




from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_snake_case            = logging.get_logger(__name__)  # pylint: disable=invalid-name

_snake_case            = "\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_=8  ):
      _A							:  Dict    =		height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      _A							:  int    =		width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor







class 		lowercase							(     UpperCamelCase__	):

     def __init__(						self	,  _a	,  _a	,  _a	,  )		->							Optional[int]:
           super().__init__()

           self.register_modules(
               unet=_a	,  scheduler=_a	,  movq=_a	,  )
           _A							:  List[Any]    =		2 ** (len(self.movq.config.block_out_channels       ) - 1)

     def       a__			(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a       )		->							Dict:
           if latents is None:
                 _A							:  Any    =		randn_tensor(_a	,  generator=_a	,  device=_a	,  dtype=_a       )
           else:
                 if latents.shape != shape:
                       raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''       )
                 _A							:  List[Any]    =		latents.to(_a       )

           _A							:  Any    =		latents * scheduler.init_noise_sigma
           return latents

     def       a__			(						self	,  _a=0       )		->							Optional[Any]:
           if is_accelerate_available():
                 from accelerate import cpu_offload
           else:
                 raise ImportError("""Please install accelerate via `pip install accelerate`"""       )

           _A							:  Any    =		torch.device(F'''cuda:{gpu_id}'''       )

           _A							:  Optional[Any]    =		[
               self.unet,
               self.movq,
           ]
           for cpu_offloaded_model in models:
                 if cpu_offloaded_model is not None:
                       cpu_offload(_a	,  _a       )

     def       a__			(						self	,  _a=0       )		->							Any:
           if is_accelerate_available() and is_accelerate_version(""">="""	,  """0.17.0.dev0"""       ):
                 from accelerate import cpu_offload_with_hook
           else:
                 raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""       )

           _A							:  List[str]    =		torch.device(F'''cuda:{gpu_id}'''       )

           if self.device.type != "cpu":
                 self.to("""cpu"""	,  silence_dtype_warnings=_a       )
                 torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

           _A							:  List[Any]    =		None
           for cpu_offloaded_model in [self.unet, self.movq]:
                 _A    ,     _A							:  List[str]    =		cpu_offload_with_hook(_a	,  _a	,  prev_module_hook=_a       )

           # We'll offload the last model manually.
           _A							:  List[str]    =		hook

     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def       a__			(						self       )		->							Optional[Any]:
           if not hasattr(self.unet	,  """_hf_hook"""       ):
                 return self.device
           for module in self.unet.modules():
                 if (
                     hasattr(_a	,  """_hf_hook"""       )
                     and hasattr(module._hf_hook	,  """execution_device"""       )
                     and module._hf_hook.execution_device is not None
                 ):
                       return torch.device(module._hf_hook.execution_device       )
           return self.device



     @torch.no_grad()
     @replace_example_docstring(_a       )
     def __call__(						self	,  _a	,  _a	,  _a	,  _a = 512	,  _a = 512	,  _a = 100	,  _a = 4.0	,  _a = 1	,  _a = None	,  _a = None	,  _a = "pil"	,  _a = True	,  )		->							Optional[int]:
           _A							:  Optional[int]    =		self._execution_device

           _A							:  Any    =		guidance_scale > 1.0

           if isinstance(_a	,  _a       ):
                 _A							:  Union[str, Any]    =		torch.cat(_a	,  dim=0       )
           if isinstance(_a	,  _a       ):
                 _A							:  Any    =		torch.cat(_a	,  dim=0       )
           if isinstance(_a	,  _a       ):
                 _A							:  List[str]    =		torch.cat(_a	,  dim=0       )

           _A							:  List[str]    =		image_embeds.shape[0] * num_images_per_prompt

           if do_classifier_free_guidance:
                 _A							:  Any    =		image_embeds.repeat_interleave(_a	,  dim=0       )
                 _A							:  Dict    =		negative_image_embeds.repeat_interleave(_a	,  dim=0       )
                 _A							:  Union[str, Any]    =		hint.repeat_interleave(_a	,  dim=0       )

