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 UpperCAmelCase						( __A  ):


				'''simple docstring'''






				def __init__(						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     ):



						"""simple docstring"""





						super().__init__()

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								    F'''The configuration file of this scheduler: {scheduler} is outdated.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

						if hasattr(scheduler.config		,     'skip_prk_steps' ) and scheduler.config.skip_prk_steps is False:
								A_		:    Dict         =				(
								    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'		,     _lowercase		,     standard_warn=_lowercase )
								A_		:    Optional[int]         =				dict(scheduler.config )
								A_		:    List[Any]         =				True
								A_		:    Any         =				FrozenDict(_lowercase )

						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=_lowercase		,     segmentation_processor=_lowercase		,     vae=_lowercase		,     text_encoder=_lowercase		,     tokenizer=_lowercase		,     unet=_lowercase		,     scheduler=_lowercase		,     safety_checker=_lowercase		,     feature_extractor=_lowercase		,     )
				def   lowerCAmelCase_       (						self		,     lowercase = "auto" ):



						"""simple docstring"""





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



						"""simple docstring"""





						self.enable_attention_slicing(_lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





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

						A_		:    Tuple         =				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(_lowercase		,     _lowercase )
				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def   lowerCAmelCase_       (						self ):



						"""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(_lowercase		,     '_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		,     lowercase		,     lowercase		,     lowercase		,     lowercase = 5_1_2		,     lowercase = 5_1_2		,     lowercase = 5_0		,     lowercase = 7.5		,     lowercase = None		,     lowercase = 1		,     lowercase = 0.0		,     lowercase = None		,     lowercase = None		,     lowercase = "pil"		,     lowercase = True		,     lowercase = None		,     lowercase = 1		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Tuple         =				self.segmentation_processor(
						    text=[text]		,     images=[image]		,     padding='max_length'		,     return_tensors='pt' ).to(self.device )
						A_		:    str         =				self.segmentation_model(**_lowercase )
						A_		:    Optional[int]         =				torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
						A_		:    Union[str, Any]         =				self.numpy_to_pil(_lowercase )[0].resize(image.size )

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







import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_UpperCAmelCase            =		logging.get_logger(__name__)





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				def __init__(						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						warnings.warn(
						    'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
						    ' use DonutImageProcessor instead.'		,     lowercase		,     )
						super().__init__(*lowercase		,     **lowercase )






