"""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	=					logging.get_logger(__name__)  # pylint: disable=invalid-name
class 			_snake_case  (       __A		):






						def __init__(							self   :      Optional[int]				,       UpperCAmelCase   :      CLIPSegForImageSegmentation				,       UpperCAmelCase   :      CLIPSegProcessor				,       UpperCAmelCase   :      AutoencoderKL				,       UpperCAmelCase   :      CLIPTextModel				,       UpperCAmelCase   :      CLIPTokenizer				,       UpperCAmelCase   :      UNetaDConditionModel				,       UpperCAmelCase   :      Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]				,       UpperCAmelCase   :      StableDiffusionSafetyChecker				,       UpperCAmelCase   :      CLIPImageProcessor				,       ):
													super().__init__()

													if hasattr(scheduler.config				,       "steps_offset"     ) and scheduler.config.steps_offset != 1:
																				__lowerCamelCase       :		Optional[int]		 =  (
																				    F"""The configuration file of this scheduler: {scheduler} is outdated.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"				,       lowerCAmelCase_				,       standard_warn=lowerCAmelCase_     )
																				__lowerCamelCase       :		str		 =  dict(scheduler.config     )
																				__lowerCamelCase       :		Tuple		 =  1
																				__lowerCamelCase       :		Optional[int]		 =  FrozenDict(lowerCAmelCase_     )

													if hasattr(scheduler.config				,       "skip_prk_steps"     ) and scheduler.config.skip_prk_steps is False:
																				__lowerCamelCase       :		Tuple		 =  (
																				    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"				,       lowerCAmelCase_				,       standard_warn=lowerCAmelCase_     )
																				__lowerCamelCase       :		Dict		 =  dict(scheduler.config     )
																				__lowerCamelCase       :		int		 =  True
																				__lowerCamelCase       :		Optional[Any]		 =  FrozenDict(lowerCAmelCase_     )

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






						def    lowerCamelCase__  (							self   :      Dict				,       UpperCAmelCase   :      Optional[Union[str, int]] = "auto"     ):
													if slice_size == "auto":
																				# half the attention head size is usually a good trade-off between
																				# speed and memory
																				__lowerCamelCase       :		Optional[int]		 =  self.unet.config.attention_head_dim // 2
													self.unet.set_attention_slice(lowerCAmelCase_     )






						def    lowerCamelCase__  (							self   :      Dict     ):
													self.enable_attention_slicing(lowerCAmelCase_     )






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

													__lowerCamelCase       :		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(lowerCAmelCase_				,       lowerCAmelCase_     )






						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def    lowerCamelCase__  (							self   :      Tuple     ):
													if self.device != torch.device("meta"     ) or not hasattr(self.unet				,       "_hf_hook"     ):
																				return self.device
													for module in self.unet.modules():
																				if (
																				    hasattr(lowerCAmelCase_				,       "_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   :      int				,       UpperCAmelCase   :      Union[str, List[str]]				,       UpperCAmelCase   :      Union[torch.FloatTensor, PIL.Image.Image]				,       UpperCAmelCase   :      str				,       UpperCAmelCase   :      int = 512				,       UpperCAmelCase   :      int = 512				,       UpperCAmelCase   :      int = 50				,       UpperCAmelCase   :      float = 7.5				,       UpperCAmelCase   :      Optional[Union[str, List[str]]] = None				,       UpperCAmelCase   :      Optional[int] = 1				,       UpperCAmelCase   :      float = 0.0				,       UpperCAmelCase   :      Optional[torch.Generator] = None				,       UpperCAmelCase   :      Optional[torch.FloatTensor] = None				,       UpperCAmelCase   :      Optional[str] = "pil"				,       UpperCAmelCase   :      bool = True				,       UpperCAmelCase   :      Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,       UpperCAmelCase   :      int = 1				,       **UpperCAmelCase   :      Tuple				,       ):
													__lowerCamelCase       :		List[str]		 =  self.segmentation_processor(
													    text=[text]				,       images=[image]				,       padding="max_length"				,       return_tensors="pt"     ).to(self.device     )
													__lowerCamelCase       :		Any		 =  self.segmentation_model(**lowerCAmelCase_     )
													__lowerCamelCase       :		Any		 =  torch.sigmoid(outputs.logits     ).cpu().detach().unsqueeze(-1     ).numpy()
													__lowerCamelCase       :		List[Any]		 =  self.numpy_to_pil(lowerCAmelCase_     )[0].resize(image.size     )

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







"""simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = '''T5Config'''







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''mt5'''
	__magic_name__       =							MTaConfig







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''mt5'''
	__magic_name__       =							MTaConfig







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''mt5'''
	__magic_name__       =							MTaConfig



