




'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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

A			   ='\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\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\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        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\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 						snake_case_							(_a     :							int   ,       _a     :							str   ,       _a     :							Dict=8					):
    UpperCAmelCase      	=		height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    UpperCAmelCase      	=		width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor





def 						snake_case_							(_a     :							Union[str, Any]   ,       _a     :							Any=5_1_2   ,       _a     :							List[Any]=5_1_2					):
    UpperCAmelCase      	=		pil_image.resize((w, h)   ,       resample=Image.BICUBIC   ,       reducing_gap=1					)
    UpperCAmelCase      	=		np.array(pil_image.convert('''RGB'''					)					)
    UpperCAmelCase      	=		arr.astype(np.floataa					) / 127.5 - 1
    UpperCAmelCase      	=		np.transpose(SCREAMING_SNAKE_CASE_   ,       [2, 0, 1]					)
    UpperCAmelCase      	=		torch.from_numpy(SCREAMING_SNAKE_CASE_					).unsqueeze(0					)
    return image







class       _a  (     _UpperCAmelCase			):
       def __init__(     self						:     int       ,    lowercase						:     Optional[int]       ,    lowercase						:     Optional[Any]       ,    lowercase						:     Any       ,    ):



           '''simple docstring'''

           super().__init__()

           self.register_modules(
               unet=lowercase__       ,    scheduler=lowercase__       ,    movq=lowercase__       ,    )
           UpperCAmelCase      	=		2 ** (len(self.movq.config.block_out_channels    ) - 1)
       def    A				(     self						:     int       ,    lowercase						:     List[str]       ,    lowercase						:     Optional[int]       ,    lowercase						:     str    ):



           '''simple docstring'''

           UpperCAmelCase      	=		min(int(num_inference_steps * strength    )       ,    lowercase__    )

           UpperCAmelCase      	=		max(num_inference_steps - init_timestep       ,    0    )
           UpperCAmelCase      	=		self.scheduler.timesteps[t_start:]

           return timesteps, num_inference_steps - t_start
       def    A				(     self						:     Optional[Any]       ,    lowercase						:     int       ,    lowercase						:     Optional[Any]       ,    lowercase						:     List[Any]       ,    lowercase						:     int       ,    lowercase						:     Optional[int]       ,    lowercase						:     List[str]       ,    lowercase						:     Union[str, Any]=None    ):



           '''simple docstring'''

           if not isinstance(lowercase__       ,    (torch.Tensor, PIL.Image.Image, list)    ):
               raise ValueError(
                   f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase__    )}"    )

           UpperCAmelCase      	=		image.to(device=lowercase__       ,    dtype=lowercase__    )

           UpperCAmelCase      	=		batch_size * num_images_per_prompt

           if image.shape[1] == 4:
               UpperCAmelCase      	=		image

           else:
               if isinstance(lowercase__       ,    lowercase__    ) and len(lowercase__    ) != batch_size:
                   raise ValueError(
                       f"You have passed a list of generators of length {len(lowercase__    )}, but requested an effective batch"
                       f" size of {batch_size}. Make sure the batch size matches the length of the generators."    )

               elif isinstance(lowercase__       ,    lowercase__    ):
                   UpperCAmelCase      	=		[
                       self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(lowercase__    )
                   ]
                   UpperCAmelCase      	=		torch.cat(lowercase__       ,    dim=0    )
               else:
                   UpperCAmelCase      	=		self.movq.encode(lowercase__    ).latent_dist.sample(lowercase__    )

               UpperCAmelCase      	=		self.movq.config.scaling_factor * init_latents

           UpperCAmelCase      	=		torch.cat([init_latents]       ,    dim=0    )

           UpperCAmelCase      	=		init_latents.shape
           UpperCAmelCase      	=		randn_tensor(lowercase__       ,    generator=lowercase__       ,    device=lowercase__       ,    dtype=lowercase__    )

           # get latents
           UpperCAmelCase      	=		self.scheduler.add_noise(lowercase__       ,    lowercase__       ,    lowercase__    )

           UpperCAmelCase      	=		init_latents

           return latents
       def    A				(     self						:     int       ,    lowercase						:     Union[str, Any]=0    ):



           '''simple docstring'''

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

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

           UpperCAmelCase      	=		[
               self.unet,
               self.movq,
           ]
           for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                   cpu_offload(lowercase__       ,    lowercase__    )
       def    A				(     self						:     Union[str, Any]       ,    lowercase						:     Tuple=0    ):



           '''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.'''    )

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

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

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

           # We'll offload the last model manually.
           UpperCAmelCase      	=		hook
       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def    A				(     self						:     int    ):



