



from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

__UpperCAmelCase			  =			'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''
def 						UpperCamelCase      (  snake_case__			:       Union[str, Any]			,	snake_case__			:       Any			,	snake_case__			:       Union[str, Any]=8      )      ->	Optional[int]:
   UpperCamelCase       :    Dict		      =      height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   UpperCamelCase       :    Tuple		      =      width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor
def 						UpperCamelCase      (  snake_case__			:       Any			,	snake_case__			:       int=512			,	snake_case__			:       List[str]=512      )      ->	Dict:
   UpperCamelCase       :    int		      =      pil_image.resize((w, h)			,	resample=Image.BICUBIC			,	reducing_gap=1      )
   UpperCamelCase       :    str		      =      np.array(pil_image.convert('RGB'      )      )
   UpperCamelCase       :    Dict		      =      arr.astype(np.floataa      ) / 127.5 - 1
   UpperCamelCase       :    int		      =      np.transpose(snake_case__			,	[2, 0, 1]      )
   UpperCamelCase       :    Optional[Any]		      =      torch.from_numpy(snake_case__      ).unsqueeze(0      )
   return image



class 							lowerCAmelCase_						(    a__				):
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     )	->						Optional[Any]:
          super().__init__()

          self.register_modules(
              unet=SCREAMING_SNAKE_CASE_,     scheduler=SCREAMING_SNAKE_CASE_,     movq=SCREAMING_SNAKE_CASE_,     )
          UpperCamelCase       :    List[Any]		      =      2 ** (len(self.movq.config.block_out_channels			) - 1)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Union[str, Any]:
          # get the original timestep using init_timestep
          UpperCamelCase       :    str		      =      min(int(num_inference_steps * strength			),     SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Optional[Any]		      =      max(num_inference_steps - init_timestep,     0			)
          UpperCamelCase       :    Dict		      =      self.scheduler.timesteps[t_start:]

          return timesteps, num_inference_steps - t_start
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=None			)	->						List[Any]:
          if not isinstance(SCREAMING_SNAKE_CASE_,     (torch.Tensor, PIL.Image.Image, list)			):
             raise ValueError(
                 F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(SCREAMING_SNAKE_CASE_			)}"""			)

          UpperCamelCase       :    Tuple		      =      image.to(device=SCREAMING_SNAKE_CASE_,     dtype=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Any		      =      batch_size * num_images_per_prompt

          if image.shape[1] == 4:
             UpperCamelCase       :    int		      =      image

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

             elif isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
                UpperCamelCase       :    List[Any]		      =      [
                    self.movq.encode(image[i : i + 1]			).latent_dist.sample(generator[i]			) for i in range(SCREAMING_SNAKE_CASE_			)
                ]
                UpperCamelCase       :    Union[str, Any]		      =      torch.cat(SCREAMING_SNAKE_CASE_,     dim=0			)
             else:
                UpperCamelCase       :    Optional[Any]		      =      self.movq.encode(SCREAMING_SNAKE_CASE_			).latent_dist.sample(SCREAMING_SNAKE_CASE_			)

             UpperCamelCase       :    Dict		      =      self.movq.config.scaling_factor * init_latents

          UpperCamelCase       :    Optional[int]		      =      torch.cat([init_latents],     dim=0			)

          UpperCamelCase       :    int		      =      init_latents.shape
          UpperCamelCase       :    Optional[int]		      =      randn_tensor(SCREAMING_SNAKE_CASE_,     generator=SCREAMING_SNAKE_CASE_,     device=SCREAMING_SNAKE_CASE_,     dtype=SCREAMING_SNAKE_CASE_			)

          # get latents
          UpperCamelCase       :    Optional[int]		      =      self.scheduler.add_noise(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    List[Any]		      =      init_latents

          return latents
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_=0			)	->						str:
          if is_accelerate_available():
             from accelerate import cpu_offload
          else:
             raise ImportError('Please install accelerate via `pip install accelerate`'			)

