
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     lowercase (		a_  ,       a_  ,       a_  ,       unittest.TestCase       ):
  __lowercase	:	int     					=    StableDiffusionInpaintPipeline
  __lowercase	:	int     					=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  __lowercase	:	Tuple     					=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  __lowercase	:	int     					=    frozenset(
      []       )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  __lowercase	:	List[str]     					=    frozenset([]       )



  def __UpperCamelCase			(			self	)  -> Dict:
    """simple docstring"""
    torch.manual_seed(0	)
    UpperCamelCase          =					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=lowercase_    ,				)
    UpperCamelCase          =					PNDMScheduler(skip_prk_steps=lowercase_	)
    torch.manual_seed(0	)
    UpperCamelCase          =					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	)
    UpperCamelCase          =					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    ,				)
    UpperCamelCase          =					CLIPTextModel(lowercase_	)
    UpperCamelCase          =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

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



  def __UpperCamelCase			(			self    ,				A_    ,				A_=0	)  -> int:
    """simple docstring"""
    UpperCamelCase          =					floats_tensor((1, 3, 32, 32)    ,				rng=random.Random(lowercase_	)	).to(lowercase_	)
    UpperCamelCase          =					image.cpu().permute(0    ,				2    ,				3    ,				1	)[0]
    UpperCamelCase          =					Image.fromarray(np.uinta(lowercase_	)	).convert('RGB'	).resize((64, 64)	)
    UpperCamelCase          =					Image.fromarray(np.uinta(image + 4	)	).convert('RGB'	).resize((64, 64)	)
    if str(lowercase_	).startswith('mps'	):
      UpperCamelCase          =					torch.manual_seed(lowercase_	)
    else:
      UpperCamelCase          =					torch.Generator(device=lowercase_	).manual_seed(lowercase_	)
    UpperCamelCase          =					{
        '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 __UpperCamelCase			(			self	)  -> str:
    """simple docstring"""
    UpperCamelCase          =					'cpu'  # ensure determinism for the device-dependent torch.Generator
    UpperCamelCase          =					self.get_dummy_components()
    UpperCamelCase          =					StableDiffusionInpaintPipeline(**lowercase_	)
    UpperCamelCase          =					sd_pipe.to(lowercase_	)
    sd_pipe.set_progress_bar_config(disable=lowercase_	)

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

    assert image.shape == (1, 64, 64, 3)
    UpperCamelCase          =					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 __UpperCamelCase			(			self	)  -> Any:
    """simple docstring"""
    super().test_inference_batch_single_identical(expected_max_diff=3e-3	)






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



  def __UpperCamelCase			(			self	)  -> Tuple:
    """simple docstring"""
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()



  def __UpperCamelCase			(			self	)  -> Optional[Any]:
    """simple docstring"""
    UpperCamelCase          =					load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/sd2-inpaint/init_image.png'	)
    UpperCamelCase          =					load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'	)
    UpperCamelCase          =					load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
        '/yellow_cat_sitting_on_a_park_bench.npy'	)

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

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

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

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



  def __UpperCamelCase			(			self	)  -> Union[str, Any]:
    """simple docstring"""
    UpperCamelCase          =					load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/sd2-inpaint/init_image.png'	)
    UpperCamelCase          =					load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'	)
    UpperCamelCase          =					load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
        '/yellow_cat_sitting_on_a_park_bench_fp16.npy'	)

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

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

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

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



  def __UpperCamelCase			(			self	)  -> List[str]:
    """simple docstring"""
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()

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

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

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

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

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


import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation


class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('swish'	)

				self.assertIsInstance(A_    ,				nn.SiLU	)

				self.assertEqual(act(torch.tensor(-100    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('silu'	)

				self.assertIsInstance(A_    ,				nn.SiLU	)

				self.assertEqual(act(torch.tensor(-100    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('mish'	)

				self.assertIsInstance(A_    ,				nn.Mish	)

				self.assertEqual(act(torch.tensor(-200    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					get_activation('gelu'	)

				self.assertIsInstance(A_    ,				nn.GELU	)

				self.assertEqual(act(torch.tensor(-100    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertNotEqual(act(torch.tensor(-1    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(0    ,				dtype=torch.floataa	)	).item()    ,				0	)
				self.assertEqual(act(torch.tensor(20    ,				dtype=torch.floataa	)	).item()    ,				20	)
