'''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 			lowerCamelCase_			(snake_case__							,				snake_case__							,				snake_case__							,				unittest.TestCase					):
      '''simple docstring'''

      __UpperCamelCase:						Tuple      			=		StableDiffusionControlNetInpaintPipeline
      __UpperCamelCase:						List[str]      			=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      __UpperCamelCase:						List[str]      			=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      __UpperCamelCase:						Optional[Any]      			=		frozenset({"control_image"}					)  # skip `image` and `mask` for now, only test for control_image
      __UpperCamelCase:						Union[str, Any]      			=		TEXT_TO_IMAGE_IMAGE_PARAMS




      def   _A			( self		:       Union[str, Any] ):
             torch.manual_seed(0 )
             _UpperCAmelCase  :						Optional[int]							   =					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 )
             _UpperCAmelCase  :						List[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 )
             _UpperCAmelCase  :						Optional[int]							   =					DDIMScheduler(
                 beta_start=0.00_085		,      beta_end=0.012		,      beta_schedule="scaled_linear"		,      clip_sample=A		,      set_alpha_to_one=A		,      )
             torch.manual_seed(0 )
             _UpperCAmelCase  :						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 )
             _UpperCAmelCase  :						Tuple							   =					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		,      )
             _UpperCAmelCase  :						Any							   =					CLIPTextModel(A )
             _UpperCAmelCase  :						Any							   =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

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




      def   _A			( self		:       List[Any]		,      A		:       List[str]		,      A		:       Optional[Any]=0 ):
             if str(A ).startswith("mps" ):
                    _UpperCAmelCase  :						Union[str, Any]							   =					torch.manual_seed(A )
             else:
                    _UpperCAmelCase  :						int							   =					torch.Generator(device=A ).manual_seed(A )

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

             _UpperCAmelCase  :						Optional[int]							   =					Image.fromarray(np.uinta(A ) ).convert("RGB" ).resize((64, 64) )
             _UpperCAmelCase  :						Optional[Any]							   =					Image.fromarray(np.uinta(init_image + 4 ) ).convert("RGB" ).resize((64, 64) )

             _UpperCAmelCase  :						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   _A			( self		:       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   _A			( self		:       Any ):
             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )





      def   _A			( self		:       List[Any] ):
             self._test_inference_batch_single_identical(expected_max_diff=2E-3 )

class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						int      			=		StableDiffusionControlNetInpaintPipeline
      __UpperCamelCase:						List[str]      			=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      __UpperCamelCase:						int      			=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      __UpperCamelCase:						Optional[int]      			=		frozenset([]					)




      def   _A			( self		:       int ):
             torch.manual_seed(0 )
             _UpperCAmelCase  :						str							   =					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 )
             _UpperCAmelCase  :						Optional[int]							   =					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 )
             _UpperCAmelCase  :						Optional[Any]							   =					DDIMScheduler(
                 beta_start=0.00_085		,      beta_end=0.012		,      beta_schedule="scaled_linear"		,      clip_sample=A		,      set_alpha_to_one=A		,      )
             torch.manual_seed(0 )
             _UpperCAmelCase  :						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 )
             _UpperCAmelCase  :						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		,      )
             _UpperCAmelCase  :						Dict							   =					CLIPTextModel(A )
             _UpperCAmelCase  :						Dict							   =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

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

class 			lowerCamelCase_			(snake_case__							,				snake_case__							,				unittest.TestCase					):
      '''simple docstring'''

      __UpperCamelCase:						Optional[Any]      			=		StableDiffusionControlNetInpaintPipeline
      __UpperCamelCase:						Dict      			=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      __UpperCamelCase:						int      			=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS




      def   _A			( self		:       Any ):
             torch.manual_seed(0 )
             _UpperCAmelCase  :						List[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(A		:       Union[str, Any] ):
                    if isinstance(A		,      torch.nn.Convad ):
                           torch.nn.init.normal(m.weight )
                           m.bias.data.fill_(1.0 )