           _A							:  Dict    =		torch.cat([negative_image_embeds, image_embeds]	,  dim=0       ).to(dtype=self.unet.dtype	,  device=_a       )
           _A							:  Union[str, Any]    =		torch.cat([hint, hint]	,  dim=0       ).to(dtype=self.unet.dtype	,  device=_a       )
           self.scheduler.set_timesteps(_a	,  device=_a       )
           _A							:  int    =		self.scheduler.timesteps

           _A							:  str    =		self.movq.config.latent_channels

           _A    ,     _A							:  int    =		downscale_height_and_width(_a	,  _a	,  self.movq_scale_factor       )

           # create initial latent
           _A							:  Union[str, Any]    =		self.prepare_latents(
               (batch_size, num_channels_latents, height, width)	,  image_embeds.dtype	,  _a	,  _a	,  _a	,  self.scheduler	,  )

           for i, t in enumerate(self.progress_bar(_a       )       ):
                 # expand the latents if we are doing classifier free guidance
                 _A							:  Dict    =		torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

                 _A							:  List[str]    =		{"""image_embeds""": image_embeds, """hint""": hint}
                 _A							:  Dict    =		self.unet(
                     sample=_a	,  timestep=_a	,  encoder_hidden_states=_a	,  added_cond_kwargs=_a	,  return_dict=_a	,  )[0]

                 if do_classifier_free_guidance:
                       _A    ,     _A							:  Optional[Any]    =		noise_pred.split(latents.shape[1]	,  dim=1       )
                       _A    ,     _A							:  Dict    =		noise_pred.chunk(2       )
                       _A    ,     _A							:  Optional[int]    =		variance_pred.chunk(2       )
                       _A							:  str    =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                       _A							:  Any    =		torch.cat([noise_pred, variance_pred_text]	,  dim=1       )

                 if not (
                     hasattr(self.scheduler.config	,  """variance_type"""       )
                     and self.scheduler.config.variance_type in ["learned", "learned_range"]
                 ):
                       _A    ,     _A							:  int    =		noise_pred.split(latents.shape[1]	,  dim=1       )

                 # compute the previous noisy sample x_t -> x_t-1
                 _A							:  Optional[int]    =		self.scheduler.step(
                     _a	,  _a	,  _a	,  generator=_a	,  )[0]
           # post-processing
           _A							:  str    =		self.movq.decode(_a	,  force_not_quantize=_a       )["""sample"""]

           if output_type not in ["pt", "np", "pil"]:
                 raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''       )

           if output_type in ["np", "pil"]:
                 _A							:  Optional[Any]    =		image * 0.5 + 0.5
                 _A							:  Union[str, Any]    =		image.clamp(0	,  1       )
                 _A							:  List[Any]    =		image.cpu().permute(0	,  2	,  3	,  1       ).float().numpy()

           if output_type == "pil":
                 _A							:  int    =		self.numpy_to_pil(_a       )

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=_a       )







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							(     UpperCamelCase__,unittest.TestCase	):
     _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       a__			(						self       )		->							int:
           return 32

     @property
     def       a__			(						self       )		->							Union[str, Any]:
           return 32

     @property
     def       a__			(						self       )		->							List[str]:
           return self.time_input_dim

     @property
     def       a__			(						self       )		->							Union[str, Any]:
           return self.time_input_dim * 4

     @property
     def       a__			(						self       )		->							str:
           return 100

     @property
     def       a__			(						self       )		->							Tuple:
           torch.manual_seed(0       )

           _A							:  str    =		{
               """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,
           }

           _A							:  Union[str, Any]    =		UNetaDConditionModel(**_a       )
           return model

