



import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
	import torch

	A__       =			True
except ImportError:
	A__       =			False


try:
	from torch.hub import _get_torch_home

	A__       =			_get_torch_home()
except ImportError:
	A__       =			os.path.expanduser(
	    os.getenv("""TORCH_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """torch"""))
	)

A__       =			os.path.join(torch_cache_home, """transformers""")

A__       =			"""https://cdn.huggingface.co"""
A__       =			"""https://s3.amazonaws.com/models.huggingface.co/bert"""
A__       =			"""/""".join(str(Path(__file__).resolve()).split("""/""")[:-1])
A__       =			os.path.join(PATH, """config.yaml""")
A__       =			os.path.join(PATH, """attributes.txt""")
A__       =			os.path.join(PATH, """objects.txt""")
A__       =			os.getenv("""PYTORCH_PRETRAINED_BERT_CACHE""", default_cache_path)
A__       =			os.getenv("""PYTORCH_TRANSFORMERS_CACHE""", PYTORCH_PRETRAINED_BERT_CACHE)
A__       =			os.getenv("""TRANSFORMERS_CACHE""", PYTORCH_TRANSFORMERS_CACHE)
A__       =			"""pytorch_model.bin"""
A__       =			"""config.yaml"""


def 						_UpperCAmelCase							( snake_case=OBJECTS      ,		snake_case=ATTRIBUTES     ):





					"""simple docstring"""

					_lowerCAmelCase         =       []
					with open(snake_case     ) as f:
										for object in f.readlines():
															vg_classes.append(object.split(""","""     )[0].lower().strip()     )

					_lowerCAmelCase         =       []
					with open(snake_case     ) as f:
										for object in f.readlines():
															vg_attrs.append(object.split(""","""     )[0].lower().strip()     )
					return vg_classes, vg_attrs


def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       OrderedDict()
					with open(snake_case      ,		"""rb"""     ) as f:
										_lowerCAmelCase         =       pkl.load(snake_case     )["""model"""]
					for k in copy.deepcopy(list(ckp.keys()     )     ):
										_lowerCAmelCase         =       ckp.pop(snake_case     )
										if isinstance(snake_case      ,		np.ndarray     ):
															_lowerCAmelCase         =       torch.tensor(snake_case     )
										else:
															assert isinstance(snake_case      ,		torch.tensor     ), type(snake_case     )
										_lowerCAmelCase         =       v
					return r





class    __lowerCAmelCase					:
				__lowerCamelCase					=							{}




				def __init__(				self		,							_snake_case		,							_snake_case = "root"		,							_snake_case=0						):



									"""simple docstring"""





									_lowerCAmelCase         =       name
									_lowerCAmelCase         =       level
									_lowerCAmelCase         =       {}
									for k, v in dictionary.items():
														if v is None:
																			raise ValueError()
														_lowerCAmelCase         =       copy.deepcopy(_snake_case						)
														_lowerCAmelCase         =       copy.deepcopy(_snake_case						)
														if isinstance(_snake_case		,							_snake_case						):
																			_lowerCAmelCase         =       Config(_snake_case		,							name=_snake_case		,							level=level + 1						)
														_lowerCAmelCase         =       v
														setattr(self		,							_snake_case		,							_snake_case						)

									_lowerCAmelCase         =       d




				def __repr__(				self						):



									"""simple docstring"""





									return str(list((self._pointer.keys())						)						)




				def __setattr__(				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       val
									_lowerCAmelCase         =       val
									_lowerCAmelCase         =       key.split("""."""						)
									_lowerCAmelCase         =       len(_snake_case						) - 1
									_lowerCAmelCase         =       self._pointer
									if len(_snake_case						) > 1:
														for i, l in enumerate(_snake_case						):
																			if hasattr(self		,							_snake_case						) and isinstance(getattr(self		,							_snake_case						)		,							_snake_case						):
																								setattr(getattr(self		,							_snake_case						)		,							""".""".join(levels[i:]						)		,							_snake_case						)
																			if l == last_level:
																								_lowerCAmelCase         =       val
																			else:
																								_lowerCAmelCase         =       pointer[l]




				def 				snake_case  (				self						):



									"""simple docstring"""





									return self._pointer




				def 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									with open(F'{file_name}'		,							"""w"""						) as stream:
														dump(_snake_case		,							_snake_case						)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									with open(F'{file_name}'		,							"""w"""						) as stream:
														json.dump(_snake_case		,							_snake_case						)




