


import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import UperNetForSemanticSegmentation
    from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    int      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    int=13      ,   __lowerCamelCase							:    Optional[Any]=32      ,   __lowerCamelCase							:    Optional[int]=3      ,   __lowerCamelCase							:    List[Any]=4      ,   __lowerCamelCase							:    Dict=[10, 20, 30, 40]      ,   __lowerCamelCase							:    List[str]=[2, 2, 3, 2]      ,   __lowerCamelCase							:    Union[str, Any]=True      ,   __lowerCamelCase							:    Optional[Any]=True      ,   __lowerCamelCase							:    str=37      ,   __lowerCamelCase							:    str="gelu"      ,   __lowerCamelCase							:    Dict=10      ,   __lowerCamelCase							:    Union[str, Any]=0.0_2      ,   __lowerCamelCase							:    List[str]=["stage2", "stage3", "stage4"]      ,   __lowerCamelCase							:    Union[str, Any]=3      ,   __lowerCamelCase							:    Any=None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						parent
             lowerCamelCase__	:       Optional[Any]					=						batch_size
             lowerCamelCase__	:       str					=						image_size
             lowerCamelCase__	:       str					=						num_channels
             lowerCamelCase__	:       Optional[Any]					=						num_stages
             lowerCamelCase__	:       Optional[int]					=						hidden_sizes
             lowerCamelCase__	:       Union[str, Any]					=						depths
             lowerCamelCase__	:       Any					=						is_training
             lowerCamelCase__	:       List[Any]					=						use_labels
             lowerCamelCase__	:       Dict					=						intermediate_size
             lowerCamelCase__	:       int					=						hidden_act
             lowerCamelCase__	:       Tuple					=						type_sequence_label_size
             lowerCamelCase__	:       Tuple					=						initializer_range
             lowerCamelCase__	:       Tuple					=						out_features
             lowerCamelCase__	:       str					=						num_labels
             lowerCamelCase__	:       List[Any]					=						scope
             lowerCamelCase__	:       int					=						num_stages





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

             lowerCamelCase__	:       List[str]					=						None
             if self.use_labels:
                   lowerCamelCase__	:       Optional[int]					=						ids_tensor([self.batch_size]      ,   self.type_sequence_label_size )

             lowerCamelCase__	:       str					=						self.get_config()

             return config, pixel_values, labels





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             return ConvNextConfig(
                 num_channels=self.num_channels      ,   num_stages=self.num_stages      ,   hidden_sizes=self.hidden_sizes      ,   depths=self.depths      ,   is_training=self.is_training      ,   intermediate_size=self.intermediate_size      ,   hidden_act=self.hidden_act      ,   out_features=self.out_features      ,   )





       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             return UperNetConfig(
                 backbone_config=self.get_backbone_config()      ,   hidden_size=512      ,   pool_scales=[1, 2, 3, 6]      ,   use_auxiliary_head=__lowerCamelCase      ,   auxiliary_loss_weight=0.4      ,   auxiliary_in_channels=40      ,   auxiliary_channels=256      ,   auxiliary_num_convs=1      ,   auxiliary_concat_input=__lowerCamelCase      ,   loss_ignore_index=255      ,   num_labels=self.num_labels      ,   )





       def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						UperNetForSemanticSegmentation(config=__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()
             lowerCamelCase__	:       int					=						model(__lowerCamelCase )
             self.parent.assertEqual(
                 result.logits.shape      ,   (self.batch_size, self.num_labels, self.image_size, self.image_size) )





       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						self.prepare_config_and_inputs()
             (
                 lowerCamelCase__     
             )	:       Optional[int]					=						config_and_inputs
             lowerCamelCase__	:       Union[str, Any]					=						{"pixel_values": pixel_values}
             return config, inputs_dict







