


'''simple docstring'''







# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()






class 		_snake_case    (				UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__  ,  unittest.TestCase		):






      '''simple docstring'''







      __snake_case   =      StableDiffusionControlNetInpaintPipeline
      __snake_case   =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      __snake_case   =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      __snake_case   =      frozenset({"control_image"}		)  # skip `image` and `mask` for now, only test for control_image
      __snake_case   =      TEXT_TO_IMAGE_IMAGE_PARAMS







      def lowerCAmelCase__ (  self:	List[str]     )							->	List[str]:
          torch.manual_seed(0     )
          __magic_name__				:					Tuple						       =       UNetaDConditionModel(
              block_out_channels=(32, 64)      ,							layers_per_block=2      ,							sample_size=32      ,							in_channels=9      ,							out_channels=4      ,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,							up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,							cross_attention_dim=32      ,							)
          torch.manual_seed(0     )
          __magic_name__				:					Any						       =       ControlNetModel(
              block_out_channels=(32, 64)      ,							layers_per_block=2      ,							in_channels=4      ,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,							cross_attention_dim=32      ,							conditioning_embedding_out_channels=(16, 32)      ,							)
          torch.manual_seed(0     )
          __magic_name__				:					Dict						       =       DDIMScheduler(
              beta_start=0.0_0_0_8_5      ,							beta_end=0.0_1_2      ,							beta_schedule="scaled_linear"      ,							clip_sample=lowerCAmelCase__      ,							set_alpha_to_one=lowerCAmelCase__      ,							)
          torch.manual_seed(0     )
          __magic_name__				:					List[Any]						       =       AutoencoderKL(
              block_out_channels=[32, 64]      ,							in_channels=3      ,							out_channels=3      ,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,							latent_channels=4      ,							)
          torch.manual_seed(0     )
          __magic_name__				:					Union[str, Any]						       =       CLIPTextConfig(
              bos_token_id=0      ,							eos_token_id=2      ,							hidden_size=32      ,							intermediate_size=37      ,							layer_norm_eps=1E-05      ,							num_attention_heads=4      ,							num_hidden_layers=5      ,							pad_token_id=1      ,							vocab_size=1000      ,							)
          __magic_name__				:					str						       =       CLIPTextModel(lowerCAmelCase__     )
          __magic_name__				:					List[Any]						       =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

          __magic_name__				:					List[str]						       =       {
              "unet": unet,
              "controlnet": controlnet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }
          return components







      def lowerCAmelCase__ (  self:	Any      ,							__UpperCamelCase:	Any      ,							__UpperCamelCase:	List[str]=0     )							->	str:




          if str(lowerCAmelCase__     ).startswith("mps"     ):
              __magic_name__				:					int						       =       torch.manual_seed(lowerCAmelCase__     )
          else:
              __magic_name__				:					List[str]						       =       torch.Generator(device=lowerCAmelCase__     ).manual_seed(lowerCAmelCase__     )

          __magic_name__				:					Optional[int]						       =       2
          __magic_name__				:					str						       =       randn_tensor(
              (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,							generator=lowerCAmelCase__      ,							device=torch.device(lowerCAmelCase__     )      ,							)
          __magic_name__				:					List[Any]						       =       floats_tensor((1, 3, 32, 32)      ,							rng=random.Random(lowerCAmelCase__     )     ).to(lowerCAmelCase__     )
          __magic_name__				:					List[Any]						       =       init_image.cpu().permute(0      ,							2      ,							3      ,							1     )[0]

          __magic_name__				:					Tuple						       =       Image.fromarray(np.uinta(lowerCAmelCase__     )     ).convert("RGB"     ).resize((64, 64)     )
          __magic_name__				:					Union[str, Any]						       =       Image.fromarray(np.uinta(init_image + 4     )     ).convert("RGB"     ).resize((64, 64)     )

          __magic_name__				:					Optional[Any]						       =       {
              "prompt": "A painting of a squirrel eating a burger",
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 6.0,
              "output_type": "numpy",
              "image": image,
              "mask_image": mask_image,
              "control_image": control_image,
          }

          return inputs







      def lowerCAmelCase__ (  self:	List[str]     )							->	List[str]:
          return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3     )