             _UpperCAmelCase  :						Optional[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(A )

             torch.manual_seed(0 )
             _UpperCAmelCase  :						Optional[int]							   =					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(A )

             torch.manual_seed(0 )
             _UpperCAmelCase  :						Tuple							   =					DDIMScheduler(
                 beta_start=0.00_085		,      beta_end=0.012		,      beta_schedule="scaled_linear"		,      clip_sample=A		,      set_alpha_to_one=A		,      )
             torch.manual_seed(0 )
             _UpperCAmelCase  :						Union[str, 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 )
             _UpperCAmelCase  :						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		,      )
             _UpperCAmelCase  :						str							   =					CLIPTextModel(A )
             _UpperCAmelCase  :						Optional[int]							   =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

             _UpperCAmelCase  :						List[Any]							   =					MultiControlNetModel([controlneta, controlneta] )

             _UpperCAmelCase  :						Optional[int]							   =					{
                 "unet": unet,
                 "controlnet": controlnet,
                 "scheduler": scheduler,
                 "vae": vae,
                 "text_encoder": text_encoder,
                 "tokenizer": tokenizer,
                 "safety_checker": None,
                 "feature_extractor": None,
             }
             return components




      def   _A			( self		:       Dict		,      A		:       int		,      A		:       Tuple=0 ):
             if str(A ).startswith("mps" ):
                    _UpperCAmelCase  :						Tuple							   =					torch.manual_seed(A )
             else:
                    _UpperCAmelCase  :						Optional[Any]							   =					torch.Generator(device=A ).manual_seed(A )

             _UpperCAmelCase  :						Tuple							   =					2

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

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

             _UpperCAmelCase  :						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   _A			( self		:       int ):
             _UpperCAmelCase  :						Optional[int]							   =					self.get_dummy_components()
             _UpperCAmelCase  :						Union[str, Any]							   =					self.pipeline_class(**A )
             pipe.to(A )

             _UpperCAmelCase  :						str							   =					10.0
             _UpperCAmelCase  :						str							   =					4

             _UpperCAmelCase  :						Any							   =					self.get_dummy_inputs(A )
             _UpperCAmelCase  :						Optional[Any]							   =					steps
             _UpperCAmelCase  :						Optional[Any]							   =					scale
             _UpperCAmelCase  :						List[str]							   =					pipe(**A )[0]

             _UpperCAmelCase  :						Dict							   =					self.get_dummy_inputs(A )
             _UpperCAmelCase  :						Tuple							   =					steps
             _UpperCAmelCase  :						int							   =					scale
             _UpperCAmelCase  :						Any							   =					pipe(**A		,      control_guidance_start=0.1		,      control_guidance_end=0.2 )[0]

             _UpperCAmelCase  :						Tuple							   =					self.get_dummy_inputs(A )
             _UpperCAmelCase  :						Any							   =					steps
             _UpperCAmelCase  :						Tuple							   =					scale
             _UpperCAmelCase  :						Union[str, Any]							   =					pipe(**A		,      control_guidance_start=[0.1, 0.3]		,      control_guidance_end=[0.2, 0.7] )[0]

             _UpperCAmelCase  :						Dict							   =					self.get_dummy_inputs(A )
             _UpperCAmelCase  :						str							   =					steps
             _UpperCAmelCase  :						Optional[int]							   =					scale
             _UpperCAmelCase  :						Union[str, Any]							   =					pipe(**A		,      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   _A			( self		:       Union[str, 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   _A			( self		:       List[str] ):
             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )




      def   _A			( self		:       Optional[Any] ):
             self._test_inference_batch_single_identical(expected_max_diff=2E-3 )





      def   _A			( self		:       int ):
             _UpperCAmelCase  :						Dict							   =					self.get_dummy_components()
             _UpperCAmelCase  :						int							   =					self.pipeline_class(**A )
             pipe.to(A )
             pipe.set_progress_bar_config(disable=A )
             with tempfile.TemporaryDirectory() as tmpdir:
                    try:
                           # save_pretrained is not implemented for Multi-ControlNet
                           pipe.save_pretrained(A )
                    except NotImplementedError:
                           pass

