



'''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,
)


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

SCREAMING_SNAKE_CASE__      					=     "\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        lowerCamelCase  (				_snake_case  :						Optional[int]     ,_snake_case  :						Dict     ,_snake_case  :						Union[str, Any]=8       ):





    '''simple docstring'''


    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 				snake_case (UpperCamelCase   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,)  ->							Dict:

           '''simple docstring'''





           super().__init__()

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



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Union[str, Any]:

           '''simple docstring'''





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

           lowercase__		       =						latents * scheduler.init_noise_sigma
           return latents



       def 							_a      (				self     ,UpperCAmelCase_=0      )  ->							Tuple:

           '''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(UpperCAmelCase_     ,UpperCAmelCase_      )



       def 							_a      (				self     ,UpperCAmelCase_=0      )  ->							List[Any]:

           '''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=UpperCAmelCase_      )
               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(UpperCAmelCase_     ,UpperCAmelCase_     ,prev_module_hook=UpperCAmelCase_      )

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



       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 							_a      (				self      )  ->							Dict:

           '''simple docstring'''





           if not hasattr(self.unet     ,"_hf_hook"      ):
               return self.device
           for module in self.unet.modules():
               if (
                   hasattr(UpperCAmelCase_     ,"_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(UpperCAmelCase_      )
       def __call__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_ = 512     ,UpperCAmelCase_ = 512     ,UpperCAmelCase_ = 100     ,UpperCAmelCase_ = 4.0     ,UpperCAmelCase_ = 1     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = "pil"     ,UpperCAmelCase_ = True     ,)  ->							List[Any]:

           '''simple docstring'''





           lowercase__		       =						self._execution_device

           lowercase__		       =						guidance_scale > 1.0

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

           lowercase__		       =						image_embeds.shape[0] * num_images_per_prompt

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

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

           lowercase__		       =						self.movq.config.latent_channels

           lowercase__       ,       lowercase__		       =						downscale_height_and_width(UpperCAmelCase_     ,UpperCAmelCase_     ,self.movq_scale_factor      )

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

           for i, t in enumerate(self.progress_bar(UpperCAmelCase_      )      ):
               # 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=UpperCAmelCase_     ,timestep=UpperCAmelCase_     ,encoder_hidden_states=UpperCAmelCase_     ,added_cond_kwargs=UpperCAmelCase_     ,return_dict=UpperCAmelCase_     ,)[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(
                   UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,generator=UpperCAmelCase_     ,)[0]
           # post-processing
           lowercase__		       =						self.movq.decode(UpperCAmelCase_     ,force_not_quantize=UpperCAmelCase_      )["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(UpperCAmelCase_      )

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=UpperCAmelCase_      )









'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						list[int]     ,_snake_case  :						list[int]       ):





    '''simple docstring'''


    lowercase__		       =						len(_snake_case       )
    print("The following activities are selected:"       )

    # The first activity is always selected
    lowercase__		       =						0
    print(_snake_case     ,end=","       )

    # Consider rest of the activities
    for j in range(_snake_case       ):
        # If this activity has start time greater than
        # or equal to the finish time of previously
        # selected activity, then select it
        if start[j] >= finish[i]:
            print(_snake_case     ,end=","       )
            lowercase__		       =						j


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 SCREAMING_SNAKE_CASE__      					=     [1, 3, 0, 5, 8, 5]
 SCREAMING_SNAKE_CASE__      					=     [2, 4, 6, 7, 9, 9]
 print_max_activities(start, finish)




