



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


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




class 							lowerCAmelCase__  (	UpperCamelCase						):







					def __init__(       self      :     Union[str, Any]			,		_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__	:							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																	    F'The configuration file of this scheduler: {scheduler} is outdated.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)
																	A__	:							Any						    = dict(scheduler.config)
																	A__	:							Dict						    = 1
																	A__	:							Optional[Any]						    = FrozenDict(_A)

											if hasattr(scheduler.config			,		"skip_prk_steps") and scheduler.config.skip_prk_steps is False:
																	A__	:							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"			,		_A			,		standard_warn=_A)
																	A__	:							Dict						    = dict(scheduler.config)
																	A__	:							Any						    = True
																	A__	:							Dict						    = 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  _lowercase			(       self      :     List[str]			,		_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__	:							List[str]						    = self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(_A)







					def  _lowercase			(       self      :     Dict):
											self.enable_attention_slicing(_A)







					def  _lowercase			(       self      :     Optional[Any]):
											if is_accelerate_available():
																	from accelerate import cpu_offload
											else:
																	raise ImportError("Please install accelerate via `pip install accelerate`")

											A__	:							Tuple						    = 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  _lowercase			(       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(_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      :     Tuple			,		_A      :     Union[str, List[str]]			,		_A      :     Union[torch.FloatTensor, PIL.Image.Image]			,		_A      :     str			,		_A      :     int = 512			,		_A      :     int = 512			,		_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      :     Dict			,		):
											A__	:							List[Any]						    = self.segmentation_processor(
											    text=[text]			,		images=[image]			,		padding="max_length"			,		return_tensors="pt").to(self.device)
											A__	:							Dict						    = self.segmentation_model(**_A)
											A__	:							Optional[Any]						    = torch.sigmoid(outputs.logits).cpu().detach().unsqueeze(-1).numpy()
											A__	:							Any						    = self.numpy_to_pil(_A)[0].resize(image.size)

											# Run inpainting pipeline with the generated mask
											A__	:							Union[str, Any]						    = 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			,		)




from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset




class 							lowerCAmelCase__  (	UpperCamelCase						):







					def  _lowercase			(       self      :     List[Any]):
											return [
											    {"col_1": 3, "col_2": "a"},
											    {"col_1": 2, "col_2": "b"},
											    {"col_1": 1, "col_2": "c"},
											    {"col_1": 0, "col_2": "d"},
											]







					def  _lowercase			(       self      :     Tuple):
											A__	:							Any						    = {"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
											return Dataset.from_dict(_A)







					def  _lowercase			(       self      :     Union[str, Any]):
											A__	:							str						    = self._create_example_records()
											A__	:							Dict						    = Dataset.from_list(_A)
											self.assertListEqual(dset.column_names			,		["col_1", "col_2"])
											for i, r in enumerate(_A):
																	self.assertDictEqual(_A			,		example_records[i])







					def  _lowercase			(       self      :     List[str]):
											A__	:							List[str]						    = self._create_example_records()
											A__	:							Any						    = Dataset.from_list(_A)
											A__	:							Optional[int]						    = Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]})
											self.assertEqual(dset.info			,		dset_from_dict.info)







					def  _lowercase			(       self      :     Any):  # checks what happens with missing columns
											A__	:							Dict						    = [{"col_1": 1}, {"col_2": "x"}]
											A__	:							Any						    = Dataset.from_list(_A)
											self.assertDictEqual(dset[0]			,		{"col_1": 1})
											self.assertDictEqual(dset[1]			,		{"col_1": None})  # NB: first record is used for columns







					def  _lowercase			(       self      :     str):  # checks if the type can be inferred from the second record
											A__	:							Dict						    = [{"col_1": []}, {"col_1": [1, 2]}]
											A__	:							Dict						    = Dataset.from_list(_A)
											self.assertEqual(dset.info.features["col_1"]			,		Sequence(Value("int64")))







					def  _lowercase			(       self      :     Union[str, Any]):
											A__	:							Dict						    = Dataset.from_list([])
											self.assertEqual(len(_A)			,		0)
											self.assertListEqual(dset.column_names			,		[])