

"""simple docstring"""

def 				__UpperCamelCase   (    a__: list ,	a__: list ,	a__: list ,	a__: int ,	a__: int			)				->			None:




     """simple docstring"""



     if target == 0:
          answer.append(path.copy()			)
     else:
          for index in range(a__ ,	len(a__			)			):
               if target >= candidates[index]:
                    path.append(candidates[index]			)
                    backtrack(a__ ,	a__ ,	a__ ,	target - candidates[index] ,	a__			)
                    path.pop(len(a__			) - 1			)





def 				__UpperCamelCase   (    a__: list ,	a__: int			)				->			list:




     """simple docstring"""



     __lowercase												=			[]  # type: list[int]
     __lowercase												=			[]  # type: list[int]
     backtrack(a__ ,	a__ ,	a__ ,	a__ ,	0			)
     return answer





def 				__UpperCamelCase   (    )				->			None:




     """simple docstring"""



     print(combination_sum([-8, 2.3, 0] ,	1			)			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()





"""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 			_snake_case     (						UpperCamelCase       ):







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







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







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_							)			->						str:
											__lowercase												=			[]
											__lowercase												=			config_dict.copy()
											for key in config_dict.keys():
																if key.startswith('''_'''							):
																					temp_dict.pop(UpperCAmelCase_							)
																					meta_keys.append(UpperCAmelCase_							)
											return (temp_dict, meta_keys)







						@torch.no_grad()
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							**UpperCAmelCase_							)			->						Optional[Any]:
											__lowercase												=			kwargs.pop('''cache_dir'''     ,							UpperCAmelCase_							)
											__lowercase												=			kwargs.pop('''resume_download'''     ,							UpperCAmelCase_							)
											__lowercase												=			kwargs.pop('''force_download'''     ,							UpperCAmelCase_							)
											__lowercase												=			kwargs.pop('''proxies'''     ,							UpperCAmelCase_							)
											__lowercase												=			kwargs.pop('''local_files_only'''     ,							UpperCAmelCase_							)
											__lowercase												=			kwargs.pop('''use_auth_token'''     ,							UpperCAmelCase_							)
											__lowercase												=			kwargs.pop('''revision'''     ,							UpperCAmelCase_							)
											__lowercase												=			kwargs.pop('''torch_dtype'''     ,							UpperCAmelCase_							)
											__lowercase												=			kwargs.pop('''device_map'''     ,							UpperCAmelCase_							)

											__lowercase												=			kwargs.pop('''alpha'''     ,							0.5							)
											__lowercase												=			kwargs.pop('''interp'''     ,							UpperCAmelCase_							)

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

											__lowercase												=			len(UpperCAmelCase_							)
											# Ignore result from model_index_json comparision of the two checkpoints
											__lowercase												=			kwargs.pop('''force'''     ,							UpperCAmelCase_							)

											# 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(
																    UpperCAmelCase_     ,							cache_dir=UpperCAmelCase_     ,							resume_download=UpperCAmelCase_     ,							force_download=UpperCAmelCase_     ,							proxies=UpperCAmelCase_     ,							local_files_only=UpperCAmelCase_     ,							use_auth_token=UpperCAmelCase_     ,							revision=UpperCAmelCase_     ,							)
																config_dicts.append(UpperCAmelCase_							)

											__lowercase												=			True
											for idx in range(1     ,							len(UpperCAmelCase_							)							):
																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(UpperCAmelCase_     ,							UpperCAmelCase_							):
																__lowercase												=			[k for k in config_dict.keys() if not k.startswith('''_'''							)]
																__lowercase												=			[os.path.join(UpperCAmelCase_     ,							'''*'''							) 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(UpperCAmelCase_							)
																    else snapshot_download(
																        UpperCAmelCase_     ,							cache_dir=UpperCAmelCase_     ,							resume_download=UpperCAmelCase_     ,							proxies=UpperCAmelCase_     ,							local_files_only=UpperCAmelCase_     ,							use_auth_token=UpperCAmelCase_     ,							revision=UpperCAmelCase_     ,							allow_patterns=UpperCAmelCase_     ,							user_agent=UpperCAmelCase_     ,							)
																)
																print('''Cached Folder'''     ,							UpperCAmelCase_							)
																cached_folders.append(UpperCAmelCase_							)

											# 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=UpperCAmelCase_     ,							device_map=UpperCAmelCase_							)
											final_pipe.to(self.device							)

											__lowercase												=			None
											if len(UpperCAmelCase_							) > 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]     ,							UpperCAmelCase_							)
																					if os.path.exists(UpperCAmelCase_							):
																										__lowercase												=			[
																										    *glob.glob(os.path.join(UpperCAmelCase_     ,							'''*.safetensors'''							)							),
																										    *glob.glob(os.path.join(UpperCAmelCase_     ,							'''*.bin'''							)							),
																										]
																										__lowercase												=			files[0] if len(UpperCAmelCase_							) > 0 else None
																					if len(UpperCAmelCase_							) < 3:
																										__lowercase												=			None
																					else:
																										__lowercase												=			os.path.join(cached_folders[2]     ,							UpperCAmelCase_							)
																										if os.path.exists(UpperCAmelCase_							):
																															__lowercase												=			[
																															    *glob.glob(os.path.join(UpperCAmelCase_     ,							'''*.safetensors'''							)							),
																															    *glob.glob(os.path.join(UpperCAmelCase_     ,							'''*.bin'''							)							),
																															]
																															__lowercase												=			files[0] if len(UpperCAmelCase_							) > 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(UpperCAmelCase_     ,							UpperCAmelCase_							)
																										if isinstance(UpperCAmelCase_     ,							UpperCAmelCase_							):  # ignore requires_safety_checker boolean
																															continue
																										__lowercase												=			getattr(UpperCAmelCase_     ,							'''state_dict'''							)
																										__lowercase												=			theta_a()

																										__lowercase												=			getattr(UpperCAmelCase_     ,							'''load_state_dict'''							)
																										__lowercase												=			(
																										    safetensors.torch.load_file(UpperCAmelCase_							)
																										    if (is_safetensors_available() and checkpoint_path_a.endswith('''.safetensors'''							))
																										    else torch.load(UpperCAmelCase_     ,							map_location='''cpu'''							)
																										)
																										__lowercase												=			None
																										if checkpoint_path_a:
																															__lowercase												=			(
																															    safetensors.torch.load_file(UpperCAmelCase_							)
																															    if (is_safetensors_available() and checkpoint_path_a.endswith('''.safetensors'''							))
																															    else torch.load(UpperCAmelCase_     ,							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(UpperCAmelCase_							)}'''							)
																										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]     ,							UpperCAmelCase_							)
																										else:
																															__lowercase												=			theta_func(theta_a[key]     ,							theta_a[key]     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)

																					del theta_a
																					del theta_a
																					update_theta_a(UpperCAmelCase_							)

																					del theta_a
											return final_pipe







						@staticmethod
						def        _UpperCamelCase    (UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)			->						List[Any]:
											return ((1 - alpha) * thetaa) + (alpha * thetaa)







						@staticmethod
						def        _UpperCamelCase    (UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)			->						List[Any]:
											__lowercase												=			alpha * alpha * (3 - (2 * alpha))
											return thetaa + ((thetaa - thetaa) * alpha)







						@staticmethod
						def        _UpperCamelCase    (UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)			->						List[Any]:
											import math

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







						@staticmethod
						def        _UpperCamelCase    (UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)			->						List[str]:
											return thetaa + (thetaa - thetaa) * (1.0 - alpha)


