






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	:							int				=logging.get_logger(__name__)  # pylint: disable=invalid-name





class 			__a  (    A__       ):







 def __init__(		self					:   int							,   SCREAMING_SNAKE_CASE					:   CLIPSegForImageSegmentation							,   SCREAMING_SNAKE_CASE					:   CLIPSegProcessor							,   SCREAMING_SNAKE_CASE					:   AutoencoderKL							,   SCREAMING_SNAKE_CASE					:   CLIPTextModel							,   SCREAMING_SNAKE_CASE					:   CLIPTokenizer							,   SCREAMING_SNAKE_CASE					:   UNetaDConditionModel							,   SCREAMING_SNAKE_CASE					:   Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]							,   SCREAMING_SNAKE_CASE					:   StableDiffusionSafetyChecker							,   SCREAMING_SNAKE_CASE					:   CLIPImageProcessor							,   ):


      '''simple docstring'''






      super().__init__()

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               F'The configuration file of this scheduler: {scheduler} is outdated.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

      if hasattr(scheduler.config							,   "skip_prk_steps"						) and scheduler.config.skip_prk_steps is False:
           UpperCamelCase__			:   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"							,   SCREAMING_SNAKE_CASE							,   standard_warn=SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   Union[str, Any]       				=  dict(scheduler.config						)
           UpperCamelCase__			:   List[Any]       				=  True
           UpperCamelCase__			:   Dict       				=  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 					__lowercase					(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, int]] = "auto"						):


      '''simple docstring'''






      if slice_size == "auto":
           # half the attention head size is usually a good trade-off between
           # speed and memory
           UpperCamelCase__			:   Any       				=  self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      self.enable_attention_slicing(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      if is_accelerate_available():
           from accelerate import cpu_offload
      else:
           raise ImportError("Please install accelerate via `pip install accelerate`"						)

      UpperCamelCase__			:   List[str]       				=  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 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      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					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Union[str, List[str]]							,   SCREAMING_SNAKE_CASE					:   Union[torch.FloatTensor, PIL.Image.Image]							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   int = 5_12							,   SCREAMING_SNAKE_CASE					:   int = 5_12							,   SCREAMING_SNAKE_CASE					:   int = 50							,   SCREAMING_SNAKE_CASE					:   float = 7.5							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, List[str]]] = None							,   SCREAMING_SNAKE_CASE					:   Optional[int] = 1							,   SCREAMING_SNAKE_CASE					:   float = 0.0							,   SCREAMING_SNAKE_CASE					:   Optional[torch.Generator] = None							,   SCREAMING_SNAKE_CASE					:   Optional[torch.FloatTensor] = None							,   SCREAMING_SNAKE_CASE					:   Optional[str] = "pil"							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,   SCREAMING_SNAKE_CASE					:   int = 1							,   **SCREAMING_SNAKE_CASE					:   str							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self.segmentation_processor(
          text=[text]							,   images=[image]							,   padding="max_length"							,   return_tensors="pt"						).to(self.device						)
      UpperCamelCase__			:   Dict       				=  self.segmentation_model(**SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  torch.sigmoid(outputs.logits						).cpu().detach().unsqueeze(-1						).numpy()
      UpperCamelCase__			:   Any       				=  self.numpy_to_pil(SCREAMING_SNAKE_CASE						)[0].resize(image.size						)

      # Run inpainting pipeline with the generated mask
      UpperCamelCase__			:   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=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							,   )







# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


lowerCamelCase	:							Optional[Any]				=subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
lowerCamelCase	:							str				=subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()

lowerCamelCase	:							List[Any]				='''|'''.join(sys.argv[1:])
lowerCamelCase	:							str				=re.compile(RF"""^({joined_dirs}).*?\.py$""")

lowerCamelCase	:							Optional[int]				=[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')