      @unittest.skipIf(
          torch_device != "cuda" or not is_xformers_available()      ,							reason="XFormers attention is only available with CUDA and `xformers` installed"      ,							)
      def lowerCAmelCase__ (  self:	Tuple     )							->	str:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3     )







      def lowerCAmelCase__ (  self:	Any     )							->	int:
          self._test_inference_batch_single_identical(expected_max_diff=2E-3     )






class 		_snake_case    (				UpperCamelCase__		):






      '''simple docstring'''







      __snake_case   =      StableDiffusionControlNetInpaintPipeline
      __snake_case   =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      __snake_case   =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      __snake_case   =      frozenset([]		)







      def lowerCAmelCase__ (  self:	Dict     )							->	Optional[Any]:
          torch.manual_seed(0     )
          __magic_name__				:					Tuple						       =       UNetaDConditionModel(
              block_out_channels=(32, 64)      ,							layers_per_block=2      ,							sample_size=32      ,							in_channels=4      ,							out_channels=4      ,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,							up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,							cross_attention_dim=32      ,							)
          torch.manual_seed(0     )
          __magic_name__				:					str						       =       ControlNetModel(
              block_out_channels=(32, 64)      ,							layers_per_block=2      ,							in_channels=4      ,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,							cross_attention_dim=32      ,							conditioning_embedding_out_channels=(16, 32)      ,							)
          torch.manual_seed(0     )
          __magic_name__				:					Optional[int]						       =       DDIMScheduler(
              beta_start=0.0_0_0_8_5      ,							beta_end=0.0_1_2      ,							beta_schedule="scaled_linear"      ,							clip_sample=lowerCAmelCase__      ,							set_alpha_to_one=lowerCAmelCase__      ,							)
          torch.manual_seed(0     )
          __magic_name__				:					Dict						       =       AutoencoderKL(
              block_out_channels=[32, 64]      ,							in_channels=3      ,							out_channels=3      ,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,							latent_channels=4      ,							)
          torch.manual_seed(0     )
          __magic_name__				:					Union[str, Any]						       =       CLIPTextConfig(
              bos_token_id=0      ,							eos_token_id=2      ,							hidden_size=32      ,							intermediate_size=37      ,							layer_norm_eps=1E-05      ,							num_attention_heads=4      ,							num_hidden_layers=5      ,							pad_token_id=1      ,							vocab_size=1000      ,							)
          __magic_name__				:					Optional[int]						       =       CLIPTextModel(lowerCAmelCase__     )
          __magic_name__				:					Optional[int]						       =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

          __magic_name__				:					str						       =       {
              "unet": unet,
              "controlnet": controlnet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }
          return components






class 		_snake_case    (				UpperCamelCase__  ,  UpperCamelCase__  ,  unittest.TestCase		):






      '''simple docstring'''







      __snake_case   =      StableDiffusionControlNetInpaintPipeline
      __snake_case   =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      __snake_case   =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS







      def lowerCAmelCase__ (  self:	str     )							->	Union[str, Any]:
          torch.manual_seed(0     )
          __magic_name__				:					str						       =       UNetaDConditionModel(
              block_out_channels=(32, 64)      ,							layers_per_block=2      ,							sample_size=32      ,							in_channels=9      ,							out_channels=4      ,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,							up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,							cross_attention_dim=32      ,							)
          torch.manual_seed(0     )

          def init_weights(__UpperCamelCase:	Tuple     ):
              if isinstance(lowerCAmelCase__      ,							torch.nn.Convad     ):
                  torch.nn.init.normal(m.weight     )
                  m.bias.data.fill_(1.0     )

          __magic_name__				:					List[str]						       =       ControlNetModel(
              block_out_channels=(32, 64)      ,							layers_per_block=2      ,							in_channels=4      ,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,							cross_attention_dim=32      ,							conditioning_embedding_out_channels=(16, 32)      ,							)
          controlneta.controlnet_down_blocks.apply(lowerCAmelCase__     )

          torch.manual_seed(0     )
          __magic_name__				:					Union[str, Any]						       =       ControlNetModel(
              block_out_channels=(32, 64)      ,							layers_per_block=2      ,							in_channels=4      ,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,							cross_attention_dim=32      ,							conditioning_embedding_out_channels=(16, 32)      ,							)
          controlneta.controlnet_down_blocks.apply(lowerCAmelCase__     )