          UpperCamelCase       :    Dict		      =      torch.device(F"""cuda:{gpu_id}"""			)

          UpperCamelCase       :    Optional[Any]		      =      [
              self.unet,
              self.movq,
          ]
          for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                cpu_offload(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_=0			)	->						Any:
          if is_accelerate_available() and is_accelerate_version('>=',     '0.17.0.dev0'			):
             from accelerate import cpu_offload_with_hook
          else:
             raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'			)

          UpperCamelCase       :    Optional[Any]		      =      torch.device(F"""cuda:{gpu_id}"""			)

          if self.device.type != "cpu":
             self.to('cpu',     silence_dtype_warnings=SCREAMING_SNAKE_CASE_			)
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

          UpperCamelCase       :    int		      =      None
          for cpu_offloaded_model in [self.unet, self.movq]:
             UpperCamelCase      ,	UpperCamelCase       :    Any		      =      cpu_offload_with_hook(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     prev_module_hook=SCREAMING_SNAKE_CASE_			)

          # We'll offload the last model manually.
          UpperCamelCase       :    List[str]		      =      hook
       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def    snake_case_			(     self			)	->						Optional[Any]:
          if not hasattr(self.unet,     '_hf_hook'			):
             return self.device
          for module in self.unet.modules():
             if (
                 hasattr(SCREAMING_SNAKE_CASE_,     '_hf_hook'			)
                 and hasattr(module._hf_hook,     'execution_device'			)
                 and module._hf_hook.execution_device is not None
             ):
                return torch.device(module._hf_hook.execution_device			)
          return self.device






       @torch.no_grad()
       @replace_example_docstring(SCREAMING_SNAKE_CASE_			)
       def __call__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = 512,     SCREAMING_SNAKE_CASE_ = 512,     SCREAMING_SNAKE_CASE_ = 100,     SCREAMING_SNAKE_CASE_ = 4.0,     SCREAMING_SNAKE_CASE_ = 0.3,     SCREAMING_SNAKE_CASE_ = 1,     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = "pil",     SCREAMING_SNAKE_CASE_ = True,     )	->						Optional[Any]:
          UpperCamelCase       :    str		      =      self._execution_device

          UpperCamelCase       :    Union[str, Any]		      =      guidance_scale > 1.0

          if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    Union[str, Any]		      =      torch.cat(SCREAMING_SNAKE_CASE_,     dim=0			)
          UpperCamelCase       :    int		      =      image_embeds.shape[0]
          if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    Union[str, Any]		      =      torch.cat(SCREAMING_SNAKE_CASE_,     dim=0			)

          if do_classifier_free_guidance:
             UpperCamelCase       :    List[str]		      =      image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_,     dim=0			)
             UpperCamelCase       :    Optional[Any]		      =      negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_,     dim=0			)

          UpperCamelCase       :    Union[str, Any]		      =      torch.cat([negative_image_embeds, image_embeds],     dim=0			).to(dtype=self.unet.dtype,     device=SCREAMING_SNAKE_CASE_			)

          if not isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    List[str]		      =      [image]
          if not all(isinstance(SCREAMING_SNAKE_CASE_,     (PIL.Image.Image, torch.Tensor)			) for i in image			):
             raise ValueError(
                 F"""Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE_			) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""			)

          UpperCamelCase       :    Any		      =      torch.cat([prepare_image(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			) for i in image],     dim=0			)
          UpperCamelCase       :    List[str]		      =      image.to(dtype=image_embeds.dtype,     device=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Any		      =      self.movq.encode(SCREAMING_SNAKE_CASE_			)['latents']
          UpperCamelCase       :    List[Any]		      =      latents.repeat_interleave(SCREAMING_SNAKE_CASE_,     dim=0			)
          self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_,     device=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase      ,	UpperCamelCase       :    str		      =      self.get_timesteps(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[int]		      =      timesteps[:1].repeat(batch_size * num_images_per_prompt			)
          UpperCamelCase      ,	UpperCamelCase       :    Union[str, Any]		      =      downscale_height_and_width(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     self.movq_scale_factor			)
          UpperCamelCase       :    Optional[int]		      =      self.prepare_latents(
              SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     image_embeds.dtype,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
          for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_			)			):
             # expand the latents if we are doing classifier free guidance
             UpperCamelCase       :    Optional[Any]		      =      torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

