
'''simple docstring'''





import math



class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			Optional[Any]						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			Optional[Any]     ) ->							Any:
        snake_case_    							=				x
        snake_case_    							=				y
        snake_case_    							=				step_size
        snake_case_    							=				function_to_optimize

  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							int:
        return self.function(self.x						,			self.y     )

  def 		_lowerCamelCase						(self	:			int     ) ->							Tuple:
        snake_case_    							=				self.step_size
        return [
            SearchProblem(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			self.function     )
            for x, y in (
                (self.x - step_size, self.y - step_size),
                (self.x - step_size, self.y),
                (self.x - step_size, self.y + step_size),
                (self.x, self.y - step_size),
                (self.x, self.y + step_size),
                (self.x + step_size, self.y - step_size),
                (self.x + step_size, self.y),
                (self.x + step_size, self.y + step_size),
            )
        ]

  def __hash__(self	:			Optional[int]     ) ->							Union[str, Any]:
        return hash(str(self     )     )

  def __eq__(self	:			List[str]						,			lowerCAmelCase_	:			str     ) ->							Dict:
        if isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
              return hash(str(self     )     ) == hash(str(lowerCAmelCase_     )     )
        return False


  def __str__(self	:			Tuple     ) ->							str:
        return f'''x: {self.x} y: {self.y}'''

def 	snake_case_							(				_a  :				Tuple			,       _a  :				bool = True			,       _a  :				float = math.inf			,       _a  :				float = -math.inf			,       _a  :				float = math.inf			,       _a  :				float = -math.inf			,       _a  :				bool = False			,       _a  :				int = 10_000			,       ):
      snake_case_    							=				search_prob
      snake_case_    							=				[]  # list to store the current score at each iteration
      snake_case_    							=				0
      snake_case_    							=				False
      snake_case_    							=				set()
      while not solution_found and iterations < max_iter:
            visited.add(_a      )
            iterations += 1
            snake_case_    							=				current_state.score()
            scores.append(_a      )
            snake_case_    							=				current_state.get_neighbors()
            snake_case_    							=				-math.inf
            snake_case_    							=				math.inf
            snake_case_    							=				None  # to hold the next best neighbor
            for neighbor in neighbors:
                  if neighbor in visited:
                        continue  # do not want to visit the same state again
                  if (
                      neighbor.x > max_x
                      or neighbor.x < min_x
                      or neighbor.y > max_y
                      or neighbor.y < min_y
                  ):
                        continue  # neighbor outside our bounds
                  snake_case_    							=				neighbor.score() - current_score
                  if find_max:  # finding max
                        # going to direction with greatest ascent
                        if change > max_change and change > 0:
                              snake_case_    							=				change
                              snake_case_    							=				neighbor
                  else:  # finding min
                        # to direction with greatest descent
                        if change < min_change and change < 0:
                              snake_case_    							=				change
                              snake_case_    							=				neighbor
            if next_state is not None:
                  # we found at least one neighbor which improved the current state
                  snake_case_    							=				next_state
            else:
                  # since we have no neighbor that improves the solution we stop the search
                  snake_case_    							=				True

      if visualization:
            from matplotlib import pyplot as plt

            plt.plot(range(_a      )			,       _a      )
            plt.xlabel("Iterations"      )
            plt.ylabel("Function values"      )
            plt.show()

      return current_state


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Dict      ):
           return (x**2) + (y**2)

     # starting the problem with initial coordinates (3, 4)
     A							     = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     A							     = hill_climbing(prob, find_max=False)
     print(
         "The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: "
         F"""{local_min.score()}"""
     )

     # starting the problem with initial coordinates (12, 47)
     A							     = SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
     A							     = hill_climbing(
         prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
     )
     print(
         "The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
         F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
     )
     def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Tuple      ):
           return (3 * x**2) - (6 * y)

     A							     = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     A							     = hill_climbing(prob, find_max=True)




     print(
         "The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: "
         F"""{local_min.score()}"""
     )





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 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Tuple     ,					UpperCamelCase		:      int     ,					UpperCamelCase		:      str=8	)     ->	Any:



	"""simple docstring"""






