



'''simple docstring'''





import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

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


if is_torch_available():
    import torch
    from torch import nn

    from transformers import MaskFormerSwinBackbone
    from transformers.models.maskformer import MaskFormerSwinModel



class  snake_case__						:
 def __init__(   self						:							int      ,     __a						:							List[str]      ,     __a						:							List[Any]=13      ,     __a						:							Optional[int]=32      ,     __a						:							Optional[int]=2      ,     __a						:							Any=3      ,     __a						:							Any=16      ,     __a						:							List[Any]=[1, 2, 1]      ,     __a						:							Union[str, Any]=[2, 2, 4]      ,     __a						:							Union[str, Any]=2      ,     __a						:							Optional[int]=2.0      ,     __a						:							int=True      ,     __a						:							int=0.0      ,     __a						:							Any=0.0      ,     __a						:							Dict=0.1      ,     __a						:							Union[str, Any]="gelu"      ,     __a						:							Tuple=False      ,     __a						:							List[Any]=True      ,     __a						:							str=0.0_2      ,     __a						:							int=1e-5      ,     __a						:							Union[str, Any]=True      ,     __a						:							Any=None      ,     __a						:							Optional[int]=True      ,     __a						:							int=10      ,     __a						:							Any=8      ,     __a						:							List[Any]=["stage1", "stage2", "stage3"]      ,     __a						:							int=[1, 2, 3]      ,     )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   List[str]													=    parent
       __snake_case :   Any													=    batch_size
       __snake_case :   int													=    image_size
       __snake_case :   Dict													=    patch_size
       __snake_case :   int													=    num_channels
       __snake_case :   Dict													=    embed_dim
       __snake_case :   List[Any]													=    depths
       __snake_case :   Optional[int]													=    num_heads
       __snake_case :   Tuple													=    window_size
       __snake_case :   Optional[Any]													=    mlp_ratio
       __snake_case :   Any													=    qkv_bias
       __snake_case :   Optional[int]													=    hidden_dropout_prob
       __snake_case :   Optional[int]													=    attention_probs_dropout_prob
       __snake_case :   Optional[int]													=    drop_path_rate
       __snake_case :   Any													=    hidden_act
       __snake_case :   List[str]													=    use_absolute_embeddings
       __snake_case :   List[str]													=    patch_norm
       __snake_case :   Optional[Any]													=    layer_norm_eps
       __snake_case :   int													=    initializer_range
       __snake_case :   Dict													=    is_training
       __snake_case :   Any													=    scope
       __snake_case :   Any													=    use_labels
       __snake_case :   int													=    type_sequence_label_size
       __snake_case :   Any													=    encoder_stride
       __snake_case :   Optional[Any]													=    out_features
       __snake_case :   str													=    out_indices
 def A_   (   self						:							List[Any]  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Dict													=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

       __snake_case :   List[Any]													=    self.get_config()

       return config, pixel_values, labels
 def A_   (   self						:							Optional[Any]  )      ->   Optional[int]:







       '''simple docstring'''
       return MaskFormerSwinConfig(
           image_size=self.image_size      ,     patch_size=self.patch_size      ,     num_channels=self.num_channels      ,     embed_dim=self.embed_dim      ,     depths=self.depths      ,     num_heads=self.num_heads      ,     window_size=self.window_size      ,     mlp_ratio=self.mlp_ratio      ,     qkv_bias=self.qkv_bias      ,     hidden_dropout_prob=self.hidden_dropout_prob      ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,     drop_path_rate=self.drop_path_rate      ,     hidden_act=self.hidden_act      ,     use_absolute_embeddings=self.use_absolute_embeddings      ,     path_norm=self.patch_norm      ,     layer_norm_eps=self.layer_norm_eps      ,     initializer_range=self.initializer_range      ,     encoder_stride=self.encoder_stride      ,     out_features=self.out_features      ,     out_indices=self.out_indices      ,     )
 def A_   (   self						:							Union[str, Any]      ,     __a						:							Union[str, Any]      ,     __a						:							Optional[Any]      ,     __a						:							Tuple  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   str													=    MaskFormerSwinModel(config=__a  )
       model.to(__a  )
       model.eval()
       __snake_case :   str													=    model(__a  )

       __snake_case :   int													=    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths  ) - 1))
       __snake_case :   Optional[Any]													=    int(config.embed_dim * 2 ** (len(config.depths  ) - 1)  )

       self.parent.assertEqual(result.last_hidden_state.shape      ,     (self.batch_size, expected_seq_len, expected_dim)  )
 def A_   (   self						:							Any      ,     __a						:							Tuple      ,     __a						:							List[str]      ,     __a						:							List[str]  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Dict													=    MaskFormerSwinBackbone(config=__a  )
       model.to(__a  )
       model.eval()
       __snake_case :   Tuple													=    model(__a  )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps  )      ,     len(config.out_features  )  )
       self.parent.assertListEqual(list(result.feature_maps[0].shape  )      ,     [13, 16, 16, 16]  )

       # verify channels
       self.parent.assertEqual(len(model.channels  )      ,     len(config.out_features  )  )
       self.parent.assertListEqual(model.channels      ,     [16, 32, 64]  )

       # verify ValueError
       with self.parent.assertRaises(__a  ):
             __snake_case :   Tuple													=    ['stem']
             __snake_case :   Optional[int]													=    MaskFormerSwinBackbone(config=__a  )





 def A_   (   self						:							Union[str, Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   int													=    self.prepare_config_and_inputs()
       __snake_case							,     __snake_case							,     __snake_case :   Optional[Any]													=    config_and_inputs
       __snake_case :   List[Any]													=    {'pixel_values': pixel_values}
       return config, inputs_dict



@require_torch
class  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= (
     (
         MaskFormerSwinModel,
         MaskFormerSwinBackbone,
     )
     if is_torch_available()
     else ()
 )
 A__				= {'''feature-extraction''': MaskFormerSwinModel} if is_torch_available() else {}
 A__				= False
 A__				= False
 A__				= False
 A__				= False
 A__				= False
 def A_   (   self						:							Optional[int]  )      ->   str:







       '''simple docstring'''
       __snake_case :   str													=    MaskFormerSwinModelTester(self  )
       __snake_case :   Any													=    ConfigTester(self      ,     config_class=__a      ,     embed_dim=37  )
 @require_torch_multi_gpu
 @unittest.skip(
     reason=(
         '`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
         ' `nn.DataParallel`'
     )  )
 def A_   (   self						:							Union[str, Any]  )      ->   str:







       '''simple docstring'''
       pass
 def A_   (   self						:							Union[str, Any]  )      ->   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 A_   (   self						:							Any  )      ->   Any:







       '''simple docstring'''
       return
 def A_   (   self						:							str  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   List[str]													=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__a  )
 def A_   (   self						:							Optional[int]  )      ->   Any:







       '''simple docstring'''
       __snake_case :   str													=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*__a  )
 @unittest.skip('Swin does not use inputs_embeds'  )
 def A_   (   self						:							List[Any]  )      ->   Dict:







       '''simple docstring'''
       pass
 @unittest.skip('Swin does not support feedforward chunking'  )
 def A_   (   self						:							Optional[int]  )      ->   Tuple:







       '''simple docstring'''
       pass
 def A_   (   self						:							Any  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case							,     __snake_case :   Tuple													=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             __snake_case :   Tuple													=    model_class(__a  )
             self.assertIsInstance(model.get_input_embeddings()      ,     (nn.Module)  )
             __snake_case :   Tuple													=    model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(__a      ,     nn.Linear  )  )
 def A_   (   self						:							Dict  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case							,     __snake_case :   Dict													=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             __snake_case :   List[Any]													=    model_class(__a  )
             __snake_case :   Tuple													=    inspect.signature(model.forward  )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             __snake_case :   Any													=    [*signature.parameters.keys()]

             __snake_case :   List[Any]													=    ['pixel_values']
             self.assertListEqual(arg_names[:1]      ,     __a  )
 @unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'  )
 def A_   (   self						:							List[str]  )      ->   Tuple:







       '''simple docstring'''
       pass
 @unittest.skip(reason='MaskFormerSwin is only used as an internal backbone'  )
 def A_   (   self						:							Dict  )      ->   str:







       '''simple docstring'''
       pass
 def A_   (   self						:							Any      ,     __a						:							int      ,     __a						:							Union[str, Any]      ,     __a						:							int      ,     __a						:							Union[str, Any]  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Dict													=    model_class(__a  )
       model.to(__a  )
       model.eval()

       with torch.no_grad():
             __snake_case :   Tuple													=    model(**self._prepare_for_class(__a      ,     __a  )  )

       __snake_case :   Optional[int]													=    outputs.hidden_states

       __snake_case :   Union[str, Any]													=    getattr(
           self.model_tester      ,     'expected_num_hidden_layers'      ,     len(self.model_tester.depths  ) + 1  )
       self.assertEqual(len(__a  )      ,     __a  )

       # Swin has a different seq_length
       __snake_case :   List[str]													=    (
           config.patch_size
           if isinstance(config.patch_size      ,     collections.abc.Iterable  )
           else (config.patch_size, config.patch_size)
       )

       __snake_case :   Optional[int]													=    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]  )      ,     [num_patches, self.model_tester.embed_dim]      ,     )
 def A_   (   self						:							str  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case							,     __snake_case :   List[Any]													=    self.model_tester.prepare_config_and_inputs_for_common()

       __snake_case :   Optional[Any]													=    (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size      ,     collections.abc.Iterable  )
           else (self.model_tester.image_size, self.model_tester.image_size)
       )

       for model_class in self.all_model_classes:
             __snake_case :   Optional[int]													=    True
             self.check_hidden_states_output(__a      ,     __a      ,     __a      ,     __a  )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             __snake_case :   Union[str, Any]													=    True

             self.check_hidden_states_output(__a      ,     __a      ,     __a      ,     __a  )
 def A_   (   self						:							str  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case							,     __snake_case :   Union[str, Any]													=    self.model_tester.prepare_config_and_inputs_for_common()
       __snake_case :   Dict													=    3

       __snake_case :   Tuple													=    (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size      ,     collections.abc.Iterable  )
           else (self.model_tester.image_size, self.model_tester.image_size)
       )
       __snake_case :   Any													=    (
           config.patch_size
           if isinstance(config.patch_size      ,     collections.abc.Iterable  )
           else (config.patch_size, config.patch_size)
       )

       __snake_case :   Tuple													=    image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       __snake_case :   str													=    image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
             __snake_case :   Dict													=    True
             self.check_hidden_states_output(__a      ,     __a      ,     __a      ,     (padded_height, padded_width)  )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             __snake_case :   str													=    True
             self.check_hidden_states_output(__a      ,     __a      ,     __a      ,     (padded_height, padded_width)  )
 @unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints'  )
 def A_   (   self						:							int  )      ->   Optional[Any]:







       '''simple docstring'''
       pass
 @unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin'  )
 def A_   (   self						:							List[str]  )      ->   Dict:







       '''simple docstring'''
       pass
 @unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin'  )
 def A_   (   self						:							int  )      ->   Optional[Any]:







       '''simple docstring'''
       pass





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







       '''simple docstring'''
       __snake_case							,     __snake_case :   Tuple													=    self.model_tester.prepare_config_and_inputs_for_common()

       def set_nan_tensor_to_zero(__a						:							Dict  ):
             __snake_case :   Tuple													=    0
             return t

       def check_equivalence(__a						:							Union[str, Any]      ,     __a						:							List[Any]      ,     __a						:							int      ,     __a						:							Tuple={}  ):
             with torch.no_grad():
                   __snake_case :   List[Any]													=    model(**__a      ,     return_dict=__a      ,     **__a  )
                   __snake_case :   Dict													=    model(**__a      ,     return_dict=__a      ,     **__a  ).to_tuple()

                   def recursive_check(__a						:							List[str]      ,     __a						:							Any  ):
                         if isinstance(__a      ,     (List, Tuple)  ):
                               for tuple_iterable_value, dict_iterable_value in zip(__a      ,     __a  ):
                                     recursive_check(__a      ,     __a  )
                         elif isinstance(__a      ,     __a  ):
                               for tuple_iterable_value, dict_iterable_value in zip(
                                   tuple_object.values()      ,     dict_object.values()  ):
                                     recursive_check(__a      ,     __a  )
                         elif tuple_object is None:
                               return
                         else:
                               self.assertTrue(
                                   torch.allclose(
                                       set_nan_tensor_to_zero(__a  )      ,     set_nan_tensor_to_zero(__a  )      ,     atol=1e-5  )      ,     msg=(
                                       'Tuple and dict output are not equal. Difference:'
                                       f''' {torch.max(torch.abs(tuple_object - dict_object  )  )}. Tuple has `nan`:'''
                                       f''' {torch.isnan(__a  ).any()} and `inf`: {torch.isinf(__a  )}. Dict has'''
                                       f''' `nan`: {torch.isnan(__a  ).any()} and `inf`: {torch.isinf(__a  )}.'''
                                   )      ,     )

                   recursive_check(__a      ,     __a  )

       for model_class in self.all_model_classes:
             __snake_case :   str													=    model_class(__a  )
             model.to(__a  )
             model.eval()

             __snake_case :   Any													=    self._prepare_for_class(__a      ,     __a  )
             __snake_case :   str													=    self._prepare_for_class(__a      ,     __a  )
             check_equivalence(__a      ,     __a      ,     __a  )

             __snake_case :   Tuple													=    self._prepare_for_class(__a      ,     __a      ,     return_labels=__a  )
             __snake_case :   Optional[Any]													=    self._prepare_for_class(__a      ,     __a      ,     return_labels=__a  )
             check_equivalence(__a      ,     __a      ,     __a  )

             __snake_case :   Optional[Any]													=    self._prepare_for_class(__a      ,     __a  )
             __snake_case :   Optional[Any]													=    self._prepare_for_class(__a      ,     __a  )
             check_equivalence(__a      ,     __a      ,     __a      ,     {'output_hidden_states': True}  )

             __snake_case :   int													=    self._prepare_for_class(__a      ,     __a      ,     return_labels=__a  )
             __snake_case :   Optional[Any]													=    self._prepare_for_class(__a      ,     __a      ,     return_labels=__a  )
             check_equivalence(__a      ,     __a      ,     __a      ,     {'output_hidden_states': True}  )



@require_torch
class  snake_case__						(    unittest.TestCase  ,     SCREAMING_SNAKE_CASE_				):
 A__				= (MaskFormerSwinBackbone,) if is_torch_available() else ()
 A__				= MaskFormerSwinConfig
 def A_   (   self						:							Dict  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    MaskFormerSwinModelTester(self  )





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







       '''simple docstring'''
       __snake_case							,     __snake_case :   Optional[Any]													=    self.model_tester.prepare_config_and_inputs_for_common()
       __snake_case :   Union[str, Any]													=    inputs_dict['pixel_values'].shape[0]

       for backbone_class in self.all_model_classes:
             __snake_case :   str													=    backbone_class(__a  )
             backbone.to(__a  )
             backbone.eval()

             __snake_case :   Union[str, Any]													=    backbone(**__a  )

             # Test default outputs and verify feature maps
             self.assertIsInstance(outputs.feature_maps      ,     __a  )
             self.assertTrue(len(outputs.feature_maps  ) == len(backbone.channels  )  )
             for feature_map, n_channels in zip(outputs.feature_maps      ,     backbone.channels  ):
                   self.assertTrue(feature_map.shape[:2]      ,     (batch_size, n_channels)  )
             self.assertIsNone(outputs.hidden_states  )
             self.assertIsNone(outputs.attentions  )

             # Test output_hidden_states=True
             __snake_case :   int													=    backbone(**__a      ,     output_hidden_states=__a  )
             self.assertIsNotNone(outputs.hidden_states  )
             self.assertTrue(len(outputs.hidden_states  )      ,     len(backbone.stage_names  )  )
             # We skip the stem layer
             for hidden_states, n_channels in zip(outputs.hidden_states[1:]      ,     backbone.channels  ):
                   for hidden_state in hidden_states:
                         # Hidden states are in the format (batch_size, (height * width), n_channels)
                         __snake_case							,     __snake_case							,     __snake_case :   int													=    hidden_state.shape
                         self.assertTrue((h_batch_size, h_n_channels)      ,     (batch_size, n_channels)  )

            # Test output_attentions=True
             if self.has_attentions:
                   __snake_case :   List[str]													=    backbone(**__a      ,     output_attentions=__a  )
                   self.assertIsNotNone(outputs.attentions  )








'''simple docstring'''





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  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= KandinskyVaaInpaintPipeline
 A__				= ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
 A__				= [
     '''image_embeds''',
     '''negative_image_embeds''',
     '''image''',
     '''mask_image''',
 ]
 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						:							int  )      ->   int:







       '''simple docstring'''
       return 32
 @property
 def A_   (   self						:							Union[str, Any]  )      ->   List[str]:







       '''simple docstring'''
       return 32
 @property
 def A_   (   self						:							List[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       return self.time_input_dim
 @property
 def A_   (   self						:							List[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       return self.time_input_dim * 4
 @property
 def A_   (   self						:							Optional[int]  )      ->   Tuple:







       '''simple docstring'''
       return 100
 @property
 def A_   (   self						:							Union[str, Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       torch.manual_seed(0  )

       __snake_case :   Optional[int]													=    {
           '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,
       }

       __snake_case :   Optional[Any]													=    UNetaDConditionModel(**__a  )
       return model
 @property
 def A_   (   self						:							Any  )      ->   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 A_   (   self						:							str  )      ->   Union[str, Any]:







       '''simple docstring'''
       torch.manual_seed(0  )
       __snake_case :   str													=    VQModel(**self.dummy_movq_kwargs  )
       return model
 def A_   (   self						:							List[str]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   int													=    self.dummy_unet
       __snake_case :   Dict													=    self.dummy_movq

       __snake_case :   int													=    DDIMScheduler(
           num_train_timesteps=1000      ,     beta_schedule='linear'      ,     beta_start=0.0_0_0_8_5      ,     beta_end=0.0_1_2      ,     clip_sample=__a      ,     set_alpha_to_one=__a      ,     steps_offset=1      ,     prediction_type='epsilon'      ,     thresholding=__a      ,     )

       __snake_case :   Optional[Any]													=    {
           'unet': unet,
           'scheduler': scheduler,
           'movq': movq,
       }

       return components
 def A_   (   self						:							str      ,     __a						:							Union[str, Any]      ,     __a						:							List[str]=0  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   Any													=    floats_tensor((1, self.text_embedder_hidden_size)      ,     rng=random.Random(__a  )  ).to(__a  )
       __snake_case :   int													=    floats_tensor((1, self.text_embedder_hidden_size)      ,     rng=random.Random(seed + 1  )  ).to(
           __a  )
       # create init_image
       __snake_case :   Union[str, Any]													=    floats_tensor((1, 3, 64, 64)      ,     rng=random.Random(__a  )  ).to(__a  )
       __snake_case :   List[Any]													=    image.cpu().permute(0      ,     2      ,     3      ,     1  )[0]
       __snake_case :   Optional[Any]													=    Image.fromarray(np.uinta(__a  )  ).convert('RGB'  ).resize((256, 256)  )
       # create mask
       __snake_case :   Optional[int]													=    np.ones((64, 64)      ,     dtype=np.floataa  )
       __snake_case :   List[str]													=    0

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







       '''simple docstring'''
       __snake_case :   Tuple													=    'cpu'

       __snake_case :   Dict													=    self.get_dummy_components()

       __snake_case :   List[Any]													=    self.pipeline_class(**__a  )
       __snake_case :   List[Any]													=    pipe.to(__a  )

       pipe.set_progress_bar_config(disable=__a  )

       __snake_case :   Optional[Any]													=    pipe(**self.get_dummy_inputs(__a  )  )
       __snake_case :   Union[str, Any]													=    output.images

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

       __snake_case :   Optional[int]													=    image[0, -3:, -3:, -1]
       __snake_case :   Tuple													=    image_from_tuple[0, -3:, -3:, -1]

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

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

       __snake_case :   Union[str, Any]													=    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 A_   (   self						:							Tuple  )      ->   Optional[int]:







       '''simple docstring'''
       super().test_inference_batch_single_identical(expected_max_diff=3e-3  )



@slow
@require_torch_gpu
class  snake_case__						(    unittest.TestCase				):
 def A_   (   self						:							Optional[int]  )      ->   int:







       '''simple docstring'''
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





 def A_   (   self						:							Union[str, Any]  )      ->   int:







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

       __snake_case :   Optional[int]													=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'  )
       __snake_case :   int													=    np.ones((768, 768)      ,     dtype=np.floataa  )
       __snake_case :   Optional[Any]													=    0

       __snake_case :   Union[str, Any]													=    'a hat'

       __snake_case :   Optional[int]													=    KandinskyVaaPriorPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-2-prior'      ,     torch_dtype=torch.floataa  )
       pipe_prior.to(__a  )

       __snake_case :   Optional[Any]													=    KandinskyVaaInpaintPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-2-decoder-inpaint'      ,     torch_dtype=torch.floataa  )
       __snake_case :   Optional[Any]													=    pipeline.to(__a  )
       pipeline.set_progress_bar_config(disable=__a  )

       __snake_case :   Dict													=    torch.Generator(device='cpu'  ).manual_seed(0  )
       __snake_case							,     __snake_case :   Optional[int]													=    pipe_prior(
           __a      ,     generator=__a      ,     num_inference_steps=5      ,     negative_prompt=''      ,     ).to_tuple()

       __snake_case :   List[str]													=    pipeline(
           image=__a      ,     mask_image=__a      ,     image_embeds=__a      ,     negative_image_embeds=__a      ,     generator=__a      ,     num_inference_steps=100      ,     height=768      ,     width=768      ,     output_type='np'      ,     )

       __snake_case :   List[str]													=    output.images[0]

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

       assert_mean_pixel_difference(__a      ,     __a  )



