

"""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					:Optional[Any]    = logging.get_logger(__name__)  # pylint: disable=invalid-name

class      __a							(UpperCamelCase_):






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    )  ->      List[Any]:


           """simple docstring"""


           super().__init__()

           if hasattr(scheduler.config	,    """steps_offset"""  ) and scheduler.config.steps_offset != 1:
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	(
                     f'''The configuration file of this scheduler: {scheduler} is outdated.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__   :      str      		=	dict(scheduler.config  )
                 SCREAMING_SNAKE_CASE__   :      Any      		=	1
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	FrozenDict(_a  )

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


           """simple docstring"""


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

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           self.enable_attention_slicing(_a  )

     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


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

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	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 					_a			(						self  )  ->      Optional[int]:


           """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	,    _a	,    _a	,    _a	,    _a = 512	,    _a = 512	,    _a = 50	,    _a = 7.5	,    _a = None	,    _a = 1	,    _a = 0.0	,    _a = None	,    _a = None	,    _a = "pil"	,    _a = True	,    _a = None	,    _a = 1	,    **_a	,    )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	self.segmentation_processor(
               text=[text]	,    images=[image]	,    padding="""max_length"""	,    return_tensors="""pt"""  ).to(self.device  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.segmentation_model(**_a  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	torch.sigmoid(outputs.logits  ).cpu().detach().unsqueeze(-1  ).numpy()
           SCREAMING_SNAKE_CASE__   :      Any      		=	self.numpy_to_pil(_a  )[0].resize(image.size  )

           # Run inpainting pipeline with the generated mask
           SCREAMING_SNAKE_CASE__   :      Dict      		=	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	,    )







"""simple docstring"""


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


a					:List[Any]    = logging.get_logger(__name__)

a					:Optional[int]    = {
    "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}

class      __a							(UpperCamelCase_   ,	UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Any       = """focalnet"""

     def __init__(						self	,    _a=224	,    _a=4	,    _a=3	,    _a=96	,    _a=False	,    _a=[192, 384, 768, 768]	,    _a=[2, 2, 6, 2]	,    _a=[2, 2, 2, 2]	,    _a=[3, 3, 3, 3]	,    _a="gelu"	,    _a=4.0	,    _a=0.0	,    _a=0.1	,    _a=False	,    _a=1E-4	,    _a=False	,    _a=False	,    _a=False	,    _a=0.02	,    _a=1E-5	,    _a=32	,    _a=None	,    _a=None	,    **_a	,    )  ->      Optional[Any]:


           """simple docstring"""


           super().__init__(**_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	image_size
           SCREAMING_SNAKE_CASE__   :      str      		=	patch_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	num_channels
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	embed_dim
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	use_conv_embed
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	hidden_sizes
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	depths
           SCREAMING_SNAKE_CASE__   :      Any      		=	focal_levels
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	focal_windows
           SCREAMING_SNAKE_CASE__   :      Any      		=	hidden_act
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	mlp_ratio
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	hidden_dropout_prob
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	drop_path_rate
           SCREAMING_SNAKE_CASE__   :      str      		=	use_layerscale
           SCREAMING_SNAKE_CASE__   :      int      		=	layerscale_value
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	use_post_layernorm
           SCREAMING_SNAKE_CASE__   :      Any      		=	use_post_layernorm_in_modulation
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	normalize_modulator
           SCREAMING_SNAKE_CASE__   :      str      		=	initializer_range
           SCREAMING_SNAKE_CASE__   :      Any      		=	layer_norm_eps
           SCREAMING_SNAKE_CASE__   :      Any      		=	encoder_stride
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	["""stem"""] + [f'''stage{idx}''' for idx in range(1	,    len(self.depths  ) + 1  )]
           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Dict      		=	get_aligned_output_features_output_indices(
               out_features=_a	,    out_indices=_a	,    stage_names=self.stage_names  )




