

'''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						(__SCREAMING_SNAKE_CASE):






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






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






			def 						UpperCamelCase__				(   self       ,_snake_case			):
				UpperCAmelCase_			:	Optional[int]      =   []
				UpperCAmelCase_			:	Dict      =   config_dict.copy()
				for key in config_dict.keys():
					if key.startswith("_"			):
						temp_dict.pop(_snake_case			)
						meta_keys.append(_snake_case			)
				return (temp_dict, meta_keys)






			@torch.no_grad()
			def 						UpperCamelCase__				(   self       ,_snake_case       ,**_snake_case			):
				UpperCAmelCase_			:	str      =   kwargs.pop("cache_dir"       ,_snake_case			)
				UpperCAmelCase_			:	int      =   kwargs.pop("resume_download"       ,_snake_case			)
				UpperCAmelCase_			:	Union[str, Any]      =   kwargs.pop("force_download"       ,_snake_case			)
				UpperCAmelCase_			:	Union[str, Any]      =   kwargs.pop("proxies"       ,_snake_case			)
				UpperCAmelCase_			:	Optional[int]      =   kwargs.pop("local_files_only"       ,_snake_case			)
				UpperCAmelCase_			:	List[Any]      =   kwargs.pop("use_auth_token"       ,_snake_case			)
				UpperCAmelCase_			:	Dict      =   kwargs.pop("revision"       ,_snake_case			)
				UpperCAmelCase_			:	Optional[int]      =   kwargs.pop("torch_dtype"       ,_snake_case			)
				UpperCAmelCase_			:	List[Any]      =   kwargs.pop("device_map"       ,_snake_case			)

				UpperCAmelCase_			:	Dict      =   kwargs.pop("alpha"       ,0.5			)
				UpperCAmelCase_			:	Optional[Any]      =   kwargs.pop("interp"       ,_snake_case			)

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

				UpperCAmelCase_			:	Optional[int]      =   len(_snake_case			)
				# Ignore result from model_index_json comparision of the two checkpoints
				UpperCAmelCase_			:	Any      =   kwargs.pop("force"       ,_snake_case			)

				# 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_			:	Any      =   []
				for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
					UpperCAmelCase_			:	Union[str, Any]      =   DiffusionPipeline.load_config(
					    _snake_case       ,cache_dir=_snake_case       ,resume_download=_snake_case       ,force_download=_snake_case       ,proxies=_snake_case       ,local_files_only=_snake_case       ,use_auth_token=_snake_case       ,revision=_snake_case       ,)
					config_dicts.append(_snake_case			)

				UpperCAmelCase_			:	str      =   True
				for idx in range(1       ,len(_snake_case			)			):
					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_			:	Union[str, Any]      =   []
				for pretrained_model_name_or_path, config_dict in zip(_snake_case       ,_snake_case			):
					UpperCAmelCase_			:	Any      =   [k for k in config_dict.keys() if not k.startswith("_"			)]
					UpperCAmelCase_			:	int      =   [os.path.join(_snake_case       ,"*"			) for k in folder_names]
					allow_patterns += [
					    WEIGHTS_NAME,
					    SCHEDULER_CONFIG_NAME,
					    CONFIG_NAME,
					    ONNX_WEIGHTS_NAME,
					    DiffusionPipeline.config_name,
					]
					UpperCAmelCase_			:	Optional[Any]      =   config_dict.get("_class_name"			)
					UpperCAmelCase_			:	Optional[Any]      =   {"diffusers": __version__, "pipeline_class": requested_pipeline_class}

					UpperCAmelCase_			:	str      =   (
					    pretrained_model_name_or_path
					    if os.path.isdir(_snake_case			)
					    else snapshot_download(
					        _snake_case       ,cache_dir=_snake_case       ,resume_download=_snake_case       ,proxies=_snake_case       ,local_files_only=_snake_case       ,use_auth_token=_snake_case       ,revision=_snake_case       ,allow_patterns=_snake_case       ,user_agent=_snake_case       ,)
					)
					print("Cached Folder"       ,_snake_case			)
					cached_folders.append(_snake_case			)

