





'''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


lowerCAmelCase_					:    Optional[int]      	=			logging.get_logger(__name__)  # pylint: disable=invalid-name
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				def __init__(  self     :					Dict					,				__a     :					CLIPSegForImageSegmentation					,				__a     :					CLIPSegProcessor					,				__a     :					AutoencoderKL					,				__a     :					CLIPTextModel					,				__a     :					CLIPTokenizer					,				__a     :					UNetaDConditionModel					,				__a     :					Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					,				__a     :					StableDiffusionSafetyChecker					,				__a     :					CLIPImageProcessor					,				):
								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												    f'The configuration file of this scheduler: {scheduler} is outdated.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"					,				__a					,				standard_warn=__a					)
												_a								= dict(scheduler.config					)
												_a								= 1
												_a								= FrozenDict(__a					)

								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"					,				__a					,				standard_warn=__a					)
												_a								= dict(scheduler.config					)
												_a								= True
												_a								= 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     :					Any					,				__a     :					Optional[Union[str, int]] = "auto"					):
								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(__a					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								self.enable_attention_slicing(__a					)




				def   UpperCamelCase__   (  self     :					Dict					):
								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(__a					,				__a					)




				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def   UpperCamelCase__   (  self     :					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(__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     :					Optional[Any]					,				__a     :					Union[str, List[str]]					,				__a     :					Union[torch.FloatTensor, PIL.Image.Image]					,				__a     :					str					,				__a     :					int = 5_12					,				__a     :					int = 5_12					,				__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     :					Tuple					,				):
								_a								= self.segmentation_processor(
								    text=[text]					,				images=[image]					,				padding="max_length"					,				return_tensors="pt"					).to(self.device					)
								_a								= self.segmentation_model(**__a					)
								_a								= torch.sigmoid(outputs.logits					).cpu().detach().unsqueeze(-1					).numpy()
								_a								= self.numpy_to_pil(__a					)[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=__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


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {}


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				str   ="llama"
       a  :				List[str]   =["past_key_values"]

       def __init__(						self		,     snake_case__=32_000		,     snake_case__=4_096		,     snake_case__=11_008		,     snake_case__=32		,     snake_case__=32		,     snake_case__=None		,     snake_case__="silu"		,     snake_case__=2_048		,     snake_case__=0.02		,     snake_case__=1e-6		,     snake_case__=True		,     snake_case__=0		,     snake_case__=1		,     snake_case__=2		,     snake_case__=1		,     snake_case__=False		,     snake_case__=None		,     **snake_case__		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							vocab_size
         lowerCAmelCase     :     str								=							max_position_embeddings
         lowerCAmelCase     :     str								=							hidden_size
         lowerCAmelCase     :     Optional[int]								=							intermediate_size
         lowerCAmelCase     :     Any								=							num_hidden_layers
         lowerCAmelCase     :     List[str]								=							num_attention_heads

         # for backward compatibility
         if num_key_value_heads is None:
           lowerCAmelCase     :     Tuple								=							num_attention_heads

         lowerCAmelCase     :     Dict								=							num_key_value_heads
         lowerCAmelCase     :     Optional[Any]								=							hidden_act
         lowerCAmelCase     :     Optional[Any]								=							initializer_range
         lowerCAmelCase     :     Any								=							rms_norm_eps
         lowerCAmelCase     :     List[Any]								=							pretraining_tp
         lowerCAmelCase     :     int								=							use_cache
         lowerCAmelCase     :     List[str]								=							rope_scaling
         self._rope_scaling_validation()

         super().__init__(
             pad_token_id=snake_case__		,     bos_token_id=snake_case__		,     eos_token_id=snake_case__		,     tie_word_embeddings=snake_case__		,     **snake_case__		,     )







       def        lowercase__							(						self						):






         """simple docstring"""





         if self.rope_scaling is None:
           return

         if not isinstance(self.rope_scaling		,     snake_case__						) or len(self.rope_scaling						) != 2:
           raise ValueError(
               "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
               f"""got {self.rope_scaling}"""						)
         lowerCAmelCase     :     Optional[Any]								=							self.rope_scaling.get("type"		,     snake_case__						)
         lowerCAmelCase     :     int								=							self.rope_scaling.get("factor"		,     snake_case__						)
         if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
           raise ValueError(
               f"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""						)
         if rope_scaling_factor is None or not isinstance(snake_case__		,     snake_case__						) or rope_scaling_factor <= 1.0:
           raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""						)





