



"""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


__A   :							Union[str, Any]										=   logging.get_logger(__name__)  # pylint: disable=invalid-name
class 			_a (					UpperCamelCase_):
			"""simple docstring"""



			def __init__(		self     :    Dict				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    List[str]				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    Any				,   __UpperCamelCase     :    Union[str, Any]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    Optional[Any]				,   __UpperCamelCase     :    Any				,   )->Any:
							super().__init__()

							if hasattr(scheduler.config				,   '''steps_offset'''   ) and scheduler.config.steps_offset != 1:
											_UpperCAmelCase					=    (
											    F'The configuration file of this scheduler: {scheduler} is outdated.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'''				,   __UpperCamelCase				,   standard_warn=__UpperCamelCase   )
											_UpperCAmelCase					=    dict(scheduler.config   )
											_UpperCAmelCase					=    1
											_UpperCAmelCase					=    FrozenDict(__UpperCamelCase   )

							if hasattr(scheduler.config				,   '''skip_prk_steps'''   ) and scheduler.config.skip_prk_steps is False:
											_UpperCAmelCase					=    (
											    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'''				,   __UpperCamelCase				,   standard_warn=__UpperCamelCase   )
											_UpperCAmelCase					=    dict(scheduler.config   )
											_UpperCAmelCase					=    True
											_UpperCAmelCase					=    FrozenDict(__UpperCamelCase   )

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



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    Tuple = "auto"   )->Dict:
							if slice_size == "auto":
											# half the attention head size is usually a good trade-off between
											# speed and memory
											_UpperCAmelCase					=    self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(__UpperCamelCase   )



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



			def  lowercase__						(		self     :    int   )->Dict:
							if is_accelerate_available():
											from accelerate import cpu_offload
							else:
											raise ImportError('''Please install accelerate via `pip install accelerate`'''   )

							_UpperCAmelCase					=    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(__UpperCamelCase				,   __UpperCamelCase   )



			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def  lowercase__						(		self     :    List[str]   )->Optional[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(__UpperCamelCase				,   '''_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     :    Tuple				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    Any				,   __UpperCamelCase     :    Optional[Any]				,   __UpperCamelCase     :    str = 5_1_2				,   __UpperCamelCase     :    int = 5_1_2				,   __UpperCamelCase     :    str = 5_0				,   __UpperCamelCase     :    int = 7.5				,   __UpperCamelCase     :    List[str] = None				,   __UpperCamelCase     :    Optional[int] = 1				,   __UpperCamelCase     :    List[Any] = 0.0				,   __UpperCamelCase     :    str = None				,   __UpperCamelCase     :    List[Any] = None				,   __UpperCamelCase     :    List[str] = "pil"				,   __UpperCamelCase     :    List[Any] = True				,   __UpperCamelCase     :    Any = None				,   __UpperCamelCase     :    Union[str, Any] = 1				,   **__UpperCamelCase     :    Union[str, Any]				,   )->Any:
							_UpperCAmelCase					=    self.segmentation_processor(
							    text=[text]				,   images=[image]				,   padding='''max_length'''				,   return_tensors='''pt'''   ).to(self.device   )
							_UpperCAmelCase					=    self.segmentation_model(**__UpperCamelCase   )
							_UpperCAmelCase					=    torch.sigmoid(outputs.logits   ).cpu().detach().unsqueeze(-1   ).numpy()
							_UpperCAmelCase					=    self.numpy_to_pil(__UpperCamelCase   )[0].resize(image.size   )

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









"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A   :							Optional[int]										=   logging.get_logger(__name__)

__A   :							Optional[Any]										=   {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class 			_a (					lowerCAmelCase			,							lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""bit"""
			UpperCamelCase__			      =	["""preactivation""", """bottleneck"""]
			UpperCamelCase__			      =	["""SAME""", """VALID"""]



			def __init__(		self     :    List[Any]				,   __UpperCamelCase     :    Optional[int]=3				,   __UpperCamelCase     :    Optional[int]=6_4				,   __UpperCamelCase     :    List[Any]=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,   __UpperCamelCase     :    Any=[3, 4, 6, 3]				,   __UpperCamelCase     :    str="preactivation"				,   __UpperCamelCase     :    Union[str, Any]="relu"				,   __UpperCamelCase     :    str=None				,   __UpperCamelCase     :    Dict=3_2				,   __UpperCamelCase     :    List[str]=0.0				,   __UpperCamelCase     :    Any=False				,   __UpperCamelCase     :    Union[str, Any]=3_2				,   __UpperCamelCase     :    List[str]=1				,   __UpperCamelCase     :    List[str]=None				,   __UpperCamelCase     :    Optional[Any]=None				,   **__UpperCamelCase     :    List[str]				,   )->List[Any]:
							super().__init__(**__UpperCamelCase   )
							if layer_type not in self.layer_types:
											raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types   )}'   )
							if global_padding is not None:
											if global_padding.upper() in self.supported_padding:
															_UpperCAmelCase					=    global_padding.upper()
											else:
															raise ValueError(F'Padding strategy {global_padding} not supported'   )
							_UpperCAmelCase					=    num_channels
							_UpperCAmelCase					=    embedding_size
							_UpperCAmelCase					=    hidden_sizes
							_UpperCAmelCase					=    depths
							_UpperCAmelCase					=    layer_type
							_UpperCAmelCase					=    hidden_act
							_UpperCAmelCase					=    global_padding
							_UpperCAmelCase					=    num_groups
							_UpperCAmelCase					=    drop_path_rate
							_UpperCAmelCase					=    embedding_dynamic_padding
							_UpperCAmelCase					=    output_stride
							_UpperCAmelCase					=    width_factor

							_UpperCAmelCase					=    ['''stem'''] + [F'stage{idx}' for idx in range(1				,   len(__UpperCamelCase   ) + 1   )]
							_UpperCAmelCase		,						_UpperCAmelCase					=    get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase				,   out_indices=__UpperCamelCase				,   stage_names=self.stage_names   )



