



def 	__UpperCamelCase	(  _lowerCAmelCase = 200		)       ->  int:



    """simple docstring"""






    A :       List[str]          =   [1, 2, 5, 10, 20, 50, 100, 200]
    A :       List[Any]          =   [0] * (pence + 1)
    A :       int          =   1  # base case: 1 way to make 0 pence

    for coin in coins:
        for i in range(_lowerCAmelCase      ,     pence + 1      ,     1		):
            number_of_ways[i] += number_of_ways[i - coin]
    return number_of_ways[pence]


if __name__ == "__main__":
     assert solution(200) == 73_682







import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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__     (		SCREAMING_SNAKE_CASE__ ,						unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: Union[str, Any]					=      KandinskyVaaControlnetPipeline
    __lowerCamelCase			: Optional[int]					=      ["image_embeds", "negative_image_embeds", "hint"]
    __lowerCamelCase			: Dict					=      ["image_embeds", "negative_image_embeds", "hint"]
    __lowerCamelCase			: List[str]					=      [
        "generator",
        "height",
        "width",
        "latents",
        "guidance_scale",
        "num_inference_steps",
        "return_dict",
        "guidance_scale",
        "num_images_per_prompt",
        "output_type",
        "return_dict",
    ]
    __lowerCamelCase			: Dict					=      False






    @property
    def 			_lowerCAmelCase					(       self		):
        return 32






    @property
    def 			_lowerCAmelCase					(       self		):
        return 32






    @property
    def 			_lowerCAmelCase					(       self		):
        return self.time_input_dim






    @property
    def 			_lowerCAmelCase					(       self		):
        return self.time_input_dim * 4






    @property
    def 			_lowerCAmelCase					(       self		):
        return 100






    @property
    def 			_lowerCAmelCase					(       self		):
        torch.manual_seed(0		)

        A :       Any          =   {
            """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,
        }

        A :       List[str]          =   UNetaDConditionModel(**lowerCamelCase__		)
        return model






    @property
    def 			_lowerCAmelCase					(       self		):
        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 			_lowerCAmelCase					(       self		):
        torch.manual_seed(0		)
        A :       List[Any]          =   VQModel(**self.dummy_movq_kwargs		)
        return model






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[Any]          =   self.dummy_unet
        A :       Tuple          =   self.dummy_movq

        A :       List[Any]          =   DDIMScheduler(
            num_train_timesteps=1000,		beta_schedule="""linear""",		beta_start=0.0_0085,		beta_end=0.012,		clip_sample=lowerCamelCase__,		set_alpha_to_one=lowerCamelCase__,		steps_offset=1,		prediction_type="""epsilon""",		thresholding=lowerCamelCase__,		)

        A :       int          =   {
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }
        return components






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=0		):
        A :       Optional[Any]          =   floats_tensor((1, self.text_embedder_hidden_size),		rng=random.Random(lowerCamelCase__		)		).to(lowerCamelCase__		)
        A :       List[Any]          =   floats_tensor((1, self.text_embedder_hidden_size),		rng=random.Random(seed + 1		)		).to(
            lowerCamelCase__		)

        # create hint
        A :       int          =   floats_tensor((1, 3, 64, 64),		rng=random.Random(lowerCamelCase__		)		).to(lowerCamelCase__		)

        if str(lowerCamelCase__		).startswith("""mps"""		):
            A :       Optional[Any]          =   torch.manual_seed(lowerCamelCase__		)
        else:
            A :       str          =   torch.Generator(device=lowerCamelCase__		).manual_seed(lowerCamelCase__		)
        A :       List[str]          =   {
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """hint""": hint,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """guidance_scale""": 4.0,
            """num_inference_steps""": 2,
            """output_type""": """np""",
        }
        return inputs






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   """cpu"""

        A :       List[str]          =   self.get_dummy_components()

        A :       Dict          =   self.pipeline_class(**lowerCamelCase__		)
        A :       Optional[Any]          =   pipe.to(lowerCamelCase__		)

        pipe.set_progress_bar_config(disable=lowerCamelCase__		)

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

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

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

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

        A :       Dict          =   np.array(
            [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]		)

        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    ):




    '''simple docstring'''






    def 			_lowerCAmelCase					(       self		):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()






    def 			_lowerCAmelCase					(       self		):
        A :       Tuple          =   load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""		)

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

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

        A :       Tuple          =   KandinskyVaaControlnetPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-controlnet-depth""",		torch_dtype=torch.floataa		)
        A :       Union[str, Any]          =   pipeline.to(lowerCamelCase__		)
        pipeline.set_progress_bar_config(disable=lowerCamelCase__		)

        A :       Optional[Any]          =   """A robot, 4k photo"""

        A :       Union[str, Any]          =   torch.Generator(device="""cuda"""		).manual_seed(0		)
        A     ,							A :       int          =   pipe_prior(
            lowerCamelCase__,		generator=lowerCamelCase__,		num_inference_steps=5,		negative_prompt="""""",		).to_tuple()

        A :       Union[str, Any]          =   torch.Generator(device="""cuda"""		).manual_seed(0		)
        A :       int          =   pipeline(
            image_embeds=lowerCamelCase__,		negative_image_embeds=lowerCamelCase__,		hint=lowerCamelCase__,		generator=lowerCamelCase__,		num_inference_steps=100,		output_type="""np""",		)

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

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

        assert_mean_pixel_difference(lowerCamelCase__,		lowerCamelCase__		)