          torch.manual_seed(0     )
          __magic_name__				:					Union[str, Any]						       =       DDIMScheduler(
              beta_start=0.0_0_0_8_5      ,							beta_end=0.0_1_2      ,							beta_schedule="scaled_linear"      ,							clip_sample=lowerCAmelCase__      ,							set_alpha_to_one=lowerCAmelCase__      ,							)
          torch.manual_seed(0     )
          __magic_name__				:					Optional[Any]						       =       AutoencoderKL(
              block_out_channels=[32, 64]      ,							in_channels=3      ,							out_channels=3      ,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,							latent_channels=4      ,							)
          torch.manual_seed(0     )
          __magic_name__				:					Optional[Any]						       =       CLIPTextConfig(
              bos_token_id=0      ,							eos_token_id=2      ,							hidden_size=32      ,							intermediate_size=37      ,							layer_norm_eps=1E-05      ,							num_attention_heads=4      ,							num_hidden_layers=5      ,							pad_token_id=1      ,							vocab_size=1000      ,							)
          __magic_name__				:					List[str]						       =       CLIPTextModel(lowerCAmelCase__     )
          __magic_name__				:					List[Any]						       =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

          __magic_name__				:					Union[str, Any]						       =       MultiControlNetModel([controlneta, controlneta]     )

          __magic_name__				:					int						       =       {
              "unet": unet,
              "controlnet": controlnet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }
          return components







      def lowerCAmelCase__ (  self:	List[str]      ,							__UpperCamelCase:	Optional[Any]      ,							__UpperCamelCase:	Any=0     )							->	str:




          if str(lowerCAmelCase__     ).startswith("mps"     ):
              __magic_name__				:					List[str]						       =       torch.manual_seed(lowerCAmelCase__     )
          else:
              __magic_name__				:					Optional[Any]						       =       torch.Generator(device=lowerCAmelCase__     ).manual_seed(lowerCAmelCase__     )

          __magic_name__				:					List[str]						       =       2

          __magic_name__				:					Optional[int]						       =       [
              randn_tensor(
                  (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,							generator=lowerCAmelCase__      ,							device=torch.device(lowerCAmelCase__     )      ,							),
              randn_tensor(
                  (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)      ,							generator=lowerCAmelCase__      ,							device=torch.device(lowerCAmelCase__     )      ,							),
          ]
          __magic_name__				:					List[str]						       =       floats_tensor((1, 3, 32, 32)      ,							rng=random.Random(lowerCAmelCase__     )     ).to(lowerCAmelCase__     )
          __magic_name__				:					Dict						       =       init_image.cpu().permute(0      ,							2      ,							3      ,							1     )[0]

          __magic_name__				:					int						       =       Image.fromarray(np.uinta(lowerCAmelCase__     )     ).convert("RGB"     ).resize((64, 64)     )
          __magic_name__				:					str						       =       Image.fromarray(np.uinta(init_image + 4     )     ).convert("RGB"     ).resize((64, 64)     )

          __magic_name__				:					Optional[Any]						       =       {
              "prompt": "A painting of a squirrel eating a burger",
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 6.0,
              "output_type": "numpy",
              "image": image,
              "mask_image": mask_image,
              "control_image": control_image,
          }

          return inputs







      def lowerCAmelCase__ (  self:	str     )							->	int:
          __magic_name__				:					Tuple						       =       self.get_dummy_components()
          __magic_name__				:					Union[str, Any]						       =       self.pipeline_class(**lowerCAmelCase__     )
          pipe.to(lowerCAmelCase__     )

          __magic_name__				:					int						       =       1_0.0
          __magic_name__				:					int						       =       4

          __magic_name__				:					Dict						       =       self.get_dummy_inputs(lowerCAmelCase__     )
          __magic_name__				:					int						       =       steps
          __magic_name__				:					List[Any]						       =       scale
          __magic_name__				:					Tuple						       =       pipe(**lowerCAmelCase__     )[0]

          __magic_name__				:					Dict						       =       self.get_dummy_inputs(lowerCAmelCase__     )
          __magic_name__				:					Dict						       =       steps
          __magic_name__				:					int						       =       scale
          __magic_name__				:					int						       =       pipe(**lowerCAmelCase__      ,							control_guidance_start=0.1      ,							control_guidance_end=0.2     )[0]

