
from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
UpperCAmelCase       =					0

UpperCAmelCase       =					[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

UpperCAmelCase       =					[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

UpperCAmelCase       =					tuple[int, int]







class 			A_  :




	'''simple docstring'''


	def __init__(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       ):
		lowercase	    =       pos_x
		lowercase	    =       pos_y
		lowercase	    =       (pos_y, pos_x)
		lowercase	    =       goal_x
		lowercase	    =       goal_y
		lowercase	    =       g_cost
		lowercase	    =       parent
		lowercase	    =       self.calculate_heuristic()
		lowercase	    =       self.g_cost + self.h_cost





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.pos_x - self.goal_x
		lowercase	    =       self.pos_y - self.goal_y
		if HEURISTIC == 1:
			return abs(snake_case			) + abs(snake_case			)
		else:
			return sqrt(dy**2 + dx**2			)





	def __lt__(			self						,       snake_case			):
		return self.f_cost < other.f_cost







class 			A_  :




	'''simple docstring'''


	def __init__(			self						,       snake_case						,       snake_case			):
		lowercase	    =       Node(start[1]						,       start[0]						,       goal[1]						,       goal[0]						,       0						,       snake_case			)
		lowercase	    =       Node(goal[1]						,       goal[0]						,       goal[1]						,       goal[0]						,       9_9999						,       snake_case			)

		lowercase	    =       [self.start]
		lowercase	    =       []

		lowercase	    =       False





	def       SCREAMING_SNAKE_CASE__		(			self			):
		while self.open_nodes:
			# Open Nodes are sorted using __lt__
			self.open_nodes.sort()
			lowercase	    =       self.open_nodes.pop(0			)

			if current_node.pos == self.target.pos:
				return self.retrace_path(snake_case			)

			self.closed_nodes.append(snake_case			)
			lowercase	    =       self.get_successors(snake_case			)

			for child_node in successors:
				if child_node in self.closed_nodes:
					continue

				if child_node not in self.open_nodes:
					self.open_nodes.append(snake_case			)
				else:
					# retrieve the best current path
					lowercase	    =       self.open_nodes.pop(self.open_nodes.index(snake_case			)			)

					if child_node.g_cost < better_node.g_cost:
						self.open_nodes.append(snake_case			)
					else:
						self.open_nodes.append(snake_case			)

		return [self.start.pos]





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		lowercase	    =       []
		for action in delta:
			lowercase	    =       parent.pos_x + action[1]
			lowercase	    =       parent.pos_y + action[0]
			if not (0 <= pos_x <= len(grid[0]			) - 1 and 0 <= pos_y <= len(snake_case			) - 1):
				continue

			if grid[pos_y][pos_x] != 0:
				continue

			successors.append(
			    Node(
			        snake_case						,       snake_case						,       self.target.pos_y						,       self.target.pos_x						,       parent.g_cost + 1						,       snake_case						,       )			)
		return successors





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		lowercase	    =       node
		lowercase	    =       []
		while current_node is not None:
			path.append((current_node.pos_y, current_node.pos_x)			)
			lowercase	    =       current_node.parent
		path.reverse()
		return path







class 			A_  :




	'''simple docstring'''


	def __init__(			self						,       snake_case						,       snake_case			):
		lowercase	    =       AStar(snake_case						,       snake_case			)
		lowercase	    =       AStar(snake_case						,       snake_case			)
		lowercase	    =       False





	def       SCREAMING_SNAKE_CASE__		(			self			):
		while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
			self.fwd_astar.open_nodes.sort()
			self.bwd_astar.open_nodes.sort()
			lowercase	    =       self.fwd_astar.open_nodes.pop(0			)
			lowercase	    =       self.bwd_astar.open_nodes.pop(0			)

			if current_bwd_node.pos == current_fwd_node.pos:
				return self.retrace_bidirectional_path(
				    snake_case						,       snake_case			)

			self.fwd_astar.closed_nodes.append(snake_case			)
			self.bwd_astar.closed_nodes.append(snake_case			)

