






"""simple docstring"""



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__       :				List[str] 					=					logging.get_logger(__name__)  # pylint: disable=invalid-name

A__       :				str 					=					"\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"

def  _snake_case				(     lowerCamelCase__	:    Optional[Any]				,						lowerCamelCase__	:    Optional[int]				,						lowerCamelCase__	:    Optional[int]=8  )				->	Tuple:
							lowerCamelCase_       :							Dict									=height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							lowerCamelCase_       :							int									=width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor

def  _snake_case				(     lowerCamelCase__	:    List[str]				,						lowerCamelCase__	:    Any=512				,						lowerCamelCase__	:    Dict=512  )				->	Union[str, Any]:
							lowerCamelCase_       :							str									=pil_image.resize((w, h)				,						resample=Image.BICUBIC				,						reducing_gap=1  )
							lowerCamelCase_       :							Any									=np.array(pil_image.convert("RGB"  )  )
							lowerCamelCase_       :							List[str]									=arr.astype(np.floataa  ) / 127.5 - 1
							lowerCamelCase_       :							List[str]									=np.transpose(__SCREAMING_SNAKE_CASE				,						[2, 0, 1]  )
							lowerCamelCase_       :							Union[str, Any]									=torch.from_numpy(__SCREAMING_SNAKE_CASE  ).unsqueeze(0  )
							return image







class    lowercase__			(							lowerCamelCase_   ):


			def __init__(      self     :  Any   ,       snake_case__     :  Union[str, Any]   ,       snake_case__     :  int   ,       snake_case__     :  Optional[int]   ,       ):
										super().__init__()

										self.register_modules(
										    unet=__SCREAMING_SNAKE_CASE   ,       scheduler=__SCREAMING_SNAKE_CASE   ,       movq=__SCREAMING_SNAKE_CASE   ,       )
										lowerCamelCase_       :							Optional[Any]									=2 ** (len(self.movq.config.block_out_channels					) - 1)


			def 	UpperCAmelCase__						(      self     :  List[str]   ,       snake_case__     :  List[str]   ,       snake_case__     :  List[str]   ,       snake_case__     :  Dict					):
										lowerCamelCase_       :							Optional[int]									=min(int(num_inference_steps * strength					)   ,       __SCREAMING_SNAKE_CASE					)

										lowerCamelCase_       :							Optional[Any]									=max(num_inference_steps - init_timestep   ,       0					)
										lowerCamelCase_       :							Tuple									=self.scheduler.timesteps[t_start:]

										return timesteps, num_inference_steps - t_start


			def 	UpperCAmelCase__						(      self     :  Dict   ,       snake_case__     :  List[Any]   ,       snake_case__     :  Optional[int]   ,       snake_case__     :  Optional[int]   ,       snake_case__     :  int   ,       snake_case__     :  Any   ,       snake_case__     :  List[Any]   ,       snake_case__     :  Optional[int]=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					)}"""					)

										lowerCamelCase_       :							Union[str, Any]									=image.to(device=__SCREAMING_SNAKE_CASE   ,       dtype=__SCREAMING_SNAKE_CASE					)

										lowerCamelCase_       :							Optional[int]									=batch_size * num_images_per_prompt

										if image.shape[1] == 4:
																	lowerCamelCase_       :							Tuple									=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					):
																								lowerCamelCase_       :							Tuple									=[
																								    self.movq.encode(image[i : i + 1]					).latent_dist.sample(generator[i]					) for i in range(__SCREAMING_SNAKE_CASE					)
																								]
																								lowerCamelCase_       :							Any									=torch.cat(__SCREAMING_SNAKE_CASE   ,       dim=0					)
																	else:
																								lowerCamelCase_       :							List[Any]									=self.movq.encode(__SCREAMING_SNAKE_CASE					).latent_dist.sample(__SCREAMING_SNAKE_CASE					)

																	lowerCamelCase_       :							Dict									=self.movq.config.scaling_factor * init_latents

										lowerCamelCase_       :							List[Any]									=torch.cat([init_latents]   ,       dim=0					)

										lowerCamelCase_       :							List[Any]									=init_latents.shape
										lowerCamelCase_       :							str									=randn_tensor(__SCREAMING_SNAKE_CASE   ,       generator=__SCREAMING_SNAKE_CASE   ,       device=__SCREAMING_SNAKE_CASE   ,       dtype=__SCREAMING_SNAKE_CASE					)

										# get latents
										lowerCamelCase_       :							str									=self.scheduler.add_noise(__SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE					)

										lowerCamelCase_       :							Any									=init_latents

										return latents


			def 	UpperCAmelCase__						(      self     :  str   ,       snake_case__     :  Optional[int]=0					):





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

										lowerCamelCase_       :							Tuple									=torch.device(F"""cuda:{gpu_id}"""					)

										lowerCamelCase_       :							str									=[
										    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 	UpperCAmelCase__						(      self     :  Union[str, Any]   ,       snake_case__     :  Tuple=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."					)

										lowerCamelCase_       :							str									=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)

										lowerCamelCase_       :							str									=None
										for cpu_offloaded_model in [self.unet, self.movq]:
																	lowerCamelCase_       :							Optional[int]									=cpu_offload_with_hook(__SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE   ,       prev_module_hook=__SCREAMING_SNAKE_CASE					)

										# We'll offload the last model manually.
										lowerCamelCase_       :							Any									=hook


			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 	UpperCAmelCase__						(      self     :  Union[str, Any]					):





