





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			(      __snake_case   ,unittest.TestCase			):
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     KandinskyVaaControlnetPipeline
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     ['''image_embeds''', '''negative_image_embeds''', '''hint''']
				SCREAMING_SNAKE_CASE__     :   str		      =     ['''image_embeds''', '''negative_image_embeds''', '''hint''']
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     [
				    '''generator''',
				    '''height''',
				    '''width''',
				    '''latents''',
				    '''guidance_scale''',
				    '''num_inference_steps''',
				    '''return_dict''',
				    '''guidance_scale''',
				    '''num_images_per_prompt''',
				    '''output_type''',
				    '''return_dict''',
				]
				SCREAMING_SNAKE_CASE__     :   Dict		      =     False






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return 32






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return 32






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return self.time_input_dim






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return self.time_input_dim * 4






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return 100






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										torch.manual_seed(0					)

										__SCREAMING_SNAKE_CASE:				List[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,
										}

										__SCREAMING_SNAKE_CASE:				Any           =   UNetaDConditionModel(**snake_case_					)
										return model






				@property
				def 			snake_case_    (  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 			snake_case_    (  self					):

										"""simple docstring"""




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






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   self.dummy_unet
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   self.dummy_movq

										__SCREAMING_SNAKE_CASE:				List[Any]           =   DDIMScheduler(
										    num_train_timesteps=1000			, beta_schedule='''linear'''			, beta_start=0.00085			, beta_end=0.012			, clip_sample=snake_case_			, set_alpha_to_one=snake_case_			, steps_offset=1			, prediction_type='''epsilon'''			, thresholding=snake_case_			, )

										__SCREAMING_SNAKE_CASE:				Any           =   {
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''movq''': movq,
										}
										return components






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase=0					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Any           =   floats_tensor((1, self.text_embedder_hidden_size)			, rng=random.Random(snake_case_					)					).to(snake_case_					)
										__SCREAMING_SNAKE_CASE:				str           =   floats_tensor((1, self.text_embedder_hidden_size)			, rng=random.Random(seed + 1					)					).to(
										    snake_case_					)

										# create hint
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   floats_tensor((1, 3, 64, 64)			, rng=random.Random(snake_case_					)					).to(snake_case_					)

										if str(snake_case_					).startswith('''mps'''					):
																__SCREAMING_SNAKE_CASE:				Tuple           =   torch.manual_seed(snake_case_					)
										else:
																__SCREAMING_SNAKE_CASE:				int           =   torch.Generator(device=snake_case_					).manual_seed(snake_case_					)
										__SCREAMING_SNAKE_CASE:				Optional[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 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   '''cpu'''

										__SCREAMING_SNAKE_CASE:				int           =   self.get_dummy_components()

										__SCREAMING_SNAKE_CASE:				Dict           =   self.pipeline_class(**snake_case_					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   pipe.to(snake_case_					)

										pipe.set_progress_bar_config(disable=snake_case_					)

										__SCREAMING_SNAKE_CASE:				Tuple           =   pipe(**self.get_dummy_inputs(snake_case_					)					)
										__SCREAMING_SNAKE_CASE:				str           =   output.images

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   pipe(
										    **self.get_dummy_inputs(snake_case_					)			, return_dict=snake_case_			, )[0]

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

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

										__SCREAMING_SNAKE_CASE:				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 				a			(      unittest.TestCase			):






				def 			snake_case_    (  self					):

										"""simple docstring"""




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






				def 			snake_case_    (  self					):

										"""simple docstring"""




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

										__SCREAMING_SNAKE_CASE:				int           =   load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinskyv22/hint_image_cat.png'''					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   torch.from_numpy(np.array(snake_case_					)					).float() / 255.0
										__SCREAMING_SNAKE_CASE:				Any           =   hint.permute(2			, 0			, 1					).unsqueeze(0					)

										__SCREAMING_SNAKE_CASE:				int           =   KandinskyVaaPriorPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-2-prior'''			, torch_dtype=torch.floataa					)
										pipe_prior.to(snake_case_					)

										__SCREAMING_SNAKE_CASE:				int           =   KandinskyVaaControlnetPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''			, torch_dtype=torch.floataa					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   pipeline.to(snake_case_					)
										pipeline.set_progress_bar_config(disable=snake_case_					)

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

										__SCREAMING_SNAKE_CASE:				List[Any]           =   torch.Generator(device='''cuda'''					).manual_seed(0					)
										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Dict           =   pipe_prior(
										    snake_case_			, generator=snake_case_			, num_inference_steps=5			, negative_prompt=''''''			, ).to_tuple()

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

										__SCREAMING_SNAKE_CASE:				Tuple           =   output.images[0]

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

										assert_mean_pixel_difference(snake_case_			, snake_case_					)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__A							:			Optional[Any]         =			{}


try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Optional[int]         =			["MLukeTokenizer"]

if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mluke import MLukeTokenizer


else:
 import sys

 __A							:			List[str]         =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





