
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,
)


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

__lowerCAmelCase			:     List[Any]           =   '\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 a__    (       A_,     A_,     A_=8					):






  '''simple docstring'''





  __magic_name__				     =       height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  __magic_name__				     =       width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor





class        UpperCAmelCase_			( lowercase_     ):


  '''simple docstring'''







  def __init__( self			: Any			,							UpperCamelCase__			: UNetaDConditionModel			,							UpperCamelCase__			: DDPMScheduler			,							UpperCamelCase__			: VQModel			,							)			->	Optional[Any]:







    """simple docstring"""






    super().__init__()

    self.register_modules(
        unet=__UpperCamelCase			,							scheduler=__UpperCamelCase			,							movq=__UpperCamelCase			,							)
    __magic_name__				     =       2 ** (len(self.movq.config.block_out_channels    ) - 1)
  def 							_lowercase							( self			: Union[str, Any]			,							UpperCamelCase__			: Tuple			,							UpperCamelCase__			: Dict			,							UpperCamelCase__			: Dict			,							UpperCamelCase__			: List[str]			,							UpperCamelCase__			: Optional[int]			,							UpperCamelCase__			: List[str]    )			->	int:







    """simple docstring"""






    if latents is None:
      __magic_name__				     =       randn_tensor(__UpperCamelCase			,							generator=__UpperCamelCase			,							device=__UpperCamelCase			,							dtype=__UpperCamelCase    )
    else:
      if latents.shape != shape:
        raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''    )
      __magic_name__				     =       latents.to(__UpperCamelCase    )

    __magic_name__				     =       latents * scheduler.init_noise_sigma
    return latents
  def 							_lowercase							( self			: Tuple			,							UpperCamelCase__			: Any=0    )			->	Dict:







    """simple docstring"""






    if is_accelerate_available():
      from accelerate import cpu_offload
    else:
      raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

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

    __magic_name__				     =       [
        self.unet,
        self.movq,
    ]
    for cpu_offloaded_model in models:
      if cpu_offloaded_model is not None:
        cpu_offload(__UpperCamelCase			,							__UpperCamelCase    )
  def 							_lowercase							( self			: Dict			,							UpperCamelCase__			: int=0    )			->	Optional[int]:







    """simple docstring"""






    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."""    )

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

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

    __magic_name__				     =       None
    for cpu_offloaded_model in [self.unet, self.movq]:
      __magic_name__     ,    __magic_name__				     =       cpu_offload_with_hook(__UpperCamelCase			,							__UpperCamelCase			,							prev_module_hook=__UpperCamelCase    )

    # We'll offload the last model manually.
    __magic_name__				     =       hook
  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 							_lowercase							( self			: str    )			->	str:







    """simple docstring"""






    if not hasattr(self.unet			,							"""_hf_hook"""    ):
      return self.device
    for module in self.unet.modules():
      if (
          hasattr(__UpperCamelCase			,							"""_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(__UpperCamelCase    )
  def __call__( self			: List[Any]			,							UpperCamelCase__			: Union[torch.FloatTensor, List[torch.FloatTensor]]			,							UpperCamelCase__			: Union[torch.FloatTensor, List[torch.FloatTensor]]			,							UpperCamelCase__			: torch.FloatTensor			,							UpperCamelCase__			: int = 512			,							UpperCamelCase__			: int = 512			,							UpperCamelCase__			: int = 100			,							UpperCamelCase__			: float = 4.0			,							UpperCamelCase__			: int = 1			,							UpperCamelCase__			: Optional[Union[torch.Generator, List[torch.Generator]]] = None			,							UpperCamelCase__			: Optional[torch.FloatTensor] = None			,							UpperCamelCase__			: Optional[str] = "pil"			,							UpperCamelCase__			: bool = True			,							)			->	str:







    """simple docstring"""






