



from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


__UpperCAmelCase						  =     logging.get_logger(__name__)  # pylint: disable=invalid-name


class 	lowerCamelCase__ (		_a   ):
						_lowerCAmelCase								= ['''safety_checker''', '''feature_extractor''']


						def __init__( self						:  List[str]      ,       _a						:  AutoencoderKL      ,       _a						:  CLIPTextModel      ,       _a						:  CLIPTokenizer      ,       _a						:  UNetaDConditionModel      ,       _a						:  Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]      ,       _a						:  StableDiffusionSafetyChecker      ,       _a						:  CLIPImageProcessor      ,       _a						:  bool = True      ,       ):
													super().__init__()
													if hasattr(scheduler.config      ,       "steps_offset"			) and scheduler.config.steps_offset != 1:
																				a__:       str											=(
																				    F"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
																				    F" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
																				    "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
																				    " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
																				    " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
																				    " file"
																				)
																				deprecate("steps_offset!=1"      ,       "1.0.0"      ,       _a      ,       standard_warn=_a			)
																				a__:       Any											=dict(scheduler.config			)
																				a__:       int											=1
																				a__:       List[str]											=FrozenDict(_a			)

													self.register_modules(
													    vae=_a      ,       text_encoder=_a      ,       tokenizer=_a      ,       unet=_a      ,       scheduler=_a      ,       safety_checker=_a      ,       feature_extractor=_a      ,       )
													self.register_to_config(requires_safety_checker=_a			)


						@property
						def   _lowerCamelCase			( self						:  List[Any]			):
													return {k: getattr(self      ,       _a			) for k in self.config.keys() if not k.startswith("_"			)}


						def   _lowerCamelCase			( self						:  str      ,       _a						:  Optional[Union[str, int]] = "auto"			):
													if slice_size == "auto":
																				# half the attention head size is usually a good trade-off between
																				# speed and memory
																				a__:       List[Any]											=self.unet.config.attention_head_dim // 2
													self.unet.set_attention_slice(_a			)


						def   _lowerCamelCase			( self						:  Optional[Any]			):
													self.enable_attention_slicing(_a			)


						@torch.no_grad()
						def   _lowerCamelCase			( self						:  Any      ,       _a						:  Union[str, List[str]]      ,       _a						:  Union[torch.FloatTensor, PIL.Image.Image]      ,       _a						:  Union[torch.FloatTensor, PIL.Image.Image]      ,       _a						:  float = 0.8      ,       _a						:  Optional[int] = 5_0      ,       _a						:  Optional[float] = 7.5      ,       _a						:  Optional[Union[str, List[str]]] = None      ,       _a						:  Optional[int] = 1      ,       _a						:  Optional[float] = 0.0      ,       _a						:  Optional[torch.Generator] = None      ,       _a						:  Optional[str] = "pil"      ,       _a						:  bool = True      ,       _a						:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,       _a						:  int = 1      ,       ):
													# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
													return StableDiffusionInpaintPipelineLegacy(**self.components			)(
													    prompt=_a      ,       image=_a      ,       mask_image=_a      ,       strength=_a      ,       num_inference_steps=_a      ,       guidance_scale=_a      ,       negative_prompt=_a      ,       num_images_per_prompt=_a      ,       eta=_a      ,       generator=_a      ,       output_type=_a      ,       return_dict=_a      ,       callback=_a      ,       )


						@torch.no_grad()
						def   _lowerCamelCase			( self						:  Dict      ,       _a						:  Union[str, List[str]]      ,       _a						:  Union[torch.FloatTensor, PIL.Image.Image]      ,       _a						:  float = 0.8      ,       _a						:  Optional[int] = 5_0      ,       _a						:  Optional[float] = 7.5      ,       _a						:  Optional[Union[str, List[str]]] = None      ,       _a						:  Optional[int] = 1      ,       _a						:  Optional[float] = 0.0      ,       _a						:  Optional[torch.Generator] = None      ,       _a						:  Optional[str] = "pil"      ,       _a						:  bool = True      ,       _a						:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,       _a						:  int = 1      ,       **_a						:  Any      ,       ):
													# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
													return StableDiffusionImgaImgPipeline(**self.components			)(
													    prompt=_a      ,       image=_a      ,       strength=_a      ,       num_inference_steps=_a      ,       guidance_scale=_a      ,       negative_prompt=_a      ,       num_images_per_prompt=_a      ,       eta=_a      ,       generator=_a      ,       output_type=_a      ,       return_dict=_a      ,       callback=_a      ,       callback_steps=_a      ,       )







						@torch.no_grad()
						def   _lowerCamelCase			( self						:  Dict      ,       _a						:  Union[str, List[str]]      ,       _a						:  int = 5_1_2      ,       _a						:  int = 5_1_2      ,       _a						:  int = 5_0      ,       _a						:  float = 7.5      ,       _a						:  Optional[Union[str, List[str]]] = None      ,       _a						:  Optional[int] = 1      ,       _a						:  float = 0.0      ,       _a						:  Optional[torch.Generator] = None      ,       _a						:  Optional[torch.FloatTensor] = None      ,       _a						:  Optional[str] = "pil"      ,       _a						:  bool = True      ,       _a						:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,       _a						:  int = 1      ,       ):
													# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
													return StableDiffusionPipeline(**self.components			)(
													    prompt=_a      ,       height=_a      ,       width=_a      ,       num_inference_steps=_a      ,       guidance_scale=_a      ,       negative_prompt=_a      ,       num_images_per_prompt=_a      ,       eta=_a      ,       generator=_a      ,       latents=_a      ,       output_type=_a      ,       return_dict=_a      ,       callback=_a      ,       callback_steps=_a      ,       )










from __future__ import annotations

from typing import Generic, TypeVar

__UpperCAmelCase						  =     TypeVar('''T''')


class 	lowerCamelCase__ (		Generic[T]   ):


						def __init__( self						:  int      ,       _a						:  T			):
													a__:       int											=data  # Assign data
													a__:       Node[T] | None											=None  # Initialize next as null
													a__:       Node[T] | None											=None  # Initialize prev as null


class 	lowerCamelCase__ (		Generic[T]   ):


						def __init__( self						:  Optional[Any]			):
													a__:       Node[T] | None											=None


						def   _lowerCamelCase			( self						:  List[Any]      ,       _a						:  T			):
													if self.head is None:
																				a__:       Dict											=Node(_a			)
													else:
																				a__:       int											=Node(_a			)
																				a__:       Optional[Any]											=new_node
																				a__:       int											=self.head
																				a__:       int											=None
																				a__:       Any											=new_node


						def   _lowerCamelCase			( self						:  Tuple			):
													if self.head is None:
																				return None
													else:
																				assert self.head is not None
																				a__:       Optional[int]											=self.head.data
																				a__:       Union[str, Any]											=self.head.next
																				if self.head is not None:
																											a__:       str											=None
																				return temp


						def   _lowerCamelCase			( self						:  int			):
													return self.head.data if self.head is not None else None


						def __len__( self						:  Union[str, Any]			):
													a__:       Tuple											=self.head
													a__:       int											=0
													while temp is not None:
																				count += 1
																				a__:       List[str]											=temp.next
													return count


						def   _lowerCamelCase			( self						:  int			):
													return self.head is None







						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													print("stack elements are:"			)
													a__:       Optional[int]											=self.head
													while temp is not None:
																				print(temp.data      ,       end="->"			)
																				a__:       Union[str, Any]											=temp.next


# Code execution starts here
if __name__ == "__main__":
		# Start with the empty stack
		__UpperCAmelCase						  =     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())





