






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

__A      :       Tuple      =	'\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 						_UpperCamelCase							(     snake_case__							,			snake_case__							,			snake_case__=8							):
      __A		   =      height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      __A		   =      width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor


def 						_UpperCamelCase							(     snake_case__							,			snake_case__=512							,			snake_case__=512							):
      __A		   =      pil_image.resize((w, h)							,			resample=Image.BICUBIC							,			reducing_gap=1							)
      __A		   =      np.array(pil_image.convert("RGB"							)							)
      __A		   =      arr.astype(np.floataa							) / 127.5 - 1
      __A		   =      np.transpose(snake_case__							,			[2, 0, 1]							)
      __A		   =      torch.from_numpy(snake_case__							).unsqueeze(0							)
      return image






class 					a__    (			lowerCamelCase						):
     '''simple docstring'''







     def __init__(self    :	Any				,				lowercase__    :	UNetaDConditionModel				,				lowercase__    :	DDPMScheduler				,				lowercase__    :	VQModel				,				)  ->       Union[str, Any]:
           super().__init__()

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






     def     _UpperCamelCase   (self    :	Dict				,				lowercase__    :	Dict				,				lowercase__    :	Union[str, Any]				,				lowercase__    :	Optional[int]					)  ->       Tuple:
           # get the original timestep using init_timestep
           __A		   =      min(int(num_inference_steps * strength					)				,				lowercase__					)

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

           return timesteps, num_inference_steps - t_start






     def     _UpperCamelCase   (self    :	Optional[int]				,				lowercase__    :	Union[str, Any]				,				lowercase__    :	List[str]				,				lowercase__    :	Optional[Any]				,				lowercase__    :	Any				,				lowercase__    :	Dict				,				lowercase__    :	List[Any]				,				lowercase__    :	List[Any]=None					)  ->       Optional[Any]:
           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__					)}"					)

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

           __A		   =      batch_size * num_images_per_prompt

           if image.shape[1] == 4:
                 __A		   =      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__					):
                       __A		   =      [
                           self.movq.encode(image[i : i + 1]					).latent_dist.sample(generator[i]					) for i in range(lowercase__					)
                       ]
                       __A		   =      torch.cat(lowercase__				,				dim=0					)
                 else:
                       __A		   =      self.movq.encode(lowercase__					).latent_dist.sample(lowercase__					)

                 __A		   =      self.movq.config.scaling_factor * init_latents

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

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

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

           __A		   =      init_latents

           return latents






     def     _UpperCamelCase   (self    :	Optional[Any]				,				lowercase__    :	List[Any]=0					)  ->       Any:
           if is_accelerate_available():
                 from accelerate import cpu_offload
           else:
                 raise ImportError("Please install accelerate via `pip install accelerate`"					)

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

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






     def     _UpperCamelCase   (self    :	str				,				lowercase__    :	Any=0					)  ->       Tuple:
           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		   =      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)

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

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






     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       int:
           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    :	Any				,				lowercase__    :	Union[torch.FloatTensor, List[torch.FloatTensor]]				,				lowercase__    :	Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]				,				lowercase__    :	Union[torch.FloatTensor, List[torch.FloatTensor]]				,				lowercase__    :	torch.FloatTensor				,				lowercase__    :	int = 512				,				lowercase__    :	int = 512				,				lowercase__    :	int = 100				,				lowercase__    :	float = 4.0				,				lowercase__    :	float = 0.3				,				lowercase__    :	int = 1				,				lowercase__    :	Optional[Union[torch.Generator, List[torch.Generator]]] = None				,				lowercase__    :	Optional[str] = "pil"				,				lowercase__    :	bool = True				,				)  ->       str:
           __A		   =      self._execution_device

           __A		   =      guidance_scale > 1.0

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

           __A		   =      image_embeds.shape[0]

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

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

           if not isinstance(lowercase__				,				lowercase__					):
                 __A		   =      [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"					)

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

           __A		   =      self.movq.encode(lowercase__					)["latents"]
           __A		   =      latents.repeat_interleave(lowercase__				,				dim=0					)
           self.scheduler.set_timesteps(lowercase__				,				device=lowercase__					)
           __A   ,				__A		   =      self.get_timesteps(lowercase__				,				lowercase__				,				lowercase__					)
           __A		   =      timesteps[:1].repeat(batch_size * num_images_per_prompt					)
           __A   ,				__A		   =      downscale_height_and_width(lowercase__				,				lowercase__				,				self.movq_scale_factor					)
           __A		   =      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
                 __A		   =      torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

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

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

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

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

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

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

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=lowercase__					)










'''simple docstring'''







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def 						_UpperCamelCase							(     ):
      __A		   =      ArgumentParser(
          description=(
              "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
          )							)

      # Optional arguments for the launch helper
      parser.add_argument("--num_cores"							,			type=snake_case__							,			default=1							,			help="Number of TPU cores to use (1 or 8)."							)

      # positional
      parser.add_argument(
          "training_script"							,			type=snake_case__							,			help=(
              "The full path to the single TPU training "
              "program/script to be launched in parallel, "
              "followed by all the arguments for the "
              "training script"
          )							,			)

      # rest from the training program
      parser.add_argument("training_script_args"							,			nargs=snake_case__							)

      return parser.parse_args()


def 						_UpperCamelCase							(     ):
      __A		   =      parse_args()

      # Import training_script as a module.
      __A		   =      Path(args.training_script							)
      sys.path.append(str(script_fpath.parent.resolve()							)							)
      __A		   =      script_fpath.stem
      __A		   =      importlib.import_module(snake_case__							)

      # Patch sys.argv
      __A		   =      [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores							)]

      xmp.spawn(mod._mp_fn							,			args=()							,			nprocs=args.num_cores							)


if __name__ == "__main__":
       main()


