



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        snake_case__    (snake_case_ ,     unittest.TestCase		):







						"""simple docstring"""




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

						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									return 3_2

						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									return 3_2

						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									return self.time_input_dim

						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									return self.time_input_dim * 4

						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									return 1_0_0

						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




									torch.manual_seed(0  )

									a__       :						Union[str, 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__       :						Union[str, Any]							=  UNetaDConditionModel(**__lowercase  )
									return model

						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									return {
									    "block_out_channels": [3_2, 3_2, 6_4, 6_4],
									    "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": 1_2,
									    "out_channels": 3,
									    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
									    "vq_embed_dim": 4,
									}

						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						List[str]							=  self.dummy_unet
									a__       :						int							=  self.dummy_movq

									a__       :						Any							=  DDIMScheduler(
									    num_train_timesteps=1_0_0_0 ,       beta_schedule="""linear""" ,       beta_start=0.0_0_0_8_5 ,       beta_end=0.0_1_2 ,       clip_sample=__lowercase ,       set_alpha_to_one=__lowercase ,       steps_offset=1 ,       prediction_type="""epsilon""" ,       thresholding=__lowercase ,       )

									a__       :						List[Any]							=  {
									    'unet': unet,
									    'scheduler': scheduler,
									    'movq': movq,
									}
									return components

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase=0  ) ->				Dict:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  floats_tensor((1, self.text_embedder_hidden_size) ,       rng=random.Random(__lowercase  )  ).to(__lowercase  )
									a__       :						List[str]							=  floats_tensor((1, self.text_embedder_hidden_size) ,       rng=random.Random(seed + 1  )  ).to(
									    __lowercase  )

									# create hint
									a__       :						List[Any]							=  floats_tensor((1, 3, 6_4, 6_4) ,       rng=random.Random(__lowercase  )  ).to(__lowercase  )

									if str(__lowercase  ).startswith("""mps"""  ):
												a__       :						Union[str, Any]							=  torch.manual_seed(__lowercase  )
									else:
												a__       :						Any							=  torch.Generator(device=__lowercase  ).manual_seed(__lowercase  )
									a__       :						List[str]							=  {
									    'image_embeds': image_embeds,
									    'negative_image_embeds': negative_image_embeds,
									    'hint': hint,
									    'generator': generator,
									    'height': 6_4,
									    'width': 6_4,
									    'guidance_scale': 4.0,
									    'num_inference_steps': 2,
									    'output_type': 'np',
									}
									return inputs

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						str							=  'cpu'

									a__       :						int							=  self.get_dummy_components()

									a__       :						int							=  self.pipeline_class(**__lowercase  )
									a__       :						Optional[int]							=  pipe.to(__lowercase  )

									pipe.set_progress_bar_config(disable=__lowercase  )

									a__       :						Any							=  pipe(**self.get_dummy_inputs(__lowercase  )  )
									a__       :						Any							=  output.images

									a__       :						Union[str, Any]							=  pipe(
									    **self.get_dummy_inputs(__lowercase  ) ,       return_dict=__lowercase ,       )[0]

									a__       :						str							=  image[0, -3:, -3:, -1]
									a__       :						Optional[Any]							=  image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)

									a__       :						Tuple							=  np.array(
									    [0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5]  )

									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        snake_case__    (unittest.TestCase		):







						"""simple docstring"""




						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




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

									a__       :						Dict							=  load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/kandinskyv22/hint_image_cat.png"""  )
									a__       :						Any							=  torch.from_numpy(np.array(__lowercase  )  ).float() / 2_5_5.0
									a__       :						Dict							=  hint.permute(2 ,       0 ,       1  ).unsqueeze(0  )

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

									a__       :						List[str]							=  KandinskyVaaControlnetPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-2-controlnet-depth""" ,       torch_dtype=torch.floataa  )
									a__       :						int							=  pipeline.to(__lowercase  )
									pipeline.set_progress_bar_config(disable=__lowercase  )

									a__       :						Any							=  'A robot, 4k photo'

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

									a__       :						Optional[Any]							=  torch.Generator(device="""cuda"""  ).manual_seed(0  )
									a__       :						Optional[Any]							=  pipeline(
									    image_embeds=__lowercase ,       negative_image_embeds=__lowercase ,       hint=__lowercase ,       generator=__lowercase ,       num_inference_steps=1_0_0 ,       output_type="""np""" ,       )

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

									assert image.shape == (5_1_2, 5_1_2, 3)

									assert_mean_pixel_difference(__lowercase ,       __lowercase  )









from __future__ import annotations

import math

def      lowerCAmelCase_					(  _lowercase    :       float						,       _lowercase    :       int)							->	float:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  u
			for i in range(1						,       _lowercase):
						a__       :						Optional[int]							=  temp * (u - i)
			return temp

def      lowerCAmelCase_					(  )							->	None:







			"""simple docstring"""
			a__       :						Tuple							=  int(input("""enter the numbers of values: """))
			a__       :						list[list[float]]							=  []
			for _ in range(_lowercase):
						y.append([])
			for i in range(_lowercase):
						for j in range(_lowercase):
									y[i].append(_lowercase)
									a__       :						Optional[Any]							=  0

			print("""enter the values of parameters in a list: """)
			a__       :						List[Any]							=  list(map(_lowercase						,       input().split()))

			print("""enter the values of corresponding parameters: """)
			for i in range(_lowercase):
						a__       :						Optional[Any]							=  float(input())

			a__       :						Tuple							=  int(input("""enter the value to interpolate: """))
			a__       :						int							=  (value - x[0]) / (x[1] - x[0])

			# for calculating forward difference table

			for i in range(1						,       _lowercase):
						for j in range(n - i):
									a__       :						int							=  y[j + 1][i - 1] - y[j][i - 1]

			a__       :						Optional[int]							=  y[0][0]
			for i in range(1						,       _lowercase):
						summ += (ucal(_lowercase						,       _lowercase) * y[0][i]) / math.factorial(_lowercase)

			print(F'''the value at {value} is {summ}''')


if __name__ == "__main__":
		main()




