"""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,
)


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

_UpperCamelCase								=		"\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  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase=8							):
							A__       :     List[Any]								=			height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							A__       :     Any								=			width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor



class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      ):







										'''simple docstring'''


										super().__init__()

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





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										if latents is None:
																	A__       :     List[str]								=			randn_tensor(snake_case_   ,      generator=snake_case_   ,      device=snake_case_   ,      dtype=snake_case_							)
										else:
																	if latents.shape != shape:
																								raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''							)
																	A__       :     Tuple								=			latents.to(snake_case_							)

										A__       :     List[str]								=			latents * scheduler.init_noise_sigma
										return latents





			def 		lowerCamelCase					(			self   ,      snake_case_=0							):







										'''simple docstring'''


										if is_accelerate_available():
																	from accelerate import cpu_offload
										else:
																	raise ImportError("""Please install accelerate via `pip install accelerate`"""							)

										A__       :     List[Any]								=			torch.device(F'''cuda:{gpu_id}'''							)

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





			def 		lowerCamelCase					(			self   ,      snake_case_=0							):







										'''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."""							)

										A__       :     int								=			torch.device(F'''cuda:{gpu_id}'''							)

										if self.device.type != "cpu":
																	self.to("""cpu"""   ,      silence_dtype_warnings=snake_case_							)
																	torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

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

										# We'll offload the last model manually.
										A__       :     Dict								=			hook





			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										if not hasattr(self.unet   ,      """_hf_hook"""							):
																	return self.device
										for module in self.unet.modules():
																	if (
																	    hasattr(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(snake_case_							)
			def __call__(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_ = 512   ,      snake_case_ = 512   ,      snake_case_ = 100   ,      snake_case_ = 4.0   ,      snake_case_ = 1   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = "pil"   ,      snake_case_ = True   ,      ):







										'''simple docstring'''


										A__       :     int								=			self._execution_device

										A__       :     int								=			guidance_scale > 1.0

										if isinstance(snake_case_   ,      snake_case_							):
																	A__       :     Tuple								=			torch.cat(snake_case_   ,      dim=0							)
										if isinstance(snake_case_   ,      snake_case_							):
																	A__       :     List[Any]								=			torch.cat(snake_case_   ,      dim=0							)
										if isinstance(snake_case_   ,      snake_case_							):
																	A__       :     Optional[Any]								=			torch.cat(snake_case_   ,      dim=0							)

										A__       :     Tuple								=			image_embeds.shape[0] * num_images_per_prompt

										if do_classifier_free_guidance:
																	A__       :     List[Any]								=			image_embeds.repeat_interleave(snake_case_   ,      dim=0							)
																	A__       :     List[Any]								=			negative_image_embeds.repeat_interleave(snake_case_   ,      dim=0							)
																	A__       :     Any								=			hint.repeat_interleave(snake_case_   ,      dim=0							)

										A__       :     Union[str, Any]								=			torch.cat([negative_image_embeds, image_embeds]   ,      dim=0							).to(dtype=self.unet.dtype   ,      device=snake_case_							)
										A__       :     int								=			torch.cat([hint, hint]   ,      dim=0							).to(dtype=self.unet.dtype   ,      device=snake_case_							)
										self.scheduler.set_timesteps(snake_case_   ,      device=snake_case_							)
										A__       :     Optional[Any]								=			self.scheduler.timesteps

										A__       :     List[Any]								=			self.movq.config.latent_channels

										A__				,							A__       :     int								=			downscale_height_and_width(snake_case_   ,      snake_case_   ,      self.movq_scale_factor							)

										# create initial latent
										A__       :     List[str]								=			self.prepare_latents(
										    (batch_size, num_channels_latents, height, width)   ,      image_embeds.dtype   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      self.scheduler   ,      )

										for i, t in enumerate(self.progress_bar(snake_case_							)							):
																	# expand the latents if we are doing classifier free guidance
																	A__       :     List[str]								=			torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

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

																	if do_classifier_free_guidance:
																								A__				,							A__       :     List[Any]								=			noise_pred.split(latents.shape[1]   ,      dim=1							)
																								A__				,							A__       :     Tuple								=			noise_pred.chunk(2							)
																								A__				,							A__       :     str								=			variance_pred.chunk(2							)
																								A__       :     Tuple								=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																								A__       :     List[str]								=			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__       :     Union[str, Any]								=			noise_pred.split(latents.shape[1]   ,      dim=1							)

																	# compute the previous noisy sample x_t -> x_t-1
																	A__       :     str								=			self.scheduler.step(
																	    snake_case_   ,      snake_case_   ,      snake_case_   ,      generator=snake_case_   ,      )[0]
										# post-processing
										A__       :     Dict								=			self.movq.decode(snake_case_   ,      force_not_quantize=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__       :     Dict								=			image * 0.5 + 0.5
																	A__       :     Optional[int]								=			image.clamp(0   ,      1							)
																	A__       :     Union[str, Any]								=			image.cpu().permute(0   ,      2   ,      3   ,      1							).float().numpy()

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

										if not return_dict:
																	return (image,)

										return ImagePipelineOutput(images=snake_case_							)






"""simple docstring"""



from collections import defaultdict
class 						__UpperCAmelCase    :







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Tuple								=			total  # total no of tasks (N)

										# DP table will have a dimension of (2^M)*N
										# initially all values are set to -1
										A__       :     Optional[int]								=			[
										    [-1 for i in range(total + 1							)] for j in range(2 ** len(snake_case_							)							)
										]

										A__       :     Optional[int]								=			defaultdict(snake_case_							)  # stores the list of persons for each task

										# final_mask is used to check if all persons are included by setting all bits
										# to 1
										A__       :     Optional[int]								=			(1 << len(snake_case_							)) - 1





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										if mask == self.final_mask:
																	return 1

										# if not everyone gets the task and no more tasks are available, return 0
										if task_no > self.total_tasks:
																	return 0

										# if case already considered
										if self.dp[mask][task_no] != -1:
																	return self.dp[mask][task_no]

										# Number of ways when we don't this task in the arrangement
										A__       :     Optional[int]								=			self.count_ways_until(snake_case_   ,      task_no + 1							)

										# now assign the tasks one by one to all possible persons and recursively
										# assign for the remaining tasks.
										if task_no in self.task:
																	for p in self.task[task_no]:
																								# if p is already given a task
																								if mask & (1 << p):
																															continue

																								# assign this task to p and change the mask value. And recursively
																								# assign tasks with the new mask value.
																								total_ways_util += self.count_ways_until(mask | (1 << p)   ,      task_no + 1							)

        # save the value.
										A__       :     List[str]								=			total_ways_util

										return self.dp[mask][task_no]





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										for i in range(len(snake_case_							)							):
																	for j in task_performed[i]:
																								self.task[j].append(snake_case_							)

        # call the function to fill the DP table, final answer is stored in dp[0][1]
										return self.count_ways_until(0   ,      1							)


if __name__ == "__main__":
				_UpperCamelCase								=		5  # total no of tasks (the value of N)

				# the list of tasks that can be done by M persons.
				_UpperCamelCase								=		[[1, 3, 4], [1, 2, 5], [3, 4]]
				print(
				    AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
				        task_performed
				    )
				)





