



"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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  :   int						=						KandinskyVaaControlnetImgaImgPipeline
   __lowerCAmelCase  :   Dict						=						['image_embeds', 'negative_image_embeds', 'image', 'hint']
   __lowerCAmelCase  :   Any						=						['image_embeds', 'negative_image_embeds', 'image', 'hint']
   __lowerCAmelCase  :   Optional[int]						=						[
       'generator',
       'height',
       'width',
       'strength',
       'guidance_scale',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   __lowerCAmelCase  :   List[Any]						=						False

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return 32

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





    '''simple docstring'''




    return 32

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





    '''simple docstring'''




    return self.time_input_dim

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    return self.time_input_dim * 4

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    return 100

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    torch.manual_seed(0						)

    UpperCAmelCase	:					List[str]	=	{
        """in_channels""": 8,
        # Out channels is double in channels because predicts mean and variance
        """out_channels""": 8,
        """addition_embed_type""": """image_hint""",
        """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	:					int	=	UNetaDConditionModel(**_SCREAMING_SNAKE_CASE						)
    return model

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





    '''simple docstring'''




    return {
        "block_out_channels": [32, 32, 64, 64],
        "down_block_types": [
            "DownEncoderBlock2D",
            "DownEncoderBlock2D",
            "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
        "vq_embed_dim": 4,
    }

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




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

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





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	self.dummy_unet
    UpperCAmelCase	:					Dict	=	self.dummy_movq

    UpperCAmelCase	:					List[str]	=	{
        """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	:					Optional[Any]	=	DDIMScheduler(**_SCREAMING_SNAKE_CASE						)

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

    return components

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





    '''simple docstring'''




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

    if str(_SCREAMING_SNAKE_CASE						).startswith("""mps"""						):
     UpperCAmelCase	:					List[Any]	=	torch.manual_seed(_SCREAMING_SNAKE_CASE						)
    else:
     UpperCAmelCase	:					Tuple	=	torch.Generator(device=_SCREAMING_SNAKE_CASE						).manual_seed(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					str	=	{
        """image""": init_image,
        """image_embeds""": image_embeds,
        """negative_image_embeds""": negative_image_embeds,
        """hint""": hint,
        """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						)  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	"""cpu"""

    UpperCAmelCase	:					Optional[Any]	=	self.get_dummy_components()

    UpperCAmelCase	:					Optional[Any]	=	self.pipeline_class(**_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	pipe.to(_SCREAMING_SNAKE_CASE						)

    pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

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

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

    UpperCAmelCase	:					Tuple	=	image[0, -3:, -3:, -1]
    UpperCAmelCase	:					Tuple	=	image_from_tuple[0, -3:, -3:, -1]

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

    UpperCAmelCase	:					Any	=	np.array(
        [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]						)
    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						)  ->      int:





    '''simple docstring'''




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




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	load_numpy(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""						)

    UpperCAmelCase	:					List[str]	=	load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""						)
    UpperCAmelCase	:					Tuple	=	init_image.resize((512, 512)						)

    UpperCAmelCase	:					List[Any]	=	load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/kandinskyv22/hint_image_cat.png"""						)
    UpperCAmelCase	:					Optional[int]	=	torch.from_numpy(np.array(_SCREAMING_SNAKE_CASE						)						).float() / 255.0
    UpperCAmelCase	:					Optional[int]	=	hint.permute(2      ,     0      ,     1						).unsqueeze(0						)

    UpperCAmelCase	:					Any	=	"""A robot, 4k photo"""

    UpperCAmelCase	:					Dict	=	KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
        """kandinsky-community/kandinsky-2-2-prior"""      ,     torch_dtype=torch.floataa						)
    pipe_prior.to(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					List[Any]	=	KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
        """kandinsky-community/kandinsky-2-2-controlnet-depth"""      ,     torch_dtype=torch.floataa						)
    UpperCAmelCase	:					Optional[Any]	=	pipeline.to(_SCREAMING_SNAKE_CASE						)

    pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Optional[int]	=	torch.Generator(device="""cpu"""						).manual_seed(0						)

    UpperCAmelCase	,		UpperCAmelCase	:					int	=	pipe_prior(
        _SCREAMING_SNAKE_CASE      ,     image=_SCREAMING_SNAKE_CASE      ,     strength=0.85      ,     generator=_SCREAMING_SNAKE_CASE      ,     negative_prompt=""""""      ,     ).to_tuple()

    UpperCAmelCase	:					Union[str, Any]	=	pipeline(
        image=_SCREAMING_SNAKE_CASE      ,     image_embeds=_SCREAMING_SNAKE_CASE      ,     negative_image_embeds=_SCREAMING_SNAKE_CASE      ,     hint=_SCREAMING_SNAKE_CASE      ,     generator=_SCREAMING_SNAKE_CASE      ,     num_inference_steps=100      ,     height=512      ,     width=512      ,     strength=0.5      ,     output_type="""np"""      ,     )

    UpperCAmelCase	:					List[Any]	=	output.images[0]

    assert image.shape == (512, 512, 3)

    assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)




import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
 import ctypes
 import msvcrt  # noqa




 class A  (   ctypes.Structure     ):
       # _fields is a specific attr expected by ctypes
       UpperCamelCase__							:  List[Any]      =[('size', ctypes.c_int), ('visible', ctypes.c_byte)]




def 	a_      (    ):
       '''simple docstring'''





       if os.name == "nt":
              _lowerCamelCase       :		Optional[Any]		=CursorInfo()
              _lowerCamelCase       :		Dict		=ctypes.windll.kernelaa.GetStdHandle(-11     )
              ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE__		,			ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
              _lowerCamelCase       :		Any		=False
              ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE__		,			ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
       elif os.name == "posix":
              sys.stdout.write('\033[?25l'     )
              sys.stdout.flush()




def 	a_      (    ):
       '''simple docstring'''





       if os.name == "nt":
              _lowerCamelCase       :		Any		=CursorInfo()
              _lowerCamelCase       :		Optional[Any]		=ctypes.windll.kernelaa.GetStdHandle(-11     )
              ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE__		,			ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
              _lowerCamelCase       :		Union[str, Any]		=True
              ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE__		,			ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
       elif os.name == "posix":
              sys.stdout.write('\033[?25h'     )
              sys.stdout.flush()




@contextmanager
def 	a_      (    ):
       '''simple docstring'''





       try:
              hide_cursor()
              yield
       finally:
              show_cursor()