    __magic_name__				     =       self._execution_device

    __magic_name__				     =       guidance_scale > 1.0

    if isinstance(__UpperCamelCase			,							__UpperCamelCase    ):
      __magic_name__				     =       torch.cat(__UpperCamelCase			,							dim=0    )
    if isinstance(__UpperCamelCase			,							__UpperCamelCase    ):
      __magic_name__				     =       torch.cat(__UpperCamelCase			,							dim=0    )
    if isinstance(__UpperCamelCase			,							__UpperCamelCase    ):
      __magic_name__				     =       torch.cat(__UpperCamelCase			,							dim=0    )

    __magic_name__				     =       image_embeds.shape[0] * num_images_per_prompt

    if do_classifier_free_guidance:
      __magic_name__				     =       image_embeds.repeat_interleave(__UpperCamelCase			,							dim=0    )
      __magic_name__				     =       negative_image_embeds.repeat_interleave(__UpperCamelCase			,							dim=0    )
      __magic_name__				     =       hint.repeat_interleave(__UpperCamelCase			,							dim=0    )

    __magic_name__				     =       torch.cat([negative_image_embeds, image_embeds]			,							dim=0    ).to(dtype=self.unet.dtype			,							device=__UpperCamelCase    )
    __magic_name__				     =       torch.cat([hint, hint]			,							dim=0    ).to(dtype=self.unet.dtype			,							device=__UpperCamelCase    )
    self.scheduler.set_timesteps(__UpperCamelCase			,							device=__UpperCamelCase    )
    __magic_name__				     =       self.scheduler.timesteps

    __magic_name__				     =       self.movq.config.latent_channels

    __magic_name__     ,    __magic_name__				     =       downscale_height_and_width(__UpperCamelCase			,							__UpperCamelCase			,							self.movq_scale_factor    )

    # create initial latent
    __magic_name__				     =       self.prepare_latents(
        (batch_size, num_channels_latents, height, width)			,							image_embeds.dtype			,							__UpperCamelCase			,							__UpperCamelCase			,							__UpperCamelCase			,							self.scheduler			,							)

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

      __magic_name__				     =       {"""image_embeds""": image_embeds, """hint""": hint}
      __magic_name__				     =       self.unet(
          sample=__UpperCamelCase			,							timestep=__UpperCamelCase			,							encoder_hidden_states=__UpperCamelCase			,							added_cond_kwargs=__UpperCamelCase			,							return_dict=__UpperCamelCase			,							)[0]

      if do_classifier_free_guidance:
        __magic_name__     ,    __magic_name__				     =       noise_pred.split(latents.shape[1]			,							dim=1    )
        __magic_name__     ,    __magic_name__				     =       noise_pred.chunk(2    )
        __magic_name__     ,    __magic_name__				     =       variance_pred.chunk(2    )
        __magic_name__				     =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
        __magic_name__				     =       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"]
      ):
        __magic_name__     ,    __magic_name__				     =       noise_pred.split(latents.shape[1]			,							dim=1    )

      # compute the previous noisy sample x_t -> x_t-1
      __magic_name__				     =       self.scheduler.step(
          __UpperCamelCase			,							__UpperCamelCase			,							__UpperCamelCase			,							generator=__UpperCamelCase			,							)[0]
    # post-processing
    __magic_name__				     =       self.movq.decode(__UpperCamelCase			,							force_not_quantize=__UpperCamelCase    )["""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"]:
      __magic_name__				     =       image * 0.5 + 0.5
      __magic_name__				     =       image.clamp(0			,							1    )
      __magic_name__				     =       image.cpu().permute(0			,							2			,							3			,							1    ).float().numpy()

    if output_type == "pil":
      __magic_name__				     =       self.numpy_to_pil(__UpperCamelCase    )

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=__UpperCamelCase    )








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

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_snake_case     :		Union[str, Any]       						= {
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case     :		int       						= [
		    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'EncodecModel',
		    'EncodecPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_encodec import (
		    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    EncodecConfig,
		)
		from .feature_extraction_encodec import EncodecFeatureExtractor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_encodec import (
				    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EncodecModel,
				    EncodecPreTrainedModel,
				)

else:
		import sys

		_snake_case     :		str       						= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


