
'''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      UpperCamelCase_			(			__a		):







						'''simple docstring'''



						def __init__( self       :int    )     ->Optional[Any]:
										self.register_to_config()
										super().__init__()





						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						lowerCAmelCase__       :Union[str, Any]		,						lowerCAmelCase__       :Any    )     ->Optional[Any]:
										if dicta == dicta:
														return True
										else:
														lowercase    ,  lowercase						    =			self._remove_meta_keys(lowerCAmelCase__    )
														lowercase    ,  lowercase						    =			self._remove_meta_keys(lowerCAmelCase__    )
														if configa == configa:
																		print(F'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'''    )
																		return True
										return False





						def 					SCREAMING_SNAKE_CASE( self       :List[Any]		,						lowerCAmelCase__       :Dict    )     ->Union[str, Any]:
										lowercase						    =			[]
										lowercase						    =			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 					SCREAMING_SNAKE_CASE( self       :List[Any]		,						lowerCAmelCase__       :List[Union[str, os.PathLike]]		,						**lowerCAmelCase__       :Tuple    )     ->Any:
										lowercase						    =			kwargs.pop("cache_dir"		,						lowerCAmelCase__    )
										lowercase						    =			kwargs.pop("resume_download"		,						lowerCAmelCase__    )
										lowercase						    =			kwargs.pop("force_download"		,						lowerCAmelCase__    )
										lowercase						    =			kwargs.pop("proxies"		,						lowerCAmelCase__    )
										lowercase						    =			kwargs.pop("local_files_only"		,						lowerCAmelCase__    )
										lowercase						    =			kwargs.pop("use_auth_token"		,						lowerCAmelCase__    )
										lowercase						    =			kwargs.pop("revision"		,						lowerCAmelCase__    )
										lowercase						    =			kwargs.pop("torch_dtype"		,						lowerCAmelCase__    )
										lowercase						    =			kwargs.pop("device_map"		,						lowerCAmelCase__    )

										lowercase						    =			kwargs.pop("alpha"		,						0.5    )
										lowercase						    =			kwargs.pop("interp"		,						lowerCAmelCase__    )

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

										lowercase						    =			len(lowerCAmelCase__    )
										# Ignore result from model_index_json comparision of the two checkpoints
										lowercase						    =			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 '_'
										lowercase						    =			[]
										for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
														lowercase						    =			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__    )

										lowercase						    =			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.
										lowercase						    =			[]
										for pretrained_model_name_or_path, config_dict in zip(lowerCAmelCase__		,						lowerCAmelCase__    ):
														lowercase						    =			[k for k in config_dict.keys() if not k.startswith("_"    )]
														lowercase						    =			[os.path.join(lowerCAmelCase__		,						"*"    ) for k in folder_names]
														allow_patterns += [
														    WEIGHTS_NAME,
														    SCHEDULER_CONFIG_NAME,
														    CONFIG_NAME,
														    ONNX_WEIGHTS_NAME,
														    DiffusionPipeline.config_name,
														]
														lowercase						    =			config_dict.get("_class_name"    )
														lowercase						    =			{"diffusers": __version__, "pipeline_class": requested_pipeline_class}

														lowercase						    =			(
														    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
										lowercase						    =			DiffusionPipeline.from_pretrained(
										    cached_folders[0]		,						torch_dtype=lowerCAmelCase__		,						device_map=lowerCAmelCase__    )
										final_pipe.to(self.device    )

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

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

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

																						lowercase						    =			getattr(lowerCAmelCase__		,						"load_state_dict"    )
																						lowercase						    =			(
																						    safetensors.torch.load_file(lowerCAmelCase__    )
																						    if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"    ))
																						    else torch.load(lowerCAmelCase__		,						map_location="cpu"    )
																						)
																						lowercase						    =			None
																						if checkpoint_path_a:
																										lowercase						    =			(
																										    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:
																										lowercase						    =			theta_func(theta_a[key]		,						theta_a[key]		,						theta_a[key]		,						lowerCAmelCase__    )
																						else:
																										lowercase						    =			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 					SCREAMING_SNAKE_CASE( lowerCAmelCase__       :Optional[int]		,						lowerCAmelCase__       :int		,						lowerCAmelCase__       :List[str]		,						lowerCAmelCase__       :Tuple    )     ->List[str]:
										return ((1 - alpha) * thetaa) + (alpha * thetaa)





						@staticmethod
						def 					SCREAMING_SNAKE_CASE( lowerCAmelCase__       :Optional[Any]		,						lowerCAmelCase__       :Dict		,						lowerCAmelCase__       :int		,						lowerCAmelCase__       :Dict    )     ->Union[str, Any]:
										lowercase						    =			alpha * alpha * (3 - (2 * alpha))
										return thetaa + ((thetaa - thetaa) * alpha)





						@staticmethod
						def 					SCREAMING_SNAKE_CASE( lowerCAmelCase__       :List[Any]		,						lowerCAmelCase__       :Optional[Any]		,						lowerCAmelCase__       :str		,						lowerCAmelCase__       :List[str]    )     ->int:
										import math

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





						@staticmethod
						def 					SCREAMING_SNAKE_CASE( lowerCAmelCase__       :Optional[Any]		,						lowerCAmelCase__       :List[str]		,						lowerCAmelCase__       :Tuple		,						lowerCAmelCase__       :List[str]    )     ->Optional[Any]:
										return thetaa + (thetaa - thetaa) * (1.0 - alpha)


from transformers import PretrainedConfig





class      UpperCamelCase_			(			__a		):







						'''simple docstring'''



						UpperCamelCase			:     List[str]							      =				'''custom'''





						def __init__( self       :Tuple		,						lowerCAmelCase__       :int=1		,						**lowerCAmelCase__       :List[Any]    )     ->int:
										lowercase						    =			attribute
										super().__init__(**lowerCAmelCase__    )





class      UpperCamelCase_			(			__a		):







						'''simple docstring'''



						UpperCamelCase			:     Union[str, Any]							      =				'''custom'''





						def __init__( self       :Union[str, Any]		,						lowerCAmelCase__       :Dict=1		,						**lowerCAmelCase__       :Dict    )     ->List[str]:
										lowercase						    =			attribute