           '''simple docstring'''

           if not hasattr(self.unet       ,    '''_hf_hook'''    ):
               return self.device
           for module in self.unet.modules():
               if (
                   hasattr(lowercase__       ,    '''_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(lowercase__    )
       def __call__(     self						:     Dict       ,    lowercase						:     Dict       ,    lowercase						:     Optional[Any]       ,    lowercase						:     Tuple       ,    lowercase						:     Dict       ,    lowercase						:     Optional[Any] = 512       ,    lowercase						:     Optional[int] = 512       ,    lowercase						:     str = 100       ,    lowercase						:     Any = 4.0       ,    lowercase						:     int = 0.3       ,    lowercase						:     Union[str, Any] = 1       ,    lowercase						:     Optional[Any] = None       ,    lowercase						:     Union[str, Any] = "pil"       ,    lowercase						:     Optional[int] = True       ,    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self._execution_device

           UpperCAmelCase      	=		guidance_scale > 1.0

           if isinstance(lowercase__       ,    lowercase__    ):
               UpperCAmelCase      	=		torch.cat(lowercase__       ,    dim=0    )
           if isinstance(lowercase__       ,    lowercase__    ):
               UpperCAmelCase      	=		torch.cat(lowercase__       ,    dim=0    )
           if isinstance(lowercase__       ,    lowercase__    ):
               UpperCAmelCase      	=		torch.cat(lowercase__       ,    dim=0    )

           UpperCAmelCase      	=		image_embeds.shape[0]

           if do_classifier_free_guidance:
               UpperCAmelCase      	=		image_embeds.repeat_interleave(lowercase__       ,    dim=0    )
               UpperCAmelCase      	=		negative_image_embeds.repeat_interleave(lowercase__       ,    dim=0    )
               UpperCAmelCase      	=		hint.repeat_interleave(lowercase__       ,    dim=0    )

           UpperCAmelCase      	=		torch.cat([negative_image_embeds, image_embeds]       ,    dim=0    ).to(dtype=self.unet.dtype       ,    device=lowercase__    )
           UpperCAmelCase      	=		torch.cat([hint, hint]       ,    dim=0    ).to(dtype=self.unet.dtype       ,    device=lowercase__    )

           if not isinstance(lowercase__       ,    lowercase__    ):
               UpperCAmelCase      	=		[image]
           if not all(isinstance(lowercase__       ,    (PIL.Image.Image, torch.Tensor)    ) for i in image    ):
               raise ValueError(
                   f"Input is in incorrect format: {[type(lowercase__    ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"    )

           UpperCAmelCase      	=		torch.cat([prepare_image(lowercase__       ,    lowercase__       ,    lowercase__    ) for i in image]       ,    dim=0    )
           UpperCAmelCase      	=		image.to(dtype=image_embeds.dtype       ,    device=lowercase__    )

           UpperCAmelCase      	=		self.movq.encode(lowercase__    )["latents"]
           UpperCAmelCase      	=		latents.repeat_interleave(lowercase__       ,    dim=0    )
           self.scheduler.set_timesteps(lowercase__       ,    device=lowercase__    )
           UpperCAmelCase      	=		self.get_timesteps(lowercase__       ,    lowercase__       ,    lowercase__    )
           UpperCAmelCase      	=		timesteps[:1].repeat(batch_size * num_images_per_prompt    )
           UpperCAmelCase      	=		downscale_height_and_width(lowercase__       ,    lowercase__       ,    self.movq_scale_factor    )
           UpperCAmelCase      	=		self.prepare_latents(
               lowercase__       ,    lowercase__       ,    lowercase__       ,    lowercase__       ,    image_embeds.dtype       ,    lowercase__       ,    lowercase__    )
           for i, t in enumerate(self.progress_bar(lowercase__    )    ):
               # expand the latents if we are doing classifier free guidance
               UpperCAmelCase      	=		torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

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

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

               # compute the previous noisy sample x_t -> x_t-1

               UpperCAmelCase      	=		self.scheduler.step(
                   lowercase__       ,    lowercase__       ,    lowercase__       ,    generator=lowercase__       ,    )[0]

           # post-processing
           UpperCAmelCase      	=		self.movq.decode(lowercase__       ,    force_not_quantize=lowercase__    )["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"]:
               UpperCAmelCase      	=		image * 0.5 + 0.5
               UpperCAmelCase      	=		image.clamp(0       ,    1    )
               UpperCAmelCase      	=		image.cpu().permute(0       ,    2       ,    3       ,    1    ).float().numpy()

           if output_type == "pil":
               UpperCAmelCase      	=		self.numpy_to_pil(lowercase__    )

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=lowercase__    )










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A			   ={'configuration_dinat': ['DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DinatConfig']}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A			   =[
       'DINAT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'DinatForImageClassification',
       'DinatModel',
       'DinatPreTrainedModel',
       'DinatBackbone',
   ]

if TYPE_CHECKING:
   from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dinat import (
          DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DinatBackbone,
          DinatForImageClassification,
          DinatModel,
          DinatPreTrainedModel,
      )

else:
   import sys

   A			   =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