				@staticmethod
				def 				snake_case  (				_snake_case						):



									"""simple docstring"""





									with open(_snake_case						) as stream:
														_lowerCAmelCase         =       load(_snake_case		,							Loader=_snake_case						)
									return data




				def __str__(				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       """    """
									if self._name != "root":
														_lowerCAmelCase         =       F'{t * (self._level-1)}{self._name}:\n'
									else:
														_lowerCAmelCase         =       """"""
									_lowerCAmelCase         =       self._level
									for i, (k, v) in enumerate(self._pointer.items()						):
														if isinstance(_snake_case		,							_snake_case						):
																			r += F'{t * (self._level)}{v}\n'
																			self._level += 1
														else:
																			r += F'{t * (self._level)}{k}: {v} ({type(_snake_case						).__name__})\n'
														_lowerCAmelCase         =       level
									return r[:-1]




				@classmethod
				def 				snake_case  (				cls		,							_snake_case		,							**_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase      ,      _lowerCAmelCase         =       cls.get_config_dict(_snake_case		,							**_snake_case						)
									return cls(_snake_case						)




				@classmethod
				def 				snake_case  (				cls		,							_snake_case		,							**_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       kwargs.pop("""cache_dir"""		,							_snake_case						)
									_lowerCAmelCase         =       kwargs.pop("""force_download"""		,							_snake_case						)
									_lowerCAmelCase         =       kwargs.pop("""resume_download"""		,							_snake_case						)
									_lowerCAmelCase         =       kwargs.pop("""proxies"""		,							_snake_case						)
									_lowerCAmelCase         =       kwargs.pop("""local_files_only"""		,							_snake_case						)

									if os.path.isdir(_snake_case						):
														_lowerCAmelCase         =       os.path.join(_snake_case		,							_snake_case						)
									elif os.path.isfile(_snake_case						) or is_remote_url(_snake_case						):
														_lowerCAmelCase         =       pretrained_model_name_or_path
									else:
														_lowerCAmelCase         =       hf_bucket_url(_snake_case		,							filename=_snake_case		,							use_cdn=_snake_case						)

									try:
														# Load from URL or cache if already cached
														_lowerCAmelCase         =       cached_path(
														    _snake_case		,							cache_dir=_snake_case		,							force_download=_snake_case		,							proxies=_snake_case		,							resume_download=_snake_case		,							local_files_only=_snake_case		,							)
														# Load config dict
														if resolved_config_file is None:
																			raise EnvironmentError

														_lowerCAmelCase         =       Config.load_yaml(_snake_case						)

									except EnvironmentError:
														_lowerCAmelCase         =       """Can't load config for"""
														raise EnvironmentError(_snake_case						)

									if resolved_config_file == config_file:
														print("""loading configuration file from path"""						)
									else:
														print("""loading configuration file cache"""						)

									return Config.load_yaml(_snake_case						), kwargs


def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       torch.load("""dump.pt"""      ,		map_location=in_tensor.device     )
					_lowerCAmelCase         =       in_tensor.numpy()
					_lowerCAmelCase         =       out_tensor.numpy()[0]
					print(na.shape      ,		na[0, 0, :5]     )
					print(na.shape      ,		na[0, 0, :5]     )
					assert np.allclose(snake_case      ,		snake_case      ,		rtol=0.01      ,		atol=0.1     ), (
					    F'{sum([1 for x in np.isclose(snake_case      ,		snake_case      ,		rtol=0.01      ,		atol=0.1     ).flatten() if x is False]     )/len(na.flatten()     )*1_00:.4f} %'
					    " element-wise mismatch"
					)
					raise Exception("""tensors are all good"""     )

					# Hugging face functions below


def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       urlparse(snake_case     )
					return parsed.scheme in ("http", "https")


def 						_UpperCAmelCase							( snake_case      ,		snake_case      ,		snake_case=True     ):





					"""simple docstring"""

					_lowerCAmelCase         =       CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
					_lowerCAmelCase         =       """/""" not in model_id
					if legacy_format:
										return F'{endpoint}/{model_id}-{filename}'
					else:
										return F'{endpoint}/{model_id}/{filename}'


def 						_UpperCAmelCase							( snake_case      ,		snake_case      ,		snake_case=None      ,		snake_case=0      ,		snake_case=None      ,		):





					"""simple docstring"""