     @property
     def       a__			(						self       )		->							int:
           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       )		->							Tuple:
           torch.manual_seed(0       )
           _A							:  Dict    =		VQModel(**self.dummy_movq_kwargs       )
           return model

     def       a__			(						self       )		->							int:
           _A							:  Any    =		self.dummy_unet
           _A							:  List[Any]    =		self.dummy_movq

           _A							:  str    =		{
               """num_train_timesteps""": 1000,
               """beta_schedule""": """linear""",
               """beta_start""": 0.00085,
               """beta_end""": 0.012,
               """clip_sample""": False,
               """set_alpha_to_one""": False,
               """steps_offset""": 0,
               """prediction_type""": """epsilon""",
               """thresholding""": False,
           }

           _A							:  int    =		DDIMScheduler(**_a       )

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

           return components

     def       a__			(						self	,  _a	,  _a=0       )		->							str:
           _A							:  Optional[int]    =		floats_tensor((1, self.text_embedder_hidden_size)	,  rng=random.Random(_a       )       ).to(_a       )
           _A							:  Dict    =		floats_tensor((1, self.text_embedder_hidden_size)	,  rng=random.Random(seed + 1       )       ).to(
               _a       )
           # create init_image
           _A							:  Optional[Any]    =		floats_tensor((1, 3, 64, 64)	,  rng=random.Random(_a       )       ).to(_a       )
           _A							:  Optional[Any]    =		image.cpu().permute(0	,  2	,  3	,  1       )[0]
           _A							:  Optional[Any]    =		Image.fromarray(np.uinta(_a       )       ).convert("""RGB"""       ).resize((256, 256)       )

           if str(_a       ).startswith("""mps"""       ):
                 _A							:  Tuple    =		torch.manual_seed(_a       )
           else:
                 _A							:  str    =		torch.Generator(device=_a       ).manual_seed(_a       )
           _A							:  Optional[Any]    =		{
               """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       a__			(						self       )		->							Union[str, Any]:
           _A							:  Dict    =		"""cpu"""

           _A							:  int    =		self.get_dummy_components()

           _A							:  Optional[int]    =		self.pipeline_class(**_a       )
           _A							:  Any    =		pipe.to(_a       )

           pipe.set_progress_bar_config(disable=_a       )

           _A							:  List[Any]    =		pipe(**self.get_dummy_inputs(_a       )       )
           _A							:  Dict    =		output.images

           _A							:  List[str]    =		pipe(
               **self.get_dummy_inputs(_a       )	,  return_dict=_a	,  )[0]

           _A							:  Dict    =		image[0, -3:, -3:, -1]
           _A							:  Union[str, Any]    =		image_from_tuple[0, -3:, -3:, -1]

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

           _A							:  Optional[int]    =		np.array(
               [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263]       )
           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	):

     def       a__			(						self       )		->							List[Any]:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



     def       a__			(						self       )		->							List[str]:
           _A							:  Any    =		load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/kandinskyv22/kandinskyv22_img2img_frog.npy"""       )

           _A							:  Union[str, Any]    =		load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""       )
           _A							:  Dict    =		"""A red cartoon frog, 4k"""

           _A							:  Dict    =		KandinskyVaaPriorPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-2-prior"""	,  torch_dtype=torch.floataa       )
           pipe_prior.to(_a       )

           _A							:  int    =		KandinskyVaaImgaImgPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-2-decoder"""	,  torch_dtype=torch.floataa       )
           _A							:  Dict    =		pipeline.to(_a       )

           pipeline.set_progress_bar_config(disable=_a       )

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

           _A							:  int    =		pipeline(
               image=_a	,  image_embeds=_a	,  negative_image_embeds=_a	,  generator=_a	,  num_inference_steps=100	,  height=768	,  width=768	,  strength=0.2	,  output_type="""np"""	,  )

           _A							:  Optional[int]    =		output.images[0]

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

           assert_mean_pixel_difference(_a	,  _a       )

