




'''simple docstring'''

import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase				,     _UpperCamelCase				,     _UpperCamelCase				,     unittest.TestCase	):
			__SCREAMING_SNAKE_CASE    =			StableDiffusionInpaintPipeline
			__SCREAMING_SNAKE_CASE    =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			__SCREAMING_SNAKE_CASE    =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__SCREAMING_SNAKE_CASE    =			frozenset(
			    []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			__SCREAMING_SNAKE_CASE    =			frozenset([]	)





			def 							A (      self						:    Tuple							):



						"""simple docstring"""




						torch.manual_seed(0							)
						__snake_case			=		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      ,		attention_head_dim=(2, 4)      ,		use_linear_projection=a_      ,		)
						__snake_case			=		PNDMScheduler(skip_prk_steps=a_							)
						torch.manual_seed(0							)
						__snake_case			=		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      ,		sample_size=128      ,		)
						torch.manual_seed(0							)
						__snake_case			=		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=1_000      ,		hidden_act="gelu"      ,		projection_dim=512      ,		)
						__snake_case			=		CLIPTextModel(a_							)
						__snake_case			=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

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





			def 							A (      self						:    Any      ,		a_						:    Optional[Any]      ,		a_						:    List[Any]=0							):



						"""simple docstring"""




						__snake_case			=		floats_tensor((1, 3, 32, 32)      ,		rng=random.Random(a_							)							).to(a_							)
						__snake_case			=		image.cpu().permute(0      ,		2      ,		3      ,		1							)[0]
						__snake_case			=		Image.fromarray(np.uinta(a_							)							).convert("RGB"							).resize((64, 64)							)
						__snake_case			=		Image.fromarray(np.uinta(image + 4							)							).convert("RGB"							).resize((64, 64)							)
						if str(a_							).startswith("mps"							):
									__snake_case			=		torch.manual_seed(a_							)
						else:
									__snake_case			=		torch.Generator(device=a_							).manual_seed(a_							)
						__snake_case			=		{
						    "prompt": "A painting of a squirrel eating a burger",
						    "image": init_image,
						    "mask_image": mask_image,
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs





			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		"cpu"  # ensure determinism for the device-dependent torch.Generator
						__snake_case			=		self.get_dummy_components()
						__snake_case			=		StableDiffusionInpaintPipeline(**a_							)
						__snake_case			=		sd_pipe.to(a_							)
						sd_pipe.set_progress_bar_config(disable=a_							)

						__snake_case			=		self.get_dummy_inputs(a_							)
						__snake_case			=		sd_pipe(**a_							).images
						__snake_case			=		image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						__snake_case			=		np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]							)

						assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2






			def 							A (      self						:    Dict							):



						"""simple docstring"""




						super().test_inference_batch_single_identical(expected_max_diff=3e-3							)







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





			def 							A (      self						:    Any							):



						"""simple docstring"""




						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





			def 							A (      self						:    int							):



						"""simple docstring"""




						__snake_case			=		load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"							)
						__snake_case			=		load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"							)
						__snake_case			=		load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
						    "/yellow_cat_sitting_on_a_park_bench.npy"							)

						__snake_case			=		"stabilityai/stable-diffusion-2-inpainting"
						__snake_case			=		StableDiffusionInpaintPipeline.from_pretrained(a_      ,		safety_checker=a_							)
						pipe.to(a_							)
						pipe.set_progress_bar_config(disable=a_							)
						pipe.enable_attention_slicing()

						__snake_case			=		"Face of a yellow cat, high resolution, sitting on a park bench"

						__snake_case			=		torch.manual_seed(0							)
						__snake_case			=		pipe(
						    prompt=a_      ,		image=a_      ,		mask_image=a_      ,		generator=a_      ,		output_type="np"      ,		)
						__snake_case			=		output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 9e-3





			def 							A (      self						:    int							):



						"""simple docstring"""




						__snake_case			=		load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"							)
						__snake_case			=		load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"							)
						__snake_case			=		load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
						    "/yellow_cat_sitting_on_a_park_bench_fp16.npy"							)