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

				UpperCAmelCase_			:	Any      =   None
				if len(_snake_case			) > 2:
					UpperCAmelCase_			:	Union[str, Any]      =   os.path.join(cached_folders[2]			)

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

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

							UpperCAmelCase_			:	str      =   getattr(_snake_case       ,"load_state_dict"			)
							UpperCAmelCase_			:	Any      =   (
							    safetensors.torch.load_file(_snake_case			)
							    if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"			))
							    else torch.load(_snake_case       ,map_location="cpu"			)
							)
							UpperCAmelCase_			:	Union[str, Any]      =   None
							if checkpoint_path_a:
								UpperCAmelCase_			:	Optional[int]      =   (
								    safetensors.torch.load_file(_snake_case			)
								    if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"			))
								    else torch.load(_snake_case       ,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(_snake_case			)}'''			)
							continue
						print(f'''MERGING {attr}'''			)

						for key in theta_a.keys():
							if theta_a:
								UpperCAmelCase_			:	Dict      =   theta_func(theta_a[key]       ,theta_a[key]       ,theta_a[key]       ,_snake_case			)
							else:
								UpperCAmelCase_			:	Dict      =   theta_func(theta_a[key]       ,theta_a[key]       ,_snake_case       ,_snake_case			)

						del theta_a
						del theta_a
						update_theta_a(_snake_case			)

						del theta_a
				return final_pipe






			@staticmethod
			def 						UpperCamelCase__				(   _snake_case       ,_snake_case       ,_snake_case       ,_snake_case			):
				return ((1 - alpha) * thetaa) + (alpha * thetaa)






			@staticmethod
			def 						UpperCamelCase__				(   _snake_case       ,_snake_case       ,_snake_case       ,_snake_case			):
				UpperCAmelCase_			:	Union[str, Any]      =   alpha * alpha * (3 - (2 * alpha))
				return thetaa + ((thetaa - thetaa) * alpha)






			@staticmethod
			def 						UpperCamelCase__				(   _snake_case       ,_snake_case       ,_snake_case       ,_snake_case			):
				import math

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






			@staticmethod
			def 						UpperCamelCase__				(   _snake_case       ,_snake_case       ,_snake_case       ,_snake_case			):
				return thetaa + (thetaa - thetaa) * (1.0 - alpha)







'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class  _snake_case						(__SCREAMING_SNAKE_CASE       ,       unittest.TestCase):
			__A   :     List[str]          =RePaintPipeline
			__A   :     List[str]          =IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
			__A   :     List[Any]          =PipelineTesterMixin.required_optional_params - {
			    "latents",
			    "num_images_per_prompt",
			    "callback",
			    "callback_steps",
			}
			__A   :     str          =IMAGE_INPAINTING_BATCH_PARAMS
			__A   :     Union[str, Any]          =False






			def 						UpperCamelCase__				(   self			):
				torch.manual_seed(0			)
				torch.manual_seed(0			)
				UpperCAmelCase_			:	Union[str, Any]      =   UNetaDModel(
				    block_out_channels=(32, 64)       ,layers_per_block=2       ,sample_size=32       ,in_channels=3       ,out_channels=3       ,down_block_types=("DownBlock2D", "AttnDownBlock2D")       ,up_block_types=("AttnUpBlock2D", "UpBlock2D")       ,)
				UpperCAmelCase_			:	Optional[int]      =   RePaintScheduler()
				UpperCAmelCase_			:	List[Any]      =   {"unet": unet, "scheduler": scheduler}
				return components