             UpperCamelCase       :    List[Any]		      =      {'image_embeds': image_embeds}
             UpperCamelCase       :    Optional[Any]		      =      self.unet(
                 sample=SCREAMING_SNAKE_CASE_,     timestep=SCREAMING_SNAKE_CASE_,     encoder_hidden_states=SCREAMING_SNAKE_CASE_,     added_cond_kwargs=SCREAMING_SNAKE_CASE_,     return_dict=SCREAMING_SNAKE_CASE_,     )[0]

             if do_classifier_free_guidance:
                UpperCamelCase      ,	UpperCamelCase       :    Tuple		      =      noise_pred.split(latents.shape[1],     dim=1			)
                UpperCamelCase      ,	UpperCamelCase       :    str		      =      noise_pred.chunk(2			)
                UpperCamelCase      ,	UpperCamelCase       :    Any		      =      variance_pred.chunk(2			)
                UpperCamelCase       :    Dict		      =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                UpperCamelCase       :    Union[str, Any]		      =      torch.cat([noise_pred, variance_pred_text],     dim=1			)

             if not (
                 hasattr(self.scheduler.config,     'variance_type'			)
                 and self.scheduler.config.variance_type in ["learned", "learned_range"]
             ):
                UpperCamelCase      ,	UpperCamelCase       :    Any		      =      noise_pred.split(latents.shape[1],     dim=1			)

             # compute the previous noisy sample x_t -> x_t-1
             UpperCamelCase       :    Dict		      =      self.scheduler.step(
                 SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     generator=SCREAMING_SNAKE_CASE_,     )[0]

          # post-processing
          UpperCamelCase       :    int		      =      self.movq.decode(SCREAMING_SNAKE_CASE_,     force_not_quantize=SCREAMING_SNAKE_CASE_			)['sample']

          if output_type not in ["pt", "np", "pil"]:
             raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""			)

          if output_type in ["np", "pil"]:
             UpperCamelCase       :    Dict		      =      image * 0.5 + 0.5
             UpperCamelCase       :    Optional[Any]		      =      image.clamp(0,     1			)
             UpperCamelCase       :    str		      =      image.cpu().permute(0,     2,     3,     1			).float().numpy()

          if output_type == "pil":
             UpperCamelCase       :    Optional[Any]		      =      self.numpy_to_pil(SCREAMING_SNAKE_CASE_			)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_			)






from typing import Any






class 						__magic_name__					:






     def __init__(				self		,						_lowercase	)->     List[str]:
         UpperCamelCase_			  =						data
         UpperCamelCase_			  =						None






     def __repr__(				self	)->     str:
         return F"Node({self.data})"






class 						__magic_name__					:






     def __init__(				self	)->     Optional[Any]:
         UpperCamelCase_			  =						None






     def __iter__(				self	)->     Any:
         UpperCamelCase_			  =						self.head
         while node:
             yield node.data
             UpperCamelCase_			  =						node.next






     def __len__(				self	)->     int:
         return sum(1 for _ in self	)






     def __repr__(				self	)->     str:
         return "->".join([str(_lowercase	) for item in self]	)






     def __getitem__(				self		,						_lowercase	)->     Any:
         if not 0 <= index < len(self	):
             raise ValueError("list index out of range."	)
         for i, node in enumerate(self	):
             if i == index:
                 return node
         return None






     def __setitem__(				self		,						_lowercase		,						_lowercase	)->     None:
         if not 0 <= index < len(self	):
             raise ValueError("list index out of range."	)
         UpperCamelCase_			  =						self.head
         for _ in range(_lowercase	):
             UpperCamelCase_			  =						current.next
         UpperCamelCase_			  =						data