						__snake_case			=		"stabilityai/stable-diffusion-2-inpainting"
						__snake_case			=		StableDiffusionInpaintPipeline.from_pretrained(
						    a_      ,		torch_dtype=torch.floataa      ,		safety_checker=a_      ,		)
						pipe.to(a_							)
						pipe.set_progress_bar_config(disable=a_							)
						pipe.enable_attention_slicing()

						__snake_case			=		"Face of a yellow cat, high resolution, sitting on a park bench"

						__snake_case			=		torch.manual_seed(0							)
						__snake_case			=		pipe(
						    prompt=a_      ,		image=a_      ,		mask_image=a_      ,		generator=a_      ,		output_type="np"      ,		)
						__snake_case			=		output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 5e-1






			def 							A (      self						:    List[Any]							):



						"""simple docstring"""




						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						__snake_case			=		load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/sd2-inpaint/init_image.png"							)
						__snake_case			=		load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"							)

						__snake_case			=		"stabilityai/stable-diffusion-2-inpainting"
						__snake_case			=		PNDMScheduler.from_pretrained(a_      ,		subfolder="scheduler"							)
						__snake_case			=		StableDiffusionInpaintPipeline.from_pretrained(
						    a_      ,		safety_checker=a_      ,		scheduler=a_      ,		torch_dtype=torch.floataa      ,		)
						pipe.to(a_							)
						pipe.set_progress_bar_config(disable=a_							)
						pipe.enable_attention_slicing(1							)
						pipe.enable_sequential_cpu_offload()

						__snake_case			=		"Face of a yellow cat, high resolution, sitting on a park bench"

						__snake_case			=		torch.manual_seed(0							)
						__snake_case			=		pipe(
						    prompt=a_      ,		image=a_      ,		mask_image=a_      ,		generator=a_      ,		num_inference_steps=2      ,		output_type="np"      ,		)

						__snake_case			=		torch.cuda.max_memory_allocated()
						# make sure that less than 2.65 GB is allocated
						assert mem_bytes < 2.65 * 10**9













import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				(PNDMScheduler,)
      snake_case_	=				(("""num_inference_steps""", 50),)


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						int:
           __lowerCAmelCase             =       {
               "num_train_timesteps": 1_0_0_0,
               "beta_start": 0.0_0_0_1,
               "beta_end": 0.0_2,
               "beta_schedule": "linear",
           }

           config.update(**__UpperCamelCase						)
           return config


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase=0       ,    **__UpperCamelCase						)->						Optional[int]:
           __lowerCAmelCase             =       dict(self.forward_default_kwargs						)
           __lowerCAmelCase             =       kwargs.pop("num_inference_steps"       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       self.dummy_sample
           __lowerCAmelCase             =       0.1 * sample
           __lowerCAmelCase             =       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

           for scheduler_class in self.scheduler_classes:
                __lowerCAmelCase             =       self.get_scheduler_config(**__UpperCamelCase						)
                __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)
                scheduler.set_timesteps(__UpperCamelCase						)
                # copy over dummy past residuals
                __lowerCAmelCase             =       dummy_past_residuals[:]

                with tempfile.TemporaryDirectory() as tmpdirname:
                     scheduler.save_config(__UpperCamelCase						)
                     __lowerCAmelCase             =       scheduler_class.from_pretrained(__UpperCamelCase						)
                     new_scheduler.set_timesteps(__UpperCamelCase						)
                     # copy over dummy past residuals
                     __lowerCAmelCase             =       dummy_past_residuals[:]

                __lowerCAmelCase             =       scheduler.step_prk(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample
                __lowerCAmelCase             =       new_scheduler.step_prk(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample

                assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"

                __lowerCAmelCase             =       scheduler.step_plms(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample
                __lowerCAmelCase             =       new_scheduler.step_plms(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample

                assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"


      def 			__UpperCAmelCase		(						self						)->						Dict:
           pass


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase=0       ,    **__UpperCamelCase						)->						Dict:
           __lowerCAmelCase             =       dict(self.forward_default_kwargs						)
           __lowerCAmelCase             =       kwargs.pop("num_inference_steps"       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       self.dummy_sample
           __lowerCAmelCase             =       0.1 * sample
           __lowerCAmelCase             =       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

           for scheduler_class in self.scheduler_classes:
                __lowerCAmelCase             =       self.get_scheduler_config()
                __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)
                scheduler.set_timesteps(__UpperCamelCase						)

