


from __future__ import annotations

from typing import Generic, TypeVar

snake_case_  :	Union[str, Any]							  =							TypeVar('''T''')

class   A__							(						Generic[T]     ):




						def __init__(				self    :					List[Any] ,			_a    :					T     )							->   str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =data  # Assign data
										_SCREAMING_SNAKE_CASE	     =None  # Initialize next as null
										_SCREAMING_SNAKE_CASE	     =None  # Initialize prev as null

class   A__							(						Generic[T]     ):




						def __init__(				self    :					Dict     )							->   None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =None




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					T     )							->   None:


										"""simple docstring"""





										if self.head is None:
														_SCREAMING_SNAKE_CASE	     =Node(_a     )
										else:
														_SCREAMING_SNAKE_CASE	     =Node(_a     )
														_SCREAMING_SNAKE_CASE	     =new_node
														_SCREAMING_SNAKE_CASE	     =self.head
														_SCREAMING_SNAKE_CASE	     =None
														_SCREAMING_SNAKE_CASE	     =new_node




						def 							__UpperCamelCase (				self    :					Any     )							->   T | None:


										"""simple docstring"""





										if self.head is None:
														return None
										else:
														assert self.head is not None
														_SCREAMING_SNAKE_CASE	     =self.head.data
														_SCREAMING_SNAKE_CASE	     =self.head.next
														if self.head is not None:
																		_SCREAMING_SNAKE_CASE	     =None
														return temp




						def 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   T | None:


										"""simple docstring"""





										return self.head.data if self.head is not None else None




						def __len__(				self    :					Optional[int]     )							->   int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.head
										_SCREAMING_SNAKE_CASE	     =0
										while temp is not None:
														count += 1
														_SCREAMING_SNAKE_CASE	     =temp.next
										return count




						def 							__UpperCamelCase (				self    :					int     )							->   bool:


										"""simple docstring"""





										return self.head is None




						def 							__UpperCamelCase (				self    :					Tuple     )							->   None:


										"""simple docstring"""





										print('''stack elements are:'''     )
										_SCREAMING_SNAKE_CASE	     =self.head
										while temp is not None:
														print(temp.data ,			end='''->'''     )
														_SCREAMING_SNAKE_CASE	     =temp.next


# Code execution starts here
if __name__ == "__main__":
							# Start with the empty stack
							snake_case_  :	Stack[int]							  =							Stack()

							# Insert 4 at the beginning. So stack becomes 4->None
							print('''Stack operations using Doubly LinkedList''')
							stack.push(4)

							# Insert 5 at the beginning. So stack becomes 4->5->None
							stack.push(5)

							# Insert 6 at the beginning. So stack becomes 4->5->6->None
							stack.push(6)

							# Insert 7 at the beginning. So stack becomes 4->5->6->7->None
							stack.push(7)

							# Print the stack
							stack.print_stack()

							# Print the top element
							print('''\nTop element is ''', stack.top())

							# Print the stack size
							print('''Size of the stack is ''', len(stack))

							# pop the top element
							stack.pop()

							# pop the top element
							stack.pop()

							# two elements have now been popped off
							stack.print_stack()

							# Print True if the stack is empty else False
							print('''\nstack is empty:''', stack.is_empty())



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class   A__							(						UpperCamelCase__     ):




						def __init__(				self    :					Any ,			_a    :					Optional[Any] ,			_a    :					List[str]     )							->   Tuple:


										"""simple docstring"""





										super().__init__()
										self.register_modules(unet=_a ,			scheduler=_a     )




						@torch.no_grad()
						def __call__(				self    :					Any ,			_a    :					int = 1 ,			_a    :					Optional[torch.Generator] = None ,			_a    :					int = 50 ,			_a    :					Optional[str] = "pil" ,			_a    :					bool = True ,			**_a    :					Dict ,			)							->   Union[ImagePipelineOutput, Tuple]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =torch.randn(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,			generator=_a ,			)
										_SCREAMING_SNAKE_CASE	     =image.to(self.device     )

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

										for t in self.progress_bar(self.scheduler.timesteps     ):
														# 1. predict noise model_output
														_SCREAMING_SNAKE_CASE	     =self.unet(_a ,			_a     ).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
														_SCREAMING_SNAKE_CASE	     =self.scheduler.step(_a ,			_a ,			_a     ).prev_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(_a     )

										if not return_dict:
														return (image,), "This is a local test"

										return ImagePipelineOutput(images=_a     ), "This is a local test"