			def 						UpperCamelCase__				(   self       ,_snake_case       ,_snake_case=0			):
				if str(_snake_case			).startswith("mps"			):
					UpperCAmelCase_			:	Optional[Any]      =   torch.manual_seed(_snake_case			)
				else:
					UpperCAmelCase_			:	Union[str, Any]      =   torch.Generator(device=_snake_case			).manual_seed(_snake_case			)
				UpperCAmelCase_			:	List[str]      =   np.random.RandomState(_snake_case			).standard_normal((1, 3, 32, 32)			)
				UpperCAmelCase_			:	str      =   torch.from_numpy(_snake_case			).to(device=_snake_case       ,dtype=torch.floataa			)
				UpperCAmelCase_			:	str      =   (image > 0).to(device=_snake_case       ,dtype=torch.floataa			)
				UpperCAmelCase_			:	str      =   {
				    "image": image,
				    "mask_image": mask,
				    "generator": generator,
				    "num_inference_steps": 5,
				    "eta": 0.0,
				    "jump_length": 2,
				    "jump_n_sample": 2,
				    "output_type": "numpy",
				}
				return inputs






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Union[str, Any]      =   "cpu"  # ensure determinism for the device-dependent torch.Generator
				UpperCAmelCase_			:	List[Any]      =   self.get_dummy_components()
				UpperCAmelCase_			:	Union[str, Any]      =   RePaintPipeline(**_snake_case			)
				UpperCAmelCase_			:	Dict      =   sd_pipe.to(_snake_case			)
				sd_pipe.set_progress_bar_config(disable=_snake_case			)

				UpperCAmelCase_			:	Union[str, Any]      =   self.get_dummy_inputs(_snake_case			)
				UpperCAmelCase_			:	Union[str, Any]      =   sd_pipe(**_snake_case			).images
				UpperCAmelCase_			:	Dict      =   image[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				UpperCAmelCase_			:	List[Any]      =   np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]			)

				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-3






			@skip_mps
			def 						UpperCamelCase__				(   self			):
				return super().test_save_load_local()






			@unittest.skip("non-deterministic pipeline"			)
			def 						UpperCamelCase__				(   self			):
				return super().test_inference_batch_single_identical()






			@skip_mps
			def 						UpperCamelCase__				(   self			):
				return super().test_dict_tuple_outputs_equivalent()






			@skip_mps
			def 						UpperCamelCase__				(   self			):
				return super().test_save_load_optional_components()






			@skip_mps
			def 						UpperCamelCase__				(   self			):
				return super().test_attention_slicing_forward_pass()




@nightly
@require_torch_gpu
class  _snake_case						(unittest.TestCase):






			def 						UpperCamelCase__				(   self			):
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Union[str, Any]      =   load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
				    "repaint/celeba_hq_256.png"			)
				UpperCAmelCase_			:	Any      =   load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"			)
				UpperCAmelCase_			:	str      =   load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
				    "repaint/celeba_hq_256_result.npy"			)

				UpperCAmelCase_			:	Optional[Any]      =   "google/ddpm-ema-celebahq-256"
				UpperCAmelCase_			:	Optional[int]      =   UNetaDModel.from_pretrained(_snake_case			)
				UpperCAmelCase_			:	Optional[int]      =   RePaintScheduler.from_pretrained(_snake_case			)

				UpperCAmelCase_			:	Union[str, Any]      =   RePaintPipeline(unet=_snake_case       ,scheduler=_snake_case			).to(_snake_case			)
				repaint.set_progress_bar_config(disable=_snake_case			)
				repaint.enable_attention_slicing()

				UpperCAmelCase_			:	Any      =   torch.manual_seed(0			)
				UpperCAmelCase_			:	Union[str, Any]      =   repaint(
				    _snake_case       ,_snake_case       ,num_inference_steps=2_50       ,eta=0.0       ,jump_length=10       ,jump_n_sample=10       ,generator=_snake_case       ,output_type="np"       ,)
				UpperCAmelCase_			:	Union[str, Any]      =   output.images[0]

				assert image.shape == (2_56, 2_56, 3)
				assert np.abs(expected_image - image			).mean() < 1E-2




