
"""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 	_lowercase       (    __UpperCAmelCase    ,	unittest.TestCase		):
	lowercase_				=     KandinskyVaaControlnetPipeline
	lowercase_				=     ['image_embeds', 'negative_image_embeds', 'hint']
	lowercase_				=     ['image_embeds', 'negative_image_embeds', 'hint']
	lowercase_				=     [
	    'generator',
	    'height',
	    'width',
	    'latents',
	    'guidance_scale',
	    'num_inference_steps',
	    'return_dict',
	    'guidance_scale',
	    'num_images_per_prompt',
	    'output_type',
	    'return_dict',
	]
	lowercase_				=     False




	@property
	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			return 32




	@property
	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			return 32




	@property
	def       _UpperCamelCase   (			self )		->							Tuple:
			return self.time_input_dim




	@property
	def       _UpperCamelCase   (			self )		->							Any:
			return self.time_input_dim * 4




	@property
	def       _UpperCamelCase   (			self )		->							Dict:
			return 100




	@property
	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			torch.manual_seed(0 )

			lowerCamelCase					:      Dict          =   {
			    '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					:      str          =   UNetaDConditionModel(**UpperCAmelCase_ )
			return model




	@property
	def       _UpperCamelCase   (			self )		->							List[Any]:
			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       _UpperCamelCase   (			self )		->							int:
			torch.manual_seed(0 )
			lowerCamelCase					:      List[str]          =   VQModel(**self.dummy_movq_kwargs )
			return model




	def       _UpperCamelCase   (			self )		->							Tuple:
			lowerCamelCase					:      Union[str, Any]          =   self.dummy_unet
			lowerCamelCase					:      Dict          =   self.dummy_movq

			lowerCamelCase					:      Optional[int]          =   DDIMScheduler(
			    num_train_timesteps=1000		,			beta_schedule='linear'		,			beta_start=0.00085		,			beta_end=0.012		,			clip_sample=UpperCAmelCase_		,			set_alpha_to_one=UpperCAmelCase_		,			steps_offset=1		,			prediction_type='epsilon'		,			thresholding=UpperCAmelCase_		,			)

			lowerCamelCase					:      Optional[Any]          =   {
			    'unet': unet,
			    'scheduler': scheduler,
			    'movq': movq,
			}
			return components




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=0 )		->							Any:
			lowerCamelCase					:      Dict          =   floats_tensor((1, self.text_embedder_hidden_size)		,			rng=random.Random(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )
			lowerCamelCase					:      Dict          =   floats_tensor((1, self.text_embedder_hidden_size)		,			rng=random.Random(seed + 1 ) ).to(
			    UpperCAmelCase_ )

			# create hint
			lowerCamelCase					:      Tuple          =   floats_tensor((1, 3, 64, 64)		,			rng=random.Random(UpperCAmelCase_ ) ).to(UpperCAmelCase_ )

			if str(UpperCAmelCase_ ).startswith('mps' ):
					lowerCamelCase					:      Union[str, Any]          =   torch.manual_seed(UpperCAmelCase_ )
			else:
					lowerCamelCase					:      Dict          =   torch.Generator(device=UpperCAmelCase_ ).manual_seed(UpperCAmelCase_ )
			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       _UpperCamelCase   (			self )		->							Any:
			lowerCamelCase					:      Any          =   'cpu'

			lowerCamelCase					:      Union[str, Any]          =   self.get_dummy_components()

			lowerCamelCase					:      str          =   self.pipeline_class(**UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   pipe.to(UpperCAmelCase_ )

			pipe.set_progress_bar_config(disable=UpperCAmelCase_ )

			lowerCamelCase					:      Any          =   pipe(**self.get_dummy_inputs(UpperCAmelCase_ ) )
			lowerCamelCase					:      Any          =   output.images

			lowerCamelCase					:      Optional[int]          =   pipe(
			    **self.get_dummy_inputs(UpperCAmelCase_ )		,			return_dict=UpperCAmelCase_		,			)[0]

			lowerCamelCase					:      Tuple          =   image[0, -3:, -3:, -1]
			lowerCamelCase					:      str          =   image_from_tuple[0, -3:, -3:, -1]

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

			lowerCamelCase					:      Optional[int]          =   np.array(
			    [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595] )

			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 	_lowercase       (    unittest.TestCase		):




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			lowerCamelCase					:      Optional[Any]          =   load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy' )

			lowerCamelCase					:      List[Any]          =   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(UpperCAmelCase_ ) ).float() / 255.0
			lowerCamelCase					:      Dict          =   hint.permute(2		,			0		,			1 ).unsqueeze(0 )

			lowerCamelCase					:      str          =   KandinskyVaaPriorPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-prior'		,			torch_dtype=torch.floataa )
			pipe_prior.to(UpperCAmelCase_ )

			lowerCamelCase					:      str          =   KandinskyVaaControlnetPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-controlnet-depth'		,			torch_dtype=torch.floataa )
			lowerCamelCase					:      Any          =   pipeline.to(UpperCAmelCase_ )
			pipeline.set_progress_bar_config(disable=UpperCAmelCase_ )

			lowerCamelCase					:      int          =   'A robot, 4k photo'

			lowerCamelCase					:      Optional[Any]          =   torch.Generator(device='cuda' ).manual_seed(0 )
			lowerCamelCase     ,   lowerCamelCase					:      Tuple          =   pipe_prior(
			    UpperCAmelCase_		,			generator=UpperCAmelCase_		,			num_inference_steps=5		,			negative_prompt=''		,			).to_tuple()

			lowerCamelCase					:      Optional[Any]          =   torch.Generator(device='cuda' ).manual_seed(0 )
			lowerCamelCase					:      Optional[int]          =   pipeline(
			    image_embeds=UpperCAmelCase_		,			negative_image_embeds=UpperCAmelCase_		,			hint=UpperCAmelCase_		,			generator=UpperCAmelCase_		,			num_inference_steps=100		,			output_type='np'		,			)

			lowerCamelCase					:      Optional[Any]          =   output.images[0]

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

			assert_mean_pixel_difference(UpperCAmelCase_		,			UpperCAmelCase_ )








"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A						=			logging.get_logger(__name__)






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'encoder-decoder'
	lowercase_				=     True




	def __init__(			self		,			**UpperCAmelCase_ )		->							str:
			super().__init__(**UpperCAmelCase_ )
			assert (
			    "encoder" in kwargs and "decoder" in kwargs
			), "Config has to be initialized with encoder and decoder config"
			lowerCamelCase					:      List[Any]          =   kwargs.pop('encoder' )
			lowerCamelCase					:      Optional[int]          =   encoder_config.pop('model_type' )
			lowerCamelCase					:      str          =   kwargs.pop('decoder' )
			lowerCamelCase					:      Dict          =   decoder_config.pop('model_type' )

			from ..auto.configuration_auto import AutoConfig

			lowerCamelCase					:      int          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   True




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ )		->							PretrainedConfig:
			logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config' )
			lowerCamelCase					:      str          =   True
			lowerCamelCase					:      Optional[Any]          =   True

			return cls(encoder=encoder_config.to_dict()		,			decoder=decoder_config.to_dict()		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Union[str, Any]          =   copy.deepcopy(self.__dict__ )
			lowerCamelCase					:      Union[str, Any]          =   self.encoder.to_dict()
			lowerCamelCase					:      List[Any]          =   self.decoder.to_dict()
			lowerCamelCase					:      Tuple          =   self.__class__.model_type
			return output






