

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,
)


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

a_						     =    '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n        >>> pipe_prior.to("cuda")\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n        >>> pipe.to("cuda")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save("cat.png")\n        ```\n'

def  lowerCamelCase__ (					_a							,			_a							,			_a=8):
	SCREAMING_SNAKE_CASE   :      Dict				     =       height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	SCREAMING_SNAKE_CASE   :      Tuple				     =       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							: Optional[Any]      ,      a							: UNetaDConditionModel      ,      a							: DDPMScheduler      ,      a							: VQModel      ,      )							->       Any:

				"""simple docstring"""

				super().__init__()

				self.register_modules(
				    unet=a      ,      scheduler=a      ,      movq=a      ,      )
				SCREAMING_SNAKE_CASE   :      Any				     =       2 ** (len(self.movq.config.block_out_channels	) - 1)
			def 							__UpperCamelCase (			self							: int      ,      a							: Dict      ,      a							: Any      ,      a							: Optional[int]      ,      a							: Union[str, Any]      ,      a							: Optional[int]      ,      a							: Optional[int]	)							->       List[Any]:

				"""simple docstring"""

				if latents is None:
					SCREAMING_SNAKE_CASE   :      Dict				     =       randn_tensor(a      ,      generator=a      ,      device=a      ,      dtype=a	)
				else:
					if latents.shape != shape:
						raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"	)
					SCREAMING_SNAKE_CASE   :      Dict				     =       latents.to(a	)

				SCREAMING_SNAKE_CASE   :      str				     =       latents * scheduler.init_noise_sigma
				return latents
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Optional[int]=0	)							->       Optional[int]:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.device(F"cuda:{gpu_id}"	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       [
				    self.unet,
				    self.movq,
				]
				for cpu_offloaded_model in models:
					if cpu_offloaded_model is not None:
						cpu_offload(a      ,      a	)
			def 							__UpperCamelCase (			self							: Any      ,      a							: Optional[Any]=0	)							->       str:

				"""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."	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       torch.device(F"cuda:{gpu_id}"	)

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

				SCREAMING_SNAKE_CASE   :      Tuple				     =       None
				for cpu_offloaded_model in [self.unet, self.movq]:
					SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Any				     =       cpu_offload_with_hook(a      ,      a      ,      prev_module_hook=a	)

				# We'll offload the last model manually.
				SCREAMING_SNAKE_CASE   :      List[str]				     =       hook
			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       List[str]:

				"""simple docstring"""

				if not hasattr(self.unet      ,      "_hf_hook"	):
					return self.device
				for module in self.unet.modules():
					if (
					    hasattr(a      ,      "_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(a	)
			def __call__(			self							: List[Any]      ,      a							: Union[torch.FloatTensor, List[torch.FloatTensor]]      ,      a							: Union[torch.FloatTensor, List[torch.FloatTensor]]      ,      a							: int = 512      ,      a							: int = 512      ,      a							: int = 100      ,      a							: float = 4.0      ,      a							: int = 1      ,      a							: Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,      a							: Optional[torch.FloatTensor] = None      ,      a							: Optional[str] = "pil"      ,      a							: bool = True      ,      )							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self._execution_device

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       guidance_scale > 1.0

				if isinstance(a      ,      a	):
					SCREAMING_SNAKE_CASE   :      int				     =       torch.cat(a      ,      dim=0	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       image_embeds.shape[0] * num_images_per_prompt
				if isinstance(a      ,      a	):
					SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.cat(a      ,      dim=0	)

				if do_classifier_free_guidance:
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       image_embeds.repeat_interleave(a      ,      dim=0	)
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       negative_image_embeds.repeat_interleave(a      ,      dim=0	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       torch.cat([negative_image_embeds, image_embeds]      ,      dim=0	).to(dtype=self.unet.dtype      ,      device=a	)

				self.scheduler.set_timesteps(a      ,      device=a	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       self.scheduler.timesteps

