



"""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 							lowerCamelCase_(   A__,				A__,				A__,				unittest.TestCase    ):





     '''simple docstring'''



     lowercase__   :       int										= StableDiffusionInpaintPipeline
     lowercase__   :       Dict										= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     lowercase__   :       Optional[int]										= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     lowercase__   :       List[str]										= frozenset(
         []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     lowercase__   :       str										= frozenset([]    )

     def 			snake_case__      (    self   ):
         torch.manual_seed(0   )
         _lowerCamelCase        =				UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)				,       layers_per_block=2				,       sample_size=3_2				,       in_channels=9				,       out_channels=4				,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,       cross_attention_dim=3_2				,       attention_head_dim=(2, 4)				,       use_linear_projection=lowerCamelCase__				,       )
         _lowerCamelCase        =				PNDMScheduler(skip_prk_steps=lowerCamelCase__   )
         torch.manual_seed(0   )
         _lowerCamelCase        =				AutoencoderKL(
             block_out_channels=[3_2, 6_4]				,       in_channels=3				,       out_channels=3				,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,       latent_channels=4				,       sample_size=1_2_8				,       )
         torch.manual_seed(0   )
         _lowerCamelCase        =				CLIPTextConfig(
             bos_token_id=0				,       eos_token_id=2				,       hidden_size=3_2				,       intermediate_size=3_7				,       layer_norm_eps=1e-05				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1_0_0_0				,       hidden_act='''gelu'''				,       projection_dim=5_1_2				,       )
         _lowerCamelCase        =				CLIPTextModel(lowerCamelCase__   )
         _lowerCamelCase        =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

         _lowerCamelCase        =				{
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }
         return components

     def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__=0   ):
         # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
         _lowerCamelCase        =				floats_tensor((1, 3, 3_2, 3_2)				,       rng=random.Random(lowerCamelCase__   )   ).to(lowerCamelCase__   )
         _lowerCamelCase        =				image.cpu().permute(0				,       2				,       3				,       1   )[0]
         _lowerCamelCase        =				Image.fromarray(np.uinta(lowerCamelCase__   )   ).convert('''RGB'''   ).resize((6_4, 6_4)   )
         _lowerCamelCase        =				Image.fromarray(np.uinta(image + 4   )   ).convert('''RGB'''   ).resize((6_4, 6_4)   )
         if str(lowerCamelCase__   ).startswith('''mps'''   ):
             _lowerCamelCase        =				torch.manual_seed(lowerCamelCase__   )
         else:
             _lowerCamelCase        =				torch.Generator(device=lowerCamelCase__   ).manual_seed(lowerCamelCase__   )
         _lowerCamelCase        =				{
             '''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 			snake_case__      (    self   ):
         _lowerCamelCase        =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         _lowerCamelCase        =				self.get_dummy_components()
         _lowerCamelCase        =				StableDiffusionInpaintPipeline(**lowerCamelCase__   )
         _lowerCamelCase        =				sd_pipe.to(lowerCamelCase__   )
         sd_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

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

         assert image.shape == (1, 6_4, 6_4, 3)
         _lowerCamelCase        =				np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]   )

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





     def 			snake_case__      (    self   ):
         super().test_inference_batch_single_identical(expected_max_diff=3e-3   )


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





     '''simple docstring'''



     def 			snake_case__      (    self   ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()

     def 			snake_case__      (    self   ):
         _lowerCamelCase        =				load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/sd2-inpaint/init_image.png'''   )
         _lowerCamelCase        =				load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''   )
         _lowerCamelCase        =				load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
             '''/yellow_cat_sitting_on_a_park_bench.npy'''   )

         _lowerCamelCase        =				'''stabilityai/stable-diffusion-2-inpainting'''
         _lowerCamelCase        =				StableDiffusionInpaintPipeline.from_pretrained(lowerCamelCase__				,       safety_checker=lowerCamelCase__   )
         pipe.to(lowerCamelCase__   )
         pipe.set_progress_bar_config(disable=lowerCamelCase__   )
         pipe.enable_attention_slicing()

         _lowerCamelCase        =				'''Face of a yellow cat, high resolution, sitting on a park bench'''

         _lowerCamelCase        =				torch.manual_seed(0   )
         _lowerCamelCase        =				pipe(
             prompt=lowerCamelCase__				,       image=lowerCamelCase__				,       mask_image=lowerCamelCase__				,       generator=lowerCamelCase__				,       output_type='''np'''				,       )
         _lowerCamelCase        =				output.images[0]

         assert image.shape == (5_1_2, 5_1_2, 3)
         assert np.abs(expected_image - image   ).max() < 9e-3

     def 			snake_case__      (    self   ):
         _lowerCamelCase        =				load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/sd2-inpaint/init_image.png'''   )
         _lowerCamelCase        =				load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''   )
         _lowerCamelCase        =				load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
             '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''   )

         _lowerCamelCase        =				'''stabilityai/stable-diffusion-2-inpainting'''
         _lowerCamelCase        =				StableDiffusionInpaintPipeline.from_pretrained(
             lowerCamelCase__				,       torch_dtype=torch.floataa				,       safety_checker=lowerCamelCase__				,       )
         pipe.to(lowerCamelCase__   )
         pipe.set_progress_bar_config(disable=lowerCamelCase__   )
         pipe.enable_attention_slicing()

         _lowerCamelCase        =				'''Face of a yellow cat, high resolution, sitting on a park bench'''

         _lowerCamelCase        =				torch.manual_seed(0   )
         _lowerCamelCase        =				pipe(
             prompt=lowerCamelCase__				,       image=lowerCamelCase__				,       mask_image=lowerCamelCase__				,       generator=lowerCamelCase__				,       output_type='''np'''				,       )
         _lowerCamelCase        =				output.images[0]

         assert image.shape == (5_1_2, 5_1_2, 3)
         assert np.abs(expected_image - image   ).max() < 5e-1





     def 			snake_case__      (    self   ):
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

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

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

         _lowerCamelCase        =				'''Face of a yellow cat, high resolution, sitting on a park bench'''

         _lowerCamelCase        =				torch.manual_seed(0   )
         _lowerCamelCase        =				pipe(
             prompt=lowerCamelCase__				,       image=lowerCamelCase__				,       mask_image=lowerCamelCase__				,       generator=lowerCamelCase__				,       num_inference_steps=2				,       output_type='''np'''				,       )

         _lowerCamelCase        =				torch.cuda.max_memory_allocated()
         # make sure that less than 2.65 GB is allocated
         assert mem_bytes < 2.6_5 * 1_0**9





"""simple docstring"""
import numpy as np



def        lowerCAmelCase_(   lowercase_     :							np.array							)	->       np.array:
				return 1 / (1 + np.exp(-vector							))



def        lowerCAmelCase_(   lowercase_     :							np.array							)	->       np.array:
				return vector * sigmoid(1.7_0_2 * vector							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
