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


lowercase_  : Optional[int]								=					logging.get_logger(__name__)






class 		_lowerCamelCase		( UpperCamelCase_     ):
    def __init__(	self			,			lowerCAmelCase )							->       List[str]:
     super().__init__()
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   nn.ModuleList(lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = False			,			lowerCAmelCase = True			,			)							->       Union[ControlNetOutput, Tuple]:
     for i, (image, scale, controlnet) in enumerate(zip(lowerCAmelCase			,			lowerCAmelCase			,			self.nets ) ):
      SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: str=   controlnet(
          lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			)

      # merge samples
      if i == 0:
       SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: str=   down_samples, mid_sample
      else:
       SCREAMING_SNAKE_CASE__: int=   [
           samples_prev + samples_curr
           for samples_prev, samples_curr in zip(lowerCAmelCase			,			lowerCAmelCase )
       ]
       mid_block_res_sample += mid_sample

     return down_block_res_samples, mid_block_res_sample
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase = True			,			lowerCAmelCase = None			,			lowerCAmelCase = False			,			lowerCAmelCase = None			,			)							->       int:
     SCREAMING_SNAKE_CASE__: Optional[int]=   0
     SCREAMING_SNAKE_CASE__: Tuple=   save_directory
     for controlnet in self.nets:
      controlnet.save_pretrained(
          lowerCAmelCase			,			is_main_process=lowerCAmelCase			,			save_function=lowerCAmelCase			,			safe_serialization=lowerCAmelCase			,			variant=lowerCAmelCase			,			)

      idx += 1
      SCREAMING_SNAKE_CASE__: List[Any]=   model_path_to_save + f'_{idx}'



    @classmethod
    def     UpperCamelCase_					(	cls			,			lowerCAmelCase			,			**lowerCAmelCase )							->       int:
     SCREAMING_SNAKE_CASE__: Any=   0
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   []

     # 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__: List[str]=   pretrained_model_path
     while os.path.isdir(lowerCAmelCase ):
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   ControlNetModel.from_pretrained(lowerCAmelCase			,			**lowerCAmelCase )
      controlnets.append(lowerCAmelCase )

      idx += 1
      SCREAMING_SNAKE_CASE__: List[Any]=   pretrained_model_path + f'_{idx}'

     logger.info(f'{len(lowerCAmelCase )} controlnets loaded from {pretrained_model_path}.' )

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

     return cls(lowerCAmelCase )





'''simple docstring'''





from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str = "" ):






			"""simple docstring"""




			__A=	url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
			__A=	BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE ).text,'html.parser' )
			__A=	soup.find_all('td',attrs='titleColumn' )
			__A=	soup.find_all('td',class_='ratingColumn imdbRating' )
			return {
			    title.a.text: float(rating.strong.text )
			    for title, rating in zip(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			}






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str = "IMDb_Top_250_Movies.csv" ):






			"""simple docstring"""




			__A=	get_imdb_top_aaa_movies()
			with open(_SCREAMING_SNAKE_CASE,'w',newline='' ) as out_file:
						__A=	csv.writer(_SCREAMING_SNAKE_CASE )
						writer.writerow(['Movie title', 'IMDb rating'] )
						for title, rating in movies.items():
									writer.writerow([title, rating] )


if __name__ == "__main__":
					write_movies()



