


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						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							def __init__(			self					):





										"""simple docstring"""




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






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										if dicta == dicta:
													return True
										else:
													SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    Dict 		=				self._remove_meta_keys(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				self._remove_meta_keys(lowerCAmelCase__					)
													if configa == configa:
																print(F'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'''					)
																return True
										return False






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				[]
										SCREAMING_SNAKE_CASE_		:    Any 		=				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 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	**lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Any 		=				kwargs.pop('cache_dir' ,	lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    Any 		=				kwargs.pop('resume_download' ,	lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				kwargs.pop('force_download' ,	lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    Any 		=				kwargs.pop('proxies' ,	lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				kwargs.pop('local_files_only' ,	lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    str 		=				kwargs.pop('use_auth_token' ,	lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				kwargs.pop('revision' ,	lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				kwargs.pop('torch_dtype' ,	lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    int 		=				kwargs.pop('device_map' ,	lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    int 		=				kwargs.pop('alpha' ,	0.5					)
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				kwargs.pop('interp' ,	lowerCAmelCase__					)

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

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

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

													SCREAMING_SNAKE_CASE_		:    str 		=				(
													    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
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				DiffusionPipeline.from_pretrained(
										    cached_folders[0] ,	torch_dtype=lowerCAmelCase__ ,	device_map=lowerCAmelCase__					)
										final_pipe.to(self.device					)

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

										if interp == "sigmoid":
													SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				CheckpointMergerPipeline.sigmoid
										elif interp == "inv_sigmoid":
													SCREAMING_SNAKE_CASE_		:    List[Any] 		=				CheckpointMergerPipeline.inv_sigmoid
										elif interp == "add_diff":
													SCREAMING_SNAKE_CASE_		:    Any 		=				CheckpointMergerPipeline.add_difference
										else:
													SCREAMING_SNAKE_CASE_		:    List[str] 		=				CheckpointMergerPipeline.weighted_sum

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

																			SCREAMING_SNAKE_CASE_		:    str 		=				getattr(lowerCAmelCase__ ,	'load_state_dict'					)
																			SCREAMING_SNAKE_CASE_		:    Tuple 		=				(
																			    safetensors.torch.load_file(lowerCAmelCase__					)
																			    if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'					))
																			    else torch.load(lowerCAmelCase__ ,	map_location='cpu'					)
																			)
																			SCREAMING_SNAKE_CASE_		:    Tuple 		=				None
																			if checkpoint_path_a:
																						SCREAMING_SNAKE_CASE_		:    Any 		=				(
																						    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:
																						SCREAMING_SNAKE_CASE_		:    List[str] 		=				theta_func(theta_a[key] ,	theta_a[key] ,	theta_a[key] ,	lowerCAmelCase__					)
																			else:
																						SCREAMING_SNAKE_CASE_		:    Any 		=				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 				UpperCamelCase__    (			lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




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






							@staticmethod
							def 				UpperCamelCase__    (			lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				alpha * alpha * (3 - (2 * alpha))
										return thetaa + ((thetaa - thetaa) * alpha)






							@staticmethod
							def 				UpperCamelCase__    (			lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										import math

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






							@staticmethod
							def 				UpperCamelCase__    (			lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




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









from itertools import compress, repeat
from math import ceil, sqrt






def 	a__ (							A__							):

			if num <= 2:
						return []
			if num == 3:
						return [2]

			# Odd sieve for numbers in range [3, num - 1]
			SCREAMING_SNAKE_CASE_		:    Any 		=				bytearray(B'\x01'							) * ((num >> 1) - 1)

			for i in range(3,			int(sqrt(A__							)							) + 1,			2							):
						if sieve[(i >> 1) - 1]:
									SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				i**2
									SCREAMING_SNAKE_CASE_		:    List[str] 		=				repeat(
									    0,			ceil((num - i_squared) / (i << 1)							)							)

			return [2] + list(compress(range(3,			A__,			2							),			A__							)							)


if __name__ == "__main__":
				import doctest

				doctest.testmod()





