
'''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   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE	,							unittest.TestCase			):






						'''simple docstring'''






						_lowercase		:				int									=       KandinskyVaaControlnetImgaImgPipeline
						_lowercase		:				List[Any]									=       ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
						_lowercase		:				Optional[int]									=       ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
						_lowercase		:				List[Any]									=       [
						    '''generator''',
						    '''height''',
						    '''width''',
						    '''strength''',
						    '''guidance_scale''',
						    '''num_inference_steps''',
						    '''return_dict''',
						    '''guidance_scale''',
						    '''num_images_per_prompt''',
						    '''output_type''',
						    '''return_dict''',
						]
						_lowercase		:				str									=       False




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return 32




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return 32




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return self.time_input_dim




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return self.time_input_dim * 4




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return 100




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												torch.manual_seed(0				)

												_lowerCAmelCase  						=			{
												    """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  						=			UNetaDConditionModel(**_lowercase				)
												return model




						@property
						def  _lowercase     ( self				):

												"""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  _lowercase     ( self				):

												"""simple docstring"""



												torch.manual_seed(0				)
												_lowerCAmelCase  						=			VQModel(**self.dummy_movq_kwargs				)
												return model




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.dummy_unet
												_lowerCAmelCase  						=			self.dummy_movq

												_lowerCAmelCase  						=			{
												    """num_train_timesteps""": 1_000,
												    """beta_schedule""": """linear""",
												    """beta_start""": 0.0_0085,
												    """beta_end""": 0.012,
												    """clip_sample""": False,
												    """set_alpha_to_one""": False,
												    """steps_offset""": 0,
												    """prediction_type""": """epsilon""",
												    """thresholding""": False,
												}

												_lowerCAmelCase  						=			DDIMScheduler(**_lowercase				)

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

												return components




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase=0				):

												"""simple docstring"""



												_lowerCAmelCase  						=			floats_tensor((1, self.text_embedder_hidden_size)   ,       rng=random.Random(_lowercase				)				).to(_lowercase				)
												_lowerCAmelCase  						=			floats_tensor((1, self.text_embedder_hidden_size)   ,       rng=random.Random(seed + 1				)				).to(
												    _lowercase				)
												# create init_image
												_lowerCAmelCase  						=			floats_tensor((1, 3, 64, 64)   ,       rng=random.Random(_lowercase				)				).to(_lowercase				)
												_lowerCAmelCase  						=			image.cpu().permute(0   ,       2   ,       3   ,       1				)[0]
												_lowerCAmelCase  						=			Image.fromarray(np.uinta(_lowercase				)				).convert("""RGB"""				).resize((256, 256)				)
												# create hint
												_lowerCAmelCase  						=			floats_tensor((1, 3, 64, 64)   ,       rng=random.Random(_lowercase				)				).to(_lowercase				)

												if str(_lowercase				).startswith("""mps"""				):
																		_lowerCAmelCase  						=			torch.manual_seed(_lowercase				)
												else:
																		_lowerCAmelCase  						=			torch.Generator(device=_lowercase				).manual_seed(_lowercase				)
												_lowerCAmelCase  						=			{
												    """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  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			"""cpu"""

												_lowerCAmelCase  						=			self.get_dummy_components()

												_lowerCAmelCase  						=			self.pipeline_class(**_lowercase				)
												_lowerCAmelCase  						=			pipe.to(_lowercase				)

												pipe.set_progress_bar_config(disable=_lowercase				)

												_lowerCAmelCase  						=			pipe(**self.get_dummy_inputs(_lowercase				)				)
												_lowerCAmelCase  						=			output.images

												_lowerCAmelCase  						=			pipe(
												    **self.get_dummy_inputs(_lowercase				)   ,       return_dict=_lowercase   ,       )[0]

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

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

												_lowerCAmelCase  						=			np.array(
												    [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]				)
												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   UpperCAmelCase_					(		unittest.TestCase			):






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			load_numpy(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""				)

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

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

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

												_lowerCAmelCase  						=			KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
												    """kandinsky-community/kandinsky-2-2-prior"""   ,       torch_dtype=torch.floataa				)
												pipe_prior.to(_lowercase				)

												_lowerCAmelCase  						=			KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
												    """kandinsky-community/kandinsky-2-2-controlnet-depth"""   ,       torch_dtype=torch.floataa				)
												_lowerCAmelCase  						=			pipeline.to(_lowercase				)

												pipeline.set_progress_bar_config(disable=_lowercase				)

												_lowerCAmelCase  						=			torch.Generator(device="""cpu"""				).manual_seed(0				)

												_lowerCAmelCase						,   _lowerCAmelCase  						=			pipe_prior(
												    _lowercase   ,       image=_lowercase   ,       strength=0.85   ,       generator=_lowercase   ,       negative_prompt=""""""   ,       ).to_tuple()

												_lowerCAmelCase  						=			pipeline(
												    image=_lowercase   ,       image_embeds=_lowercase   ,       negative_image_embeds=_lowercase   ,       hint=_lowercase   ,       generator=_lowercase   ,       num_inference_steps=100   ,       height=512   ,       width=512   ,       strength=0.5   ,       output_type="""np"""   ,       )

												_lowerCAmelCase  						=			output.images[0]

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

												assert_mean_pixel_difference(_lowercase   ,       _lowercase				)


'''simple docstring'''



from __future__ import annotations




class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			order

												# a_{0} ... a_{k}
												_lowerCAmelCase  						=			[1.0] + [0.0] * order
												# b_{0} ... b_{k}
												_lowerCAmelCase  						=			[1.0] + [0.0] * order

												# x[n-1] ... x[n-k]
												_lowerCAmelCase  						=			[0.0] * self.order
												# y[n-1] ... y[n-k]
												_lowerCAmelCase  						=			[0.0] * self.order




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												if len(_lowercase				) < self.order:
																		_lowerCAmelCase  						=			[1.0, *a_coeffs]

												if len(_lowercase				) != self.order + 1:
																		_lowerCAmelCase  						=			(
																		    F'Expected a_coeffs to have {self.order + 1} elements '
																		    F'for {self.order}-order filter, got {len(_lowercase				)}'
																		)
																		raise ValueError(_lowercase				)

												if len(_lowercase				) != self.order + 1:
																		_lowerCAmelCase  						=			(
																		    F'Expected b_coeffs to have {self.order + 1} elements '
																		    F'for {self.order}-order filter, got {len(_lowercase				)}'
																		)
																		raise ValueError(_lowercase				)

												_lowerCAmelCase  						=			a_coeffs
												_lowerCAmelCase  						=			b_coeffs




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			0.0

												# Start at index 1 and do index 0 at the end.
												for i in range(1   ,       self.order + 1				):
																		result += (
																		    self.b_coeffs[i] * self.input_history[i - 1]
																		    - self.a_coeffs[i] * self.output_history[i - 1]
																		)

												_lowerCAmelCase  						=			(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

												_lowerCAmelCase  						=			self.input_history[:-1]
												_lowerCAmelCase  						=			self.output_history[:-1]

												_lowerCAmelCase  						=			sample
												_lowerCAmelCase  						=			result

												return result