          __magic_name__				:					Tuple						       =       self.get_dummy_inputs(lowerCAmelCase__     )
          __magic_name__				:					Optional[Any]						       =       steps
          __magic_name__				:					List[Any]						       =       scale
          __magic_name__				:					int						       =       pipe(**lowerCAmelCase__      ,							control_guidance_start=[0.1, 0.3]      ,							control_guidance_end=[0.2, 0.7]     )[0]

          __magic_name__				:					Any						       =       self.get_dummy_inputs(lowerCAmelCase__     )
          __magic_name__				:					int						       =       steps
          __magic_name__				:					Dict						       =       scale
          __magic_name__				:					Union[str, Any]						       =       pipe(**lowerCAmelCase__      ,							control_guidance_start=0.4      ,							control_guidance_end=[0.5, 0.8]     )[0]

          # make sure that all outputs are different
          assert np.sum(np.abs(output_a - output_a     )     ) > 1E-3
          assert np.sum(np.abs(output_a - output_a     )     ) > 1E-3
          assert np.sum(np.abs(output_a - output_a     )     ) > 1E-3







      def lowerCAmelCase__ (  self:	Any     )							->	Any:
          return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3     )







      @unittest.skipIf(
          torch_device != "cuda" or not is_xformers_available()      ,							reason="XFormers attention is only available with CUDA and `xformers` installed"      ,							)
      def lowerCAmelCase__ (  self:	Tuple     )							->	Dict:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3     )







      def lowerCAmelCase__ (  self:	Optional[int]     )							->	int:
          self._test_inference_batch_single_identical(expected_max_diff=2E-3     )







      def lowerCAmelCase__ (  self:	Dict     )							->	str:
          __magic_name__				:					Union[str, Any]						       =       self.get_dummy_components()
          __magic_name__				:					Optional[Any]						       =       self.pipeline_class(**lowerCAmelCase__     )
          pipe.to(lowerCAmelCase__     )
          pipe.set_progress_bar_config(disable=lowerCAmelCase__     )
          with tempfile.TemporaryDirectory() as tmpdir:
              try:
                  # save_pretrained is not implemented for Multi-ControlNet
                  pipe.save_pretrained(lowerCAmelCase__     )
              except NotImplementedError:
                  pass






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






      '''simple docstring'''







      def lowerCAmelCase__ (  self:	Dict     )							->	Dict:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







      def lowerCAmelCase__ (  self:	str     )							->	Any:
          __magic_name__				:					Optional[int]						       =       ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"     )

          __magic_name__				:					Dict						       =       StableDiffusionControlNetInpaintPipeline.from_pretrained(
              "runwayml/stable-diffusion-inpainting"      ,							safety_checker=lowerCAmelCase__      ,							controlnet=lowerCAmelCase__     )
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=lowerCAmelCase__     )

          __magic_name__				:					int						       =       torch.Generator(device="cpu"     ).manual_seed(0     )
          __magic_name__				:					List[Any]						       =       load_image(
              "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"     ).resize((512, 512)     )

          __magic_name__				:					Union[str, Any]						       =       load_image(
              "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
              "/stable_diffusion_inpaint/input_bench_mask.png"     ).resize((512, 512)     )

          __magic_name__				:					Optional[Any]						       =       "pitch black hole"

          __magic_name__				:					Tuple						       =       load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"     ).resize((512, 512)     )

          __magic_name__				:					List[str]						       =       pipe(
              lowerCAmelCase__      ,							image=lowerCAmelCase__      ,							mask_image=lowerCAmelCase__      ,							control_image=lowerCAmelCase__      ,							generator=lowerCAmelCase__      ,							output_type="np"      ,							num_inference_steps=3      ,							)

          __magic_name__				:					Tuple						       =       output.images[0]

          assert image.shape == (512, 512, 3)

          __magic_name__				:					List[Any]						       =       load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"     )

          assert np.abs(expected_image - image     ).max() < 9E-2







      def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Any:
          __magic_name__				:					Any						       =       ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint"     )