										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     :  Any   ,       snake_case__     :  Any   ,       snake_case__     :  List[Any]   ,       snake_case__     :  Dict   ,       snake_case__     :  str = 512   ,       snake_case__     :  int = 512   ,       snake_case__     :  Dict = 100   ,       snake_case__     :  str = 4.0   ,       snake_case__     :  Optional[Any] = 0.3   ,       snake_case__     :  List[Any] = 1   ,       snake_case__     :  List[Any] = None   ,       snake_case__     :  Dict = "pil"   ,       snake_case__     :  Optional[Any] = True   ,       ):
										lowerCamelCase_       :							Tuple									=self._execution_device

										lowerCamelCase_       :							str									=guidance_scale > 1.0

										if isinstance(__SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE					):
																	lowerCamelCase_       :							str									=torch.cat(__SCREAMING_SNAKE_CASE   ,       dim=0					)
										lowerCamelCase_       :							Union[str, Any]									=image_embeds.shape[0]
										if isinstance(__SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE					):
																	lowerCamelCase_       :							str									=torch.cat(__SCREAMING_SNAKE_CASE   ,       dim=0					)

										if do_classifier_free_guidance:
																	lowerCamelCase_       :							List[Any]									=image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE   ,       dim=0					)
																	lowerCamelCase_       :							Optional[Any]									=negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE   ,       dim=0					)

										lowerCamelCase_       :							Union[str, Any]									=torch.cat([negative_image_embeds, image_embeds]   ,       dim=0					).to(dtype=self.unet.dtype   ,       device=__SCREAMING_SNAKE_CASE					)

										if not isinstance(__SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE					):
																	lowerCamelCase_       :							Dict									=[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"""					)

										lowerCamelCase_       :							Any									=torch.cat([prepare_image(__SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE					) for i in image]   ,       dim=0					)
										lowerCamelCase_       :							Optional[Any]									=image.to(dtype=image_embeds.dtype   ,       device=__SCREAMING_SNAKE_CASE					)

										lowerCamelCase_       :							List[str]									=self.movq.encode(__SCREAMING_SNAKE_CASE					)['''latents''']
										lowerCamelCase_       :							List[Any]									=latents.repeat_interleave(__SCREAMING_SNAKE_CASE   ,       dim=0					)
										self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE   ,       device=__SCREAMING_SNAKE_CASE					)
										lowerCamelCase_       :							List[str]									=self.get_timesteps(__SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE					)
										lowerCamelCase_       :							List[Any]									=timesteps[:1].repeat(batch_size * num_images_per_prompt					)
										lowerCamelCase_       :							Optional[Any]									=downscale_height_and_width(__SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE   ,       self.movq_scale_factor					)
										lowerCamelCase_       :							Tuple									=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
																	lowerCamelCase_       :							List[Any]									=torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

																	lowerCamelCase_       :							Optional[int]									={'''image_embeds''': image_embeds}
																	lowerCamelCase_       :							List[Any]									=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:
																								lowerCamelCase_       :							List[Any]									=noise_pred.split(latents.shape[1]   ,       dim=1					)
																								lowerCamelCase_       :							str									=noise_pred.chunk(2					)
																								lowerCamelCase_       :							Optional[int]									=variance_pred.chunk(2					)
																								lowerCamelCase_       :							Union[str, Any]									=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																								lowerCamelCase_       :							Dict									=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"]
																	):
																								lowerCamelCase_       :							str									=noise_pred.split(latents.shape[1]   ,       dim=1					)

																	# compute the previous noisy sample x_t -> x_t-1
																	lowerCamelCase_       :							Tuple									=self.scheduler.step(
																	    __SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE   ,       generator=__SCREAMING_SNAKE_CASE   ,       )[0]

										# post-processing
										lowerCamelCase_       :							Optional[int]									=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"]:
																	lowerCamelCase_       :							Tuple									=image * 0.5 + 0.5
																	lowerCamelCase_       :							int									=image.clamp(0   ,       1					)
																	lowerCamelCase_       :							Any									=image.cpu().permute(0   ,       2   ,       3   ,       1					).float().numpy()

										if output_type == "pil":
																	lowerCamelCase_       :							Optional[Any]									=self.numpy_to_pil(__SCREAMING_SNAKE_CASE					)

										if not return_dict:
																	return (image,)

										return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE					)





'''simple docstring'''







import unittest

from knapsack import greedy_knapsack as kp






class  lowerCAmelCase__   (  unittest.TestCase     ):






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       [10, 20, 30, 40, 50, 60]
											lowercase_	:     Optional[Any]													=       [2, 4, 6, 8, 10, 12]
											lowercase_	:     Union[str, Any]													=       1_00
											self.assertEqual(kp.calc_profit(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)				,							2_10		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.assertRaisesRegex(__SCREAMING_SNAKE_CASE				,							'''max_weight must greater than zero.'''		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.assertRaisesRegex(__SCREAMING_SNAKE_CASE				,							'''Weight can not be negative.'''		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.assertRaisesRegex(__SCREAMING_SNAKE_CASE				,							'''Profit can not be negative.'''		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.assertRaisesRegex(__SCREAMING_SNAKE_CASE				,							'''max_weight must greater than zero.'''		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.assertRaisesRegex(
											    __SCREAMING_SNAKE_CASE				,							'''The length of profit and weight must be same.'''		)


if __name__ == "__main__":
							unittest.main()



