


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

class 						A__							(		UpperCamelCase__  ):




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


										"""simple docstring"""





										super().__init__()

										if hasattr(scheduler.config				,    '''steps_offset'''  ) and scheduler.config.steps_offset != 1:
														_SCREAMING_SNAKE_CASE			  =(
														    f"The configuration file of this scheduler: {scheduler} is outdated.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  )
														_SCREAMING_SNAKE_CASE			  =dict(scheduler.config  )
														_SCREAMING_SNAKE_CASE			  =1
														_SCREAMING_SNAKE_CASE			  =FrozenDict(_a  )

										if hasattr(scheduler.config				,    '''skip_prk_steps'''  ) and scheduler.config.skip_prk_steps is False:
														_SCREAMING_SNAKE_CASE			  =(
														    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  )
														_SCREAMING_SNAKE_CASE			  =dict(scheduler.config  )
														_SCREAMING_SNAKE_CASE			  =True
														_SCREAMING_SNAKE_CASE			  =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 							__UpperCamelCase				(       self					:      Tuple				,    _a					:      Optional[Union[str, int]] = "auto"  )							->     List[str]:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Union[str, Any]:


										"""simple docstring"""





										self.enable_attention_slicing(_a  )




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     int:


										"""simple docstring"""





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

										_SCREAMING_SNAKE_CASE			  =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 							__UpperCamelCase				(       self					:      List[str]  )							->     Optional[Any]:


										"""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(_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					:      Any				,    _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					:      Any				,    )							->     Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.segmentation_processor(
										    text=[text]				,    images=[image]				,    padding='''max_length'''				,    return_tensors='''pt'''  ).to(self.device  )
										_SCREAMING_SNAKE_CASE			  =self.segmentation_model(**_a  )
										_SCREAMING_SNAKE_CASE			  =torch.sigmoid(outputs.logits  ).cpu().detach().unsqueeze(-1  ).numpy()
										_SCREAMING_SNAKE_CASE			  =self.numpy_to_pil(_a  )[0].resize(image.size  )

										# Run inpainting pipeline with the generated mask
										_SCREAMING_SNAKE_CASE			  =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				,    )



import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment







def 						lowerCamelCase(   a__	,a__=()	,a__=None	,a__="no"	,a__="29500"):
				_SCREAMING_SNAKE_CASE			  =False
				_SCREAMING_SNAKE_CASE			  =False
				if any(key.startswith('''KAGGLE''') for key in os.environ.keys()):
								_SCREAMING_SNAKE_CASE			  =True
				elif "IPython" in sys.modules:
								_SCREAMING_SNAKE_CASE			  ='''google.colab''' in str(sys.modules['''IPython'''].get_ipython())

				try:
								_SCREAMING_SNAKE_CASE			  =PrecisionType(mixed_precision.lower())
				except ValueError:
								raise ValueError(
								    f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.")

				if (in_colab or in_kaggle) and (os.environ.get('''TPU_NAME'''	,a__) is not None):
								# TPU launch
								import torch_xla.distributed.xla_multiprocessing as xmp

								if len(AcceleratorState._shared_state) > 0:
												raise ValueError(
												    '''To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '''
												    '''your training function. Restart your notebook and make sure no cells initializes an '''
												    '''`Accelerator`.''')
								if num_processes is None:
												_SCREAMING_SNAKE_CASE			  =8

								_SCREAMING_SNAKE_CASE			  =PrepareForLaunch(a__	,distributed_type='''TPU''')
								print(f"Launching a training on {num_processes} TPU cores.")
								xmp.spawn(a__	,args=a__	,nprocs=a__	,start_method='''fork''')
				elif in_colab:
								# No need for a distributed launch otherwise as it's either CPU or one GPU.
								if torch.cuda.is_available():
												print('''Launching training on one GPU.''')
								else:
												print('''Launching training on one CPU.''')
								function(*a__)
				else:
								if num_processes is None:
												raise ValueError(
												    '''You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.''')

								if num_processes > 1:
												# Multi-GPU launch
												from torch.multiprocessing import start_processes
												from torch.multiprocessing.spawn import ProcessRaisedException

												if len(AcceleratorState._shared_state) > 0:
																raise ValueError(
																    '''To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '''
																    '''inside your training function. Restart your notebook and make sure no cells initializes an '''
																    '''`Accelerator`.''')

												if torch.cuda.is_initialized():
																raise ValueError(
																    '''To launch a multi-GPU training from your notebook, you need to avoid running any instruction '''
																    '''using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '''
																    '''function.''')

												# torch.distributed will expect a few environment variable to be here. We set the ones common to each
												# process here (the other ones will be set be the launcher).
												with patch_environment(
												    world_size=a__	,master_addr='''127.0.01'''	,master_port=a__	,mixed_precision=a__):
																_SCREAMING_SNAKE_CASE			  =PrepareForLaunch(a__	,distributed_type='''MULTI_GPU''')
																print(f"Launching training on {num_processes} GPUs.")
																try:
																				start_processes(a__	,args=a__	,nprocs=a__	,start_method='''fork''')
																except ProcessRaisedException as e:
																				if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																								raise RuntimeError(
																								    '''CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '''
																								    '''This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '''
																								    '''Please review your imports and test them when running the `notebook_launcher()` to identify '''
																								    '''which one is problematic.''') from e

								else:
												# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
												if is_mps_available():
																_SCREAMING_SNAKE_CASE			  ='''1'''
																print('''Launching training on MPS.''')
												elif torch.cuda.is_available():
																print('''Launching training on one GPU.''')
												else:
																print('''Launching training on CPU.''')
												function(*a__)







def 						lowerCamelCase(   a__	,a__=()	,a__=2):
				from torch.multiprocessing import start_processes

				with tempfile.NamedTemporaryFile() as tmp_file:
								# torch.distributed will expect a few environment variable to be here. We set the ones common to each
								# process here (the other ones will be set be the launcher).
								with patch_environment(
								    world_size=a__	,master_addr='''127.0.01'''	,master_port='''29500'''	,accelerate_mixed_precision='''no'''	,accelerate_debug_rdv_file=tmp_file.name	,accelerate_use_cpu='''yes'''	,):
												_SCREAMING_SNAKE_CASE			  =PrepareForLaunch(a__	,debug=a__)
												start_processes(a__	,args=a__	,nprocs=a__	,start_method='''fork''')