          __magic_name__				:					Tuple						       =       StableDiffusionControlNetInpaintPipeline.from_pretrained(
              "runwayml/stable-diffusion-v1-5"      ,							safety_checker=lowerCAmelCase__      ,							controlnet=lowerCAmelCase__     )
          __magic_name__				:					Optional[int]						       =       DDIMScheduler.from_config(pipe.scheduler.config     )
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=lowerCAmelCase__     )

          __magic_name__				:					int						       =       torch.Generator(device="cpu"     ).manual_seed(33     )

          __magic_name__				:					Any						       =       load_image(
              "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"     )
          __magic_name__				:					Union[str, Any]						       =       init_image.resize((512, 512)     )

          __magic_name__				:					List[str]						       =       load_image(
              "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"     )
          __magic_name__				:					Optional[Any]						       =       mask_image.resize((512, 512)     )

          __magic_name__				:					str						       =       "a handsome man with ray-ban sunglasses"

          def make_inpaint_condition(__UpperCamelCase:	Any      ,							__UpperCamelCase:	str     ):
              __magic_name__				:					List[Any]						       =       np.array(image.convert("RGB"     )     ).astype(np.floataa     ) / 255.0
              __magic_name__				:					str						       =       np.array(image_mask.convert("L"     )     ).astype(np.floataa     ) / 255.0

              assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
              __magic_name__				:					Any						       =       -1.0  # set as masked pixel
              __magic_name__				:					int						       =       np.expand_dims(lowerCAmelCase__      ,							0     ).transpose(0      ,							3      ,							1      ,							2     )
              __magic_name__				:					Tuple						       =       torch.from_numpy(lowerCAmelCase__     )
              return image

          __magic_name__				:					List[str]						       =       make_inpaint_condition(lowerCAmelCase__      ,							lowerCAmelCase__     )

          __magic_name__				:					int						       =       pipe(
              lowerCAmelCase__      ,							image=lowerCAmelCase__      ,							mask_image=lowerCAmelCase__      ,							control_image=lowerCAmelCase__      ,							guidance_scale=9.0      ,							eta=1.0      ,							generator=lowerCAmelCase__      ,							num_inference_steps=20      ,							output_type="np"      ,							)
          __magic_name__				:					Dict						       =       output.images[0]

          assert image.shape == (512, 512, 3)

          __magic_name__				:					Tuple						       =       load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"     )

          assert np.abs(expected_image - image     ).max() < 9E-2



'''simple docstring'''







import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 		_snake_case    (				snake_case_		):






						'''simple docstring'''







						__snake_case   =      (LMSDiscreteScheduler,)
						__snake_case   =      1_0







						def lowerCAmelCase__ (  self:	Union[str, Any]      ,							**__UpperCamelCase:	Any     )							->	List[str]:
										__magic_name__				:					List[str]						       =       {
										    "num_train_timesteps": 1100,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										}

										config.update(**__UpperCamelCase     )
										return config







						def lowerCAmelCase__ (  self:	Any     )							->	str:
										for timesteps in [10, 50, 100, 1000]:
														self.check_over_configs(num_train_timesteps=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	str     )							->	int:
										for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]      ,							[0.0_0_0_2, 0.0_0_2, 0.0_2]     ):
														self.check_over_configs(beta_start=__UpperCamelCase      ,							beta_end=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	str     )							->	Any:
										for schedule in ["linear", "scaled_linear"]:
														self.check_over_configs(beta_schedule=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Dict     )							->	int:
										for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Optional[Any]:
										for t in [0, 500, 800]:
														self.check_over_forward(time_step=__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	List[str]     )							->	Optional[int]:
										__magic_name__				:					Dict						       =       self.scheduler_classes[0]
										__magic_name__				:					List[str]						       =       self.get_scheduler_config()
										__magic_name__				:					Dict						       =       scheduler_class(**__UpperCamelCase     )

										scheduler.set_timesteps(self.num_inference_steps     )

										__magic_name__				:					str						       =       self.dummy_model()
										__magic_name__				:					str						       =       self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(scheduler.timesteps     ):
														__magic_name__				:					Dict						       =       scheduler.scale_model_input(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					Optional[Any]						       =       model(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					Union[str, Any]						       =       scheduler.step(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
														__magic_name__				:					Dict						       =       output.prev_sample