			lowercase	    =       current_bwd_node
			lowercase	    =       current_fwd_node

			lowercase	    =       {
			    self.fwd_astar: self.fwd_astar.get_successors(snake_case			),
			    self.bwd_astar: self.bwd_astar.get_successors(snake_case			),
			}

			for astar in [self.fwd_astar, self.bwd_astar]:
				for child_node in successors[astar]:
					if child_node in astar.closed_nodes:
						continue

					if child_node not in astar.open_nodes:
						astar.open_nodes.append(snake_case			)
					else:
						# retrieve the best current path
						lowercase	    =       astar.open_nodes.pop(
						    astar.open_nodes.index(snake_case			)			)

						if child_node.g_cost < better_node.g_cost:
							astar.open_nodes.append(snake_case			)
						else:
							astar.open_nodes.append(snake_case			)

		return [self.fwd_astar.start.pos]





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case			):
		lowercase	    =       self.fwd_astar.retrace_path(snake_case			)
		lowercase	    =       self.bwd_astar.retrace_path(snake_case			)
		bwd_path.pop()
		bwd_path.reverse()
		lowercase	    =       fwd_path + bwd_path
		return path


if __name__ == "__main__":
	# all coordinates are given in format [y,x]
	UpperCAmelCase       =					(0, 0)
	UpperCAmelCase       =					(len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	UpperCAmelCase       =					time.time()
	UpperCAmelCase       =					AStar(init, goal)
	UpperCAmelCase       =					a_star.search()
	UpperCAmelCase       =					time.time() - start_time
	print(F"""AStar execution time = {end_time:f} seconds""")

	UpperCAmelCase       =					time.time()
	UpperCAmelCase       =					BidirectionalAStar(init, goal)
	UpperCAmelCase       =					time.time() - bd_start_time
	print(F"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")







'''simple docstring'''





from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

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


UpperCamelCase_		:     List[Any]          =		logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCamelCase_		:     Dict          =		'''
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
        >>> pipe_prior.to("cuda")

        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
        >>> pipe.to("cuda")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

        >>> image[0].save("cat.png")
        ```
'''





def       __a      (  _UpperCamelCase:       Any   ,		_UpperCamelCase:       List[Any]   ,		_UpperCamelCase:       Union[str, Any]=8       )		->					Optional[int]:

     """simple docstring"""




     _snake_case				= h // scale_factor**2
     if h % scale_factor**2 != 0:
          new_h += 1
     _snake_case				= w // scale_factor**2
     if w % scale_factor**2 != 0:
          new_w += 1
     return new_h * scale_factor, new_w * scale_factor





class   _a							(		__lowerCAmelCase				):






     def __init__(					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,)				->     str:
          super().__init__()

          self.register_modules(
              text_encoder=_SCREAMING_SNAKE_CASE   ,tokenizer=_SCREAMING_SNAKE_CASE   ,unet=_SCREAMING_SNAKE_CASE   ,scheduler=_SCREAMING_SNAKE_CASE   ,movq=_SCREAMING_SNAKE_CASE   ,)
          _snake_case				= 2 ** (len(self.movq.config.block_out_channels       ) - 1)






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Any:
          if latents is None:
               _snake_case				= randn_tensor(_SCREAMING_SNAKE_CASE   ,generator=_SCREAMING_SNAKE_CASE   ,device=_SCREAMING_SNAKE_CASE   ,dtype=_SCREAMING_SNAKE_CASE       )
          else:
               if latents.shape != shape:
                    raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""       )
               _snake_case				= latents.to(_SCREAMING_SNAKE_CASE       )

          _snake_case				= latents * scheduler.init_noise_sigma
          return latents






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=None   ,)				->     Union[str, Any]:
          _snake_case				= len(_SCREAMING_SNAKE_CASE       ) if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ) else 1
          # get prompt text embeddings
          _snake_case				= self.tokenizer(
              _SCREAMING_SNAKE_CASE   ,padding="max_length"   ,truncation=_SCREAMING_SNAKE_CASE   ,max_length=77   ,return_attention_mask=_SCREAMING_SNAKE_CASE   ,add_special_tokens=_SCREAMING_SNAKE_CASE   ,return_tensors="pt"   ,)

