
'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 	snake_case__       (      snake_case_					):







		"""simple docstring"""

		def __init__(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			Optional[Any]      )						->			Optional[Any]:






				"""simple docstring"""







				super().__init__()

				# make sure scheduler can always be converted to DDIM
				snake_case  :       Dict     			=						DDIMScheduler.from_config(scheduler.config      )

				self.register_modules(unet=UpperCamelCase__  ,	scheduler=UpperCamelCase__      )



		@torch.no_grad()
		def __call__(      self				:			Optional[int]  ,	UpperCamelCase__				:			List[str] = 1  ,	UpperCamelCase__				:			Union[str, Any] = None  ,	UpperCamelCase__				:			Optional[Any] = 0.0  ,	UpperCamelCase__				:			Optional[int] = 50  ,	UpperCamelCase__				:			Tuple = None  ,	UpperCamelCase__				:			Optional[int] = "pil"  ,	UpperCamelCase__				:			Optional[int] = True  ,	)						->			Union[ImagePipelineOutput, Tuple]:






				"""simple docstring"""







				if isinstance(self.unet.config.sample_size  ,	UpperCamelCase__      ):
						snake_case  :       int     			=						(
						    batch_size,
						    self.unet.config.in_channels,
						    self.unet.config.sample_size,
						    self.unet.config.sample_size,
						)
				else:
						snake_case  :       str     			=						(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

				if isinstance(UpperCamelCase__  ,	UpperCamelCase__      ) and len(UpperCamelCase__      ) != batch_size:
						raise ValueError(
						    f'You have passed a list of generators of length {len(UpperCamelCase__      )}, but requested an effective batch'
						    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'      )

				snake_case  :       Any     			=						randn_tensor(UpperCamelCase__  ,	generator=UpperCamelCase__  ,	device=self.device  ,	dtype=self.unet.dtype      )

				# set step values
				self.scheduler.set_timesteps(UpperCamelCase__      )

				for t in self.progress_bar(self.scheduler.timesteps      ):
						# 1. predict noise model_output
						snake_case  :       int     			=						self.unet(UpperCamelCase__  ,	UpperCamelCase__      ).sample

						# 2. predict previous mean of image x_t-1 and add variance depending on eta
						# eta corresponds to η in paper and should be between [0, 1]
						# do x_t -> x_t-1
						snake_case  :       Union[str, Any]     			=						self.scheduler.step(
						    UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	eta=UpperCamelCase__  ,	use_clipped_model_output=UpperCamelCase__  ,	generator=UpperCamelCase__      ).prev_sample

				snake_case  :       str     			=						(image / 2 + 0.5).clamp(0  ,	1      )
				snake_case  :       Optional[int]     			=						image.cpu().permute(0  ,	2  ,	3  ,	1      ).numpy()
				if output_type == "pil":
						snake_case  :       Dict     			=						self.numpy_to_pil(UpperCamelCase__      )

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=UpperCamelCase__      )










from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

lowercase    				=   TypeVar("T")




class 					UpperCamelCase_ (     Generic[T]							):



	'''simple docstring'''




	lowerCAmelCase						=					42  # Cache store of keys
	lowerCAmelCase						=					42  # References of the keys in cache
	lowerCAmelCase						=					1_0  # Maximum capacity of cache




	def __init__(			self			,     a   )			->       None:
								snake_case_           =				deque()
								snake_case_           =				set()
								if not n:
															snake_case_           =				sys.maxsize
								elif n < 0:
															raise ValueError('n should be an integer greater than 0.'   )
								else:
															snake_case_           =				n




	def 				_UpperCamelCase     (			self			,     a   )			->       None:
								if x not in self.key_reference:
															if len(self.dq_store   ) == LRUCache._MAX_CAPACITY:
																						snake_case_           =				self.dq_store.pop()
																						self.key_reference.remove(a   )
								else:
															self.dq_store.remove(a   )

								self.dq_store.appendleft(a   )
								self.key_reference.add(a   )




	def 				_UpperCamelCase     (			self   )			->       None:
								for k in self.dq_store:
															print(a   )






	def __repr__(			self   )			->       str:
								return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store   )}'''


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	lowercase    				=   LRUCache(4)
	lru_cache.refer("A")
	lru_cache.refer(2)
	lru_cache.refer(3)
	lru_cache.refer("A")
	lru_cache.refer(4)
	lru_cache.refer(5)
	lru_cache.display()

	print(lru_cache)
	assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"