										__magic_name__				:					Tuple						       =       torch.sum(torch.abs(__UpperCamelCase     )     )
										__magic_name__				:					Union[str, Any]						       =       torch.mean(torch.abs(__UpperCamelCase     )     )

										assert abs(result_sum.item() - 1_0_0_6.3_8_8     ) < 1E-2
										assert abs(result_mean.item() - 1.3_1     ) < 1E-3







						def lowerCAmelCase__ (  self:	int     )							->	Tuple:
										__magic_name__				:					Optional[int]						       =       self.scheduler_classes[0]
										__magic_name__				:					Tuple						       =       self.get_scheduler_config(prediction_type="v_prediction"     )
										__magic_name__				:					List[Any]						       =       scheduler_class(**__UpperCamelCase     )

										scheduler.set_timesteps(self.num_inference_steps     )

										__magic_name__				:					Tuple						       =       self.dummy_model()
										__magic_name__				:					str						       =       self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(scheduler.timesteps     ):
														__magic_name__				:					int						       =       scheduler.scale_model_input(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					int						       =       model(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					Dict						       =       scheduler.step(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
														__magic_name__				:					Dict						       =       output.prev_sample

										__magic_name__				:					Optional[Any]						       =       torch.sum(torch.abs(__UpperCamelCase     )     )
										__magic_name__				:					List[Any]						       =       torch.mean(torch.abs(__UpperCamelCase     )     )

										assert abs(result_sum.item() - 0.0_0_1_7     ) < 1E-2
										assert abs(result_mean.item() - 2.2_676E-06     ) < 1E-3







						def lowerCAmelCase__ (  self:	Union[str, Any]     )							->	str:
										__magic_name__				:					Optional[int]						       =       self.scheduler_classes[0]
										__magic_name__				:					List[Any]						       =       self.get_scheduler_config()
										__magic_name__				:					Any						       =       scheduler_class(**__UpperCamelCase     )

										scheduler.set_timesteps(self.num_inference_steps      ,							device=__UpperCamelCase     )

										__magic_name__				:					Optional[Any]						       =       self.dummy_model()
										__magic_name__				:					Union[str, Any]						       =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										__magic_name__				:					int						       =       sample.to(__UpperCamelCase     )

										for i, t in enumerate(scheduler.timesteps     ):
														__magic_name__				:					Optional[int]						       =       scheduler.scale_model_input(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					Union[str, Any]						       =       model(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					int						       =       scheduler.step(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
														__magic_name__				:					List[Any]						       =       output.prev_sample

										__magic_name__				:					Any						       =       torch.sum(torch.abs(__UpperCamelCase     )     )
										__magic_name__				:					int						       =       torch.mean(torch.abs(__UpperCamelCase     )     )

										assert abs(result_sum.item() - 1_0_0_6.3_8_8     ) < 1E-2
										assert abs(result_mean.item() - 1.3_1     ) < 1E-3







						def lowerCAmelCase__ (  self:	List[str]     )							->	str:
										__magic_name__				:					Dict						       =       self.scheduler_classes[0]
										__magic_name__				:					Optional[Any]						       =       self.get_scheduler_config()
										__magic_name__				:					Dict						       =       scheduler_class(**__UpperCamelCase      ,							use_karras_sigmas=__UpperCamelCase     )

										scheduler.set_timesteps(self.num_inference_steps      ,							device=__UpperCamelCase     )

										__magic_name__				:					int						       =       self.dummy_model()
										__magic_name__				:					Optional[int]						       =       self.dummy_sample_deter.to(__UpperCamelCase     ) * scheduler.init_noise_sigma
										__magic_name__				:					List[str]						       =       sample.to(__UpperCamelCase     )

										for t in scheduler.timesteps:
														__magic_name__				:					str						       =       scheduler.scale_model_input(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					List[Any]						       =       model(__UpperCamelCase      ,							__UpperCamelCase     )

														__magic_name__				:					str						       =       scheduler.step(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
														__magic_name__				:					int						       =       output.prev_sample

										__magic_name__				:					str						       =       torch.sum(torch.abs(__UpperCamelCase     )     )
										__magic_name__				:					Dict						       =       torch.mean(torch.abs(__UpperCamelCase     )     )

										assert abs(result_sum.item() - 3_8_1_2.9_9_2_7     ) < 2E-2
										assert abs(result_mean.item() - 4.9_6_4_8     ) < 1E-3