


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   A__							(						UpperCamelCase__     ):




						def __init__(				self    :					Any     )							->   Union[str, Any]:


										"""simple docstring"""





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




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					Tuple ,			_a    :					int     )							->   str:


										"""simple docstring"""





										if dicta == dicta:
														return True
										else:
														_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self._remove_meta_keys(_a     )
														_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self._remove_meta_keys(_a     )
														if configa == configa:
																		print(f"Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}."     )
																		return True
										return False




						def 							__UpperCamelCase (				self    :					Union[str, Any] ,			_a    :					Dict     )							->   Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[]
										_SCREAMING_SNAKE_CASE	     =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 							__UpperCamelCase (				self    :					List[str] ,			_a    :					List[Union[str, os.PathLike]] ,			**_a    :					List[Any]     )							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =kwargs.pop('''cache_dir''' ,			_a     )
										_SCREAMING_SNAKE_CASE	     =kwargs.pop('''resume_download''' ,			_a     )
										_SCREAMING_SNAKE_CASE	     =kwargs.pop('''force_download''' ,			_a     )
										_SCREAMING_SNAKE_CASE	     =kwargs.pop('''proxies''' ,			_a     )
										_SCREAMING_SNAKE_CASE	     =kwargs.pop('''local_files_only''' ,			_a     )
										_SCREAMING_SNAKE_CASE	     =kwargs.pop('''use_auth_token''' ,			_a     )
										_SCREAMING_SNAKE_CASE	     =kwargs.pop('''revision''' ,			_a     )
										_SCREAMING_SNAKE_CASE	     =kwargs.pop('''torch_dtype''' ,			_a     )
										_SCREAMING_SNAKE_CASE	     =kwargs.pop('''device_map''' ,			_a     )

										_SCREAMING_SNAKE_CASE	     =kwargs.pop('''alpha''' ,			0.5     )
										_SCREAMING_SNAKE_CASE	     =kwargs.pop('''interp''' ,			_a     )

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

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

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

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

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

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

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

																						_SCREAMING_SNAKE_CASE	     =getattr(_a ,			'''load_state_dict'''     )
																						_SCREAMING_SNAKE_CASE	     =(
																						    safetensors.torch.load_file(_a     )
																						    if (is_safetensors_available() and checkpoint_path_a.endswith('''.safetensors'''     ))
																						    else torch.load(_a ,			map_location='''cpu'''     )
																						)
																						_SCREAMING_SNAKE_CASE	     =None
																						if checkpoint_path_a:
																										_SCREAMING_SNAKE_CASE	     =(
																										    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:
																										_SCREAMING_SNAKE_CASE	     =theta_func(theta_a[key] ,			theta_a[key] ,			theta_a[key] ,			_a     )
																						else:
																										_SCREAMING_SNAKE_CASE	     =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 							__UpperCamelCase (				_a    :					Tuple ,			_a    :					Tuple ,			_a    :					Dict ,			_a    :					str     )							->   Optional[Any]:


										"""simple docstring"""





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




						@staticmethod
						def 							__UpperCamelCase (				_a    :					str ,			_a    :					str ,			_a    :					str ,			_a    :					List[Any]     )							->   int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =alpha * alpha * (3 - (2 * alpha))
										return thetaa + ((thetaa - thetaa) * alpha)




						@staticmethod
						def 							__UpperCamelCase (				_a    :					List[str] ,			_a    :					List[Any] ,			_a    :					Optional[Any] ,			_a    :					Optional[Any]     )							->   Union[str, Any]:


										"""simple docstring"""





										import math

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




						@staticmethod
						def 							__UpperCamelCase (				_a    :					Any ,			_a    :					int ,			_a    :					Union[str, Any] ,			_a    :					Union[str, Any]     )							->   Any:


										"""simple docstring"""





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



from __future__ import annotations

from collections.abc import Callable







def   lowerCamelCase(      a__		,a__		,a__		,a__ = 100		,):
				_SCREAMING_SNAKE_CASE	     =x_start
				_SCREAMING_SNAKE_CASE	     =fnc(a__)
				_SCREAMING_SNAKE_CASE	     =0.0

				for _ in range(a__):
								# Approximates small segments of curve as linear and solve
								# for trapezoidal area
								_SCREAMING_SNAKE_CASE	     =(x_end - x_start) / steps + xa
								_SCREAMING_SNAKE_CASE	     =fnc(a__)
								area += abs(fxa + fxa) * (xa - xa) / 2

								# Increment step
								_SCREAMING_SNAKE_CASE	     =xa
								_SCREAMING_SNAKE_CASE	     =fxa
				return area


if __name__ == "__main__":







							def   lowerCamelCase(      a__):
											return x**3

							print('''f(x) = x^3''')
							print('''The area between the curve, x = -10, x = 10 and the x axis is:''')
							snake_case_  :	Any							  =							10
							while i <= 10_00_00:
														snake_case_  :	Tuple							  =							trapezoidal_area(f, -5, 5, i)
														print(f"""with {i} steps: {area}""")
														i *= 10