          _snake_case				= text_inputs.input_ids
          _snake_case				= self.tokenizer(_SCREAMING_SNAKE_CASE   ,padding="longest"   ,return_tensors="pt"       ).input_ids

          if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]       )
               logger.warning(
                   "The following part of your input was truncated because CLIP can only handle sequences up to"
                   f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""       )

          _snake_case				= text_input_ids.to(_SCREAMING_SNAKE_CASE       )
          _snake_case				= text_inputs.attention_mask.to(_SCREAMING_SNAKE_CASE       )

          _snake_case				,   _snake_case				= self.text_encoder(
              input_ids=_SCREAMING_SNAKE_CASE   ,attention_mask=_SCREAMING_SNAKE_CASE       )

          _snake_case				= prompt_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE   ,dim=0       )
          _snake_case				= text_encoder_hidden_states.repeat_interleave(_SCREAMING_SNAKE_CASE   ,dim=0       )
          _snake_case				= text_mask.repeat_interleave(_SCREAMING_SNAKE_CASE   ,dim=0       )

          if do_classifier_free_guidance:
               _snake_case				= 42
               if negative_prompt is None:
                    _snake_case				= [""] * batch_size
               elif type(_SCREAMING_SNAKE_CASE       ) is not type(_SCREAMING_SNAKE_CASE       ):
                    raise TypeError(
                        f"""`negative_prompt` should be the same type to `prompt`, but got {type(_SCREAMING_SNAKE_CASE       )} !="""
                        f""" {type(_SCREAMING_SNAKE_CASE       )}."""       )
               elif isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
                    _snake_case				= [negative_prompt]
               elif batch_size != len(_SCREAMING_SNAKE_CASE       ):
                    raise ValueError(
                        f"""`negative_prompt`: {negative_prompt} has batch size {len(_SCREAMING_SNAKE_CASE       )}, but `prompt`:"""
                        f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                        " the batch size of `prompt`."       )
               else:
                    _snake_case				= negative_prompt

               _snake_case				= self.tokenizer(
                   _SCREAMING_SNAKE_CASE   ,padding="max_length"   ,max_length=77   ,truncation=_SCREAMING_SNAKE_CASE   ,return_attention_mask=_SCREAMING_SNAKE_CASE   ,add_special_tokens=_SCREAMING_SNAKE_CASE   ,return_tensors="pt"   ,)
               _snake_case				= uncond_input.input_ids.to(_SCREAMING_SNAKE_CASE       )
               _snake_case				= uncond_input.attention_mask.to(_SCREAMING_SNAKE_CASE       )

               _snake_case				,   _snake_case				= self.text_encoder(
                   input_ids=_SCREAMING_SNAKE_CASE   ,attention_mask=_SCREAMING_SNAKE_CASE       )

               # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

               _snake_case				= negative_prompt_embeds.shape[1]
               _snake_case				= negative_prompt_embeds.repeat(1   ,_SCREAMING_SNAKE_CASE       )
               _snake_case				= negative_prompt_embeds.view(batch_size * num_images_per_prompt   ,_SCREAMING_SNAKE_CASE       )

               _snake_case				= uncond_text_encoder_hidden_states.shape[1]
               _snake_case				= uncond_text_encoder_hidden_states.repeat(1   ,_SCREAMING_SNAKE_CASE   ,1       )
               _snake_case				= uncond_text_encoder_hidden_states.view(
                   batch_size * num_images_per_prompt   ,_SCREAMING_SNAKE_CASE   ,-1       )
               _snake_case				= uncond_text_mask.repeat_interleave(_SCREAMING_SNAKE_CASE   ,dim=0       )

               # done duplicates

               # For classifier free guidance, we need to do two forward passes.
               # Here we concatenate the unconditional and text embeddings into a single batch
               # to avoid doing two forward passes
               _snake_case				= torch.cat([negative_prompt_embeds, prompt_embeds]       )
               _snake_case				= torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]       )

               _snake_case				= torch.cat([uncond_text_mask, text_mask]       )

          return prompt_embeds, text_encoder_hidden_states, text_mask






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE=0       )				->     List[Any]:
          if is_accelerate_available():
               from accelerate import cpu_offload
          else:
               raise ImportError("Please install accelerate via `pip install accelerate`"       )

