
'''simple docstring'''





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 			UpperCamelCase__	(     lowerCAmelCase__   ,  lowerCAmelCase__   ,  lowerCAmelCase__   ,  unittest.TestCase):
 UpperCAmelCase__     :     Any    					=       StableDiffusionControlNetInpaintPipeline
 UpperCAmelCase__     :     Optional[Any]    					=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 UpperCAmelCase__     :     Optional[Any]    					=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 UpperCAmelCase__     :     Dict    					=       frozenset({'control_image'})  # skip `image` and `mask` for now, only test for control_image
 UpperCAmelCase__     :     Dict    					=       TEXT_TO_IMAGE_IMAGE_PARAMS






 def 						lowercase_    (      self	:Optional[Any]      )					->      Optional[Any]:







       '''simple docstring'''



       torch.manual_seed(0      )
       __A													=      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      )
       __A													=      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      )
       __A													=      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      )
       __A													=      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      )
       __A													=      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						,					)
       __A													=      CLIPTextModel(_A      )
       __A													=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

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






 def 						lowercase_    (      self	:Union[str, Any]						,					_A	:int						,					_A	:Dict=0      )					->      Optional[Any]:







       '''simple docstring'''



       if str(_A      ).startswith('mps'      ):
             __A													=      torch.manual_seed(_A      )
       else:
             __A													=      torch.Generator(device=_A      ).manual_seed(_A      )

       __A													=      2
       __A													=      randn_tensor(
           (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)						,					generator=_A						,					device=torch.device(_A      )						,					)
       __A													=      floats_tensor((1, 3, 32, 32)						,					rng=random.Random(_A      )      ).to(_A      )
       __A													=      init_image.cpu().permute(0						,					2						,					3						,					1      )[0]

       __A													=      Image.fromarray(np.uinta(_A      )      ).convert('RGB'      ).resize((64, 64)      )
       __A													=      Image.fromarray(np.uinta(init_image + 4      )      ).convert('RGB'      ).resize((64, 64)      )

       __A													=      {
           '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 						lowercase_    (      self	:Optional[Any]      )					->      Optional[int]:







       '''simple docstring'''



       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 						lowercase_    (      self	:int      )					->      Union[str, Any]:







       '''simple docstring'''



       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3      )






 def 						lowercase_    (      self	:Tuple      )					->      Optional[Any]:







       '''simple docstring'''



       self._test_inference_batch_single_identical(expected_max_diff=2E-3      )


class 			UpperCamelCase__	(     lowerCAmelCase__):
 UpperCAmelCase__     :     List[Any]    					=       StableDiffusionControlNetInpaintPipeline
 UpperCAmelCase__     :     int    					=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 UpperCAmelCase__     :     Optional[Any]    					=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 UpperCAmelCase__     :     Tuple    					=       frozenset([])






 def 						lowercase_    (      self	:Any      )					->      Optional[int]:







       '''simple docstring'''



       torch.manual_seed(0      )
       __A													=      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      )
       __A													=      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      )
       __A													=      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      )
       __A													=      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      )
       __A													=      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						,					)
       __A													=      CLIPTextModel(_A      )
       __A													=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

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


class 			UpperCamelCase__	(     lowerCAmelCase__   ,  lowerCAmelCase__   ,  unittest.TestCase):
 UpperCAmelCase__     :     str    					=       StableDiffusionControlNetInpaintPipeline
 UpperCAmelCase__     :     List[str]    					=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 UpperCAmelCase__     :     List[Any]    					=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS






 def 						lowercase_    (      self	:List[Any]      )					->      str:







       '''simple docstring'''



       torch.manual_seed(0      )
       __A													=      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	:Dict      ):
             if isinstance(_A						,					torch.nn.Convad      ):
                   torch.nn.init.normal(m.weight      )
                   m.bias.data.fill_(1.0      )

       __A													=      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      )
       __A													=      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      )
       __A													=      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      )
       __A													=      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      )
       __A													=      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						,					)
       __A													=      CLIPTextModel(_A      )
       __A													=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

       __A													=      MultiControlNetModel([controlneta, controlneta]      )

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






 def 						lowercase_    (      self	:List[str]						,					_A	:Optional[Any]						,					_A	:Union[str, Any]=0      )					->      Union[str, Any]:







       '''simple docstring'''



       if str(_A      ).startswith('mps'      ):
             __A													=      torch.manual_seed(_A      )
       else:
             __A													=      torch.Generator(device=_A      ).manual_seed(_A      )

