


'''simple docstring'''







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,
)


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

A__:       int          =			'''\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       SCREAMING_SNAKE_CASE_   (    _UpperCAmelCase      :      Tuple       ,_UpperCAmelCase      :      Optional[int]       ,_UpperCAmelCase      :      Tuple=8    )    ->     Optional[int]:
       _a       :					Dict	   =height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       _a       :					List[Any]	   =width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor







class A__			(		UpperCAmelCase__ ):






      def __init__(					self     :Any	,		SCREAMING_SNAKE_CASE     :Tuple	,		SCREAMING_SNAKE_CASE     :Any	,		SCREAMING_SNAKE_CASE     :Optional[int]	,		)			->   Optional[int]:




             '''simple docstring'''
             super().__init__()

             self.register_modules(
                 unet=_lowerCAmelCase	,		scheduler=_lowerCAmelCase	,		movq=_lowerCAmelCase	,		)
             _a       :					str	   =2 ** (len(self.movq.config.block_out_channels      ) - 1)






      def __UpperCAmelCase			(					self     :Union[str, Any]	,		SCREAMING_SNAKE_CASE     :Dict	,		SCREAMING_SNAKE_CASE     :Tuple	,		SCREAMING_SNAKE_CASE     :int	,		SCREAMING_SNAKE_CASE     :int	,		SCREAMING_SNAKE_CASE     :Any	,		SCREAMING_SNAKE_CASE     :Optional[int]      )			->   Optional[Any]:




             '''simple docstring'''
             if latents is None:
                    _a       :					Dict	   =randn_tensor(_lowerCAmelCase	,		generator=_lowerCAmelCase	,		device=_lowerCAmelCase	,		dtype=_lowerCAmelCase      )
             else:
                    if latents.shape != shape:
                           raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"      )
                    _a       :					Optional[int]	   =latents.to(_lowerCAmelCase      )

             _a       :					Optional[Any]	   =latents * scheduler.init_noise_sigma
             return latents






      def __UpperCAmelCase			(					self     :int	,		SCREAMING_SNAKE_CASE     :List[str]=0      )			->   Optional[Any]:




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

             _a       :					int	   =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(_lowerCAmelCase	,		_lowerCAmelCase      )






      def __UpperCAmelCase			(					self     :Union[str, Any]	,		SCREAMING_SNAKE_CASE     :str=0      )			->   Tuple:




             '''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."""      )

             _a       :					str	   =torch.device(f"cuda:{gpu_id}"      )

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

             _a       :					str	   =None
             for cpu_offloaded_model in [self.unet, self.movq]:
                    _a   ,       _a       :					Dict	   =cpu_offload_with_hook(_lowerCAmelCase	,		_lowerCAmelCase	,		prev_module_hook=_lowerCAmelCase      )

             # We'll offload the last model manually.
             _a       :					Union[str, Any]	   =hook






      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def __UpperCAmelCase			(					self     :Union[str, Any]      )			->   int:




             '''simple docstring'''
             if not hasattr(self.unet	,		"""_hf_hook"""      ):
                    return self.device
             for module in self.unet.modules():
                    if (
                        hasattr(_lowerCAmelCase	,		"""_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(_lowerCAmelCase      )
      def __call__(					self     :Optional[int]	,		SCREAMING_SNAKE_CASE     :Any	,		SCREAMING_SNAKE_CASE     :int	,		SCREAMING_SNAKE_CASE     :Dict	,		SCREAMING_SNAKE_CASE     :Union[str, Any] = 5_1_2	,		SCREAMING_SNAKE_CASE     :int = 5_1_2	,		SCREAMING_SNAKE_CASE     :Tuple = 1_0_0	,		SCREAMING_SNAKE_CASE     :Dict = 4.0	,		SCREAMING_SNAKE_CASE     :Tuple = 1	,		SCREAMING_SNAKE_CASE     :Optional[Any] = None	,		SCREAMING_SNAKE_CASE     :List[str] = None	,		SCREAMING_SNAKE_CASE     :Tuple = "pil"	,		SCREAMING_SNAKE_CASE     :Union[str, Any] = True	,		)			->   List[str]:




             '''simple docstring'''
             _a       :					Union[str, Any]	   =self._execution_device

             _a       :					Optional[int]	   =guidance_scale > 1.0

             if isinstance(_lowerCAmelCase	,		_lowerCAmelCase      ):
                    _a       :					int	   =torch.cat(_lowerCAmelCase	,		dim=0      )
             if isinstance(_lowerCAmelCase	,		_lowerCAmelCase      ):
                    _a       :					Optional[Any]	   =torch.cat(_lowerCAmelCase	,		dim=0      )
             if isinstance(_lowerCAmelCase	,		_lowerCAmelCase      ):
                    _a       :					Union[str, Any]	   =torch.cat(_lowerCAmelCase	,		dim=0      )

             _a       :					Any	   =image_embeds.shape[0] * num_images_per_prompt

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

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

             _a       :					Any	   =self.movq.config.latent_channels

             _a   ,       _a       :					Dict	   =downscale_height_and_width(_lowerCAmelCase	,		_lowerCAmelCase	,		self.movq_scale_factor      )

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

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

                    _a       :					Optional[Any]	   ={"""image_embeds""": image_embeds, """hint""": hint}
                    _a       :					Any	   =self.unet(
                        sample=_lowerCAmelCase	,		timestep=_lowerCAmelCase	,		encoder_hidden_states=_lowerCAmelCase	,		added_cond_kwargs=_lowerCAmelCase	,		return_dict=_lowerCAmelCase	,		)[0]

                    if do_classifier_free_guidance:
                           _a   ,       _a       :					Any	   =noise_pred.split(latents.shape[1]	,		dim=1      )
                           _a   ,       _a       :					List[Any]	   =noise_pred.chunk(2      )
                           _a   ,       _a       :					Union[str, Any]	   =variance_pred.chunk(2      )
                           _a       :					Any	   =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                           _a       :					Optional[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       :					Union[str, Any]	   =noise_pred.split(latents.shape[1]	,		dim=1      )

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

             if output_type == "pil":
                    _a       :					List[Any]	   =self.numpy_to_pil(_lowerCAmelCase      )

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=_lowerCAmelCase      )









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "swinv2"

 __lowerCamelCase    :				int								=       {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }




 def __init__(						self      ,  _lowerCAmelCase=224      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=96      ,  _lowerCAmelCase=[2, 2, 6, 2]      ,  _lowerCAmelCase=[3, 6, 12, 24]      ,  _lowerCAmelCase=7      ,  _lowerCAmelCase=4.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase=32      ,  **_lowerCAmelCase      ,  )     ->				Tuple:
     super().__init__(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   embed_dim
     _lowerCAmelCase						      =   depths
     _lowerCAmelCase						      =   len(_lowerCAmelCase		)
     _lowerCAmelCase						      =   num_heads
     _lowerCAmelCase						      =   window_size
     _lowerCAmelCase						      =   mlp_ratio
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   drop_path_rate
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   use_absolute_embeddings
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   encoder_stride
     # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     _lowerCAmelCase						      =   int(embed_dim * 2 ** (len(_lowerCAmelCase		) - 1)		)
     _lowerCAmelCase						      =   (0, 0, 0, 0)