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     None:
         self.insert_nth(len(self	)		,						_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     None:
         self.insert_nth(0		,						_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase	)->     None:
         if not 0 <= index <= len(self	):
             raise IndexError("list index out of range"	)
         UpperCamelCase_			  =						Node(_lowercase	)
         if self.head is None:
             UpperCamelCase_			  =						new_node
         elif index == 0:
             UpperCamelCase_			  =						self.head  # link new_node to head
             UpperCamelCase_			  =						new_node
         else:
             UpperCamelCase_			  =						self.head
             for _ in range(index - 1	):
                 UpperCamelCase_			  =						temp.next
             UpperCamelCase_			  =						temp.next
             UpperCamelCase_			  =						new_node






     def 	UpperCAmelCase_				(				self	)->     None:  # print every node data
         print(self	)






     def 	UpperCAmelCase_				(				self	)->     Any:
         return self.delete_nth(0	)






     def 	UpperCAmelCase_				(				self	)->     Any:  # delete from tail
         return self.delete_nth(len(self	) - 1	)






     def 	UpperCAmelCase_				(				self		,						_lowercase = 0	)->     Any:
         if not 0 <= index <= len(self	) - 1:  # test if index is valid
             raise IndexError("List index out of range."	)
         UpperCamelCase_			  =						self.head  # default first node
         if index == 0:
             UpperCamelCase_			  =						self.head.next
         else:
             UpperCamelCase_			  =						self.head
             for _ in range(index - 1	):
                 UpperCamelCase_			  =						temp.next
             UpperCamelCase_			  =						temp.next
             UpperCamelCase_			  =						temp.next.next
         return delete_node.data






     def 	UpperCAmelCase_				(				self	)->     bool:
         return self.head is None






     def 	UpperCAmelCase_				(				self	)->     None:
         UpperCamelCase_			  =						None
         UpperCamelCase_			  =						self.head

         while current:
             # Store the current node's next node.
             UpperCamelCase_			  =						current.next
             # Make the current node's next point backwards
             UpperCamelCase_			  =						prev
             # Make the previous node be the current node
             UpperCamelCase_			  =						current
             # Make the current node the next node (to progress iteration)
             UpperCamelCase_			  =						next_node
         # Return prev in order to put the head at the end
         UpperCamelCase_			  =						prev




def 			lowerCAmelCase(      )->						None:



    """simple docstring"""






    UpperCamelCase_			  =						LinkedList()
    assert linked_list.is_empty() is True
    assert str(SCREAMING_SNAKE_CASE_			) == ""

    try:
        linked_list.delete_head()
        raise AssertionError  # This should not happen.
    except IndexError:
        assert True  # This should happen.

    try:
        linked_list.delete_tail()
        raise AssertionError  # This should not happen.
    except IndexError:
        assert True  # This should happen.

    for i in range(1_0			):
        assert len(SCREAMING_SNAKE_CASE_			) == i
        linked_list.insert_nth(SCREAMING_SNAKE_CASE_			,						i + 1			)
    assert str(SCREAMING_SNAKE_CASE_			) == "->".join(str(SCREAMING_SNAKE_CASE_			) for i in range(1			,						1_1			)			)

    linked_list.insert_head(0			)
    linked_list.insert_tail(1_1			)
    assert str(SCREAMING_SNAKE_CASE_			) == "->".join(str(SCREAMING_SNAKE_CASE_			) for i in range(0			,						1_2			)			)

    assert linked_list.delete_head() == 0
    assert linked_list.delete_nth(9			) == 1_0
    assert linked_list.delete_tail() == 1_1
    assert len(SCREAMING_SNAKE_CASE_			) == 9
    assert str(SCREAMING_SNAKE_CASE_			) == "->".join(str(SCREAMING_SNAKE_CASE_			) for i in range(1			,						1_0			)			)

    assert all(linked_list[i] == i + 1 for i in range(0			,						9			)			) is True

    for i in range(0			,						9			):
        UpperCamelCase_			  =						-i
    assert all(linked_list[i] == -i for i in range(0			,						9			)			) is True

    linked_list.reverse()
    assert str(SCREAMING_SNAKE_CASE_			) == "->".join(str(SCREAMING_SNAKE_CASE_			) for i in range(-8			,						1			)			)




def 			lowerCAmelCase(      )->						None:



