


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







class 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :Optional[int]      ,__snake_case       :CLIPSegForImageSegmentation      ,__snake_case       :CLIPSegProcessor      ,__snake_case       :AutoencoderKL      ,__snake_case       :CLIPTextModel      ,__snake_case       :CLIPTokenizer      ,__snake_case       :UNetaDConditionModel      ,__snake_case       :Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]      ,__snake_case       :StableDiffusionSafetyChecker      ,__snake_case       :CLIPImageProcessor      ,) ->   Tuple:
												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																			    F'The configuration file of this scheduler: {scheduler} is outdated.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'      ,__snake_case      ,standard_warn=__snake_case				)
																			a__					=					dict(scheduler.config				)
																			a__					=					1
																			a__					=					FrozenDict(__snake_case				)

												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'      ,__snake_case      ,standard_warn=__snake_case				)
																			a__					=					dict(scheduler.config				)
																			a__					=					True
																			a__					=					FrozenDict(__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=__snake_case      ,segmentation_processor=__snake_case      ,vae=__snake_case      ,text_encoder=__snake_case      ,tokenizer=__snake_case      ,unet=__snake_case      ,scheduler=__snake_case      ,safety_checker=__snake_case      ,feature_extractor=__snake_case      ,)
					def 			lowerCamelCase__(				self       :int      ,__snake_case       :Optional[Union[str, int]] = "auto"				) ->   Union[str, 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(__snake_case				)
					def 			lowerCamelCase__(				self       :str				) ->   Dict:
												self.enable_attention_slicing(__snake_case				)
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   Union[str, 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(__snake_case      ,__snake_case				)
					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 			lowerCamelCase__(				self       :str				) ->   str:
												if self.device != torch.device('meta'				) or not hasattr(self.unet      ,'_hf_hook'				):
																			return self.device
												for module in self.unet.modules():
																			if (
																			    hasattr(__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[Any]      ,__snake_case       :Union[str, List[str]]      ,__snake_case       :Union[torch.FloatTensor, PIL.Image.Image]      ,__snake_case       :str      ,__snake_case       :int = 5_12      ,__snake_case       :int = 5_12      ,__snake_case       :int = 50      ,__snake_case       :float = 7.5      ,__snake_case       :Optional[Union[str, List[str]]] = None      ,__snake_case       :Optional[int] = 1      ,__snake_case       :float = 0.0      ,__snake_case       :Optional[torch.Generator] = None      ,__snake_case       :Optional[torch.FloatTensor] = None      ,__snake_case       :Optional[str] = "pil"      ,__snake_case       :bool = True      ,__snake_case       :Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,__snake_case       :int = 1      ,**__snake_case       :Tuple      ,) ->   List[str]:
												a__					=					self.segmentation_processor(
												    text=[text]      ,images=[image]      ,padding='max_length'      ,return_tensors='pt'				).to(self.device				)
												a__					=					self.segmentation_model(**__snake_case				)
												a__					=					torch.sigmoid(outputs.logits				).cpu().detach().unsqueeze(-1				).numpy()
												a__					=					self.numpy_to_pil(__snake_case				)[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=__snake_case      ,image=__snake_case      ,mask_image=__snake_case      ,height=__snake_case      ,width=__snake_case      ,num_inference_steps=__snake_case      ,guidance_scale=__snake_case      ,negative_prompt=__snake_case      ,num_images_per_prompt=__snake_case      ,eta=__snake_case      ,generator=__snake_case      ,latents=__snake_case      ,output_type=__snake_case      ,return_dict=__snake_case      ,callback=__snake_case      ,callback_steps=__snake_case      ,)








from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


snake_case							:	str					      =				logging.get_logger(__name__)






def 							__lowercase       (			__lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    Optional[Any]       ):
							a__					=					b.T
							a__					=					np.sum(np.square(__lowerCAmelCase       ) ,   axis=1       )
							a__					=					np.sum(np.square(__lowerCAmelCase       ) ,   axis=0       )
							a__					=					np.matmul(__lowerCAmelCase ,   __lowerCAmelCase       )
							a__					=					aa[:, None] - 2 * ab + ba[None, :]
							return d






def 							__lowercase       (			__lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    str       ):
							a__					=					x.reshape(-1 ,   3       )
							a__					=					squared_euclidean_distance(__lowerCAmelCase ,   __lowerCAmelCase       )
							return np.argmin(__lowerCAmelCase ,   axis=1       )







