"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 					_A		(			lowerCAmelCase    ,       unittest.TestCase      ):
      snake_case__     :  Dict				    =      KandinskyVaaInpaintPipeline
      snake_case__     :  Any				    =      ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
      snake_case__     :  Any				    =      [
          'image_embeds',
          'negative_image_embeds',
          'image',
          'mask_image',
      ]
      snake_case__     :  Optional[int]				    =      [
          'generator',
          'height',
          'width',
          'latents',
          'guidance_scale',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      snake_case__     :  List[Any]				    =      False

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return 32

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return 32

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self.time_input_dim

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self.time_input_dim * 4

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return 100

      @property
      def 							A__			(		self		):







            """simple docstring"""



            torch.manual_seed(0		)

            lowercase								=      {
                """in_channels""": 9,
                # Out channels is double in channels because predicts mean and variance
                """out_channels""": 8,
                """addition_embed_type""": """image""",
                """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
                """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
                """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
                """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
                """layers_per_block""": 1,
                """encoder_hid_dim""": self.text_embedder_hidden_size,
                """encoder_hid_dim_type""": """image_proj""",
                """cross_attention_dim""": self.cross_attention_dim,
                """attention_head_dim""": 4,
                """resnet_time_scale_shift""": """scale_shift""",
                """class_embed_type""": None,
            }

            lowercase								=      UNetaDConditionModel(**__lowerCAmelCase		)
            return model

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return {
                "block_out_channels": [32, 64],
                "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
                "in_channels": 3,
                "latent_channels": 4,
                "layers_per_block": 1,
                "norm_num_groups": 8,
                "norm_type": "spatial",
                "num_vq_embeddings": 12,
                "out_channels": 3,
                "up_block_types": [
                    "AttnUpDecoderBlock2D",
                    "UpDecoderBlock2D",
                ],
                "vq_embed_dim": 4,
            }

      @property
      def 							A__			(		self		):







            """simple docstring"""



            torch.manual_seed(0		)
            lowercase								=      VQModel(**self.dummy_movq_kwargs		)
            return model

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.dummy_unet
            lowercase								=      self.dummy_movq

            lowercase								=      DDIMScheduler(
                num_train_timesteps=1000						,			beta_schedule="""linear"""						,			beta_start=0.0_0_0_8_5						,			beta_end=0.0_1_2						,			clip_sample=__lowerCAmelCase						,			set_alpha_to_one=__lowerCAmelCase						,			steps_offset=1						,			prediction_type="""epsilon"""						,			thresholding=__lowerCAmelCase						,			)

            lowercase								=      {
                """unet""": unet,
                """scheduler""": scheduler,
                """movq""": movq,
            }

            return components

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase=0		):







            """simple docstring"""



            lowercase								=      floats_tensor((1, self.text_embedder_hidden_size)						,			rng=random.Random(__lowerCAmelCase		)		).to(__lowerCAmelCase		)
            lowercase								=      floats_tensor((1, self.text_embedder_hidden_size)						,			rng=random.Random(seed + 1		)		).to(
                __lowerCAmelCase		)
            # create init_image
            lowercase								=      floats_tensor((1, 3, 64, 64)						,			rng=random.Random(__lowerCAmelCase		)		).to(__lowerCAmelCase		)
            lowercase								=      image.cpu().permute(0						,			2						,			3						,			1		)[0]
            lowercase								=      Image.fromarray(np.uinta(__lowerCAmelCase		)		).convert("""RGB"""		).resize((256, 256)		)
            # create mask
            lowercase								=      np.ones((64, 64)						,			dtype=np.floataa		)
            lowercase								=      0

            if str(__lowerCAmelCase		).startswith("""mps"""		):
                  lowercase								=      torch.manual_seed(__lowerCAmelCase		)
            else:
                  lowercase								=      torch.Generator(device=__lowerCAmelCase		).manual_seed(__lowerCAmelCase		)
            lowercase								=      {
                """image""": init_image,
                """mask_image""": mask,
                """image_embeds""": image_embeds,
                """negative_image_embeds""": negative_image_embeds,
                """generator""": generator,
                """height""": 64,
                """width""": 64,
                """num_inference_steps""": 2,
                """guidance_scale""": 4.0,
                """output_type""": """np""",
            }
            return inputs

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      """cpu"""

            lowercase								=      self.get_dummy_components()

            lowercase								=      self.pipeline_class(**__lowerCAmelCase		)
            lowercase								=      pipe.to(__lowerCAmelCase		)

            pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

            lowercase								=      pipe(**self.get_dummy_inputs(__lowerCAmelCase		)		)
            lowercase								=      output.images

            lowercase								=      pipe(
                **self.get_dummy_inputs(__lowerCAmelCase		)						,			return_dict=__lowerCAmelCase						,			)[0]

            lowercase								=      image[0, -3:, -3:, -1]
            lowercase								=      image_from_tuple[0, -3:, -3:, -1]

            print(f'image.shape {image.shape}'		)

            assert image.shape == (1, 64, 64, 3)

            lowercase								=      np.array(
                [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]		)

            assert (
                np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
            ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
            assert (
                np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2
            ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







      def 							A__			(		self		):







            """simple docstring"""



            super().test_inference_batch_single_identical(expected_max_diff=3E-3		)






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

      def 							A__			(		self		):







            """simple docstring"""



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







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      load_numpy(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""		)

            lowercase								=      load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""		)
            lowercase								=      np.ones((768, 768)						,			dtype=np.floataa		)
            lowercase								=      0

            lowercase								=      """a hat"""

            lowercase								=      KandinskyVaaPriorPipeline.from_pretrained(
                """kandinsky-community/kandinsky-2-2-prior"""						,			torch_dtype=torch.floataa		)
            pipe_prior.to(__lowerCAmelCase		)

            lowercase								=      KandinskyVaaInpaintPipeline.from_pretrained(
                """kandinsky-community/kandinsky-2-2-decoder-inpaint"""						,			torch_dtype=torch.floataa		)
            lowercase								=      pipeline.to(__lowerCAmelCase		)
            pipeline.set_progress_bar_config(disable=__lowerCAmelCase		)

            lowercase								=      torch.Generator(device="""cpu"""		).manual_seed(0		)
            lowercase				,				lowercase								=      pipe_prior(
                __lowerCAmelCase						,			generator=__lowerCAmelCase						,			num_inference_steps=5						,			negative_prompt=""""""						,			).to_tuple()

            lowercase								=      pipeline(
                image=__lowerCAmelCase						,			mask_image=__lowerCAmelCase						,			image_embeds=__lowerCAmelCase						,			negative_image_embeds=__lowerCAmelCase						,			generator=__lowerCAmelCase						,			num_inference_steps=100						,			height=768						,			width=768						,			output_type="""np"""						,			)

            lowercase								=      output.images[0]

            assert image.shape == (768, 768, 3)

            assert_mean_pixel_difference(__lowerCAmelCase						,			__lowerCAmelCase		)




"""simple docstring"""






import torch

from diffusers import StableDiffusionPipeline


__lowerCAmelCase       :			str   	="""path-to-your-trained-model"""
__lowerCAmelCase       :			int   	=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

__lowerCAmelCase       :			Optional[int]   	="""A photo of sks dog in a bucket"""
__lowerCAmelCase       :			int   	=pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")



