





"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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 			__UpperCamelCase							(		UpperCAmelCase_						,			unittest.TestCase		):
							lowerCamelCase  :   str    =KandinskyVaaImgaImgPipeline
							lowerCamelCase  :   List[Any]    =['''image_embeds''', '''negative_image_embeds''', '''image''']
							lowerCamelCase  :   Optional[Any]    =[
							    '''image_embeds''',
							    '''negative_image_embeds''',
							    '''image''',
							]
							lowerCamelCase  :   List[str]    =[
							    '''generator''',
							    '''height''',
							    '''width''',
							    '''strength''',
							    '''guidance_scale''',
							    '''num_inference_steps''',
							    '''return_dict''',
							    '''guidance_scale''',
							    '''num_images_per_prompt''',
							    '''output_type''',
							    '''return_dict''',
							]
							lowerCamelCase  :   int    =False






							@property
							def        __a  (  self )      ->					str:
								return 32






							@property
							def        __a  (  self )      ->					Dict:
								return 32






							@property
							def        __a  (  self )      ->					str:
								return self.time_input_dim






							@property
							def        __a  (  self )      ->					Union[str, Any]:
								return self.time_input_dim * 4






							@property
							def        __a  (  self )      ->					Optional[int]:
								return 100






							@property
							def        __a  (  self )      ->					List[str]:
								torch.manual_seed(0 )

								a :						Tuple      =							{
								    """in_channels""": 4,
								    # 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 :						Dict      =							UNetaDConditionModel(**lowerCamelCase__ )
								return model






							@property
							def        __a  (  self )      ->					Tuple:
								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 )      ->					Union[str, Any]:
								torch.manual_seed(0 )
								a :						Optional[Any]      =							VQModel(**self.dummy_movq_kwargs )
								return model






							def        __a  (  self )      ->					str:
								a :						int      =							self.dummy_unet
								a :						List[Any]      =							self.dummy_movq

								a :						Union[str, Any]      =							{
								    """num_train_timesteps""": 1000,
								    """beta_schedule""": """linear""",
								    """beta_start""": 0.00_085,
								    """beta_end""": 0.012,
								    """clip_sample""": False,
								    """set_alpha_to_one""": False,
								    """steps_offset""": 0,
								    """prediction_type""": """epsilon""",
								    """thresholding""": False,
								}

								a :						Union[str, Any]      =							DDIMScheduler(**lowerCamelCase__ )

								a :						List[Any]      =							{
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """movq""": movq,
								}

								return components






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__=0 )      ->					Union[str, Any]:
								a :						Any      =							floats_tensor((1, self.text_embedder_hidden_size) ,   rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )
								a :						Dict      =							floats_tensor((1, self.text_embedder_hidden_size) ,   rng=random.Random(seed + 1 ) ).to(
								    lowerCamelCase__ )
								# create init_image
								a :						Optional[int]      =							floats_tensor((1, 3, 64, 64) ,   rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )
								a :						List[str]      =							image.cpu().permute(0 ,   2 ,   3 ,   1 )[0]
								a :						Dict      =							Image.fromarray(np.uinta(lowerCamelCase__ ) ).convert("RGB" ).resize((256, 256) )

								if str(lowerCamelCase__ ).startswith("mps" ):
									a :						Tuple      =							torch.manual_seed(lowerCamelCase__ )
								else:
									a :						List[str]      =							torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
								a :						Dict      =							{
								    """image""": init_image,
								    """image_embeds""": image_embeds,
								    """negative_image_embeds""": negative_image_embeds,
								    """generator""": generator,
								    """height""": 64,
								    """width""": 64,
								    """num_inference_steps""": 10,
								    """guidance_scale""": 7.0,
								    """strength""": 0.2,
								    """output_type""": """np""",
								}
								return inputs






							def        __a  (  self )      ->					Union[str, Any]:
								a :						Optional[int]      =							"""cpu"""

								a :						List[Any]      =							self.get_dummy_components()

								a :						List[Any]      =							self.pipeline_class(**lowerCamelCase__ )
								a :						Any      =							pipe.to(lowerCamelCase__ )

								pipe.set_progress_bar_config(disable=lowerCamelCase__ )

								a :						Union[str, Any]      =							pipe(**self.get_dummy_inputs(lowerCamelCase__ ) )
								a :						Tuple      =							output.images

								a :						str      =							pipe(
								    **self.get_dummy_inputs(lowerCamelCase__ ) ,   return_dict=lowerCamelCase__ ,   )[0]

								a :						Optional[Any]      =							image[0, -3:, -3:, -1]
								a :						int      =							image_from_tuple[0, -3:, -3:, -1]

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

								a :						List[str]      =							np.array(
								    [0.6_199_778, 0.63_984_406, 0.46_145_785, 0.62_944_984, 0.5_622_215, 0.47_306_132, 0.47_441_456, 0.4_607_606, 0.48_719_263] )
								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()}"""


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






							def        __a  (  self )      ->					List[Any]:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






							def        __a  (  self )      ->					str:
								a :						int      =							load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/kandinskyv22/kandinskyv22_img2img_frog.npy" )

								a :						Optional[Any]      =							load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
								a :						str      =							"""A red cartoon frog, 4k"""

								a :						List[Any]      =							KandinskyVaaPriorPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-2-prior" ,   torch_dtype=torch.floataa )
								pipe_prior.to(lowerCamelCase__ )

								a :						int      =							KandinskyVaaImgaImgPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-2-decoder" ,   torch_dtype=torch.floataa )
								a :						Optional[Any]      =							pipeline.to(lowerCamelCase__ )

								pipeline.set_progress_bar_config(disable=lowerCamelCase__ )

								a :						List[str]      =							torch.Generator(device="cpu" ).manual_seed(0 )
								a :						Any      =							pipe_prior(
								    lowerCamelCase__ ,   generator=lowerCamelCase__ ,   num_inference_steps=5 ,   negative_prompt="" ,   ).to_tuple()

								a :						int      =							pipeline(
								    image=lowerCamelCase__ ,   image_embeds=lowerCamelCase__ ,   negative_image_embeds=lowerCamelCase__ ,   generator=lowerCamelCase__ ,   num_inference_steps=100 ,   height=768 ,   width=768 ,   strength=0.2 ,   output_type="np" ,   )

								a :						Dict      =							output.images[0]

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

								assert_mean_pixel_difference(lowerCamelCase__ ,   lowerCamelCase__ )










from collections.abc import Iterable
from typing import Generic, TypeVar

UpperCamelCase    							=      TypeVar('_T')







class 		_A						( Generic[_T] ):

  def __init__(    self						:	int   ,				lowerCamelCase__						:	Iterable[_T] | None = None			):
     """simple docstring"""

     __UpperCamelCase	:			list[_T]					=					list(iterable or []			)
     __UpperCamelCase	:			list[_T]					=					[]

  def __len__(    self						:	str			):
     """simple docstring"""

     return len(self._stacka			) + len(self._stacka			)

  def __repr__(    self						:	Dict			):
     """simple docstring"""

     return f'Queue({tuple(self._stacka[::-1] + self._stacka			)})'

  def    a					(    self						:	Union[str, Any]   ,				lowerCamelCase__						:	_T			):
     """simple docstring"""

     self._stacka.append(lowerCamelCase__			)

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Any					=					self._stacka.pop
     __UpperCamelCase	:			int					=					self._stacka.append

     if not self._stacka:
        while self._stacka:
           stacka_append(stacka_pop()			)

     if not self._stacka:
        raise IndexError("""Queue is empty"""			)
     return self._stacka.pop()


if __name__ == "__main__":
       from doctest import testmod

       testmod()



