




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 	_UpperCamelCase					( UpperCAmelCase__		,				unittest.TestCase						):
      UpperCAmelCase_						=						KandinskyVaaControlnetImgaImgPipeline
      UpperCAmelCase_						=						['image_embeds', 'negative_image_embeds', 'image', 'hint']
      UpperCAmelCase_						=						['image_embeds', 'negative_image_embeds', 'image', 'hint']
      UpperCAmelCase_						=						[
          'generator',
          'height',
          'width',
          'strength',
          'guidance_scale',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      UpperCAmelCase_						=						False
      @property
      def 		UpperCAmelCase_  (	self      :Dict					)   ->	int:
       return 32
      @property
      def 		UpperCAmelCase_  (	self      :Tuple					)   ->	Optional[int]:
       return 32
      @property
      def 		UpperCAmelCase_  (	self      :Dict					)   ->	List[Any]:
       return self.time_input_dim
      @property
      def 		UpperCAmelCase_  (	self      :List[Any]					)   ->	Tuple:
       return self.time_input_dim * 4
      @property
      def 		UpperCAmelCase_  (	self      :List[str]					)   ->	Union[str, Any]:
       return 100
      @property
      def 		UpperCAmelCase_  (	self      :Optional[int]					)   ->	Any:
       torch.manual_seed(0					)

       UpperCAmelCase__							       =  {
           """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,
       }

       UpperCAmelCase__							       =  UNetaDConditionModel(**_SCREAMING_SNAKE_CASE					)
       return model
      @property
      def 		UpperCAmelCase_  (	self      :Any					)   ->	Optional[Any]:
       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 		UpperCAmelCase_  (	self      :Any					)   ->	Dict:
       torch.manual_seed(0					)
       UpperCAmelCase__							       =  VQModel(**self.dummy_movq_kwargs					)
       return model
      def 		UpperCAmelCase_  (	self      :List[str]					)   ->	Optional[Any]:
       UpperCAmelCase__							       =  self.dummy_unet
       UpperCAmelCase__							       =  self.dummy_movq

       UpperCAmelCase__							       =  {
           """num_train_timesteps""": 1000,
           """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,
       }

       UpperCAmelCase__							       =  DDIMScheduler(**_SCREAMING_SNAKE_CASE					)

       UpperCAmelCase__							       =  {
           """unet""": unet,
           """scheduler""": scheduler,
           """movq""": movq,
       }

       return components
      def 		UpperCAmelCase_  (	self      :Tuple       ,					lowerCamelCase      :int       ,					lowerCamelCase      :List[Any]=0					)   ->	Any:
       UpperCAmelCase__							       =  floats_tensor((1, self.text_embedder_hidden_size)       ,					rng=random.Random(_SCREAMING_SNAKE_CASE					)					).to(_SCREAMING_SNAKE_CASE					)
       UpperCAmelCase__							       =  floats_tensor((1, self.text_embedder_hidden_size)       ,					rng=random.Random(seed + 1					)					).to(
           _SCREAMING_SNAKE_CASE					)
       # create init_image
       UpperCAmelCase__							       =  floats_tensor((1, 3, 64, 64)       ,					rng=random.Random(_SCREAMING_SNAKE_CASE					)					).to(_SCREAMING_SNAKE_CASE					)
       UpperCAmelCase__							       =  image.cpu().permute(0       ,					2       ,					3       ,					1					)[0]
       UpperCAmelCase__							       =  Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE					)					).convert("RGB"					).resize((256, 256)					)
       # create hint
       UpperCAmelCase__							       =  floats_tensor((1, 3, 64, 64)       ,					rng=random.Random(_SCREAMING_SNAKE_CASE					)					).to(_SCREAMING_SNAKE_CASE					)

       if str(_SCREAMING_SNAKE_CASE					).startswith("mps"					):
        UpperCAmelCase__							       =  torch.manual_seed(_SCREAMING_SNAKE_CASE					)
       else:
        UpperCAmelCase__							       =  torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(_SCREAMING_SNAKE_CASE					)
       UpperCAmelCase__							       =  {
           """image""": init_image,
           """image_embeds""": image_embeds,
           """negative_image_embeds""": negative_image_embeds,
           """hint""": hint,
           """generator""": generator,
           """height""": 64,
           """width""": 64,
           """num_inference_steps""": 10,
           """guidance_scale""": 7.0,
           """strength""": 0.2,
           """output_type""": """np""",
       }
       return inputs







      def 		UpperCAmelCase_  (	self      :Optional[Any]					)   ->	Union[str, Any]:
       UpperCAmelCase__							       =  """cpu"""

