



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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 			_snake_case     (   lowercase__      ,		unittest.TestCase):
				UpperCamelCase__			:   int       						=KandinskyVaaInpaintPipeline
				UpperCamelCase__			:   Any       						=["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
				UpperCamelCase__			:   int       						=[
				    """image_embeds""",
				    """negative_image_embeds""",
				    """image""",
				    """mask_image""",
				]
				UpperCamelCase__			:   Optional[int]       						=[
				    """generator""",
				    """height""",
				    """width""",
				    """latents""",
				    """guidance_scale""",
				    """num_inference_steps""",
				    """return_dict""",
				    """guidance_scale""",
				    """num_images_per_prompt""",
				    """output_type""",
				    """return_dict""",
				]
				UpperCamelCase__			:   Any       						=False




				@property
				def 							A__ (  self   :  int  ):
							return 32




				@property
				def 							A__ (  self   :  Optional[int]  ):
							return 32




				@property
				def 							A__ (  self   :  Any  ):
							return self.time_input_dim




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




				@property
				def 							A__ (  self   :  List[Any]  ):
							return 100




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

							lowercase__   					=			{
							    "in_channels": 9,
							    # 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,
							}

							lowercase__   					=			UNetaDConditionModel(**__lowercase  )
							return model




				@property
				def 							A__ (  self   :  str  ):
							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   :  Any  ):
							torch.manual_seed(0  )
							lowercase__   					=			VQModel(**self.dummy_movq_kwargs  )
							return model




				def 							A__ (  self   :  str  ):
							lowercase__   					=			self.dummy_unet
							lowercase__   					=			self.dummy_movq

							lowercase__   					=			DDIMScheduler(
							    num_train_timesteps=1000,   beta_schedule="linear",   beta_start=0.00085,   beta_end=0.012,   clip_sample=__lowercase,   set_alpha_to_one=__lowercase,   steps_offset=1,   prediction_type="epsilon",   thresholding=__lowercase,   )

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

							return components




				def 							A__ (  self   :  Optional[Any],   __lowercase   :  str,   __lowercase   :  List[Any]=0  ):
							lowercase__   					=			floats_tensor((1, self.text_embedder_hidden_size),   rng=random.Random(__lowercase  )  ).to(__lowercase  )
							lowercase__   					=			floats_tensor((1, self.text_embedder_hidden_size),   rng=random.Random(seed + 1  )  ).to(
							    __lowercase  )
							# create init_image
							lowercase__   					=			floats_tensor((1, 3, 64, 64),   rng=random.Random(__lowercase  )  ).to(__lowercase  )
							lowercase__   					=			image.cpu().permute(0,   2,   3,   1  )[0]
							lowercase__   					=			Image.fromarray(np.uinta(__lowercase  )  ).convert("RGB"  ).resize((256, 256)  )
							# create mask
							lowercase__   					=			np.ones((64, 64),   dtype=np.floataa  )
							lowercase__   					=			0

							if str(__lowercase  ).startswith("mps"  ):
										lowercase__   					=			torch.manual_seed(__lowercase  )
							else:
										lowercase__   					=			torch.Generator(device=__lowercase  ).manual_seed(__lowercase  )
							lowercase__   					=			{
							    "image": init_image,
							    "mask_image": mask,
							    "image_embeds": image_embeds,
							    "negative_image_embeds": negative_image_embeds,
							    "generator": generator,
							    "height": 64,
							    "width": 64,
							    "num_inference_steps": 2,
							    "guidance_scale": 4.0,
							    "output_type": "np",
							}
							return inputs




				def 							A__ (  self   :  Any  ):
							lowercase__   					=			"cpu"

							lowercase__   					=			self.get_dummy_components()

							lowercase__   					=			self.pipeline_class(**__lowercase  )
							lowercase__   					=			pipe.to(__lowercase  )

							pipe.set_progress_bar_config(disable=__lowercase  )

							lowercase__   					=			pipe(**self.get_dummy_inputs(__lowercase  )  )
							lowercase__   					=			output.images

							lowercase__   					=			pipe(
							    **self.get_dummy_inputs(__lowercase  ),   return_dict=__lowercase,   )[0]

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

							print(F'''image.shape {image.shape}'''  )

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

							lowercase__   					=			np.array(
							    [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]  )

							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()}'''




				def 							A__ (  self   :  List[Any]  ):
							super().test_inference_batch_single_identical(expected_max_diff=3e-3  )
@slow
@require_torch_gpu
class 			_snake_case     (   unittest.TestCase):




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




				def 							A__ (  self   :  Optional[int]  ):
							lowercase__   					=			load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"  )

							lowercase__   					=			load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"  )
							lowercase__   					=			np.ones((768, 768),   dtype=np.floataa  )
							lowercase__   					=			0

							lowercase__   					=			"a hat"

							lowercase__   					=			KandinskyVaaPriorPipeline.from_pretrained(
							    "kandinsky-community/kandinsky-2-2-prior",   torch_dtype=torch.floataa  )
							pipe_prior.to(__lowercase  )

							lowercase__   					=			KandinskyVaaInpaintPipeline.from_pretrained(
							    "kandinsky-community/kandinsky-2-2-decoder-inpaint",   torch_dtype=torch.floataa  )
							lowercase__   					=			pipeline.to(__lowercase  )
							pipeline.set_progress_bar_config(disable=__lowercase  )

							lowercase__   					=			torch.Generator(device="cpu"  ).manual_seed(0  )
							lowercase__					,    lowercase__   					=			pipe_prior(
							    __lowercase,   generator=__lowercase,   num_inference_steps=5,   negative_prompt="",   ).to_tuple()

							lowercase__   					=			pipeline(
							    image=__lowercase,   mask_image=__lowercase,   image_embeds=__lowercase,   negative_image_embeds=__lowercase,   generator=__lowercase,   num_inference_steps=100,   height=768,   width=768,   output_type="np",   )

							lowercase__   					=			output.images[0]

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

							assert_mean_pixel_difference(__lowercase,   __lowercase  )









def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			1  # To kept the Calculated Value
			# Since C(n, k) = C(n, n-k)
			if k > (n - k):
						lowercase__   					=			n - k
			# Calculate C(n,k)
			for i in range(SCREAMING_SNAKE_CASE_					):
						result *= n - i
						result //= i + 1
			return result



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return binomial_coefficient(2 * node_count  ,					SCREAMING_SNAKE_CASE_					) // (node_count + 1)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if n < 0:
						raise ValueError("factorial() not defined for negative values"					)
			lowercase__   					=			1
			for i in range(1  ,					n + 1					):
						result *= i
			return result



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return catalan_number(SCREAMING_SNAKE_CASE_					) * factorial(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
							lowercase_						=       int(input("""Enter the number of nodes: """).strip() or 0)
							if node_count <= 0:
														raise ValueError("""We need some nodes to work with.""")
							print(
							    F'Given {node_count} nodes, there are {binary_tree_count(node_count)} '
							    F'binary trees and {catalan_number(node_count)} binary search trees.'
							)