       __A													=      2

       __A													=      [
           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      )						,					),
       ]
       __A													=      floats_tensor((1, 3, 32, 32)						,					rng=random.Random(_A      )      ).to(_A      )
       __A													=      init_image.cpu().permute(0						,					2						,					3						,					1      )[0]

       __A													=      Image.fromarray(np.uinta(_A      )      ).convert('RGB'      ).resize((64, 64)      )
       __A													=      Image.fromarray(np.uinta(init_image + 4      )      ).convert('RGB'      ).resize((64, 64)      )

       __A													=      {
           '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 						lowercase_    (      self	:str      )					->      Any:







       '''simple docstring'''



       __A													=      self.get_dummy_components()
       __A													=      self.pipeline_class(**_A      )
       pipe.to(_A      )

       __A													=      10.0
       __A													=      4

       __A													=      self.get_dummy_inputs(_A      )
       __A													=      steps
       __A													=      scale
       __A													=      pipe(**_A      )[0]

       __A													=      self.get_dummy_inputs(_A      )
       __A													=      steps
       __A													=      scale
       __A													=      pipe(**_A						,					control_guidance_start=0.1						,					control_guidance_end=0.2      )[0]

       __A													=      self.get_dummy_inputs(_A      )
       __A													=      steps
       __A													=      scale
       __A													=      pipe(**_A						,					control_guidance_start=[0.1, 0.3]						,					control_guidance_end=[0.2, 0.7]      )[0]

       __A													=      self.get_dummy_inputs(_A      )
       __A													=      steps
       __A													=      scale
       __A													=      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 						lowercase_    (      self	:Tuple      )					->      Optional[int]:







       '''simple docstring'''



       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 						lowercase_    (      self	:Dict      )					->      Optional[Any]:







       '''simple docstring'''



       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3      )






 def 						lowercase_    (      self	:Optional[Any]      )					->      str:







       '''simple docstring'''



       self._test_inference_batch_single_identical(expected_max_diff=2E-3      )






 def 						lowercase_    (      self	:Tuple      )					->      str:







       '''simple docstring'''



       __A													=      self.get_dummy_components()
       __A													=      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 			UpperCamelCase__	(     unittest.TestCase):






 def 						lowercase_    (      self	:Union[str, Any]      )					->      Any:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:List[str]      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'      )

       __A													=      StableDiffusionControlNetInpaintPipeline.from_pretrained(
           'runwayml/stable-diffusion-inpainting'						,					safety_checker=_A						,					controlnet=_A      )
       pipe.enable_model_cpu_offload()
       pipe.set_progress_bar_config(disable=_A      )

       __A													=      torch.Generator(device='cpu'      ).manual_seed(0      )
       __A													=      load_image(
           'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'      ).resize((512, 512)      )

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

       __A													=      'pitch black hole'

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

       __A													=      pipe(
           _A						,					image=_A						,					mask_image=_A						,					control_image=_A						,					generator=_A						,					output_type='np'						,					num_inference_steps=3						,					)

       __A													=      output.images[0]

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

       __A													=      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 						lowercase_    (      self	:Optional[int]      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'      )

       __A													=      StableDiffusionControlNetInpaintPipeline.from_pretrained(
           'runwayml/stable-diffusion-v1-5'						,					safety_checker=_A						,					controlnet=_A      )
       __A													=      DDIMScheduler.from_config(pipe.scheduler.config      )
       pipe.enable_model_cpu_offload()
       pipe.set_progress_bar_config(disable=_A      )

       __A													=      torch.Generator(device='cpu'      ).manual_seed(33      )

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

       __A													=      load_image(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'      )
       __A													=      mask_image.resize((512, 512)      )

       __A													=      'a handsome man with ray-ban sunglasses'

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

       __A													=      make_inpaint_condition(_A						,					_A      )

       __A													=      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'						,					)
       __A													=      output.images[0]

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

       __A													=      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'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE__     : Dict				=   {"""configuration_detr""": ["""DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetrConfig""", """DetrOnnxConfig"""]}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__     : List[Any]				=   ["""DetrFeatureExtractor"""]
							SCREAMING_SNAKE_CASE__     : str				=   ["""DetrImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__     : Optional[Any]				=   [
							    """DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DetrForObjectDetection""",
							    """DetrForSegmentation""",
							    """DetrModel""",
							    """DetrPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_detr import DetrFeatureExtractor
														from .image_processing_detr import DetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_detr import (
														    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DetrForObjectDetection,
														    DetrForSegmentation,
														    DetrModel,
														    DetrPreTrainedModel,
														)

else:
							import sys

							SCREAMING_SNAKE_CASE__     : Optional[Any]				=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
