




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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   (		_a					,unittest.TestCase   ):
							lowercase_          =    KandinskyVaaInpaintPipeline
							lowercase_          =    ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
							lowercase_          =    [
							    'image_embeds',
							    'negative_image_embeds',
							    'image',
							    'mask_image',
							]
							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        __lowerCAmelCase      (  self	:  Tuple )      ->							List[str]:


														"""simple docstring"""


														return 32







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


														"""simple docstring"""


														return 32







							@property
							def        __lowerCAmelCase      (  self	:  Any )      ->							str:


														"""simple docstring"""


														return self.time_input_dim







							@property
							def        __lowerCAmelCase      (  self	:  Any )      ->							str:


														"""simple docstring"""


														return self.time_input_dim * 4







							@property
							def        __lowerCAmelCase      (  self	:  Any )      ->							Tuple:


														"""simple docstring"""


														return 1_00







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


														"""simple docstring"""


														torch.manual_seed(0 )

														_a     					=    {
														    '''in_channels''': 9,
														    # Out channels is double in channels because predicts mean and variance
														    '''out_channels''': 8,
														    '''addition_embed_type''': '''image''',
														    '''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,
														}

														_a     					=    UNetaDConditionModel(**lowerCAmelCase_ )
														return model







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


														"""simple docstring"""


														return {
														    "block_out_channels": [32, 64],
														    "down_block_types": ["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",
														    ],
														    "vq_embed_dim": 4,
														}







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


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  List[Any] )      ->							Any:


														"""simple docstring"""


														_a     					=    self.dummy_unet
														_a     					=    self.dummy_movq

														_a     					=    DDIMScheduler(
														    num_train_timesteps=10_00							,      beta_schedule='''linear'''							,      beta_start=0.0_0_0_8_5							,      beta_end=0.0_1_2							,      clip_sample=lowerCAmelCase_							,      set_alpha_to_one=lowerCAmelCase_							,      steps_offset=1							,      prediction_type='''epsilon'''							,      thresholding=lowerCAmelCase_							,      )

														_a     					=    {
														    '''unet''': unet,
														    '''scheduler''': scheduler,
														    '''movq''': movq,
														}

														return components







							def        __lowerCAmelCase      (  self	:  Tuple							,      lowerCAmelCase_	:  Optional[int]							,      lowerCAmelCase_	:  Union[str, Any]=0 )      ->							str:


														"""simple docstring"""


														_a     					=    floats_tensor((1, self.text_embedder_hidden_size)							,      rng=random.Random(lowerCAmelCase_ ) ).to(lowerCAmelCase_ )
														_a     					=    floats_tensor((1, self.text_embedder_hidden_size)							,      rng=random.Random(seed + 1 ) ).to(
														    lowerCAmelCase_ )
														# create init_image
														_a     					=    floats_tensor((1, 3, 64, 64)							,      rng=random.Random(lowerCAmelCase_ ) ).to(lowerCAmelCase_ )
														_a     					=    image.cpu().permute(0							,      2							,      3							,      1 )[0]
														_a     					=    Image.fromarray(np.uinta(lowerCAmelCase_ ) ).convert('''RGB''' ).resize((2_56, 2_56) )
														# create mask
														_a     					=    np.ones((64, 64)							,      dtype=np.floataa )
														_a     					=    0

														if str(lowerCAmelCase_ ).startswith('''mps''' ):
																					_a     					=    torch.manual_seed(lowerCAmelCase_ )
														else:
																					_a     					=    torch.Generator(device=lowerCAmelCase_ ).manual_seed(lowerCAmelCase_ )
														_a     					=    {
														    '''image''': init_image,
														    '''mask_image''': mask,
														    '''image_embeds''': image_embeds,
														    '''negative_image_embeds''': negative_image_embeds,
														    '''generator''': generator,
														    '''height''': 64,
														    '''width''': 64,
														    '''num_inference_steps''': 2,
														    '''guidance_scale''': 4.0,
														    '''output_type''': '''np''',
														}
														return inputs







							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							Dict:


														"""simple docstring"""


														_a     					=    '''cpu'''

														_a     					=    self.get_dummy_components()

														_a     					=    self.pipeline_class(**lowerCAmelCase_ )
														_a     					=    pipe.to(lowerCAmelCase_ )

														pipe.set_progress_bar_config(disable=lowerCAmelCase_ )

														_a     					=    pipe(**self.get_dummy_inputs(lowerCAmelCase_ ) )
														_a     					=    output.images

														_a     					=    pipe(
														    **self.get_dummy_inputs(lowerCAmelCase_ )							,      return_dict=lowerCAmelCase_							,      )[0]

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

														print(F'image.shape {image.shape}' )

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

														_a     					=    np.array(
														    [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8] )

														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()}'







							def        __lowerCAmelCase      (  self	:  str )      ->							Optional[int]:


														"""simple docstring"""


														super().test_inference_batch_single_identical(expected_max_diff=3e-3 )



@slow
@require_torch_gpu
class       A   (		unittest.TestCase   ):







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


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							int:


														"""simple docstring"""


														_a     					=    load_numpy(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy''' )

														_a     					=    load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
														_a     					=    np.ones((7_68, 7_68)							,      dtype=np.floataa )
														_a     					=    0

														_a     					=    '''a hat'''

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

														_a     					=    KandinskyVaaInpaintPipeline.from_pretrained(
														    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''							,      torch_dtype=torch.floataa )
														_a     					=    pipeline.to(lowerCAmelCase_ )
														pipeline.set_progress_bar_config(disable=lowerCAmelCase_ )

														_a     					=    torch.Generator(device='''cpu''' ).manual_seed(0 )
														_a					,							_a     					=    pipe_prior(
														    lowerCAmelCase_							,      generator=lowerCAmelCase_							,      num_inference_steps=5							,      negative_prompt=''''''							,      ).to_tuple()

														_a     					=    pipeline(
														    image=lowerCAmelCase_							,      mask_image=lowerCAmelCase_							,      image_embeds=lowerCAmelCase_							,      negative_image_embeds=lowerCAmelCase_							,      generator=lowerCAmelCase_							,      num_inference_steps=1_00							,      height=7_68							,      width=7_68							,      output_type='''np'''							,      )

														_a     					=    output.images[0]

														assert image.shape == (7_68, 7_68, 3)

														assert_mean_pixel_difference(lowerCAmelCase_							,      lowerCAmelCase_ )







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_snake_case				:				int       							=		{'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				List[str]       							=		['YolosFeatureExtractor']
				_snake_case				:				Optional[int]       							=		['YolosImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				Tuple       							=		[
				    'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'YolosForObjectDetection',
				    'YolosModel',
				    'YolosPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_yolos import YolosFeatureExtractor
								from .image_processing_yolos import YolosImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_yolos import (
								    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    YolosForObjectDetection,
								    YolosModel,
								    YolosPreTrainedModel,
								)


else:
				import sys

				_snake_case				:				Dict       							=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