                # copy over dummy past residuals (must be after setting timesteps)
                __lowerCAmelCase             =       dummy_past_residuals[:]

                with tempfile.TemporaryDirectory() as tmpdirname:
                     scheduler.save_config(__UpperCamelCase						)
                     __lowerCAmelCase             =       scheduler_class.from_pretrained(__UpperCamelCase						)
                     # copy over dummy past residuals
                     new_scheduler.set_timesteps(__UpperCamelCase						)

                     # copy over dummy past residual (must be after setting timesteps)
                     __lowerCAmelCase             =       dummy_past_residuals[:]

                __lowerCAmelCase             =       scheduler.step_prk(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample
                __lowerCAmelCase             =       new_scheduler.step_prk(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample

                assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"

                __lowerCAmelCase             =       scheduler.step_plms(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample
                __lowerCAmelCase             =       new_scheduler.step_plms(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample

                assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						Tuple:
           __lowerCAmelCase             =       self.scheduler_classes[0]
           __lowerCAmelCase             =       self.get_scheduler_config(**__UpperCamelCase						)
           __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)

           __lowerCAmelCase             =       1_0
           __lowerCAmelCase             =       self.dummy_model()
           __lowerCAmelCase             =       self.dummy_sample_deter
           scheduler.set_timesteps(__UpperCamelCase						)

           for i, t in enumerate(scheduler.prk_timesteps						):
                __lowerCAmelCase             =       model(__UpperCamelCase       ,    __UpperCamelCase						)
                __lowerCAmelCase             =       scheduler.step_prk(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						).prev_sample

           for i, t in enumerate(scheduler.plms_timesteps						):
                __lowerCAmelCase             =       model(__UpperCamelCase       ,    __UpperCamelCase						)
                __lowerCAmelCase             =       scheduler.step_plms(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						).prev_sample

           return sample


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       dict(self.forward_default_kwargs						)

           __lowerCAmelCase             =       kwargs.pop("num_inference_steps"       ,    __UpperCamelCase						)

           for scheduler_class in self.scheduler_classes:
                __lowerCAmelCase             =       self.get_scheduler_config()
                __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)

                __lowerCAmelCase             =       self.dummy_sample
                __lowerCAmelCase             =       0.1 * sample

                if num_inference_steps is not None and hasattr(__UpperCamelCase       ,    "set_timesteps"						):
                     scheduler.set_timesteps(__UpperCamelCase						)
                elif num_inference_steps is not None and not hasattr(__UpperCamelCase       ,    "set_timesteps"						):
                     __lowerCAmelCase             =       num_inference_steps

                # copy over dummy past residuals (must be done after set_timesteps)
                __lowerCAmelCase             =       [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
                __lowerCAmelCase             =       dummy_past_residuals[:]

                __lowerCAmelCase             =       scheduler.step_prk(__UpperCamelCase       ,    0       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample
                __lowerCAmelCase             =       scheduler.step_prk(__UpperCamelCase       ,    1       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample

                self.assertEqual(output_a.shape       ,    sample.shape						)
                self.assertEqual(output_a.shape       ,    output_a.shape						)

                __lowerCAmelCase             =       scheduler.step_plms(__UpperCamelCase       ,    0       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample
                __lowerCAmelCase             =       scheduler.step_plms(__UpperCamelCase       ,    1       ,    __UpperCamelCase       ,    **__UpperCamelCase						).prev_sample

                self.assertEqual(output_a.shape       ,    sample.shape						)
                self.assertEqual(output_a.shape       ,    output_a.shape						)


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           for timesteps in [1_0_0, 1_0_0_0]:
                self.check_over_configs(num_train_timesteps=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Any:
           for steps_offset in [0, 1]:
                self.check_over_configs(steps_offset=__UpperCamelCase						)

