

from collections import deque

from .hash_table import HashTable






class      lowerCamelCase_      (	_lowercase	):




	def __init__(     self						:     List[Any]					,      *__A						:     str					,      **__A						:     Union[str, Any]							):
		super().__init__(*__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     str					,      __A						:     str					,      __A						:     Optional[Any]							):
		__A :			Tuple		    =			deque([]							) if self.values[key] is None else self.values[key]
		self.values[key].appendleft(__A							)
		__A :			Optional[int]		    =			self.values[key]




	def        lowerCAmelCase_       (     self						:     Any							):
		return (
		    sum(self.charge_factor - len(__A							) for slot in self.values							)
		    / self.size_table
		    * self.charge_factor
		)




	def        lowerCAmelCase_       (     self						:     Optional[int]					,      __A						:     int					,      __A						:     Union[str, Any]=None							):
		if not (
		    len(self.values[key]							) == self.charge_factor and self.values.count(__A							) == 0
		):
			return key
		return super()._collision_resolution(__A					,      __A							)








'''simple docstring'''

import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							List[Any]:
											super().__init__()
											self.register_modules(vqvae=_lowercase,     unet=_lowercase,     scheduler=_lowercase       )





				@torch.no_grad()
				def __call__(    self,     _lowercase = 1,     _lowercase = None,     _lowercase = 0.0,     _lowercase = 50,     _lowercase = "pil",     _lowercase = True,     **_lowercase,     )     ->							Union[Tuple, ImagePipelineOutput]:
											SCREAMING_SNAKE_CASE_				=				randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),     generator=_lowercase,     )
											SCREAMING_SNAKE_CASE_				=				latents.to(self.device       )

											# scale the initial noise by the standard deviation required by the scheduler
											SCREAMING_SNAKE_CASE_				=				latents * self.scheduler.init_noise_sigma

											self.scheduler.set_timesteps(_lowercase       )

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											SCREAMING_SNAKE_CASE_				=				'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

											SCREAMING_SNAKE_CASE_				=				{}
											if accepts_eta:
																		SCREAMING_SNAKE_CASE_				=				eta

											for t in self.progress_bar(self.scheduler.timesteps       ):
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.scale_model_input(_lowercase,     _lowercase       )
																		# predict the noise residual
																		SCREAMING_SNAKE_CASE_				=				self.unet(_lowercase,     _lowercase       ).sample
																		# compute the previous noisy sample x_t -> x_t-1
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.step(_lowercase,     _lowercase,     _lowercase,     **_lowercase       ).prev_sample

											# decode the image latents with the VAE
											SCREAMING_SNAKE_CASE_				=				self.vqvae.decode(_lowercase       ).sample

											SCREAMING_SNAKE_CASE_				=				(image / 2 + 0.5).clamp(0,     1       )
											SCREAMING_SNAKE_CASE_				=				image.cpu().permute(0,     2,     3,     1       ).numpy()
											if output_type == "pil":
																		SCREAMING_SNAKE_CASE_				=				self.numpy_to_pil(_lowercase       )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=_lowercase       )





