



"""simple docstring"""







from __future__ import annotations

from typing import Any





class   UpperCamelCase	:




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_				,UpperCAmelCase_   ):  # noqa: A002
			_lowercase				: Optional[Any]     =   item
			_lowercase				: List[str]     =   next







class   UpperCamelCase	:




	"""simple docstring"""






	def __init__(     self   ):
			_lowercase				: Node | None     =   None
			_lowercase				: Union[str, Any]     =   0






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: int     =   Node(UpperCAmelCase_				,self.head   )
			self.size += 1






	def    lowerCamelCase__					(     self   ):
			# Switched 'self.is_empty()' to 'self.head is None'
			# because mypy was considering the possibility that 'self.head'
			# can be None in below else part and giving error
			if self.head is None:
					return None
			else:
					_lowercase				: Any     =   self.head.item
					_lowercase				: Tuple     =   self.head.next
					self.size -= 1
					return item






	def    lowerCamelCase__					(     self   ):
			return self.head is None






	def __str__(     self   ):
			if self.is_empty():
					return ""
			else:
					_lowercase				: Tuple     =   self.head
					_lowercase				: Union[str, Any]     =   """"""
					_lowercase				: list[str]     =   []
					while iterate:
							item_list.append(str(iterate.item   )   )
							_lowercase				: str     =   iterate.next

					_lowercase				: Tuple     =   """ --> """.join(UpperCAmelCase_   )

					return item_str






	def __len__(     self   ):
			return self.size











'''simple docstring'''


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE						):


			'''simple docstring'''





			__a				:				Union[List[PIL.Image.Image], np.ndarray]
			__a				:				Optional[List[bool]]


if is_transformers_available() and is_torch_available():
					from .modeling_roberta_series import RobertaSeriesModelWithTransformation
					from .pipeline_alt_diffusion import AltDiffusionPipeline
					from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
