

"""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):
			def __init__(						self       )      ->      int:






				'''simple docstring'''







				self.register_to_config()
				super().__init__()
			def 							__snake_case	(						self ,						_A ,						_A       )      ->      int:






				'''simple docstring'''







				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]:






				'''simple docstring'''







				_UpperCAmelCase      :							Tuple       =   []
				_UpperCAmelCase      :							List[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 							__snake_case	(						self ,						_A ,						**_A       )      ->      Union[str, Any]:






				'''simple docstring'''







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

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

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

				_UpperCAmelCase      :							Optional[Any]       =   len(_A       )
				# Ignore result from model_index_json comparision of the two checkpoints
				_UpperCAmelCase      :							Any       =   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      :							str       =   []
				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      :							List[str]       =   []
				for pretrained_model_name_or_path, config_dict in zip(_A ,						_A       ):
					_UpperCAmelCase      :							Optional[int]       =   [k for k in config_dict.keys() if not k.startswith("""_"""       )]
					_UpperCAmelCase      :							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[str]       =   config_dict.get("""_class_name"""       )
					_UpperCAmelCase      :							Tuple       =   {"""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      :							Dict       =   DiffusionPipeline.from_pretrained(
				    cached_folders[0] ,						torch_dtype=_A ,						device_map=_A       )
				final_pipe.to(self.device       )

				_UpperCAmelCase      :							List[str]       =   None
				if len(_A       ) > 2:
					_UpperCAmelCase      :							Optional[int]       =   os.path.join(cached_folders[2]       )

				if interp == "sigmoid":
					_UpperCAmelCase      :							Dict       =   CheckpointMergerPipeline.sigmoid
				elif interp == "inv_sigmoid":
					_UpperCAmelCase      :							Any       =   CheckpointMergerPipeline.inv_sigmoid
				elif interp == "add_diff":
					_UpperCAmelCase      :							int       =   CheckpointMergerPipeline.add_difference
				else:
					_UpperCAmelCase      :							Tuple       =   CheckpointMergerPipeline.weighted_sum

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

							_UpperCAmelCase      :							Optional[int]       =   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      :							List[Any]       =   None
							if checkpoint_path_a:
								_UpperCAmelCase      :							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      :							List[str]       =   theta_func(theta_a[key] ,						theta_a[key] ,						theta_a[key] ,						_A       )
							else:
								_UpperCAmelCase      :							List[str]       =   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       )      ->      Optional[int]:






				'''simple docstring'''







				return ((1 - alpha) * thetaa) + (alpha * thetaa)
			@staticmethod
			def 							__snake_case	(						_A ,						_A ,						_A ,						_A       )      ->      str:






				'''simple docstring'''







				_UpperCAmelCase      :							List[Any]       =   alpha * alpha * (3 - (2 * alpha))
				return thetaa + ((thetaa - thetaa) * alpha)
			@staticmethod
			def 							__snake_case	(						_A ,						_A ,						_A ,						_A       )      ->      List[str]:






				'''simple docstring'''







				import math

				_UpperCAmelCase      :							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:






				'''simple docstring'''







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








"""simple docstring"""






import math
def  UpperCamelCase   (     _lowerCAmelCase       :  int	)			->     bool:

	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5,					int(math.sqrt(_lowerCAmelCase	) + 1	),					6	):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True
def  UpperCamelCase   (     _lowerCAmelCase       :  int = 2000000	)			->     int:
	return sum(num for num in range(3,					_lowerCAmelCase,					2	) if is_prime(_lowerCAmelCase	)	) + 2 if n > 2 else 0


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





