


"""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_       :       List[str]      						=  logging.get_logger(__name__)  # pylint: disable=invalid-name






class 				__A    (	lowerCamelCase_      ):






 """simple docstring"""




 def __init__(		self			,   __A			,   __A			,   __A			,   __A			,   __A			,   __A			,   __A			,   __A			,   __A			,   ) ->					Any:
     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             f'''The configuration file of this scheduler: {scheduler} is outdated.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'''			,   lowerCAmelCase__			,   standard_warn=lowerCAmelCase__						)
         a					=dict(scheduler.config						)
         a					=1
         a					=FrozenDict(lowerCAmelCase__						)

     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'''			,   lowerCAmelCase__			,   standard_warn=lowerCAmelCase__						)
         a					=dict(scheduler.config						)
         a					=True
         a					=FrozenDict(lowerCAmelCase__						)

     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=lowerCAmelCase__			,   segmentation_processor=lowerCAmelCase__			,   vae=lowerCAmelCase__			,   text_encoder=lowerCAmelCase__			,   tokenizer=lowerCAmelCase__			,   unet=lowerCAmelCase__			,   scheduler=lowerCAmelCase__			,   safety_checker=lowerCAmelCase__			,   feature_extractor=lowerCAmelCase__			,   )


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A = "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(lowerCAmelCase__						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     self.enable_attention_slicing(lowerCAmelCase__						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[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(lowerCAmelCase__			,   lowerCAmelCase__						)


 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     if self.device != torch.device('''meta'''						) or not hasattr(self.unet			,   '''_hf_hook'''						):
         return self.device
     for module in self.unet.modules():
         if (
             hasattr(lowerCAmelCase__			,   '''_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			,   __A			,   __A			,   __A			,   __A = 512			,   __A = 512			,   __A = 50			,   __A = 7.5			,   __A = None			,   __A = 1			,   __A = 0.0			,   __A = None			,   __A = None			,   __A = "pil"			,   __A = True			,   __A = None			,   __A = 1			,   **__A			,   ) ->					Optional[int]:
     a					=self.segmentation_processor(
         text=[text]			,   images=[image]			,   padding='''max_length'''			,   return_tensors='''pt'''						).to(self.device						)
     a					=self.segmentation_model(**lowerCAmelCase__						)
     a					=torch.sigmoid(outputs.logits						).cpu().detach().unsqueeze(-1						).numpy()
     a					=self.numpy_to_pil(lowerCAmelCase__						)[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=lowerCAmelCase__			,   image=lowerCAmelCase__			,   mask_image=lowerCAmelCase__			,   height=lowerCAmelCase__			,   width=lowerCAmelCase__			,   num_inference_steps=lowerCAmelCase__			,   guidance_scale=lowerCAmelCase__			,   negative_prompt=lowerCAmelCase__			,   num_images_per_prompt=lowerCAmelCase__			,   eta=lowerCAmelCase__			,   generator=lowerCAmelCase__			,   latents=lowerCAmelCase__			,   output_type=lowerCAmelCase__			,   return_dict=lowerCAmelCase__			,   callback=lowerCAmelCase__			,   callback_steps=lowerCAmelCase__			,   )



"""simple docstring"""







from __future__ import annotations






class 				__A    :






 """simple docstring"""




 def __init__(		self			,   __A = 0						) ->					Dict:
     a					=key


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					list[str]:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key is an appropriate size
     key %= 255

     return [chr(ord(__A						) ^ key						) for ch in content]


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					list[str]:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key is an appropriate size
     key %= 255

     return [chr(ord(__A						) ^ key						) for ch in content]


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = 0						) ->					str:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key can be any size
     while key > 255:
         key -= 255

     # This will be returned
     a					=''''''

     for ch in content:
         ans += chr(ord(__A						) ^ key						)

     return ans


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = 0						) ->					str:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key can be any size
     while key > 255:
         key -= 255

     # This will be returned
     a					=''''''

     for ch in content:
         ans += chr(ord(__A						) ^ key						)

     return ans


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = 0						) ->					bool:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     try:
         with open(__A						) as fin, open('''encrypt.out'''			,   '''w+'''						) as fout:
             # actual encrypt-process
             for line in fin:
                 fout.write(self.encrypt_string(__A			,   __A						)						)

     except OSError:
         return False

     return True





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					bool:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     try:
         with open(__A						) as fin, open('''decrypt.out'''			,   '''w+'''						) as fout:
             # actual encrypt-process
             for line in fin:
                 fout.write(self.decrypt_string(__A			,   __A						)						)

     except OSError:
         return False

     return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")