



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__):







							'''simple docstring'''






							def __init__(							self						):
											self.register_to_config()
											super().__init__()


							def _a					(							self     ,     a_     ,     a_						):





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


							def _a					(							self     ,     a_						):
											a__ 					=						[]
											a__ 					=						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 _a					(							self     ,     a_     ,     **a_						):
											a__ 					=						kwargs.pop("""cache_dir"""     ,     _a						)
											a__ 					=						kwargs.pop("""resume_download"""     ,     _a						)
											a__ 					=						kwargs.pop("""force_download"""     ,     _a						)
											a__ 					=						kwargs.pop("""proxies"""     ,     _a						)
											a__ 					=						kwargs.pop("""local_files_only"""     ,     _a						)
											a__ 					=						kwargs.pop("""use_auth_token"""     ,     _a						)
											a__ 					=						kwargs.pop("""revision"""     ,     _a						)
											a__ 					=						kwargs.pop("""torch_dtype"""     ,     _a						)
											a__ 					=						kwargs.pop("""device_map"""     ,     _a						)

											a__ 					=						kwargs.pop("""alpha"""     ,     0.5						)
											a__ 					=						kwargs.pop("""interp"""     ,     _a						)

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

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

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

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

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

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

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

																							a__ 					=						getattr(_a     ,     """load_state_dict"""						)
																							a__ 					=						(
																							    safetensors.torch.load_file(_a						)
																							    if (is_safetensors_available() and checkpoint_path_a.endswith(""".safetensors"""						))
																							    else torch.load(_a     ,     map_location="""cpu"""						)
																							)
																							a__ 					=						None
																							if checkpoint_path_a:
																											a__ 					=						(
																											    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:
																											a__ 					=						theta_func(theta_a[key]     ,     theta_a[key]     ,     theta_a[key]     ,     _a						)
																							else:
																											a__ 					=						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 _a					(							a_     ,     a_     ,     a_     ,     a_						):
											return ((1 - alpha) * thetaa) + (alpha * thetaa)


							@staticmethod
							def _a					(							a_     ,     a_     ,     a_     ,     a_						):
											a__ 					=						alpha * alpha * (3 - (2 * alpha))
											return thetaa + ((thetaa - thetaa) * alpha)


							@staticmethod
							def _a					(							a_     ,     a_     ,     a_     ,     a_						):
											import math

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







							@staticmethod
							def _a					(							a_     ,     a_     ,     a_     ,     a_						):
											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