	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 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[Any]     ,					UpperCamelCase		:      Dict=512     ,					UpperCamelCase		:      Optional[Any]=512	)     ->	List[Any]:



	"""simple docstring"""






	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(UpperCamelCase     ,					[2, 0, 1]	)
	a_     			=   torch.from_numpy(UpperCamelCase	).unsqueeze(0	)
	return image

class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





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

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





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
								# get the original timestep using init_timestep
								a_     			=   min(int(num_inference_steps * strength    )			,       _SCREAMING_SNAKE_CASE    )

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

								return timesteps, num_inference_steps - t_start





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=None    ):
								if not isinstance(_SCREAMING_SNAKE_CASE			,       (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(_SCREAMING_SNAKE_CASE    )}"""    )

								a_     			=   image.to(device=_SCREAMING_SNAKE_CASE			,       dtype=_SCREAMING_SNAKE_CASE    )

								a_     			=   batch_size * num_images_per_prompt

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

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

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

									a_     			=   self.movq.config.scaling_factor * init_latents

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

								a_     			=   init_latents.shape
								a_     			=   randn_tensor(_SCREAMING_SNAKE_CASE			,       generator=_SCREAMING_SNAKE_CASE			,       device=_SCREAMING_SNAKE_CASE			,       dtype=_SCREAMING_SNAKE_CASE    )

								# get latents
								a_     			=   self.scheduler.add_noise(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )

								a_     			=   init_latents

								return latents





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=0    ):
								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(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=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."""    )

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

								if self.device.type != "cpu":
									self.to("""cpu"""			,       silence_dtype_warnings=_SCREAMING_SNAKE_CASE    )
									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(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       prev_module_hook=_SCREAMING_SNAKE_CASE    )

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





							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def   __magic_name__      (							self    ):
								if not hasattr(self.unet			,       """_hf_hook"""    ):
									return self.device
								for module in self.unet.modules():
									if (
									    hasattr(_SCREAMING_SNAKE_CASE			,       """_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(_SCREAMING_SNAKE_CASE    )
							def __call__(							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE = 512			,       _SCREAMING_SNAKE_CASE = 512			,       _SCREAMING_SNAKE_CASE = 100			,       _SCREAMING_SNAKE_CASE = 4.0			,       _SCREAMING_SNAKE_CASE = 0.3			,       _SCREAMING_SNAKE_CASE = 1			,       _SCREAMING_SNAKE_CASE = None			,       _SCREAMING_SNAKE_CASE = "pil"			,       _SCREAMING_SNAKE_CASE = True			,       ):
								a_     			=   self._execution_device

								a_     			=   guidance_scale > 1.0

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

								a_     			=   image_embeds.shape[0]

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

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

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

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

								a_     			=   self.movq.encode(_SCREAMING_SNAKE_CASE    )["""latents"""]
								a_     			=   latents.repeat_interleave(_SCREAMING_SNAKE_CASE			,       dim=0    )
								self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			,       device=_SCREAMING_SNAKE_CASE    )
								a_			, a_     			=   self.get_timesteps(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )
								a_     			=   timesteps[:1].repeat(batch_size * num_images_per_prompt    )
								a_			, a_     			=   downscale_height_and_width(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       self.movq_scale_factor    )
								a_     			=   self.prepare_latents(
								    _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       image_embeds.dtype			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )
								for i, t in enumerate(self.progress_bar(_SCREAMING_SNAKE_CASE    )    ):
									# 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=_SCREAMING_SNAKE_CASE			,       timestep=_SCREAMING_SNAKE_CASE			,       encoder_hidden_states=_SCREAMING_SNAKE_CASE			,       added_cond_kwargs=_SCREAMING_SNAKE_CASE			,       return_dict=_SCREAMING_SNAKE_CASE			,       )[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(
									    _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       generator=_SCREAMING_SNAKE_CASE			,       )[0]

								# post-processing
								a_     			=   self.movq.decode(_SCREAMING_SNAKE_CASE			,       force_not_quantize=_SCREAMING_SNAKE_CASE    )["""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(_SCREAMING_SNAKE_CASE    )

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE    )