
'''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,
)


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

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

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> 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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.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)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

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







def   lowerCAmelCase(  a__    :  str      , a__    :  Tuple      , a__    :  Tuple=8      ):







       '''simple docstring'''






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







def   lowerCAmelCase(  a__    :  str      , a__    :  List[Any]=512      , a__    :  Dict=512      ):







       '''simple docstring'''






       lowerCamelCase__				   =      pil_image.resize((w, h)      , resample=Image.BICUBIC      , reducing_gap=1      )
       lowerCamelCase__				   =      np.array(pil_image.convert("RGB"      )      )
       lowerCamelCase__				   =      arr.astype(np.floataa      ) / 1_2_7.5 - 1
       lowerCamelCase__				   =      np.transpose(a__      , [2, 0, 1]      )
       lowerCamelCase__				   =      torch.from_numpy(a__      ).unsqueeze(0      )
       return image







class 		snake_case_       (						_lowerCAmelCase						):



   """simple docstring"""





   def __init__( self			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase			,   ):
          super().__init__()

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







   def     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase):
          # get the original timestep using init_timestep
          lowerCamelCase__				   =      min(int(num_inference_steps * strength)			,   A__)

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

          return timesteps, num_inference_steps - t_start







   def     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase=None):
          if not isinstance(A__			,   (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(A__)}""")

          lowerCamelCase__				   =      image.to(device=A__			,   dtype=A__)

          lowerCamelCase__				   =      batch_size * num_images_per_prompt

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

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

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

                 lowerCamelCase__				   =      self.movq.config.scaling_factor * init_latents

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

          lowerCamelCase__				   =      init_latents.shape
          lowerCamelCase__				   =      randn_tensor(A__			,   generator=A__			,   device=A__			,   dtype=A__)

          # get latents
          lowerCamelCase__				   =      self.scheduler.add_noise(A__			,   A__			,   A__)

          lowerCamelCase__				   =      init_latents

          return latents







   def     __UpperCAmelCase      ( self			,   UpperCamelCase=0):
          if is_accelerate_available():
                 from accelerate import cpu_offload
          else:
                 raise ImportError("Please install accelerate via `pip install accelerate`")

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

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







   def     __UpperCAmelCase      ( self			,   UpperCamelCase=0):
          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.")

          lowerCamelCase__				   =      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)

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

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







   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def     __UpperCAmelCase      ( self):
          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			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase = 5_12			,   UpperCamelCase = 5_12			,   UpperCamelCase = 1_00			,   UpperCamelCase = 4.0			,   UpperCamelCase = 0.3			,   UpperCamelCase = 1			,   UpperCamelCase = None			,   UpperCamelCase = "pil"			,   UpperCamelCase = True			,   ):
          lowerCamelCase__				   =      self._execution_device

          lowerCamelCase__				   =      guidance_scale > 1.0

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

          lowerCamelCase__				   =      image_embeds.shape[0]

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

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

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

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

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

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

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

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

                 lowerCamelCase__				   =      self.scheduler.step(
                     A__			,   A__			,   A__			,   generator=A__			,   )[0]

          # post-processing
          lowerCamelCase__				   =      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"]:
                 lowerCamelCase__				   =      image * 0.5 + 0.5
                 lowerCamelCase__				   =      image.clamp(0			,   1)
                 lowerCamelCase__				   =      image.cpu().permute(0			,   2			,   3			,   1).float().numpy()

          if output_type == "pil":
                 lowerCamelCase__				   =      self.numpy_to_pil(A__)

          if not return_dict:
                 return (image,)

          return ImagePipelineOutput(images=A__)







def       lowerCAmelCase_      (	lowerCamelCase:							str    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__   	=					str(lowerCamelCase    ).strip()
      if not bin_string:
            raise ValueError('Empty string was passed to the function'    )
      lowerCamelCase__   	=					bin_string[0] == '-'
      if is_negative:
            lowerCamelCase__   	=					bin_string[1:]
      if not all(char in '01' for char in bin_string    ):
            raise ValueError('Non-binary value was passed to the function'    )
      lowerCamelCase__   	=					0
      for char in bin_string:
            lowerCamelCase__   	=					2 * decimal_number + int(lowerCamelCase    )
      return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
      from doctest import testmod

      testmod()