


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 			lowerCamelCase_		(			UpperCAmelCase__   ):





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		):
								super().__init__()

								if hasattr(scheduler.config       ,		"steps_offset"     ) and scheduler.config.steps_offset != 1:
												SCREAMING_SNAKE_CASE__     							=				(
												    f'''The configuration file of this scheduler: {scheduler} is outdated.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__     )
												SCREAMING_SNAKE_CASE__     							=				dict(scheduler.config     )
												SCREAMING_SNAKE_CASE__     							=				1
												SCREAMING_SNAKE_CASE__     							=				FrozenDict(UpperCAmelCase__     )

								if hasattr(scheduler.config       ,		"skip_prk_steps"     ) and scheduler.config.skip_prk_steps is False:
												SCREAMING_SNAKE_CASE__     							=				(
												    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__     )
												SCREAMING_SNAKE_CASE__     							=				dict(scheduler.config     )
												SCREAMING_SNAKE_CASE__     							=				True
												SCREAMING_SNAKE_CASE__     							=				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   lowerCAmelCase__		(						self       ,		UpperCAmelCase__ = "auto"     ):




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



				def   lowerCAmelCase__		(						self     ):
								self.enable_attention_slicing(UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):




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

								SCREAMING_SNAKE_CASE__     							=				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   lowerCAmelCase__		(						self     ):




								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       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = 512       ,		UpperCAmelCase__ = 512       ,		UpperCAmelCase__ = 50       ,		UpperCAmelCase__ = 7.5       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = 1       ,		UpperCAmelCase__ = 0.0       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = "pil"       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = 1       ,		**UpperCAmelCase__       ,		):
								SCREAMING_SNAKE_CASE__     							=				self.segmentation_processor(
								    text=[text]       ,		images=[image]       ,		padding="max_length"       ,		return_tensors="pt"     ).to(self.device     )
								SCREAMING_SNAKE_CASE__     							=				self.segmentation_model(**UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				torch.sigmoid(outputs.logits     ).cpu().detach().unsqueeze(-1     ).numpy()
								SCREAMING_SNAKE_CASE__     							=				self.numpy_to_pil(UpperCAmelCase__     )[0].resize(image.size     )

								# Run inpainting pipeline with the generated mask
								SCREAMING_SNAKE_CASE__     							=				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"""






import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__ = "▁"       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = "<unk>"       ,		UpperCAmelCase__ = "</s>"       ,		UpperCAmelCase__ = "<pad>"       ,		):
								SCREAMING_SNAKE_CASE__     							=				{
								    "pad": {"id": 0, "token": pad_token},
								    "eos": {"id": 1, "token": eos_token},
								    "unk": {"id": 2, "token": unk_token},
								}

								SCREAMING_SNAKE_CASE__     							=				[None] * len(self.special_tokens     )
								for token_dict in self.special_tokens.values():
												SCREAMING_SNAKE_CASE__     							=				token_dict["token"]

								SCREAMING_SNAKE_CASE__     							=				Tokenizer(Unigram()     )

								SCREAMING_SNAKE_CASE__     							=				normalizers.Sequence(
								    [
								        normalizers.Nmt(),
								        normalizers.NFKC(),
								        normalizers.Replace(Regex(" {2,}"     )       ,		" "     ),
								        normalizers.Lowercase(),
								    ]     )
								SCREAMING_SNAKE_CASE__     							=				pre_tokenizers.Sequence(
								    [
								        pre_tokenizers.Metaspace(replacement=UpperCAmelCase__       ,		add_prefix_space=UpperCAmelCase__     ),
								        pre_tokenizers.Digits(individual_digits=UpperCAmelCase__     ),
								        pre_tokenizers.Punctuation(),
								    ]     )
								SCREAMING_SNAKE_CASE__     							=				decoders.Metaspace(replacement=UpperCAmelCase__       ,		add_prefix_space=UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				TemplateProcessing(
								    single=f'''$A {self.special_tokens["eos"]["token"]}'''       ,		special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])]       ,		)

								SCREAMING_SNAKE_CASE__     							=				{
								    "model": "SentencePieceUnigram",
								    "replacement": replacement,
								    "add_prefix_space": add_prefix_space,
								}

								super().__init__(UpperCAmelCase__       ,		UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = 8000       ,		UpperCAmelCase__ = True       ,		):
								SCREAMING_SNAKE_CASE__     							=				trainers.UnigramTrainer(
								    vocab_size=UpperCAmelCase__       ,		special_tokens=self.special_tokens_list       ,		show_progress=UpperCAmelCase__       ,		)

								if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												SCREAMING_SNAKE_CASE__     							=				[files]
								self._tokenizer.train(UpperCAmelCase__       ,		trainer=UpperCAmelCase__     )

								self.add_unk_id()



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = 8000       ,		UpperCAmelCase__ = True       ,		):
								SCREAMING_SNAKE_CASE__     							=				trainers.UnigramTrainer(
								    vocab_size=UpperCAmelCase__       ,		special_tokens=self.special_tokens_list       ,		show_progress=UpperCAmelCase__       ,		)

								self._tokenizer.train_from_iterator(UpperCAmelCase__       ,		trainer=UpperCAmelCase__     )

								self.add_unk_id()






				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				json.loads(self._tokenizer.to_str()     )

								SCREAMING_SNAKE_CASE__     							=				self.special_tokens["unk"]["id"]

								SCREAMING_SNAKE_CASE__     							=				Tokenizer.from_str(json.dumps(UpperCAmelCase__     )     )
