'''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 	_lowercase(  SCREAMING_SNAKE_CASE__ ,unittest.TestCase					):



   """simple docstring"""





   __lowerCamelCase     =	KandinskyVaaControlnetImgaImgPipeline
   __lowerCamelCase     =	['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
   __lowerCamelCase     =	['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
   __lowerCamelCase     =	[
       '''generator''',
       '''height''',
       '''width''',
       '''strength''',
       '''guidance_scale''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   __lowerCamelCase     =	False





   @property
   def       snake_case						(				self:     Optional[Any]						):
    return 32





   @property
   def       snake_case						(				self:     List[str]						):
    return 32





   @property
   def       snake_case						(				self:     Union[str, Any]						):
    return self.time_input_dim





   @property
   def       snake_case						(				self:     List[Any]						):
    return self.time_input_dim * 4





   @property
   def       snake_case						(				self:     List[str]						):
    return 100





   @property
   def       snake_case						(				self:     Dict						):
    torch.manual_seed(0						)

    __UpperCAmelCase								=       {
        """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								=       UNetaDConditionModel(**__lowerCAmelCase						)
    return model





   @property
   def       snake_case						(				self:     Union[str, Any]						):
    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       snake_case						(				self:     Tuple						):
    torch.manual_seed(0						)
    __UpperCAmelCase								=       VQModel(**self.dummy_movq_kwargs						)
    return model





   def       snake_case						(				self:     Any						):
    __UpperCAmelCase								=       self.dummy_unet
    __UpperCAmelCase								=       self.dummy_movq

    __UpperCAmelCase								=       {
        """num_train_timesteps""": 1000,
        """beta_schedule""": """linear""",
        """beta_start""": 0.00085,
        """beta_end""": 0.012,
        """clip_sample""": False,
        """set_alpha_to_one""": False,
        """steps_offset""": 0,
        """prediction_type""": """epsilon""",
        """thresholding""": False,
    }

    __UpperCAmelCase								=       DDIMScheduler(**__lowerCAmelCase						)

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

    return components





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

    if str(__lowerCAmelCase						).startswith('mps'						):
     __UpperCAmelCase								=       torch.manual_seed(__lowerCAmelCase						)
    else:
     __UpperCAmelCase								=       torch.Generator(device=__lowerCAmelCase						).manual_seed(__lowerCAmelCase						)
    __UpperCAmelCase								=       {
        """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       snake_case						(				self:     Optional[int]						):
    __UpperCAmelCase								=       """cpu"""

    __UpperCAmelCase								=       self.get_dummy_components()

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

    pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

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

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

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

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

    __UpperCAmelCase								=       np.array(
        [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]						)
    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 	_lowercase(  unittest.TestCase					):



   """simple docstring"""





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





   def       snake_case						(				self:     Dict						):
    __UpperCAmelCase								=       load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'						)

    __UpperCAmelCase								=       load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'						)
    __UpperCAmelCase								=       init_image.resize((512, 512)						)

    __UpperCAmelCase								=       load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/kandinskyv22/hint_image_cat.png'						)
    __UpperCAmelCase								=       torch.from_numpy(np.array(__lowerCAmelCase						)						).float() / 255.0
    __UpperCAmelCase								=       hint.permute(2      ,0      ,1						).unsqueeze(0						)

    __UpperCAmelCase								=       """A robot, 4k photo"""

    __UpperCAmelCase								=       KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
        'kandinsky-community/kandinsky-2-2-prior'      ,torch_dtype=torch.floataa						)
    pipe_prior.to(__lowerCAmelCase						)

    __UpperCAmelCase								=       KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
        'kandinsky-community/kandinsky-2-2-controlnet-depth'      ,torch_dtype=torch.floataa						)
    __UpperCAmelCase								=       pipeline.to(__lowerCAmelCase						)

    pipeline.set_progress_bar_config(disable=__lowerCAmelCase						)

    __UpperCAmelCase								=       torch.Generator(device='cpu'						).manual_seed(0						)

    __UpperCAmelCase								=       pipe_prior(
        __lowerCAmelCase      ,image=__lowerCAmelCase      ,strength=0.85      ,generator=__lowerCAmelCase      ,negative_prompt=''      ,).to_tuple()

    __UpperCAmelCase								=       pipeline(
        image=__lowerCAmelCase      ,image_embeds=__lowerCAmelCase      ,negative_image_embeds=__lowerCAmelCase      ,hint=__lowerCAmelCase      ,generator=__lowerCAmelCase      ,num_inference_steps=100      ,height=512      ,width=512      ,strength=0.5      ,output_type='np'      ,)

    __UpperCAmelCase								=       output.images[0]

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

    assert_mean_pixel_difference(__lowerCAmelCase      ,__lowerCAmelCase						)











'''simple docstring'''

def 				snake_case       (  a_				:			str     , a_				:			Optional[int]						)							->					Any:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				(boundary[1] - boundary[0]) / steps
      UpperCamelCase_					: Dict            =				boundary[0]
      UpperCamelCase_					: Any            =				boundary[1]
      UpperCamelCase_					: Union[str, Any]            =				make_points(a_     , a_     , a_						)
      UpperCamelCase_					: Any            =				0.0
      y += (h / 2.0) * f(a_						)
      for i in x_i:
            # print(i)
            y += h * f(a_						)
      y += (h / 2.0) * f(a_						)
      return y



def 				snake_case       (  a_				:			Tuple     , a_				:			Any     , a_				:			Tuple						)							->					Tuple:





      """simple docstring"""

      UpperCamelCase_					: Optional[int]            =				a + h
      while x < (b - h):
            yield x
            UpperCamelCase_					: List[str]            =				x + h



def 				snake_case       (  a_				:			List[str]						)							->					Tuple:  # enter your function here





      """simple docstring"""

      UpperCamelCase_					: int            =				(x - 0) * (x - 0)
      return y



def 				snake_case       (  )							->					Dict:





      """simple docstring"""

      UpperCamelCase_					: Optional[Any]            =				0.0  # Lower bound of integration
      UpperCamelCase_					: Optional[int]            =				1.0  # Upper bound of integration
      UpperCamelCase_					: Optional[Any]            =				10.0  # define number of steps or resolution
      UpperCamelCase_					: Optional[Any]            =				[a, b]  # define boundary of integration
      UpperCamelCase_					: Any            =				method_a(a_     , a_						)
      print(f"y = {y}"						)


if __name__ == "__main__":
      main()