          _snake_case				= torch.device(f"""cuda:{gpu_id}"""       )

          _snake_case				= [
              self.unet,
              self.text_encoder,
              self.movq,
          ]
          for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                    cpu_offload(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE=0       )				->     List[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."       )

          _snake_case				= 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)

          _snake_case				= None
          for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
               _snake_case				,   _snake_case				= cpu_offload_with_hook(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,prev_module_hook=_SCREAMING_SNAKE_CASE       )

          if self.safety_checker is not None:
               _snake_case				,   _snake_case				= cpu_offload_with_hook(self.safety_checker   ,_SCREAMING_SNAKE_CASE   ,prev_module_hook=_SCREAMING_SNAKE_CASE       )

          # We'll offload the last model manually.
          _snake_case				= hook






     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 							_lowercase      (					self       )				->     Optional[int]:
          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 = None   ,_SCREAMING_SNAKE_CASE = 512   ,_SCREAMING_SNAKE_CASE = 512   ,_SCREAMING_SNAKE_CASE = 100   ,_SCREAMING_SNAKE_CASE = 4.0   ,_SCREAMING_SNAKE_CASE = 1   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = "pil"   ,_SCREAMING_SNAKE_CASE = True   ,)				->     Union[str, Any]:

          if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= 1
          elif isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= len(_SCREAMING_SNAKE_CASE       )
          else:
               raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(_SCREAMING_SNAKE_CASE       )}"""       )

          _snake_case				= self._execution_device

          _snake_case				= batch_size * num_images_per_prompt
          _snake_case				= guidance_scale > 1.0

          _snake_case				,   _snake_case				,   _snake_case				= self._encode_prompt(
              _SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )

          if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= torch.cat(_SCREAMING_SNAKE_CASE   ,dim=0       )
          if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= torch.cat(_SCREAMING_SNAKE_CASE   ,dim=0       )

          if do_classifier_free_guidance:
               _snake_case				= image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE   ,dim=0       )
               _snake_case				= negative_image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE   ,dim=0       )

          _snake_case				= torch.cat([negative_image_embeds, image_embeds]   ,dim=0       ).to(
              dtype=prompt_embeds.dtype   ,device=_SCREAMING_SNAKE_CASE       )

          self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE   ,device=_SCREAMING_SNAKE_CASE       )
          _snake_case				= self.scheduler.timesteps

          _snake_case				= self.unet.config.in_channels

          _snake_case				,   _snake_case				= get_new_h_w(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,self.movq_scale_factor       )

          # create initial latent
          _snake_case				= self.prepare_latents(
              (batch_size, num_channels_latents, height, width)   ,text_encoder_hidden_states.dtype   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,self.scheduler   ,)

          for i, t in enumerate(self.progress_bar(_SCREAMING_SNAKE_CASE       )       ):
               # expand the latents if we are doing classifier free guidance
               _snake_case				= torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

               _snake_case				= {"text_embeds": prompt_embeds, "image_embeds": image_embeds}
               _snake_case				= 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:
                    _snake_case				,   _snake_case				= noise_pred.split(latents.shape[1]   ,dim=1       )
                    _snake_case				,   _snake_case				= noise_pred.chunk(2       )
                    _snake_case				,   _snake_case				= variance_pred.chunk(2       )
                    _snake_case				= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                    _snake_case				= 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"]
               ):
                    _snake_case				,   _snake_case				= noise_pred.split(latents.shape[1]   ,dim=1       )

               # compute the previous noisy sample x_t -> x_t-1
               _snake_case				= self.scheduler.step(
                   _SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,generator=_SCREAMING_SNAKE_CASE   ,).prev_sample
          # post-processing
          _snake_case				= 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"]:
               _snake_case				= image * 0.5 + 0.5
               _snake_case				= image.clamp(0   ,1       )
               _snake_case				= image.cpu().permute(0   ,2   ,3   ,1       ).float().numpy()

          if output_type == "pil":
               _snake_case				= self.numpy_to_pil(_SCREAMING_SNAKE_CASE       )

          if not return_dict:
               return (image,)

          return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE       )


