




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,
)


__lowerCAmelCase	:       Union[str, Any]     =	logging.get_logger(__name__)  # pylint: disable=invalid-name

__lowerCAmelCase	:       List[str]     =	'\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     __magic_name__       (				A	:      str,						A	:      Optional[Any],						A	:      Optional[int]=8     ):






			'''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






class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				def __init__(   self       :  Optional[Any]      ,   __lowerCamelCase       :  UNetaDConditionModel      ,   __lowerCamelCase       :  DDPMScheduler      ,   __lowerCamelCase       :  VQModel      ,   )      ->  List[str]:
							super().__init__()

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





				def 							__UpperCAmelCase							(   self       :  Any      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  List[Any]							)      ->  Optional[int]:
							if latents is None:
										a					=    randn_tensor(__lowerCamelCase      ,   generator=__lowerCamelCase      ,   device=__lowerCamelCase      ,   dtype=__lowerCamelCase							)
							else:
										if latents.shape != shape:
													raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""							)
										a					=    latents.to(__lowerCamelCase							)

							a					=    latents * scheduler.init_noise_sigma
							return latents





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  List[str]=0							)      ->  Union[str, 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(__lowerCamelCase      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   __lowerCamelCase       :  Optional[Any]=0							)      ->  Union[str, Any]:
							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=__lowerCamelCase							)
										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(__lowerCamelCase      ,   __lowerCamelCase      ,   prev_module_hook=__lowerCamelCase							)

							# 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       :  List[str]							)      ->  List[str]:
							if not hasattr(self.unet      ,   "_hf_hook"							):
										return self.device
							for module in self.unet.modules():
										if (
										    hasattr(__lowerCamelCase      ,   "_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(__lowerCamelCase							)
				def __call__(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  Union[torch.FloatTensor, List[torch.FloatTensor]]      ,   __lowerCamelCase       :  Union[torch.FloatTensor, List[torch.FloatTensor]]      ,   __lowerCamelCase       :  torch.FloatTensor      ,   __lowerCamelCase       :  int = 5_12      ,   __lowerCamelCase       :  int = 5_12      ,   __lowerCamelCase       :  int = 1_00      ,   __lowerCamelCase       :  float = 4.0      ,   __lowerCamelCase       :  int = 1      ,   __lowerCamelCase       :  Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,   __lowerCamelCase       :  Optional[torch.FloatTensor] = None      ,   __lowerCamelCase       :  Optional[str] = "pil"      ,   __lowerCamelCase       :  bool = True      ,   )      ->  List[Any]:
							a					=    self._execution_device

							a					=    guidance_scale > 1.0

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

							a					=    image_embeds.shape[0] * num_images_per_prompt

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

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

							a					=    self.movq.config.latent_channels

							a					,	a					=    downscale_height_and_width(__lowerCamelCase      ,   __lowerCamelCase      ,   self.movq_scale_factor							)

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

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

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=__lowerCamelCase							)












from __future__ import annotations

import time

import numpy as np

__lowerCAmelCase	:       List[str]     =	[8, 5, 9, 7]
__lowerCAmelCase	:       str     =	[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__lowerCAmelCase	:       Optional[Any]     =	[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]




class 					snake_case__							:
				"""simple docstring"""



				def __init__(   self       :  Any      ,   __lowerCamelCase       :  list[int]      ,   __lowerCamelCase       :  list[list[int]]      ,   __lowerCamelCase       :  list[list[int]]      ,   )      ->  None:
							a					=    claim_vector
							a					=    allocated_resources_table
							a					=    maximum_claim_table





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  list[int]:
							return [
							    sum(p_item[i] for p_item in self.__allocated_resources_table							)
							    for i in range(len(self.__allocated_resources_table[0]							)							)
							]





				def 							__UpperCAmelCase							(   self       :  str							)      ->  list[int]:
							return np.array(self.__claim_vector							) - np.array(
							    self.__processes_resource_summation()							)





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  list[list[int]]:
							return [
							    list(np.array(self.__maximum_claim_table[i]							) - np.array(__lowerCamelCase							)							)
							    for i, allocated_resource in enumerate(self.__allocated_resources_table							)
							]





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  dict[int, list[int]]:
							return {self.__need().index(__lowerCamelCase							): i for i in self.__need()}





				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   **__lowerCamelCase       :  Any							)      ->  None:
							a					=    self.__need()
							a					=    self.__allocated_resources_table
							a					=    self.__available_resources()
							a					=    self.__need_index_manager()
							for kw, val in kwargs.items():
										if kw and val is True:
													self.__pretty_data()
							print("_" * 50 + "\n"							)
							while need_list:
										a					=    False
										for each_need in need_list:
													a					=    True
													for index, need in enumerate(__lowerCamelCase							):
																if need > available_resources[index]:
																			a					=    False
																			break
													if execution:
																a					=    True
																# get the original index of the process from ind_ctrl db
																for original_need_index, need_clone in need_index_manager.items():
																			if each_need == need_clone:
																						a					=    original_need_index
																print(f"""Process {process_number + 1} is executing."""							)
																# remove the process run from stack
																need_list.remove(__lowerCamelCase							)
																# update available/freed resources stack
																a					=    np.array(__lowerCamelCase							) + np.array(
																    alloc_resources_table[process_number]							)
																print(
																    "Updated available resource stack for processes: "
																    + " ".join([str(__lowerCamelCase							) for x in available_resources]							)							)
																break
										if safe:
													print("The process is in a safe state.\n"							)
										else:
													print("System in unsafe state. Aborting...\n"							)
													break





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  str:
							print(" " * 9 + "Allocated Resource Table"							)
							for item in self.__allocated_resources_table:
										print(
										    f"""P{self.__allocated_resources_table.index(__lowerCamelCase							) + 1}"""
										    + " ".join(f"""{it:>8}""" for it in item							)
										    + "\n"							)
							print(" " * 9 + "System Resource Table"							)
							for item in self.__maximum_claim_table:
										print(
										    f"""P{self.__maximum_claim_table.index(__lowerCamelCase							) + 1}"""
										    + " ".join(f"""{it:>8}""" for it in item							)
										    + "\n"							)
							print(
							    "Current Usage by Active Processes: "
							    + " ".join(str(__lowerCamelCase							) for x in self.__claim_vector							)							)
							print(
							    "Initial Available Resources:       "
							    + " ".join(str(__lowerCamelCase							) for x in self.__available_resources()							)							)
							time.sleep(1							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()