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.unet.config.in_channels

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Dict				     =       downscale_height_and_width(a      ,      a      ,      self.movq_scale_factor	)

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

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

					SCREAMING_SNAKE_CASE   :      int				     =       {"image_embeds": image_embeds}
					SCREAMING_SNAKE_CASE   :      int				     =       self.unet(
					    sample=a      ,      timestep=a      ,      encoder_hidden_states=a      ,      added_cond_kwargs=a      ,      return_dict=a      ,      )[0]

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

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

				if output_type == "pil":
					SCREAMING_SNAKE_CASE   :      Dict				     =       self.numpy_to_pil(a	)

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=a	)


def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	global f  # a global dp table for knapsack
	if f[i][j] < 0:
		if j < wt[i - 1]:
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       mf_knapsack(i - 1							,			_a							,			_a							,			_a)
		else:
			SCREAMING_SNAKE_CASE   :      Any				     =       max(
			    mf_knapsack(i - 1							,			_a							,			_a							,			_a)							,			mf_knapsack(i - 1							,			_a							,			_a							,			j - wt[i - 1]) + val[i - 1]							,			)
		SCREAMING_SNAKE_CASE   :      Tuple				     =       val
	return f[i][j]

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [[0] * (w + 1) for _ in range(n + 1)]

	for i in range(1							,			n + 1):
		for w_ in range(1							,			w + 1):
			if wt[i - 1] <= w_:
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]							,			dp[i - 1][w_])
			else:
				SCREAMING_SNAKE_CASE   :      int				     =       dp[i - 1][w_]

	return dp[n][w_], dp

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	if not (isinstance(_a							,			(list, tuple)) and isinstance(_a							,			(list, tuple))):
		raise ValueError(
		    "Both the weights and values vectors must be either lists or tuples")

	SCREAMING_SNAKE_CASE   :      int				     =       len(_a)
	if num_items != len(_a):
		SCREAMING_SNAKE_CASE   :      List[str]				     =       (
		    "The number of weights must be the same as the number of values.\n"
		    f"But got {num_items} weights and {len(_a)} values"
		)
		raise ValueError(_a)
	for i in range(_a):
		if not isinstance(wt[i]							,			_a):
			SCREAMING_SNAKE_CASE   :      int				     =       (
			    "All weights must be integers but got weight of "
			    f"type {type(wt[i])} at index {i}"
			)
			raise TypeError(_a)

	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       knapsack(_a							,			_a							,			_a							,			_a)
	SCREAMING_SNAKE_CASE   :      set				     =       set()
	_construct_solution(_a							,			_a							,			_a							,			_a							,			_a)

	return optimal_val, example_optional_set

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a							,			_a):
	# for the current item i at a maximum weight j to be part of an optimal subset,
	# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
	# where i - 1 means considering only the previous items at the given maximum weight
	if i > 0 and j > 0:
		if dp[i - 1][j] == dp[i][j]:
			_construct_solution(_a							,			_a							,			i - 1							,			_a							,			_a)
		else:
			optimal_set.add(_a)
			_construct_solution(_a							,			_a							,			i - 1							,			j - wt[i - 1]							,			_a)


if __name__ == "__main__":
			a_						     =    [3, 2, 4, 4]
			a_						     =    [4, 3, 2, 3]
			a_						     =    4
			a_						     =    6
			a_						     =    [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
			a_     ,	a_						     =    knapsack(w, wt, val, n)
			print(optimal_solution)
			print(mf_knapsack(n, wt, val, w))  # switched the n and w

			# testing the dynamic programming problem with example
			# the optimal subset for the above example are items 3 and 4
			a_     ,	a_						     =    knapsack_with_example_solution(w, wt, val)
			assert optimal_solution == 8
			assert optimal_subset == {3, 4}
			print('optimal_value = ', optimal_solution)
			print('An optimal subset corresponding to the optimal value', optimal_subset)