




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



class 					A		( lowerCamelCase_		):




				'''simple docstring'''





				def __init__(self ,  _UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase ,  )	->       str:
							super().__init__()

							if hasattr(scheduler.config ,  "steps_offset"			) and scheduler.config.steps_offset != 1:
										__UpperCamelCase      :			Tuple									=   (
										    f"The configuration file of this scheduler: {scheduler} is outdated.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			)
										__UpperCamelCase      :			str									=   dict(scheduler.config			)
										__UpperCamelCase      :			List[str]									=   1
										__UpperCamelCase      :			Tuple									=   FrozenDict(__a			)

							if hasattr(scheduler.config ,  "skip_prk_steps"			) and scheduler.config.skip_prk_steps is False:
										__UpperCamelCase      :			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" ,  __a ,  standard_warn=__a			)
										__UpperCamelCase      :			Optional[int]									=   dict(scheduler.config			)
										__UpperCamelCase      :			Optional[Any]									=   True
										__UpperCamelCase      :			int									=   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 ,  _UpperCAmelCase = "auto"			)	->       List[str]:
							if slice_size == "auto":
										# half the attention head size is usually a good trade-off between
										# speed and memory
										__UpperCamelCase      :			str									=   self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(__a			)
				def        a_       (self			)	->       List[Any]:
							self.enable_attention_slicing(__a			)
				def        a_       (self			)	->       Any:
							if is_accelerate_available():
										from accelerate import cpu_offload
							else:
										raise ImportError("Please install accelerate via `pip install accelerate`"			)

							__UpperCamelCase      :			Optional[Any]									=   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]:
							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 ,  _UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase = 5_1_2 ,  _UpperCAmelCase = 5_1_2 ,  _UpperCAmelCase = 5_0 ,  _UpperCAmelCase = 7.5 ,  _UpperCAmelCase = None ,  _UpperCAmelCase = 1 ,  _UpperCAmelCase = 0.0 ,  _UpperCAmelCase = None ,  _UpperCAmelCase = None ,  _UpperCAmelCase = "pil" ,  _UpperCAmelCase = True ,  _UpperCAmelCase = None ,  _UpperCAmelCase = 1 ,  **_UpperCAmelCase ,  )	->       List[Any]:
							__UpperCamelCase      :			Dict									=   self.segmentation_processor(
							    text=[text] ,  images=[image] ,  padding="max_length" ,  return_tensors="pt"			).to(self.device			)
							__UpperCamelCase      :			List[Any]									=   self.segmentation_model(**__a			)
							__UpperCamelCase      :			Union[str, Any]									=   torch.sigmoid(outputs.logits			).cpu().detach().unsqueeze(-1			).numpy()
							__UpperCamelCase      :			Optional[int]									=   self.numpy_to_pil(__a			)[0].resize(image.size			)

							# Run inpainting pipeline with the generated mask
							__UpperCamelCase      :			int									=   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 typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase_					:    int      	=			{'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_					:    Optional[int]      	=			['GPTNeoXTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_					:    List[str]      	=			[
					    'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GPTNeoXForCausalLM',
					    'GPTNeoXForQuestionAnswering',
					    'GPTNeoXForSequenceClassification',
					    'GPTNeoXForTokenClassification',
					    'GPTNeoXLayer',
					    'GPTNeoXModel',
					    'GPTNeoXPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gpt_neox import (
										    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTNeoXForCausalLM,
										    GPTNeoXForQuestionAnswering,
										    GPTNeoXForSequenceClassification,
										    GPTNeoXForTokenClassification,
										    GPTNeoXLayer,
										    GPTNeoXModel,
										    GPTNeoXPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase_					:    List[str]      	=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
