'''simple docstring'''




import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_UpperCamelCase					:		Union[str, Any]							      =		os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_UpperCamelCase					:		int							      =		'    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'

class 	_lowercase(  unittest.TestCase					):



   """simple docstring"""





   def       snake_case						(				self:     str						):
    __UpperCAmelCase								=       tempfile.mkdtemp()
    os.makedirs(os.path.join(self.diffusers_dir      ,'schedulers/'						)						)
    __UpperCAmelCase								=       self.diffusers_dir
    shutil.copy(
        os.path.join(_a      ,'src/diffusers/schedulers/scheduling_ddpm.py'						)      ,os.path.join(self.diffusers_dir      ,'schedulers/scheduling_ddpm.py'						)      ,)





   def       snake_case						(				self:     Dict						):
    __UpperCAmelCase								=       'src/diffusers'
    shutil.rmtree(self.diffusers_dir						)





   def       snake_case						(				self:     List[Any]      ,a:     Tuple      ,a:     List[str]      ,a:     List[str]      ,a:     int=None						):
    __UpperCAmelCase								=       comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
    if overwrite_result is not None:
     __UpperCAmelCase								=       comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
    __UpperCAmelCase								=       black.Mode(target_versions={black.TargetVersion.PYaa}      ,line_length=119						)
    __UpperCAmelCase								=       black.format_str(_a      ,mode=_a						)
    __UpperCAmelCase								=       os.path.join(self.diffusers_dir      ,'new_code.py'						)
    with open(_a      ,'w'      ,newline='\n'						) as f:
     f.write(_a						)
    if overwrite_result is None:
     self.assertTrue(len(check_copies.is_copy_consistent(_a						)						) == 0						)
    else:
     check_copies.is_copy_consistent(f.name      ,overwrite=_a						)
     with open(_a      ,'r'						) as f:
      self.assertTrue(f.read()      ,_a						)





   def       snake_case						(				self:     int						):
    __UpperCAmelCase								=       check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'						)
    self.assertEqual(_a      ,_a						)





   def       snake_case						(				self:     Any						):
    # Base copy consistency
    self.check_copy_consistency(
        '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'      ,'DDPMSchedulerOutput'      ,REFERENCE_CODE + '\n'      ,)

    # With no empty line at the end
    self.check_copy_consistency(
        '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'      ,'DDPMSchedulerOutput'      ,_a      ,)

    # Copy consistency with rename
    self.check_copy_consistency(
        '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'      ,'TestSchedulerOutput'      ,re.sub('DDPM'      ,'Test'      ,_a						)      ,)

    # Copy consistency with a really long name
    __UpperCAmelCase								=       'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
    self.check_copy_consistency(
        f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""      ,f"""{long_class_name}SchedulerOutput"""      ,re.sub('Bert'      ,_a      ,_a						)      ,)

    # Copy consistency with overwrite
    self.check_copy_consistency(
        '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'      ,'TestSchedulerOutput'      ,_a      ,overwrite_result=re.sub('DDPM'      ,'Test'      ,_a						)      ,)








'''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


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






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  def __init__(					self :      Union[str, Any]     ,      _a :      CLIPSegForImageSegmentation     ,      _a :      CLIPSegProcessor     ,      _a :      AutoencoderKL     ,      _a :      CLIPTextModel     ,      _a :      CLIPTokenizer     ,      _a :      UNetaDConditionModel     ,      _a :      Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     ,      _a :      StableDiffusionSafetyChecker     ,      _a :      CLIPImageProcessor     ,      )   ->   int:
    super().__init__()

    if hasattr(scheduler.config     ,      'steps_offset'  ) and scheduler.config.steps_offset != 1:
      __lowerCamelCase			:			int       			=					(
          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'     ,      _a     ,      standard_warn=_a  )
      __lowerCamelCase			:			int       			=					dict(scheduler.config  )
      __lowerCamelCase			:			Any       			=					1
      __lowerCamelCase			:			List[Any]       			=					FrozenDict(_a  )

    if hasattr(scheduler.config     ,      'skip_prk_steps'  ) and scheduler.config.skip_prk_steps is False:
      __lowerCamelCase			:			str       			=					(
          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'     ,      _a     ,      standard_warn=_a  )
      __lowerCamelCase			:			Dict       			=					dict(scheduler.config  )
      __lowerCamelCase			:			Tuple       			=					True
      __lowerCamelCase			:			Union[str, Any]       			=					FrozenDict(_a  )

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




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




  def 		_lowercase	(					self :      Optional[Any]  )   ->   int:
    self.enable_attention_slicing(_a  )




  def 		_lowercase	(					self :      str  )   ->   Any:
    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(_a     ,      _a  )




  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 		_lowercase	(					self :      Dict  )   ->   int:
    if self.device != torch.device('meta'  ) or not hasattr(self.unet     ,      '_hf_hook'  ):
      return self.device
    for module in self.unet.modules():
      if (
          hasattr(_a     ,      '_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]     ,      _a :      Union[str, List[str]]     ,      _a :      Union[torch.FloatTensor, PIL.Image.Image]     ,      _a :      str     ,      _a :      int = 512     ,      _a :      int = 512     ,      _a :      int = 50     ,      _a :      float = 7.5     ,      _a :      Optional[Union[str, List[str]]] = None     ,      _a :      Optional[int] = 1     ,      _a :      float = 0.0     ,      _a :      Optional[torch.Generator] = None     ,      _a :      Optional[torch.FloatTensor] = None     ,      _a :      Optional[str] = "pil"     ,      _a :      bool = True     ,      _a :      Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,      _a :      int = 1     ,      **_a :      Optional[Any]     ,      )   ->   int:
    __lowerCamelCase			:			Union[str, Any]       			=					self.segmentation_processor(
        text=[text]     ,      images=[image]     ,      padding='max_length'     ,      return_tensors='pt'  ).to(self.device  )
    __lowerCamelCase			:			Union[str, Any]       			=					self.segmentation_model(**_a  )
    __lowerCamelCase			:			List[str]       			=					torch.sigmoid(outputs.logits  ).cpu().detach().unsqueeze(-1  ).numpy()
    __lowerCamelCase			:			Optional[Any]       			=					self.numpy_to_pil(_a  )[0].resize(image.size  )

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






