


"""simple docstring"""





from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


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



class 						snake_case_(				a__	):






     def __init__(		self     :    List[Any]    , UpperCamelCase_     :    CLIPSegForImageSegmentation    , UpperCamelCase_     :    CLIPSegProcessor    , UpperCamelCase_     :    AutoencoderKL    , UpperCamelCase_     :    CLIPTextModel    , UpperCamelCase_     :    CLIPTokenizer    , UpperCamelCase_     :    UNetaDConditionModel    , UpperCamelCase_     :    Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]    , UpperCamelCase_     :    StableDiffusionSafetyChecker    , UpperCamelCase_     :    CLIPImageProcessor    , ):
           super().__init__()

           if hasattr(scheduler.config    , '''steps_offset''' ) and scheduler.config.steps_offset != 1:
                 lowerCAmelCase    :		List[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                     F'''The configuration file of this scheduler: {scheduler} is outdated.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'''    , UpperCamelCase_    , standard_warn=UpperCamelCase_ )
                 lowerCAmelCase    :		Optional[Any]				=      dict(scheduler.config )
                 lowerCAmelCase    :		List[str]				=      1
                 lowerCAmelCase    :		Any				=      FrozenDict(UpperCamelCase_ )

           if hasattr(scheduler.config    , '''skip_prk_steps''' ) and scheduler.config.skip_prk_steps is False:
                 lowerCAmelCase    :		Optional[int]				=      (
                     F'''The configuration file of this scheduler: {scheduler} has not set the configuration'''
                     ''' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'''
                     ''' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead 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('''skip_prk_steps not set'''    , '''1.0.0'''    , UpperCamelCase_    , standard_warn=UpperCamelCase_ )
                 lowerCAmelCase    :		Union[str, Any]				=      dict(scheduler.config )
                 lowerCAmelCase    :		Tuple				=      True
                 lowerCAmelCase    :		Union[str, Any]				=      FrozenDict(UpperCamelCase_ )

           if safety_checker is None:
                 logger.warning(
                     F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
                     ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
                     ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
                     ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
                     ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
                     ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .''' )

           self.register_modules(
               segmentation_model=UpperCamelCase_    , segmentation_processor=UpperCamelCase_    , vae=UpperCamelCase_    , text_encoder=UpperCamelCase_    , tokenizer=UpperCamelCase_    , unet=UpperCamelCase_    , scheduler=UpperCamelCase_    , safety_checker=UpperCamelCase_    , feature_extractor=UpperCamelCase_    , )






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






     def    lowerCamelCase__   (		self     :    Dict ):
           self.enable_attention_slicing(UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Dict ):
           if is_accelerate_available():
                 from accelerate import cpu_offload
           else:
                 raise ImportError('''Please install accelerate via `pip install accelerate`''' )

           lowerCAmelCase    :		Dict				=      torch.device('''cuda''' )

           for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
                 if cpu_offloaded_model is not None:
                       cpu_offload(UpperCamelCase_    , UpperCamelCase_ )






     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def    lowerCamelCase__   (		self     :    Dict ):
           if self.device != torch.device('''meta''' ) or not hasattr(self.unet    , '''_hf_hook''' ):
                 return self.device
           for module in self.unet.modules():
                 if (
                     hasattr(UpperCamelCase_    , '''_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()
     def __call__(		self     :    Any    , UpperCamelCase_     :    Union[str, List[str]]    , UpperCamelCase_     :    Union[torch.FloatTensor, PIL.Image.Image]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    int = 5_1_2    , UpperCamelCase_     :    int = 5_1_2    , UpperCamelCase_     :    int = 5_0    , UpperCamelCase_     :    float = 7.5    , UpperCamelCase_     :    Optional[Union[str, List[str]]] = None    , UpperCamelCase_     :    Optional[int] = 1    , UpperCamelCase_     :    float = 0.0    , UpperCamelCase_     :    Optional[torch.Generator] = None    , UpperCamelCase_     :    Optional[torch.FloatTensor] = None    , UpperCamelCase_     :    Optional[str] = "pil"    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None    , UpperCamelCase_     :    int = 1    , **UpperCamelCase_     :    List[Any]    , ):
           lowerCAmelCase    :		Dict				=      self.segmentation_processor(
               text=[text]    , images=[image]    , padding='''max_length'''    , return_tensors='''pt''' ).to(self.device )
           lowerCAmelCase    :		int				=      self.segmentation_model(**UpperCamelCase_ )
           lowerCAmelCase    :		Optional[Any]				=      torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
           lowerCAmelCase    :		Optional[int]				=      self.numpy_to_pil(UpperCamelCase_ )[0].resize(image.size )

           # Run inpainting pipeline with the generated mask
           lowerCAmelCase    :		str				=      StableDiffusionInpaintPipeline(
               vae=self.vae    , text_encoder=self.text_encoder    , tokenizer=self.tokenizer    , unet=self.unet    , scheduler=self.scheduler    , safety_checker=self.safety_checker    , feature_extractor=self.feature_extractor    , )
           return inpainting_pipeline(
               prompt=UpperCamelCase_    , image=UpperCamelCase_    , mask_image=UpperCamelCase_    , height=UpperCamelCase_    , width=UpperCamelCase_    , num_inference_steps=UpperCamelCase_    , guidance_scale=UpperCamelCase_    , negative_prompt=UpperCamelCase_    , num_images_per_prompt=UpperCamelCase_    , eta=UpperCamelCase_    , generator=UpperCamelCase_    , latents=UpperCamelCase_    , output_type=UpperCamelCase_    , return_dict=UpperCamelCase_    , callback=UpperCamelCase_    , callback_steps=UpperCamelCase_    , )






from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str = ""				):
     __UpperCamelCase    							=url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
     __UpperCamelCase    							=BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE__				).text			,     'html.parser'				)
     __UpperCamelCase    							=soup.find_all('td'			,     attrs='titleColumn'				)
     __UpperCamelCase    							=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"				):
     __UpperCamelCase    							=get_imdb_top_aaa_movies()
     with open(SCREAMING_SNAKE_CASE__			,     'w'			,     newline=''				) as out_file:
          __UpperCamelCase    							=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()





