




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_       (     SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= KandinskyVaaControlnetPipeline
	A    							= ['''image_embeds''', '''negative_image_embeds''', '''hint''']
	A    							= ['''image_embeds''', '''negative_image_embeds''', '''hint''']
	A    							= [
	    '''generator''',
	    '''height''',
	    '''width''',
	    '''latents''',
	    '''guidance_scale''',
	    '''num_inference_steps''',
	    '''return_dict''',
	    '''guidance_scale''',
	    '''num_images_per_prompt''',
	    '''output_type''',
	    '''return_dict''',
	]
	A    							= False





	@property
	def 						A_(							self      )  ->					Optional[int]:




				"""simple docstring"""



				return 32





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




				"""simple docstring"""



				return 32





	@property
	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



				return self.time_input_dim





	@property
	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				return self.time_input_dim * 4





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




				"""simple docstring"""



				return 100





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




				"""simple docstring"""



				torch.manual_seed(0      )

				SCREAMING_SNAKE_CASE_								=						{
				    '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,
				}

				SCREAMING_SNAKE_CASE_								=						UNetaDConditionModel(**SCREAMING_SNAKE_CASE      )
				return model





	@property
	def 						A_(							self      )  ->					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 						A_(							self      )  ->					Optional[int]:




				"""simple docstring"""



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





	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.dummy_unet
				SCREAMING_SNAKE_CASE_								=						self.dummy_movq

				SCREAMING_SNAKE_CASE_								=						DDIMScheduler(
				    num_train_timesteps=1000      ,     beta_schedule='linear'      ,     beta_start=0.0_0_0_8_5      ,     beta_end=0.0_1_2      ,     clip_sample=SCREAMING_SNAKE_CASE      ,     set_alpha_to_one=SCREAMING_SNAKE_CASE      ,     steps_offset=1      ,     prediction_type='epsilon'      ,     thresholding=SCREAMING_SNAKE_CASE      ,     )

				SCREAMING_SNAKE_CASE_								=						{
				    'unet': unet,
				    'scheduler': scheduler,
				    'movq': movq,
				}
				return components





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=0      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						floats_tensor((1, self.text_embedder_hidden_size)      ,     rng=random.Random(SCREAMING_SNAKE_CASE      )      ).to(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						floats_tensor((1, self.text_embedder_hidden_size)      ,     rng=random.Random(seed + 1      )      ).to(
				    SCREAMING_SNAKE_CASE      )

				# create hint
				SCREAMING_SNAKE_CASE_								=						floats_tensor((1, 3, 64, 64)      ,     rng=random.Random(SCREAMING_SNAKE_CASE      )      ).to(SCREAMING_SNAKE_CASE      )

				if str(SCREAMING_SNAKE_CASE      ).startswith('mps'      ):
							SCREAMING_SNAKE_CASE_								=						torch.manual_seed(SCREAMING_SNAKE_CASE      )
				else:
							SCREAMING_SNAKE_CASE_								=						torch.Generator(device=SCREAMING_SNAKE_CASE      ).manual_seed(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						{
				    '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 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						'cpu'

				SCREAMING_SNAKE_CASE_								=						self.get_dummy_components()

				SCREAMING_SNAKE_CASE_								=						self.pipeline_class(**SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						pipe.to(SCREAMING_SNAKE_CASE      )

				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE      )      )
				SCREAMING_SNAKE_CASE_								=						output.images

				SCREAMING_SNAKE_CASE_								=						pipe(
				    **self.get_dummy_inputs(SCREAMING_SNAKE_CASE      )      ,     return_dict=SCREAMING_SNAKE_CASE      ,     )[0]

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

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

				SCREAMING_SNAKE_CASE_								=						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       a_       (     unittest.TestCase     ):





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



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





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



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

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

				SCREAMING_SNAKE_CASE_								=						KandinskyVaaPriorPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-2-prior'      ,     torch_dtype=torch.floataa      )
				pipe_prior.to(SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						KandinskyVaaControlnetPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-2-controlnet-depth'      ,     torch_dtype=torch.floataa      )
				SCREAMING_SNAKE_CASE_								=						pipeline.to(SCREAMING_SNAKE_CASE      )
				pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						'A robot, 4k photo'

				SCREAMING_SNAKE_CASE_								=						torch.Generator(device='cuda'      ).manual_seed(0      )
				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						pipe_prior(
				    SCREAMING_SNAKE_CASE      ,     generator=SCREAMING_SNAKE_CASE      ,     num_inference_steps=5      ,     negative_prompt=''      ,     ).to_tuple()

				SCREAMING_SNAKE_CASE_								=						torch.Generator(device='cuda'      ).manual_seed(0      )
				SCREAMING_SNAKE_CASE_								=						pipeline(
				    image_embeds=SCREAMING_SNAKE_CASE      ,     negative_image_embeds=SCREAMING_SNAKE_CASE      ,     hint=SCREAMING_SNAKE_CASE      ,     generator=SCREAMING_SNAKE_CASE      ,     num_inference_steps=100      ,     output_type='np'      ,     )

				SCREAMING_SNAKE_CASE_								=						output.images[0]

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

				assert_mean_pixel_difference(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )






import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


SCREAMING_SNAKE_CASE__     :     List[Any]		    =       logging.get_logger(__name__)



class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     *SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				warnings.warn(
				    'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
				    ' Please use PerceiverImageProcessor instead.'      ,     SCREAMING_SNAKE_CASE      ,     )
				super().__init__(*SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )
