





"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:int						    =   KandinskyVaaControlnetImgaImgPipeline
       __magic_name__	:Tuple						    =   ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
       __magic_name__	:List[Any]						    =   ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
       __magic_name__	:List[Any]						    =   [
           """generator""",
           """height""",
           """width""",
           """strength""",
           """guidance_scale""",
           """num_inference_steps""",
           """return_dict""",
           """guidance_scale""",
           """num_images_per_prompt""",
           """output_type""",
           """return_dict""",
       ]
       __magic_name__	:Any						    =   False






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return 3_2






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return 3_2






       @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 1_0_0






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         torch.manual_seed(0 )

         lowerCAmelCase__     :Union[str, 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,
         }

         lowerCAmelCase__     :int											=				UNetaDConditionModel(**__UpperCAmelCase )
         return model






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return {
             "block_out_channels": [3_2, 3_2, 6_4, 6_4],
             "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": 1_2,
             "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 )
         lowerCAmelCase__     :Any											=				VQModel(**self.dummy_movq_kwargs )
         return model






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				self.dummy_unet
         lowerCAmelCase__     :List[str]											=				self.dummy_movq

         lowerCAmelCase__     :List[Any]											=				{
             'num_train_timesteps': 1_0_0_0,
             'beta_schedule': 'linear',
             'beta_start': 0.0_00_85,
             'beta_end': 0.0_12,
             'clip_sample': False,
             'set_alpha_to_one': False,
             'steps_offset': 0,
             'prediction_type': 'epsilon',
             'thresholding': False,
         }

         lowerCAmelCase__     :Union[str, Any]											=				DDIMScheduler(**__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				{
             'unet': unet,
             'scheduler': scheduler,
             'movq': movq,
         }

         return components






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=0 ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				floats_tensor((1, self.text_embedder_hidden_size)       ,					rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )
         lowerCAmelCase__     :str											=				floats_tensor((1, self.text_embedder_hidden_size)       ,					rng=random.Random(seed + 1 ) ).to(
             __UpperCAmelCase )
         # create init_image
         lowerCAmelCase__     :int											=				floats_tensor((1, 3, 6_4, 6_4)       ,					rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )
         lowerCAmelCase__     :Union[str, Any]											=				image.cpu().permute(0       ,					2       ,					3       ,					1 )[0]
         lowerCAmelCase__     :Optional[int]											=				Image.fromarray(np.uinta(__UpperCAmelCase ) ).convert('RGB' ).resize((2_5_6, 2_5_6) )
         # create hint
         lowerCAmelCase__     :str											=				floats_tensor((1, 3, 6_4, 6_4)       ,					rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )

         if str(__UpperCAmelCase ).startswith('mps' ):
           lowerCAmelCase__     :Optional[int]											=				torch.manual_seed(__UpperCAmelCase )
         else:
           lowerCAmelCase__     :Tuple											=				torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
         lowerCAmelCase__     :str											=				{
             'image': init_image,
             'image_embeds': image_embeds,
             'negative_image_embeds': negative_image_embeds,
             'hint': hint,
             'generator': generator,
             'height': 6_4,
             'width': 6_4,
             'num_inference_steps': 1_0,
             'guidance_scale': 7.0,
             'strength': 0.2,
             'output_type': 'np',
         }
         return inputs






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				'cpu'

         lowerCAmelCase__     :Optional[Any]											=				self.get_dummy_components()

         lowerCAmelCase__     :str											=				self.pipeline_class(**__UpperCAmelCase )
         lowerCAmelCase__     :Optional[Any]											=				pipe.to(__UpperCAmelCase )

         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         lowerCAmelCase__     :Dict											=				pipe(**self.get_dummy_inputs(__UpperCAmelCase ) )
         lowerCAmelCase__     :List[str]											=				output.images

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

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

         assert image.shape == (1, 6_4, 6_4, 3)

         lowerCAmelCase__     :List[Any]											=				np.array(
             [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36] )
         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    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy' )

         lowerCAmelCase__     :Dict											=				load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
         lowerCAmelCase__     :Dict											=				init_image.resize((5_1_2, 5_1_2) )

         lowerCAmelCase__     :int											=				load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/kandinskyv22/hint_image_cat.png' )
         lowerCAmelCase__     :Tuple											=				torch.from_numpy(np.array(__UpperCAmelCase ) ).float() / 2_55.0
         lowerCAmelCase__     :Union[str, Any]											=				hint.permute(2       ,					0       ,					1 ).unsqueeze(0 )

         lowerCAmelCase__     :List[Any]											=				'A robot, 4k photo'

         lowerCAmelCase__     :int											=				KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-2-prior'       ,					torch_dtype=torch.floataa )
         pipe_prior.to(__UpperCAmelCase )

         lowerCAmelCase__     :Tuple											=				KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-2-controlnet-depth'       ,					torch_dtype=torch.floataa )
         lowerCAmelCase__     :int											=				pipeline.to(__UpperCAmelCase )

         pipeline.set_progress_bar_config(disable=__UpperCAmelCase )

         lowerCAmelCase__     :Tuple											=				torch.Generator(device='cpu' ).manual_seed(0 )

         lowerCAmelCase__				,							lowerCAmelCase__     :Any											=				pipe_prior(
             __UpperCAmelCase       ,					image=__UpperCAmelCase       ,					strength=0.85       ,					generator=__UpperCAmelCase       ,					negative_prompt=''       ,					).to_tuple()

         lowerCAmelCase__     :int											=				pipeline(
             image=__UpperCAmelCase       ,					image_embeds=__UpperCAmelCase       ,					negative_image_embeds=__UpperCAmelCase       ,					hint=__UpperCAmelCase       ,					generator=__UpperCAmelCase       ,					num_inference_steps=1_0_0       ,					height=5_1_2       ,					width=5_1_2       ,					strength=0.5       ,					output_type='np'       ,					)

         lowerCAmelCase__     :Any											=				output.images[0]

         assert image.shape == (5_1_2, 5_1_2, 3)

         assert_mean_pixel_difference(__UpperCAmelCase       ,					__UpperCAmelCase )







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__A											=						{}


try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						["""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											=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