@require_torch
class     _lowercase				(							lowercase__							,						lowercase__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    (UperNetForSemanticSegmentation,) if is_torch_available() else ()
       A__      		=    {"image-segmentation": UperNetForSemanticSegmentation} if is_torch_available() else {}
       A__      		=    False
       A__      		=    False
       A__      		=    False
       A__      		=    False
       A__      		=    False
       A__      		=    False





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						UperNetModelTester(self )
             lowerCamelCase__	:       Any					=						ConfigTester(self      ,   config_class=__lowerCamelCase      ,   has_text_modality=__lowerCamelCase      ,   hidden_size=37 )





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             self.create_and_test_config_common_properties()
             self.config_tester.create_and_test_config_to_json_string()
             self.config_tester.create_and_test_config_to_json_file()
             self.config_tester.create_and_test_config_from_and_save_pretrained()
             self.config_tester.create_and_test_config_with_num_labels()
             self.config_tester.check_config_can_be_init_without_params()
             self.config_tester.check_config_arguments_init()





       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             return





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   lowerCamelCase__	:       List[Any]					=						model_class(__lowerCamelCase )
                   lowerCamelCase__	:       Union[str, Any]					=						inspect.signature(model.forward )
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   lowerCamelCase__	:       Any					=						[*signature.parameters.keys()]

                   lowerCamelCase__	:       List[Any]					=						["pixel_values"]
                   self.assertListEqual(arg_names[:1]      ,   __lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase )





       @unittest.skip(reason="UperNet does not use inputs_embeds" )
       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             pass





       @unittest.skip(reason="UperNet does not support input and output embeddings" )
       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             pass





       @unittest.skip(reason="UperNet does not have a base model" )
       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             pass





       @unittest.skip(reason="UperNet does not have a base model" )
       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             pass





       @require_torch_multi_gpu
       @unittest.skip(reason="UperNet has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             pass





       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             pass





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             def check_hidden_states_output(__lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    List[Any] ):
                   lowerCamelCase__	:       Dict					=						model_class(__lowerCamelCase )
                   model.to(__lowerCamelCase )
                   model.eval()

                   with torch.no_grad():
                         lowerCamelCase__	:       Tuple					=						model(**self._prepare_for_class(__lowerCamelCase      ,   __lowerCamelCase ) )

                   lowerCamelCase__	:       Any					=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                   lowerCamelCase__	:       Optional[Any]					=						self.model_tester.num_stages
                   self.assertEqual(len(__lowerCamelCase )      ,   expected_num_stages + 1 )

                   # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
                   self.assertListEqual(
                       list(hidden_states[0].shape[-2:] )      ,   [self.model_tester.image_size // 4, self.model_tester.image_size // 4]      ,   )

             lowerCamelCase__	:       Union[str, Any]					=						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   lowerCamelCase__	:       List[Any]					=						True
                   check_hidden_states_output(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   lowerCamelCase__	:       List[Any]					=						True

                   check_hidden_states_output(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						self.model_tester.prepare_config_and_inputs_for_common()

             lowerCamelCase__	:       Optional[int]					=						_config_zero_init(__lowerCamelCase )
             lowerCamelCase__	:       Any					=						_config_zero_init(configs_no_init.backbone_config )
             for model_class in self.all_model_classes:
                   lowerCamelCase__	:       Tuple					=						model_class(config=__lowerCamelCase )
                   for name, param in model.named_parameters():
                         if param.requires_grad:
                               self.assertIn(
                                   ((param.data.mean() * 1E9).round() / 1E9).item()      ,   [0.0, 1.0]      ,   msg=f"Parameter {name} of model {model_class} seems not properly initialized"      ,   )





       @unittest.skip(reason="UperNet does not have tied weights" )
       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             pass





       @slow
       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   lowerCamelCase__	:       List[Any]					=						UperNetForSemanticSegmentation.from_pretrained(__lowerCamelCase )
                   self.assertIsNotNone(__lowerCamelCase )



def    lowercase_	(	):
      lowerCamelCase__	:       Optional[int]					=						hf_hub_download(
          repo_id="hf-internal-testing/fixtures_ade20k"					,      repo_type="dataset"					,      filename="ADE_val_00000001.jpg"		)
      lowerCamelCase__	:       List[str]					=						Image.open(_A		).convert("RGB"		)
      return image







