'''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 lowercase_       (		_UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   KandinskyVaaInpaintPipeline
   __lowerCAmelCase         =   ["image_embeds", "negative_image_embeds", "image", "mask_image"]
   __lowerCAmelCase         =   [
       "image_embeds",
       "negative_image_embeds",
       "image",
       "mask_image",
   ]
   __lowerCAmelCase         =   [
       "generator",
       "height",
       "width",
       "latents",
       "guidance_scale",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   __lowerCAmelCase         =   False
   @property
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Dict:
        return 32
   @property
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[int]:
        return 32
   @property
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			str:
        return self.time_input_dim
   @property
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			int:
        return self.time_input_dim * 4
   @property
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[int]:
        return 1_00
   @property
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			str:
        torch.manual_seed(0     )

        _A    		=  {
            '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,
        }

        _A    		=  UNetaDConditionModel(**UpperCamelCase__     )
        return model
   @property
   def 	__UpperCAmelCase   (      self      :			str     )  ->			int:
        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 	__UpperCAmelCase   (      self      :			str     )  ->			Tuple:
        torch.manual_seed(0     )
        _A    		=  VQModel(**self.dummy_movq_kwargs     )
        return model
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Optional[int]:
        _A    		=  self.dummy_unet
        _A    		=  self.dummy_movq

        _A    		=  DDIMScheduler(
            num_train_timesteps=10_00, beta_schedule='linear', beta_start=0.00_085, beta_end=0.012, clip_sample=UpperCamelCase__, set_alpha_to_one=UpperCamelCase__, steps_offset=1, prediction_type='epsilon', thresholding=UpperCamelCase__, )

        _A    		=  {
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }

        return components
   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			int, UpperCamelCase__      :			List[Any]=0     )  ->			str:
        _A    		=  floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(UpperCamelCase__     )     ).to(UpperCamelCase__     )
        _A    		=  floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed + 1     )     ).to(
            UpperCamelCase__     )
        # create init_image
        _A    		=  floats_tensor((1, 3, 64, 64), rng=random.Random(UpperCamelCase__     )     ).to(UpperCamelCase__     )
        _A    		=  image.cpu().permute(0, 2, 3, 1     )[0]
        _A    		=  Image.fromarray(np.uinta(UpperCamelCase__     )     ).convert('RGB'     ).resize((2_56, 2_56)     )
        # create mask
        _A    		=  np.ones((64, 64), dtype=np.floataa     )
        _A    		=  0

        if str(UpperCamelCase__     ).startswith('mps'     ):
             _A    		=  torch.manual_seed(UpperCamelCase__     )
        else:
             _A    		=  torch.Generator(device=UpperCamelCase__     ).manual_seed(UpperCamelCase__     )
        _A    		=  {
            '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 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Dict:
        _A    		=  'cpu'

        _A    		=  self.get_dummy_components()

        _A    		=  self.pipeline_class(**UpperCamelCase__     )
        _A    		=  pipe.to(UpperCamelCase__     )

        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  pipe(**self.get_dummy_inputs(UpperCamelCase__     )     )
        _A    		=  output.images

        _A    		=  pipe(
            **self.get_dummy_inputs(UpperCamelCase__     ), return_dict=UpperCamelCase__, )[0]

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

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

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

        _A    		=  np.array(
            [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848]     )

        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 	__UpperCAmelCase   (      self      :			int     )  ->			Tuple:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3     )







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







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Union[str, Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Tuple:
        _A    		=  load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'     )

        _A    		=  load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'     )
        _A    		=  np.ones((7_68, 7_68), dtype=np.floataa     )
        _A    		=  0

        _A    		=  'a hat'

        _A    		=  KandinskyVaaPriorPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-2-prior', torch_dtype=torch.floataa     )
        pipe_prior.to(UpperCamelCase__     )

        _A    		=  KandinskyVaaInpaintPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-2-decoder-inpaint', torch_dtype=torch.floataa     )
        _A    		=  pipeline.to(UpperCamelCase__     )
        pipeline.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  torch.Generator(device='cpu'     ).manual_seed(0     )
        _A		,       _A    		=  pipe_prior(
            UpperCamelCase__, generator=UpperCamelCase__, num_inference_steps=5, negative_prompt='', ).to_tuple()

        _A    		=  pipeline(
            image=UpperCamelCase__, mask_image=UpperCamelCase__, image_embeds=UpperCamelCase__, negative_image_embeds=UpperCamelCase__, generator=UpperCamelCase__, num_inference_steps=1_00, height=7_68, width=7_68, output_type='np', )

        _A    		=  output.images[0]

        assert image.shape == (7_68, 7_68, 3)

        assert_mean_pixel_difference(UpperCamelCase__, UpperCamelCase__     )








from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream



class        lowerCAmelCase__					(  _lowerCamelCase       ):


				def __init__(							self      :  int						, __UpperCamelCase      :  Callable						, __UpperCamelCase      :  Optional[Features] = None						, __UpperCamelCase      :  str = None						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  Optional[dict] = None						, __UpperCamelCase      :  Optional[int] = None						, **__UpperCamelCase      :  str						, )		->     List[str]:
											super().__init__(
											    features=__UpperCamelCase						, cache_dir=__UpperCamelCase						, keep_in_memory=__UpperCamelCase						, streaming=__UpperCamelCase						, num_proc=__UpperCamelCase						, **__UpperCamelCase						, )
											A      						=    Generator(
											    cache_dir=__UpperCamelCase						, features=__UpperCamelCase						, generator=__UpperCamelCase						, gen_kwargs=__UpperCamelCase						, **__UpperCamelCase						, )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											# Build iterable dataset
											if self.streaming:
																		A      						=    self.builder.as_streaming_dataset(split='train'       )
											# Build regular (map-style) dataset
											else:
																		A      						=    None
																		A      						=    None
																		A      						=    None
																		A      						=    None

																		self.builder.download_and_prepare(
																		    download_config=__UpperCamelCase						, download_mode=__UpperCamelCase						, verification_mode=__UpperCamelCase						, base_path=__UpperCamelCase						, num_proc=self.num_proc						, )
																		A      						=    self.builder.as_dataset(
																		    split='train'						, verification_mode=__UpperCamelCase						, in_memory=self.keep_in_memory       )
											return dataset