           __lowerCAmelCase             =       self.scheduler_classes[0]
           __lowerCAmelCase             =       self.get_scheduler_config(steps_offset=1						)
           __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)
           scheduler.set_timesteps(1_0						)
           assert torch.equal(
               scheduler.timesteps       ,    torch.LongTensor(
                   [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]						)       ,    )


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


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           for schedule in ["linear", "squaredcos_cap_v2"]:
                self.check_over_configs(beta_schedule=__UpperCamelCase						)


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


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           for t in [1, 5, 1_0]:
                self.check_over_forward(time_step=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           for t, num_inference_steps in zip([1, 5, 1_0]       ,    [1_0, 5_0, 1_0_0]						):
                self.check_over_forward(num_inference_steps=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						int:
           # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
           __lowerCAmelCase             =       2_7

           for scheduler_class in self.scheduler_classes:
                __lowerCAmelCase             =       self.dummy_sample
                __lowerCAmelCase             =       0.1 * sample

                __lowerCAmelCase             =       self.get_scheduler_config()
                __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)

                scheduler.set_timesteps(__UpperCamelCase						)

                # before power of 3 fix, would error on first step, so we only need to do two
                for i, t in enumerate(scheduler.prk_timesteps[:2]						):
                     __lowerCAmelCase             =       scheduler.step_prk(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						).prev_sample


      def 			__UpperCAmelCase		(						self						)->						Dict:
           with self.assertRaises(__UpperCamelCase						):
                __lowerCAmelCase             =       self.scheduler_classes[0]
                __lowerCAmelCase             =       self.get_scheduler_config()
                __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)

                scheduler.step_plms(self.dummy_sample       ,    1       ,    self.dummy_sample						).prev_sample


      def 			__UpperCAmelCase		(						self						)->						Any:
           __lowerCAmelCase             =       self.full_loop()
           __lowerCAmelCase             =       torch.sum(torch.abs(__UpperCamelCase						)						)
           __lowerCAmelCase             =       torch.mean(torch.abs(__UpperCamelCase						)						)

           assert abs(result_sum.item() - 1_9_8.1_3_1_8						) < 1e-2
           assert abs(result_mean.item() - 0.2_5_8_0						) < 1e-3


      def 			__UpperCAmelCase		(						self						)->						str:
           __lowerCAmelCase             =       self.full_loop(prediction_type="v_prediction"						)
           __lowerCAmelCase             =       torch.sum(torch.abs(__UpperCamelCase						)						)
           __lowerCAmelCase             =       torch.mean(torch.abs(__UpperCamelCase						)						)

           assert abs(result_sum.item() - 6_7.3_9_8_6						) < 1e-2
           assert abs(result_mean.item() - 0.0_8_7_8						) < 1e-3


      def 			__UpperCAmelCase		(						self						)->						Any:
           # We specify different beta, so that the first alpha is 0.99
           __lowerCAmelCase             =       self.full_loop(set_alpha_to_one=__UpperCamelCase       ,    beta_start=0.0_1						)
           __lowerCAmelCase             =       torch.sum(torch.abs(__UpperCamelCase						)						)
           __lowerCAmelCase             =       torch.mean(torch.abs(__UpperCamelCase						)						)

           assert abs(result_sum.item() - 2_3_0.0_3_9_9						) < 1e-2
           assert abs(result_mean.item() - 0.2_9_9_5						) < 1e-3


      def 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           # We specify different beta, so that the first alpha is 0.99
           __lowerCAmelCase             =       self.full_loop(set_alpha_to_one=__UpperCamelCase       ,    beta_start=0.0_1						)
           __lowerCAmelCase             =       torch.sum(torch.abs(__UpperCamelCase						)						)
           __lowerCAmelCase             =       torch.mean(torch.abs(__UpperCamelCase						)						)

           assert abs(result_sum.item() - 1_8_6.9_4_8_2						) < 1e-2
           assert abs(result_mean.item() - 0.2_4_3_4						) < 1e-3