@require_torch
@require_vision
@slow
class     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						AutoImageProcessor.from_pretrained("openmmlab/upernet-swin-tiny" )
             lowerCamelCase__	:       int					=						UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-swin-tiny" ).to(__lowerCamelCase )

             lowerCamelCase__	:       str					=						prepare_img()
             lowerCamelCase__	:       Any					=						processor(images=__lowerCamelCase      ,   return_tensors="pt" ).to(__lowerCamelCase )

             with torch.no_grad():
                   lowerCamelCase__	:       str					=						model(**__lowerCamelCase )

             lowerCamelCase__	:       Any					=						torch.Size((1, model.config.num_labels, 512, 512) )
             self.assertEqual(outputs.logits.shape      ,   __lowerCamelCase )

             lowerCamelCase__	:       Optional[int]					=						torch.tensor(
                 [[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]] ).to(__lowerCamelCase )
             self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]      ,   __lowerCamelCase      ,   atol=1E-4 ) )





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-tiny" )
             lowerCamelCase__	:       Union[str, Any]					=						UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-tiny" ).to(__lowerCamelCase )

             lowerCamelCase__	:       Optional[Any]					=						prepare_img()
             lowerCamelCase__	:       str					=						processor(images=__lowerCamelCase      ,   return_tensors="pt" ).to(__lowerCamelCase )

             with torch.no_grad():
                   lowerCamelCase__	:       Union[str, Any]					=						model(**__lowerCamelCase )

             lowerCamelCase__	:       Optional[Any]					=						torch.Size((1, model.config.num_labels, 512, 512) )
             self.assertEqual(outputs.logits.shape      ,   __lowerCamelCase )

             lowerCamelCase__	:       Optional[Any]					=						torch.tensor(
                 [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]] ).to(__lowerCamelCase )
             self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]      ,   __lowerCamelCase      ,   atol=1E-4 ) )






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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				(							lowercase__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    KandinskyVaaImgaImgPipeline
       A__      		=    ["image_embeds", "negative_image_embeds", "image"]
       A__      		=    [
           "image_embeds",
           "negative_image_embeds",
           "image",
       ]
       A__      		=    [
           "generator",
           "height",
           "width",
           "strength",
           "guidance_scale",
           "num_inference_steps",
           "return_dict",
           "guidance_scale",
           "num_images_per_prompt",
           "output_type",
           "return_dict",
       ]
       A__      		=    False





       @property
       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             return 32





       @property
       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             return 32





       @property
       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             return self.time_input_dim





       @property
       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             return self.time_input_dim * 4





       @property
       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             return 100





       @property
       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             torch.manual_seed(0 )

             lowerCamelCase__	:       Optional[Any]					=						{
                 "in_channels": 4,
                 # 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,
             }

             lowerCamelCase__	:       Tuple					=						UNetaDConditionModel(**__lowerCamelCase )
             return model





       @property
       def 		lowerCAmelCase  (     self							:    int ):


             '''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							:    Optional[int] ):


             '''simple docstring'''



             torch.manual_seed(0 )
             lowerCamelCase__	:       int					=						VQModel(**self.dummy_movq_kwargs )
             return model





