





'''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


__lowerCAmelCase							:	Union[str, Any]			=logging.get_logger(__name__)  # pylint: disable=invalid-name






class    UpperCAmelCase	(       lowercase_				):
 def __init__(				self      :Tuple						,       lowercase_      :CLIPSegForImageSegmentation						,       lowercase_      :CLIPSegProcessor						,       lowercase_      :AutoencoderKL						,       lowercase_      :CLIPTextModel						,       lowercase_      :CLIPTokenizer						,       lowercase_      :UNetaDConditionModel						,       lowercase_      :Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]						,       lowercase_      :StableDiffusionSafetyChecker						,       lowercase_      :CLIPImageProcessor						,       )->							str:
     super().__init__()

     if hasattr(scheduler.config						,       "steps_offset"							) and scheduler.config.steps_offset != 1:
         A__				=			(
             F"The configuration file of this scheduler: {scheduler} is outdated.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"						,       SCREAMING_SNAKE_CASE_						,       standard_warn=SCREAMING_SNAKE_CASE_							)
         A__				=			dict(scheduler.config							)
         A__				=			1
         A__				=			FrozenDict(SCREAMING_SNAKE_CASE_							)

     if hasattr(scheduler.config						,       "skip_prk_steps"							) and scheduler.config.skip_prk_steps is False:
         A__				=			(
             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"						,       SCREAMING_SNAKE_CASE_						,       standard_warn=SCREAMING_SNAKE_CASE_							)
         A__				=			dict(scheduler.config							)
         A__				=			True
         A__				=			FrozenDict(SCREAMING_SNAKE_CASE_							)

     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=SCREAMING_SNAKE_CASE_						,       segmentation_processor=SCREAMING_SNAKE_CASE_						,       vae=SCREAMING_SNAKE_CASE_						,       text_encoder=SCREAMING_SNAKE_CASE_						,       tokenizer=SCREAMING_SNAKE_CASE_						,       unet=SCREAMING_SNAKE_CASE_						,       scheduler=SCREAMING_SNAKE_CASE_						,       safety_checker=SCREAMING_SNAKE_CASE_						,       feature_extractor=SCREAMING_SNAKE_CASE_						,       )
 def      UpperCAmelCase_							(				self      :Any						,       lowercase_      :Optional[Union[str, int]] = "auto"							)->							Optional[Any]:
     if slice_size == "auto":
         # half the attention head size is usually a good trade-off between
         # speed and memory
         A__				=			self.unet.config.attention_head_dim // 2
     self.unet.set_attention_slice(SCREAMING_SNAKE_CASE_							)
 def      UpperCAmelCase_							(				self      :List[str]							)->							Optional[int]:
     self.enable_attention_slicing(SCREAMING_SNAKE_CASE_							)
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Union[str, Any]:
     if is_accelerate_available():
         from accelerate import cpu_offload
     else:
         raise ImportError("Please install accelerate via `pip install accelerate`"							)

     A__				=			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(SCREAMING_SNAKE_CASE_						,       SCREAMING_SNAKE_CASE_							)
 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							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(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()
 def __call__(				self      :str						,       lowercase_      :Union[str, List[str]]						,       lowercase_      :Union[torch.FloatTensor, PIL.Image.Image]						,       lowercase_      :str						,       lowercase_      :int = 5_12						,       lowercase_      :int = 5_12						,       lowercase_      :int = 50						,       lowercase_      :float = 7.5						,       lowercase_      :Optional[Union[str, List[str]]] = None						,       lowercase_      :Optional[int] = 1						,       lowercase_      :float = 0.0						,       lowercase_      :Optional[torch.Generator] = None						,       lowercase_      :Optional[torch.FloatTensor] = None						,       lowercase_      :Optional[str] = "pil"						,       lowercase_      :bool = True						,       lowercase_      :Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,       lowercase_      :int = 1						,       **lowercase_      :int						,       )->							Tuple:
     A__				=			self.segmentation_processor(
         text=[text]						,       images=[image]						,       padding="max_length"						,       return_tensors="pt"							).to(self.device							)
     A__				=			self.segmentation_model(**SCREAMING_SNAKE_CASE_							)
     A__				=			torch.sigmoid(outputs.logits							).cpu().detach().unsqueeze(-1							).numpy()
     A__				=			self.numpy_to_pil(SCREAMING_SNAKE_CASE_							)[0].resize(image.size							)

     # Run inpainting pipeline with the generated mask
     A__				=			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=SCREAMING_SNAKE_CASE_						,       image=SCREAMING_SNAKE_CASE_						,       mask_image=SCREAMING_SNAKE_CASE_						,       height=SCREAMING_SNAKE_CASE_						,       width=SCREAMING_SNAKE_CASE_						,       num_inference_steps=SCREAMING_SNAKE_CASE_						,       guidance_scale=SCREAMING_SNAKE_CASE_						,       negative_prompt=SCREAMING_SNAKE_CASE_						,       num_images_per_prompt=SCREAMING_SNAKE_CASE_						,       eta=SCREAMING_SNAKE_CASE_						,       generator=SCREAMING_SNAKE_CASE_						,       latents=SCREAMING_SNAKE_CASE_						,       output_type=SCREAMING_SNAKE_CASE_						,       return_dict=SCREAMING_SNAKE_CASE_						,       callback=SCREAMING_SNAKE_CASE_						,       callback_steps=SCREAMING_SNAKE_CASE_						,       )








"""simple docstring"""







from __future__ import annotations

def 				UpperCamelCase_    (    lowerCAmelCase__      :				list[int]    ,   lowerCAmelCase__      :				int						)						->   bool:

  """simple docstring"""




  if len(lowerCAmelCase__						) == 0:
    return False
  lowerCAmelCase_   :				Union[str, Any]								= len(lowerCAmelCase__						) // 2
  if a_list[midpoint] == item:
    return True
  if item < a_list[midpoint]:
    return binary_search(a_list[:midpoint]    ,   lowerCAmelCase__						)
  else:
    return binary_search(a_list[midpoint + 1 :]    ,   lowerCAmelCase__						)


if __name__ == "__main__":
     lowercase__		:				str          =	input("""Enter numbers separated by comma:\n""").strip()
     lowercase__		:				Optional[int]          =	[int(item.strip()) for item in user_input.split(""",""")]
     lowercase__		:				Optional[Any]          =	int(input("""Enter the number to be found in the list:\n""").strip())
     lowercase__		:				int          =	"""""" if binary_search(sequence, target) else """not """
     print(f'{target} was {not_str}found in {sequence}')