    """simple docstring"""






    UpperCamelCase_			  =						[
        -9,
        1_0_0,
        Node(7_7_3_4_5_1_1_2			),
        "dlrow olleH",
        7,
        5_5_5_5,
        0,
        -192.5_5555,
        "Hello, world!",
        77.9,
        Node(1_0			),
        None,
        None,
        12.20,
    ]
    UpperCamelCase_			  =						LinkedList()

    for i in test_input:
        linked_list.insert_tail(SCREAMING_SNAKE_CASE_			)

    # Check if it's empty or not
    assert linked_list.is_empty() is False
    assert (
        str(SCREAMING_SNAKE_CASE_			) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
        "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
    )

    # Delete the head
    UpperCamelCase_			  =						linked_list.delete_head()
    assert result == -9
    assert (
        str(SCREAMING_SNAKE_CASE_			) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
        "Hello, world!->77.9->Node(10)->None->None->12.2"
    )

    # Delete the tail
    UpperCamelCase_			  =						linked_list.delete_tail()
    assert result == 12.2
    assert (
        str(SCREAMING_SNAKE_CASE_			) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
        "Hello, world!->77.9->Node(10)->None->None"
    )

    # Delete a node in specific location in linked list
    UpperCamelCase_			  =						linked_list.delete_nth(1_0			)
    assert result is None
    assert (
        str(SCREAMING_SNAKE_CASE_			) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
        "Hello, world!->77.9->Node(10)->None"
    )

    # Add a Node instance to its head
    linked_list.insert_head(Node("Hello again, world!"			)			)
    assert (
        str(SCREAMING_SNAKE_CASE_			)
        == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
        "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
    )

    # Add None to its tail
    linked_list.insert_tail(SCREAMING_SNAKE_CASE_			)
    assert (
        str(SCREAMING_SNAKE_CASE_			)
        == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
        "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
    )

    # Reverse the linked list
    linked_list.reverse()
    assert (
        str(SCREAMING_SNAKE_CASE_			)
        == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
        "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
    )




def 			lowerCAmelCase(      )->						List[Any]:



    """simple docstring"""






    from doctest import testmod

    testmod()

    UpperCamelCase_			  =						LinkedList()
    linked_list.insert_head(input("Inserting 1st at head "			).strip()			)
    linked_list.insert_head(input("Inserting 2nd at head "			).strip()			)
    print("\nPrint list:"			)
    linked_list.print_list()
    linked_list.insert_tail(input("\nInserting 1st at tail "			).strip()			)
    linked_list.insert_tail(input("Inserting 2nd at tail "			).strip()			)
    print("\nPrint list:"			)
    linked_list.print_list()
    print("\nDelete head"			)
    linked_list.delete_head()
    print("Delete tail"			)
    linked_list.delete_tail()
    print("\nPrint list:"			)
    linked_list.print_list()
    print("\nReverse linked list"			)
    linked_list.reverse()
    print("\nPrint list:"			)
    linked_list.print_list()
    print("\nString representation of linked list:"			)
    print(SCREAMING_SNAKE_CASE_			)
    print("\nReading/changing Node data using indexing:"			)
    print(f"Element at Position 1: {linked_list[1]}"			)
    UpperCamelCase_			  =						input("Enter New Value: "			).strip()
    print("New list:"			)
    print(SCREAMING_SNAKE_CASE_			)
    print(f"length of linked_list is : {len(SCREAMING_SNAKE_CASE_			)}"			)


if __name__ == "__main__":
 main()
