



"""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       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   Any						=						KandinskyVaaImgaImgPipeline
   __lowerCAmelCase  :   Any						=						['image_embeds', 'negative_image_embeds', 'image']
   __lowerCAmelCase  :   Optional[int]						=						[
       'image_embeds',
       'negative_image_embeds',
       'image',
   ]
   __lowerCAmelCase  :   Tuple						=						[
       'generator',
       'height',
       'width',
       'strength',
       'guidance_scale',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   __lowerCAmelCase  :   Optional[Any]						=						False

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    return 32

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    return 32

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    return self.time_input_dim

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    return self.time_input_dim * 4

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    return 100

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    torch.manual_seed(0						)

    UpperCAmelCase	:					Optional[Any]	=	{
        """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,
    }

    UpperCAmelCase	:					Optional[int]	=	UNetaDConditionModel(**_SCREAMING_SNAKE_CASE						)
    return model

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[int]:





    '''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 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    torch.manual_seed(0						)
    UpperCAmelCase	:					Tuple	=	VQModel(**self.dummy_movq_kwargs						)
    return model

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	self.dummy_unet
    UpperCAmelCase	:					int	=	self.dummy_movq

    UpperCAmelCase	:					Any	=	{
        """num_train_timesteps""": 1000,
        """beta_schedule""": """linear""",
        """beta_start""": 0.0_0085,
        """beta_end""": 0.012,
        """clip_sample""": False,
        """set_alpha_to_one""": False,
        """steps_offset""": 0,
        """prediction_type""": """epsilon""",
        """thresholding""": False,
    }

    UpperCAmelCase	:					int	=	DDIMScheduler(**_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Optional[int]	=	{
        """unet""": unet,
        """scheduler""": scheduler,
        """movq""": movq,
    }

    return components

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=0						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	floats_tensor((1, self.text_embedder_hidden_size)      ,     rng=random.Random(_SCREAMING_SNAKE_CASE						)						).to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Dict	=	floats_tensor((1, self.text_embedder_hidden_size)      ,     rng=random.Random(seed + 1						)						).to(
        _SCREAMING_SNAKE_CASE						)
    # create init_image
    UpperCAmelCase	:					Any	=	floats_tensor((1, 3, 64, 64)      ,     rng=random.Random(_SCREAMING_SNAKE_CASE						)						).to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Any	=	image.cpu().permute(0      ,     2      ,     3      ,     1						)[0]
    UpperCAmelCase	:					str	=	Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE						)						).convert("""RGB"""						).resize((256, 256)						)

    if str(_SCREAMING_SNAKE_CASE						).startswith("""mps"""						):
     UpperCAmelCase	:					int	=	torch.manual_seed(_SCREAMING_SNAKE_CASE						)
    else:
     UpperCAmelCase	:					Dict	=	torch.Generator(device=_SCREAMING_SNAKE_CASE						).manual_seed(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					int	=	{
        """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 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	"""cpu"""

    UpperCAmelCase	:					int	=	self.get_dummy_components()

    UpperCAmelCase	:					Tuple	=	self.pipeline_class(**_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					str	=	pipe.to(_SCREAMING_SNAKE_CASE						)

    pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Optional[Any]	=	pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE						)						)
    UpperCAmelCase	:					Tuple	=	output.images

    UpperCAmelCase	:					Optional[int]	=	pipe(
        **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE						)      ,     return_dict=_SCREAMING_SNAKE_CASE      ,     )[0]

    UpperCAmelCase	:					str	=	image[0, -3:, -3:, -1]
    UpperCAmelCase	:					List[Any]	=	image_from_tuple[0, -3:, -3:, -1]

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

    UpperCAmelCase	:					List[str]	=	np.array(
        [0.619_9778, 0.6398_4406, 0.4614_5785, 0.6294_4984, 0.562_2215, 0.4730_6132, 0.4744_1456, 0.460_7606, 0.4871_9263]						)
    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       SCREAMING_SNAKE_CASE__  (					unittest.TestCase ):

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[int]:





    '''simple docstring'''




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




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





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	load_numpy(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/kandinskyv22/kandinskyv22_img2img_frog.npy"""						)

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

    UpperCAmelCase	:					List[str]	=	KandinskyVaaPriorPipeline.from_pretrained(
        """kandinsky-community/kandinsky-2-2-prior"""      ,     torch_dtype=torch.floataa						)
    pipe_prior.to(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Union[str, Any]	=	KandinskyVaaImgaImgPipeline.from_pretrained(
        """kandinsky-community/kandinsky-2-2-decoder"""      ,     torch_dtype=torch.floataa						)
    UpperCAmelCase	:					int	=	pipeline.to(_SCREAMING_SNAKE_CASE						)

    pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					str	=	torch.Generator(device="""cpu"""						).manual_seed(0						)
    UpperCAmelCase	,		UpperCAmelCase	:					Any	=	pipe_prior(
        _SCREAMING_SNAKE_CASE      ,     generator=_SCREAMING_SNAKE_CASE      ,     num_inference_steps=5      ,     negative_prompt=""""""      ,     ).to_tuple()

    UpperCAmelCase	:					List[Any]	=	pipeline(
        image=_SCREAMING_SNAKE_CASE      ,     image_embeds=_SCREAMING_SNAKE_CASE      ,     negative_image_embeds=_SCREAMING_SNAKE_CASE      ,     generator=_SCREAMING_SNAKE_CASE      ,     num_inference_steps=100      ,     height=768      ,     width=768      ,     strength=0.2      ,     output_type="""np"""      ,     )

    UpperCAmelCase	:					Optional[int]	=	output.images[0]

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

    assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)





"""simple docstring"""
A:   Union[str, Any]								=		{
    0: "0",
    1: "1",
    2: "2",
    3: "3",
    4: "4",
    5: "5",
    6: "6",
    7: "7",
    8: "8",
    9: "9",
    1_0: "a",
    1_1: "b",
    1_2: "c",
    1_3: "d",
    1_4: "e",
    1_5: "f",
}

def       _snake_case						(						UpperCamelCase						:		float						):
 assert type(UpperCamelCase						) in (int, float) and decimal == int(UpperCamelCase						)
 UpperCAmelCase	:					str	=	int(UpperCamelCase						)
 UpperCAmelCase	:					Optional[int]	=	""""""
 UpperCAmelCase	:					List[str]	=	False
 if decimal < 0:
  UpperCAmelCase	:					Any	=	True
  decimal *= -1
 while decimal > 0:
  UpperCAmelCase	,		UpperCAmelCase	:					Dict	=	divmod(UpperCamelCase			,		16						)
  UpperCAmelCase	:					Union[str, Any]	=	values[remainder] + hexadecimal
 UpperCAmelCase	:					int	=	"""0x""" + hexadecimal
 if negative:
  UpperCAmelCase	:					Optional[int]	=	"""-""" + hexadecimal
 return hexadecimal


if __name__ == "__main__":
      import doctest

      doctest.testmod()
