'''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,
)


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

__snake_case						  =		"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> 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\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=8						)			->Tuple:
  lowercase_     =       height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  lowercase_     =       width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor





class       _a    (		__a      ):


  """simple docstring"""

  def __init__(		self      :       Tuple   ,				lowercase_      :       UNetaDConditionModel   ,				lowercase_      :       DDPMScheduler   ,				lowercase_      :       VQModel   ,				):







    '''simple docstring'''


    super().__init__()

    self.register_modules(
        unet=lowercase_   ,				scheduler=lowercase_   ,				movq=lowercase_   ,				)
    lowercase_     =       2 ** (len(self.movq.config.block_out_channels      ) - 1)




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       int   ,				lowercase_      :       Union[str, Any]   ,				lowercase_      :       Tuple   ,				lowercase_      :       int   ,				lowercase_      :       List[Any]   ,				lowercase_      :       List[str]      ):







    '''simple docstring'''


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

    lowercase_     =       latents * scheduler.init_noise_sigma
    return latents




  def 		lowerCamelCase__  (		self      :       Optional[Any]   ,				lowercase_      :       str=0      ):







    '''simple docstring'''


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

    lowercase_     =       torch.device(F"""cuda:{gpu_id}"""      )

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




  def 		lowerCamelCase__  (		self      :       List[str]   ,				lowercase_      :       List[str]=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."""      )

    lowercase_     =       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)

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

    # We'll offload the last model manually.
    lowercase_     =       hook




  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 		lowerCamelCase__  (		self      :       List[Any]      ):







    '''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      :       Optional[int]   ,				lowercase_      :       Union[torch.FloatTensor, List[torch.FloatTensor]]   ,				lowercase_      :       Union[torch.FloatTensor, List[torch.FloatTensor]]   ,				lowercase_      :       torch.FloatTensor   ,				lowercase_      :       int = 512   ,				lowercase_      :       int = 512   ,				lowercase_      :       int = 100   ,				lowercase_      :       float = 4.0   ,				lowercase_      :       int = 1   ,				lowercase_      :       Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,				lowercase_      :       Optional[torch.FloatTensor] = None   ,				lowercase_      :       Optional[str] = "pil"   ,				lowercase_      :       bool = True   ,				):







    '''simple docstring'''


    lowercase_     =       self._execution_device

    lowercase_     =       guidance_scale > 1.0

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

    lowercase_     =       image_embeds.shape[0] * num_images_per_prompt

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

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

    lowercase_     =       self.movq.config.latent_channels

    lowercase_     ,	lowercase_     =       downscale_height_and_width(lowercase_   ,				lowercase_   ,				self.movq_scale_factor      )

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

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

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

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

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

    if output_type == "pil":
      lowercase_     =       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


__snake_case						  =		{
    """configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
    """processing_git""": ["""GitProcessor"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __snake_case						  =		[
           """GIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GitForCausalLM""",
           """GitModel""",
           """GitPreTrainedModel""",
           """GitVisionModel""",
       ]

if TYPE_CHECKING:
       from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
       from .processing_git import GitProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_git import (
                  GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GitForCausalLM,
                  GitModel,
                  GitPreTrainedModel,
                  GitVisionModel,
              )

else:
       import sys

       __snake_case						  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

