - thetaa) * alpha)







 @staticmethod
 def _snake_case					(				a_    ,a_    ,a_    ,a_				)						->  int:
        return thetaa + (thetaa - thetaa) * (1.0 - alpha)





from __future__ import annotations

from typing import Any

class        UpperCamelCase__:

	"""simple docstring"""

	def __init__(      self				:			Dict							,			snake_case__				:			Any							,			snake_case__				:			Any ):  # noqa: A002
			"""simple docstring"""





			A				=item
			A				=next


class        UpperCamelCase__:

	"""simple docstring"""

	def __init__(      self				:			Optional[Any] ):
			"""simple docstring"""





			A				=None
			A				=0







	def        _a					(      self				:			str							,			snake_case__				:			Any ):
			"""simple docstring"""





			A				=Node(snake_case__							,			self.head )
			self.size += 1







	def        _a					(      self				:			Dict ):
			"""simple docstring"""





			if self.head is None:
					return None
			else:
					A				=self.head.item
					A				=self.head.next
					self.size -= 1
					return item







	def        _a					(      self				:			Any ):
			"""simple docstring"""





			return self.head is None







	def __str__(      self				:			Dict ):
			"""simple docstring"""





			if self.is_empty():
					return ""
			else:
					A				=self.head
					A				=""
					A				=[]
					while iterate:
							item_list.append(str(iterate.item ) )
							A				=iterate.next

					A				=" --> ".join(snake_case__ )

					return item_str







	def __len__(      self				:			Union[str, Any] ):
			"""simple docstring"""





			return self.size