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						self.dummy_unet
             lowerCamelCase__	:       Optional[Any]					=						self.dummy_movq

             lowerCamelCase__	:       Optional[int]					=						{
                 "num_train_timesteps": 1000,
                 "beta_schedule": "linear",
                 "beta_start": 0.0_0_0_8_5,
                 "beta_end": 0.0_1_2,
                 "clip_sample": False,
                 "set_alpha_to_one": False,
                 "steps_offset": 0,
                 "prediction_type": "epsilon",
                 "thresholding": False,
             }

             lowerCamelCase__	:       List[Any]					=						DDIMScheduler(**__lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						{
                 "unet": unet,
                 "scheduler": scheduler,
                 "movq": movq,
             }

             return components





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    int=0 ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						floats_tensor((1, self.text_embedder_hidden_size)      ,   rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
             lowerCamelCase__	:       Union[str, Any]					=						floats_tensor((1, self.text_embedder_hidden_size)      ,   rng=random.Random(seed + 1 ) ).to(
                 __lowerCamelCase )
             # create init_image
             lowerCamelCase__	:       Union[str, Any]					=						floats_tensor((1, 3, 64, 64)      ,   rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						image.cpu().permute(0      ,   2      ,   3      ,   1 )[0]
             lowerCamelCase__	:       Optional[int]					=						Image.fromarray(np.uinta(__lowerCamelCase ) ).convert("RGB" ).resize((256, 256) )

             if str(__lowerCamelCase ).startswith("mps" ):
                   lowerCamelCase__	:       Optional[int]					=						torch.manual_seed(__lowerCamelCase )
             else:
                   lowerCamelCase__	:       List[str]					=						torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						{
                 "image": init_image,
                 "image_embeds": image_embeds,
                 "negative_image_embeds": negative_image_embeds,
                 "generator": generator,
                 "height": 64,
                 "width": 64,
                 "num_inference_steps": 10,
                 "guidance_scale": 7.0,
                 "strength": 0.2,
                 "output_type": "np",
             }
             return inputs





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						"cpu"

             lowerCamelCase__	:       str					=						self.get_dummy_components()

             lowerCamelCase__	:       Optional[int]					=						self.pipeline_class(**__lowerCamelCase )
             lowerCamelCase__	:       List[str]					=						pipe.to(__lowerCamelCase )

             pipe.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       Optional[Any]					=						pipe(**self.get_dummy_inputs(__lowerCamelCase ) )
             lowerCamelCase__	:       List[str]					=						output.images

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

             lowerCamelCase__	:       int					=						image[0, -3:, -3:, -1]
             lowerCamelCase__	:       int					=						image_from_tuple[0, -3:, -3:, -1]

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

             lowerCamelCase__	:       str					=						np.array(
                 [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3] )
             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):


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/kandinskyv22/kandinskyv22_img2img_frog.npy" )

             lowerCamelCase__	:       Tuple					=						load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
             lowerCamelCase__	:       Any					=						"A red cartoon frog, 4k"

             lowerCamelCase__	:       str					=						KandinskyVaaPriorPipeline.from_pretrained(
                 "kandinsky-community/kandinsky-2-2-prior"      ,   torch_dtype=torch.floataa )
             pipe_prior.to(__lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						KandinskyVaaImgaImgPipeline.from_pretrained(
                 "kandinsky-community/kandinsky-2-2-decoder"      ,   torch_dtype=torch.floataa )
             lowerCamelCase__	:       str					=						pipeline.to(__lowerCamelCase )

             pipeline.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						torch.Generator(device="cpu" ).manual_seed(0 )
             lowerCamelCase__					,     lowerCamelCase__	:       List[str]					=						pipe_prior(
                 __lowerCamelCase      ,   generator=__lowerCamelCase      ,   num_inference_steps=5      ,   negative_prompt=""      ,   ).to_tuple()

             lowerCamelCase__	:       Optional[Any]					=						pipeline(
                 image=__lowerCamelCase      ,   image_embeds=__lowerCamelCase      ,   negative_image_embeds=__lowerCamelCase      ,   generator=__lowerCamelCase      ,   num_inference_steps=100      ,   height=768      ,   width=768      ,   strength=0.2      ,   output_type="np"      ,   )

             lowerCamelCase__	:       Union[str, Any]					=						output.images[0]

             assert image.shape == (768, 768, 3)

             assert_mean_pixel_difference(__lowerCamelCase      ,   __lowerCamelCase )