@slow
@require_torch_gpu
class 			lowerCamelCase_			(unittest.TestCase					):
      '''simple docstring'''

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




      def   _A			( self		:       str ):
             _UpperCAmelCase  :						Dict							   =					ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny" )

             _UpperCAmelCase  :						int							   =					StableDiffusionControlNetInpaintPipeline.from_pretrained(
                 "runwayml/stable-diffusion-inpainting"		,      safety_checker=A		,      controlnet=A )
             pipe.enable_model_cpu_offload()
             pipe.set_progress_bar_config(disable=A )

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

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

             _UpperCAmelCase  :						Any							   =					"pitch black hole"

             _UpperCAmelCase  :						Optional[int]							   =					load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" ).resize((512, 512) )

             _UpperCAmelCase  :						Optional[Any]							   =					pipe(
                 A		,      image=A		,      mask_image=A		,      control_image=A		,      generator=A		,      output_type="np"		,      num_inference_steps=3		,      )

             _UpperCAmelCase  :						Union[str, Any]							   =					output.images[0]

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

             _UpperCAmelCase  :						int							   =					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   _A			( self		:       int ):
             _UpperCAmelCase  :						Optional[Any]							   =					ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint" )

             _UpperCAmelCase  :						Any							   =					StableDiffusionControlNetInpaintPipeline.from_pretrained(
                 "runwayml/stable-diffusion-v1-5"		,      safety_checker=A		,      controlnet=A )
             _UpperCAmelCase  :						Union[str, Any]							   =					DDIMScheduler.from_config(pipe.scheduler.config )
             pipe.enable_model_cpu_offload()
             pipe.set_progress_bar_config(disable=A )

             _UpperCAmelCase  :						Optional[Any]							   =					torch.Generator(device="cpu" ).manual_seed(33 )

             _UpperCAmelCase  :						Any							   =					load_image(
                 "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png" )
             _UpperCAmelCase  :						Dict							   =					init_image.resize((512, 512) )

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

             _UpperCAmelCase  :						Tuple							   =					"a handsome man with ray-ban sunglasses"

             def make_inpaint_condition(A		:       Optional[Any]		,      A		:       List[str] ):
                    _UpperCAmelCase  :						int							   =					np.array(image.convert("RGB" ) ).astype(np.floataa ) / 255.0
                    _UpperCAmelCase  :						Dict							   =					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"
                    _UpperCAmelCase  :						int							   =					-1.0  # set as masked pixel
                    _UpperCAmelCase  :						Optional[int]							   =					np.expand_dims(A		,      0 ).transpose(0		,      3		,      1		,      2 )
                    _UpperCAmelCase  :						List[str]							   =					torch.from_numpy(A )
                    return image

             _UpperCAmelCase  :						List[Any]							   =					make_inpaint_condition(A		,      A )

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

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

             _UpperCAmelCase  :						List[str]							   =					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 pytest

from datasets.utils.version import Version

@pytest.mark.parametrize(
    "other, expected_equality"    ,			[
        (Version("1.0.0"					), True),
        ("1.0.0", True),
        (Version("2.0.0"					), False),
        ("2.0.0", False),
        ("1", False),
        ("a", False),
        (1, False),
        (None, False),
    ]    ,			)
def 			UpperCamelCase_							(      _UpperCAmelCase						:   Tuple    ,			_UpperCAmelCase						:   List[Any]					)					->					int:


       """simple docstring"""







       _UpperCAmelCase  :						Union[str, Any]							   =					Version("1.0.0"					)
       assert (version == other) is expected_equality
       assert (version != other) is not expected_equality
       assert (hash(_UpperCAmelCase					) == hash(_UpperCAmelCase					)) is expected_equality

