



"""simple docstring"""






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 			A__   (  lowerCamelCase_  ,		unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	KandinskyVaaControlnetPipeline
	SCREAMING_SNAKE_CASE          =	['image_embeds', 'negative_image_embeds', 'hint']
	SCREAMING_SNAKE_CASE          =	['image_embeds', 'negative_image_embeds', 'hint']
	SCREAMING_SNAKE_CASE          =	[
	    'generator',
	    'height',
	    'width',
	    'latents',
	    'guidance_scale',
	    'num_inference_steps',
	    'return_dict',
	    'guidance_scale',
	    'num_images_per_prompt',
	    'output_type',
	    'return_dict',
	]
	SCREAMING_SNAKE_CASE          =	False

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

				"""simple docstring"""

				return 32

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

				"""simple docstring"""

				return 32

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Dict:

				"""simple docstring"""

				return self.time_input_dim

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       List[str]:

				"""simple docstring"""

				return self.time_input_dim * 4

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

				"""simple docstring"""

				return 100

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       str:

				"""simple docstring"""

				torch.manual_seed(0)

				__lowerCAmelCase      :  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,
				}

				__lowerCAmelCase      :  Dict												=       UNetaDConditionModel(**_lowercase)
				return model

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       Tuple:

				"""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:		Optional[Any])				->       Tuple:

				"""simple docstring"""

				torch.manual_seed(0)
				__lowerCAmelCase      :  List[str]												=       VQModel(**self.dummy_movq_kwargs)
				return model

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       self.dummy_unet
				__lowerCAmelCase      :  Optional[int]												=       self.dummy_movq

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

				__lowerCAmelCase      :  Any												=       {
				    "unet": unet,
				    "scheduler": scheduler,
				    "movq": movq,
				}
				return components

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		str=0)				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       floats_tensor((1, self.text_embedder_hidden_size)       ,			rng=random.Random(_lowercase)).to(_lowercase)
				__lowerCAmelCase      :  Dict												=       floats_tensor((1, self.text_embedder_hidden_size)       ,			rng=random.Random(seed + 1)).to(
				    _lowercase)

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

				if str(_lowercase).startswith("mps"):
							__lowerCAmelCase      :  str												=       torch.manual_seed(_lowercase)
				else:
							__lowerCAmelCase      :  str												=       torch.Generator(device=_lowercase).manual_seed(_lowercase)
				__lowerCAmelCase      :  Union[str, Any]												=       {
				    "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 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       "cpu"

				__lowerCAmelCase      :  str												=       self.get_dummy_components()

				__lowerCAmelCase      :  Union[str, Any]												=       self.pipeline_class(**_lowercase)
				__lowerCAmelCase      :  str												=       pipe.to(_lowercase)

				pipe.set_progress_bar_config(disable=_lowercase)

				__lowerCAmelCase      :  List[str]												=       pipe(**self.get_dummy_inputs(_lowercase))
				__lowerCAmelCase      :  Any												=       output.images

				__lowerCAmelCase      :  str												=       pipe(
				    **self.get_dummy_inputs(_lowercase)       ,			return_dict=_lowercase       ,			)[0]

				__lowerCAmelCase      :  Optional[int]												=       image[0, -3:, -3:, -1]
				__lowerCAmelCase      :  Tuple												=       image_from_tuple[0, -3:, -3:, -1]

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

				__lowerCAmelCase      :  int												=       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 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Any:

				"""simple docstring"""

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







	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       Optional[Any]:

				"""simple docstring"""

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

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

				__lowerCAmelCase      :  List[Any]												=       KandinskyVaaPriorPipeline.from_pretrained(
				    "kandinsky-community/kandinsky-2-2-prior"       ,			torch_dtype=torch.floataa)
				pipe_prior.to(_lowercase)

				__lowerCAmelCase      :  Optional[Any]												=       KandinskyVaaControlnetPipeline.from_pretrained(
				    "kandinsky-community/kandinsky-2-2-controlnet-depth"       ,			torch_dtype=torch.floataa)
				__lowerCAmelCase      :  str												=       pipeline.to(_lowercase)
				pipeline.set_progress_bar_config(disable=_lowercase)

				__lowerCAmelCase      :  Dict												=       "A robot, 4k photo"

				__lowerCAmelCase      :  Dict												=       torch.Generator(device="cuda").manual_seed(0)
				__lowerCAmelCase						,	__lowerCAmelCase      :  Any												=       pipe_prior(
				    _lowercase       ,			generator=_lowercase       ,			num_inference_steps=5       ,			negative_prompt=""       ,			).to_tuple()

				__lowerCAmelCase      :  int												=       torch.Generator(device="cuda").manual_seed(0)
				__lowerCAmelCase      :  Optional[int]												=       pipeline(
				    image_embeds=_lowercase       ,			negative_image_embeds=_lowercase       ,			hint=_lowercase       ,			generator=_lowercase       ,			num_inference_steps=100       ,			output_type="np"       ,			)

				__lowerCAmelCase      :  Tuple												=       output.images[0]

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

				assert_mean_pixel_difference(_lowercase       ,			_lowercase)
import doctest
from collections import deque

import numpy as np







class __snake_case				:




   def __init__(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      [2, 1, 2, -1]
     SCREAMING_SNAKE_CASE__										=      [1, 2, 3, 4]




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      len(self.first_signal      )
     SCREAMING_SNAKE_CASE__										=      len(self.second_signal      )

     SCREAMING_SNAKE_CASE__										=      max(_lowercase       ,	_lowercase      )

     # create a zero matrix of max_length x max_length
     SCREAMING_SNAKE_CASE__										=      [[0] * max_length for i in range(_lowercase      )]

     # fills the smaller signal with zeros to make both signals of same length
     if length_first_signal < length_second_signal:
       self.first_signal += [0] * (max_length - length_first_signal)
     elif length_first_signal > length_second_signal:
       self.second_signal += [0] * (max_length - length_second_signal)
     for i in range(_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      deque(self.second_signal      )
       rotated_signal.rotate(_lowercase      )
       for j, item in enumerate(_lowercase      ):
         matrix[i][j] += item

        # multiply the matrix with the first signal
     SCREAMING_SNAKE_CASE__										=      np.matmul(np.transpose(_lowercase      )       ,	np.transpose(self.first_signal      )      )

     # rounding-off to two decimal places
     return [round(_lowercase       ,	2      ) for i in final_signal]


if __name__ == "__main__":
   doctest.testmod()
