
"""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 							snake_case       (     SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       unittest.TestCase		):
   a_					:		List[Any]     					=     StableDiffusionControlNetInpaintPipeline
   a_					:		Optional[int]     					=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   a_					:		str     					=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   a_					:		Tuple     					=     frozenset({"""control_image"""}		)  # skip `image` and `mask` for now, only test for control_image
   a_					:		List[str]     					=     TEXT_TO_IMAGE_IMAGE_PARAMS







   def    UpperCAmelCase__	( self)   ->List[Any]:
      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=UpperCAmelCase_       ,							set_alpha_to_one=UpperCAmelCase_       ,							)
      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=10_00       ,							)
      a_						   =     CLIPTextModel(UpperCAmelCase_)
      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    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase=0)   ->Dict:
      if str(UpperCAmelCase_).startswith("mps"):
         a_						   =     torch.manual_seed(UpperCAmelCase_)
      else:
         a_						   =     torch.Generator(device=UpperCAmelCase_).manual_seed(UpperCAmelCase_)

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

      a_						   =     Image.fromarray(np.uinta(UpperCAmelCase_)).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    UpperCAmelCase__	( self)   ->Tuple:
      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    UpperCAmelCase__	( self)   ->List[str]:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3)







   def    UpperCAmelCase__	( self)   ->Any:
      self._test_inference_batch_single_identical(expected_max_diff=2E-3)






class 							snake_case       (     SCREAMING_SNAKE_CASE_		):
   a_					:		Any     					=     StableDiffusionControlNetInpaintPipeline
   a_					:		Dict     					=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   a_					:		List[Any]     					=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   a_					:		Union[str, Any]     					=     frozenset([]		)







   def    UpperCAmelCase__	( self)   ->List[Any]:
      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=UpperCAmelCase_       ,							set_alpha_to_one=UpperCAmelCase_       ,							)
      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=10_00       ,							)
      a_						   =     CLIPTextModel(UpperCAmelCase_)
      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 							snake_case       (     SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       unittest.TestCase		):
   a_					:		Any     					=     StableDiffusionControlNetInpaintPipeline
   a_					:		Optional[Any]     					=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   a_					:		str     					=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS







   def    UpperCAmelCase__	( self)   ->Dict:
      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(__UpperCAmelCase):
         if isinstance(UpperCAmelCase_       ,							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(UpperCAmelCase_)

      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(UpperCAmelCase_)

      torch.manual_seed(0)
      a_						   =     DDIMScheduler(
          beta_start=0.00_085       ,							beta_end=0.012       ,							beta_schedule="scaled_linear"       ,							clip_sample=UpperCAmelCase_       ,							set_alpha_to_one=UpperCAmelCase_       ,							)
      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=10_00       ,							)
      a_						   =     CLIPTextModel(UpperCAmelCase_)
      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    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase=0)   ->Optional[Any]:
      if str(UpperCAmelCase_).startswith("mps"):
         a_						   =     torch.manual_seed(UpperCAmelCase_)
      else:
         a_						   =     torch.Generator(device=UpperCAmelCase_).manual_seed(UpperCAmelCase_)

      a_						   =     2

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

      a_						   =     Image.fromarray(np.uinta(UpperCAmelCase_)).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    UpperCAmelCase__	( self)   ->Any:
      a_						   =     self.get_dummy_components()
      a_						   =     self.pipeline_class(**UpperCAmelCase_)
      pipe.to(UpperCAmelCase_)

      a_						   =     10.0
      a_						   =     4

      a_						   =     self.get_dummy_inputs(UpperCAmelCase_)
      a_						   =     steps
      a_						   =     scale
      a_						   =     pipe(**UpperCAmelCase_)[0]

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

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

      a_						   =     self.get_dummy_inputs(UpperCAmelCase_)
      a_						   =     steps
      a_						   =     scale
      a_						   =     pipe(**UpperCAmelCase_       ,							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    UpperCAmelCase__	( 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    UpperCAmelCase__	( self)   ->str:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3)







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







   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      a_						   =     self.get_dummy_components()
      a_						   =     self.pipeline_class(**UpperCAmelCase_)
      pipe.to(UpperCAmelCase_)
      pipe.set_progress_bar_config(disable=UpperCAmelCase_)
      with tempfile.TemporaryDirectory() as tmpdir:
         try:
            # save_pretrained is not implemented for Multi-ControlNet
            pipe.save_pretrained(UpperCAmelCase_)
         except NotImplementedError:
            pass






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







   def    UpperCAmelCase__	( self)   ->Optional[int]:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







   def    UpperCAmelCase__	( self)   ->Optional[int]:
      a_						   =     ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")

      a_						   =     StableDiffusionControlNetInpaintPipeline.from_pretrained(
          "runwayml/stable-diffusion-inpainting"       ,							safety_checker=UpperCAmelCase_       ,							controlnet=UpperCAmelCase_)
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=UpperCAmelCase_)

      a_						   =     torch.Generator(device="cpu").manual_seed(0)
      a_						   =     load_image(
          "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png").resize((5_12, 5_12))

      a_						   =     load_image(
          "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
          "/stable_diffusion_inpaint/input_bench_mask.png").resize((5_12, 5_12))

      a_						   =     """pitch black hole"""

      a_						   =     load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png").resize((5_12, 5_12))

      a_						   =     pipe(
          UpperCAmelCase_       ,							image=UpperCAmelCase_       ,							mask_image=UpperCAmelCase_       ,							control_image=UpperCAmelCase_       ,							generator=UpperCAmelCase_       ,							output_type="np"       ,							num_inference_steps=3       ,							)

      a_						   =     output.images[0]

      assert image.shape == (5_12, 5_12, 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    UpperCAmelCase__	( self)   ->str:
      a_						   =     ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint")

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

      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((5_12, 5_12))

      a_						   =     load_image(
          "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png")
      a_						   =     mask_image.resize((5_12, 5_12))

      a_						   =     """a handsome man with ray-ban sunglasses"""

      def make_inpaint_condition(__UpperCAmelCase       ,							__UpperCAmelCase):
         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(UpperCAmelCase_       ,							0).transpose(0       ,							3       ,							1       ,							2)
         a_						   =     torch.from_numpy(UpperCAmelCase_)
         return image

      a_						   =     make_inpaint_condition(UpperCAmelCase_       ,							UpperCAmelCase_)

      a_						   =     pipe(
          UpperCAmelCase_       ,							image=UpperCAmelCase_       ,							mask_image=UpperCAmelCase_       ,							control_image=UpperCAmelCase_       ,							guidance_scale=9.0       ,							eta=1.0       ,							generator=UpperCAmelCase_       ,							num_inference_steps=20       ,							output_type="np"       ,							)
      a_						   =     output.images[0]

      assert image.shape == (5_12, 5_12, 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 _LazyModule


UpperCAmelCase:  Union[str, Any]			     =				{"""tokenization_phobert""": ["""PhobertTokenizer"""]}


if TYPE_CHECKING:
		from .tokenization_phobert import PhobertTokenizer

else:
		import sys

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