       UpperCAmelCase__							       =  self.get_dummy_components()

       UpperCAmelCase__							       =  self.pipeline_class(**_SCREAMING_SNAKE_CASE					)
       UpperCAmelCase__							       =  pipe.to(_SCREAMING_SNAKE_CASE					)

       pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

       UpperCAmelCase__							       =  pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE					)					)
       UpperCAmelCase__							       =  output.images

       UpperCAmelCase__							       =  pipe(
           **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE					)       ,					return_dict=_SCREAMING_SNAKE_CASE       ,					)[0]

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

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

       UpperCAmelCase__							       =  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 	_UpperCamelCase					( unittest.TestCase						):
      def 		UpperCAmelCase_  (	self      :List[str]					)   ->	int:
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







      def 		UpperCAmelCase_  (	self      :Optional[int]					)   ->	str:
       UpperCAmelCase__							       =  load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"					)

       UpperCAmelCase__							       =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"					)
       UpperCAmelCase__							       =  init_image.resize((512, 512)					)

       UpperCAmelCase__							       =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinskyv22/hint_image_cat.png"					)
       UpperCAmelCase__							       =  torch.from_numpy(np.array(_SCREAMING_SNAKE_CASE					)					).float() / 2_55.0
       UpperCAmelCase__							       =  hint.permute(2       ,					0       ,					1					).unsqueeze(0					)

       UpperCAmelCase__							       =  """A robot, 4k photo"""

       UpperCAmelCase__							       =  KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-prior"       ,					torch_dtype=torch.floataa					)
       pipe_prior.to(_SCREAMING_SNAKE_CASE					)

       UpperCAmelCase__							       =  KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-controlnet-depth"       ,					torch_dtype=torch.floataa					)
       UpperCAmelCase__							       =  pipeline.to(_SCREAMING_SNAKE_CASE					)

       pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

       UpperCAmelCase__							       =  torch.Generator(device="cpu"					).manual_seed(0					)

       UpperCAmelCase__							       =  pipe_prior(
           _SCREAMING_SNAKE_CASE       ,					image=_SCREAMING_SNAKE_CASE       ,					strength=0.85       ,					generator=_SCREAMING_SNAKE_CASE       ,					negative_prompt=""       ,					).to_tuple()

       UpperCAmelCase__							       =  pipeline(
           image=_SCREAMING_SNAKE_CASE       ,					image_embeds=_SCREAMING_SNAKE_CASE       ,					negative_image_embeds=_SCREAMING_SNAKE_CASE       ,					hint=_SCREAMING_SNAKE_CASE       ,					generator=_SCREAMING_SNAKE_CASE       ,					num_inference_steps=100       ,					height=512       ,					width=512       ,					strength=0.5       ,					output_type="np"       ,					)

       UpperCAmelCase__							       =  output.images[0]

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

       assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE       ,					_SCREAMING_SNAKE_CASE					)










"""simple docstring"""
from typing import TYPE_CHECKING

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


A:   List[str]								=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Tuple								=		["DPTFeatureExtractor"]
      A:   int								=		["DPTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[Any]								=		[
          "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPTForDepthEstimation",
          "DPTForSemanticSegmentation",
          "DPTModel",
          "DPTPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_dpt import DPTFeatureExtractor
            from .image_processing_dpt import DPTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dpt import (
                DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPTForDepthEstimation,
                DPTForSemanticSegmentation,
                DPTModel,
                DPTPreTrainedModel,
            )


else:
      import sys

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