


"""simple docstring"""







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

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


a__    :							Tuple            =					logging.get_logger(__name__)




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	def __init__(			self      :    Optional[int]      ,      UpperCAmelCase__      :    Union[List[ControlNetModel], Tuple[ControlNetModel]]       )	->     int:
						super().__init__()
						__SCREAMING_SNAKE_CASE						       =    nn.ModuleList(UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Tuple      ,      UpperCAmelCase__      :    torch.FloatTensor      ,      UpperCAmelCase__      :    Union[torch.Tensor, float, int]      ,      UpperCAmelCase__      :    torch.Tensor      ,      UpperCAmelCase__      :    List[torch.tensor]      ,      UpperCAmelCase__      :    List[float]      ,      UpperCAmelCase__      :    Optional[torch.Tensor] = None      ,      UpperCAmelCase__      :    Optional[torch.Tensor] = None      ,      UpperCAmelCase__      :    Optional[torch.Tensor] = None      ,      UpperCAmelCase__      :    Optional[Dict[str, Any]] = None      ,      UpperCAmelCase__      :    bool = False      ,      UpperCAmelCase__      :    bool = True      ,      )	->     Union[ControlNetOutput, Tuple]:
						for i, (image, scale, controlnet) in enumerate(zip(UpperCAmelCase__      ,      UpperCAmelCase__      ,      self.nets       )       ):
											__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    controlnet(
											    UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__      ,      )

											# merge samples
											if i == 0:
																__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    down_samples, mid_sample
											else:
																__SCREAMING_SNAKE_CASE						       =    [
																    samples_prev + samples_curr
																    for samples_prev, samples_curr in zip(UpperCAmelCase__      ,      UpperCAmelCase__       )
																]
																mid_block_res_sample += mid_sample

						return down_block_res_samples, mid_block_res_sample




	def   UpperCAmelCase_				(			self      :    Union[str, Any]      ,      UpperCAmelCase__      :    Union[str, os.PathLike]      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    Callable = None      ,      UpperCAmelCase__      :    bool = False      ,      UpperCAmelCase__      :    Optional[str] = None      ,      )	->     int:
						__SCREAMING_SNAKE_CASE						       =    0
						__SCREAMING_SNAKE_CASE						       =    save_directory
						for controlnet in self.nets:
											controlnet.save_pretrained(
											    UpperCAmelCase__      ,      is_main_process=UpperCAmelCase__      ,      save_function=UpperCAmelCase__      ,      safe_serialization=UpperCAmelCase__      ,      variant=UpperCAmelCase__      ,      )

											idx += 1
											__SCREAMING_SNAKE_CASE						       =    model_path_to_save + F"""_{idx}"""




	@classmethod
	def   UpperCAmelCase_				(			cls      :    List[str]      ,      UpperCAmelCase__      :    Optional[Union[str, os.PathLike]]      ,      **UpperCAmelCase__      :    Optional[Any]       )	->     List[Any]:
						__SCREAMING_SNAKE_CASE						       =    0
						__SCREAMING_SNAKE_CASE						       =    []

						# load controlnet and append to list until no controlnet directory exists anymore
						# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
						# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
						__SCREAMING_SNAKE_CASE						       =    pretrained_model_path
						while os.path.isdir(UpperCAmelCase__       ):
											__SCREAMING_SNAKE_CASE						       =    ControlNetModel.from_pretrained(UpperCAmelCase__      ,      **UpperCAmelCase__       )
											controlnets.append(UpperCAmelCase__       )

											idx += 1
											__SCREAMING_SNAKE_CASE						       =    pretrained_model_path + F"""_{idx}"""

						logger.info(F"""{len(UpperCAmelCase__       )} controlnets loaded from {pretrained_model_path}."""       )

						if len(UpperCAmelCase__       ) == 0:
											raise ValueError(
											    F"""No ControlNets found under {os.path.dirname(UpperCAmelCase__       )}. Expected at least {pretrained_model_path + '_0'}."""       )

						return cls(UpperCAmelCase__       )









"""simple docstring"""







from __future__ import annotations

import requests




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
					return requests.get(lowerCAmelCase_       ).json()




def        UpperCAmelCase__	(lowerCAmelCase_ = 10       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    "https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"
					__SCREAMING_SNAKE_CASE						       =    requests.get(lowerCAmelCase_       ).json()[:max_stories]
					return [get_hackernews_story(lowerCAmelCase_       ) for story_id in story_ids]




def        UpperCAmelCase__	(lowerCAmelCase_ = 10       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    hackernews_top_stories(lowerCAmelCase_       )
					return "\n".join("* [{title}]({url})".format(**lowerCAmelCase_       ) for story in stories       )


if __name__ == "__main__":
		print(hackernews_top_stories_as_markdown())