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   Optional[Any]   				=      ['''pixel_values''']
					def __init__(				self       :Dict      ,__snake_case       :Optional[Union[List[List[int]], np.ndarray]] = None      ,__snake_case       :bool = True      ,__snake_case       :Dict[str, int] = None      ,__snake_case       :PILImageResampling = PILImageResampling.BILINEAR      ,__snake_case       :bool = True      ,__snake_case       :bool = True      ,**__snake_case       :Optional[int]      ,) ->   None:
												super().__init__(**__snake_case				)
												a__					=					size if size is not None else {'height': 2_56, 'width': 2_56}
												a__					=					get_size_dict(__snake_case				)
												a__					=					np.array(__snake_case				) if clusters is not None else None
												a__					=					do_resize
												a__					=					size
												a__					=					resample
												a__					=					do_normalize
												a__					=					do_color_quantize
					def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :np.ndarray      ,__snake_case       :Dict[str, int]      ,__snake_case       :PILImageResampling = PILImageResampling.BILINEAR      ,__snake_case       :Optional[Union[str, ChannelDimension]] = None      ,**__snake_case       :Any      ,) ->   np.ndarray:
												a__					=					get_size_dict(__snake_case				)
												if "height" not in size or "width" not in size:
																			raise ValueError(F'Size dictionary must contain both height and width keys. Got {size.keys()}'				)
												return resize(
												    __snake_case      ,size=(size['height'], size['width'])      ,resample=__snake_case      ,data_format=__snake_case      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :List[Any]      ,__snake_case       :np.ndarray      ,__snake_case       :Optional[Union[str, ChannelDimension]] = None      ,) ->   np.ndarray:
												a__					=					rescale(image=__snake_case      ,scale=1 / 1_27.5      ,data_format=__snake_case				)
												a__					=					image - 1
												return image



					def 			lowerCamelCase__(				self       :Optional[Any]      ,__snake_case       :ImageInput      ,__snake_case       :bool = None      ,__snake_case       :Dict[str, int] = None      ,__snake_case       :PILImageResampling = None      ,__snake_case       :bool = None      ,__snake_case       :Optional[bool] = None      ,__snake_case       :Optional[Union[List[List[int]], np.ndarray]] = None      ,__snake_case       :Optional[Union[str, TensorType]] = None      ,__snake_case       :Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST      ,**__snake_case       :List[str]      ,) ->   PIL.Image.Image:
												a__					=					do_resize if do_resize is not None else self.do_resize
												a__					=					size if size is not None else self.size
												a__					=					get_size_dict(__snake_case				)
												a__					=					resample if resample is not None else self.resample
												a__					=					do_normalize if do_normalize is not None else self.do_normalize
												a__					=					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
												a__					=					clusters if clusters is not None else self.clusters
												a__					=					np.array(__snake_case				)

												a__					=					make_list_of_images(__snake_case				)

												if not valid_images(__snake_case				):
																			raise ValueError(
																			    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																			    'torch.Tensor, tf.Tensor or jax.ndarray.'				)

												if do_resize and size is None or resample is None:
																			raise ValueError('Size and resample must be specified if do_resize is True.'				)

												if do_color_quantize and clusters is None:
																			raise ValueError('Clusters must be specified if do_color_quantize is True.'				)

												# All transformations expect numpy arrays.
												a__					=					[to_numpy_array(__snake_case				) for image in images]

												if do_resize:
																			a__					=					[self.resize(image=__snake_case      ,size=__snake_case      ,resample=__snake_case				) for image in images]

												if do_normalize:
																			a__					=					[self.normalize(image=__snake_case				) for image in images]

												if do_color_quantize:
																			a__					=					[to_channel_dimension_format(__snake_case      ,ChannelDimension.LAST				) for image in images]
																			# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																			a__					=					np.array(__snake_case				)
																			a__					=					color_quantize(__snake_case      ,__snake_case				).reshape(images.shape[:-1]				)

																			# flatten to (batch_size, height*width)
																			a__					=					images.shape[0]
																			a__					=					images.reshape(__snake_case      ,-1				)

																			# We need to convert back to a list of images to keep consistent behaviour across processors.
																			a__					=					list(__snake_case				)
												else:
																			a__					=					[to_channel_dimension_format(__snake_case      ,__snake_case				) for image in images]

												a__					=					{'input_ids': images}
												return BatchFeature(data=__snake_case      ,tensor_type=__snake_case				)