					_lowerCAmelCase         =       """python/{}""".format(sys.version.split()[0]     )
					if _torch_available:
										ua += "; torch/{}".format(torch.__version__     )
					if isinstance(snake_case      ,		snake_case     ):
										ua += "; " + "; ".join("""{}/{}""".format(snake_case      ,		snake_case     ) for k, v in user_agent.items()     )
					elif isinstance(snake_case      ,		snake_case     ):
										ua += "; " + user_agent
					_lowerCAmelCase         =       {"""user-agent""": ua}
					if resume_size > 0:
										_lowerCAmelCase         =       """bytes=%d-""" % (resume_size,)
					_lowerCAmelCase         =       requests.get(snake_case      ,		stream=snake_case      ,		proxies=snake_case      ,		headers=snake_case     )
					if response.status_code == 4_16:  # Range not satisfiable
										return
					_lowerCAmelCase         =       response.headers.get("""Content-Length"""     )
					_lowerCAmelCase         =       resume_size + int(snake_case     ) if content_length is not None else None
					_lowerCAmelCase         =       tqdm(
					    unit="""B"""      ,		unit_scale=snake_case      ,		total=snake_case      ,		initial=snake_case      ,		desc="""Downloading"""      ,		)
					for chunk in response.iter_content(chunk_size=10_24     ):
										if chunk:  # filter out keep-alive new chunks
															progress.update(len(snake_case     )     )
															temp_file.write(snake_case     )
					progress.close()


def 						_UpperCAmelCase							( snake_case      ,		snake_case=None      ,		snake_case=False      ,		snake_case=None      ,		snake_case=10      ,		snake_case=False      ,		snake_case=None      ,		snake_case=False      ,		):





					"""simple docstring"""

					if cache_dir is None:
										_lowerCAmelCase         =       TRANSFORMERS_CACHE
					if isinstance(snake_case      ,		snake_case     ):
										_lowerCAmelCase         =       str(snake_case     )

					os.makedirs(snake_case      ,		exist_ok=snake_case     )

					_lowerCAmelCase         =       None
					if not local_files_only:
										try:
															_lowerCAmelCase         =       requests.head(snake_case      ,		allow_redirects=snake_case      ,		proxies=snake_case      ,		timeout=snake_case     )
															if response.status_code == 2_00:
																				_lowerCAmelCase         =       response.headers.get("""ETag"""     )
										except (EnvironmentError, requests.exceptions.Timeout):
															# etag is already None
															pass

					_lowerCAmelCase         =       url_to_filename(snake_case      ,		snake_case     )

					# get cache path to put the file
					_lowerCAmelCase         =       os.path.join(snake_case      ,		snake_case     )

					# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
					# try to get the last downloaded one
					if etag is None:
										if os.path.exists(snake_case     ):
															return cache_path
										else:
															_lowerCAmelCase         =       [
															    file
															    for file in fnmatch.filter(os.listdir(snake_case     )      ,		filename + """.*"""     )
															    if not file.endswith(""".json"""     ) and not file.endswith(""".lock"""     )
															]
															if len(snake_case     ) > 0:
																				return os.path.join(snake_case      ,		matching_files[-1]     )
															else:
																				# If files cannot be found and local_files_only=True,
																				# the models might've been found if local_files_only=False
																				# Notify the user about that
																				if local_files_only:
																									raise ValueError(
																									    """Cannot find the requested files in the cached path and outgoing traffic has been"""
																									    """ disabled. To enable model look-ups and downloads online, set 'local_files_only'"""
																									    """ to False."""     )
																				return None

    # From now on, etag is not None.
					if os.path.exists(snake_case     ) and not force_download:
										return cache_path

					# Prevent parallel downloads of the same file with a lock.
					_lowerCAmelCase         =       cache_path + """.lock"""
					with FileLock(snake_case     ):
										# If the download just completed while the lock was activated.
										if os.path.exists(snake_case     ) and not force_download:
															# Even if returning early like here, the lock will be released.
															return cache_path

										if resume_download:
															_lowerCAmelCase         =       cache_path + """.incomplete"""

															@contextmanager
															def _resumable_file_manager():
																				with open(snake_case      ,		"""a+b"""     ) as f:
																									yield f

															_lowerCAmelCase         =       _resumable_file_manager
															if os.path.exists(snake_case     ):
																				_lowerCAmelCase         =       os.stat(snake_case     ).st_size
															else:
																				_lowerCAmelCase         =       0
										else:
															_lowerCAmelCase         =       partial(tempfile.NamedTemporaryFile      ,		dir=snake_case      ,		delete=snake_case     )
															_lowerCAmelCase         =       0

										# Download to temporary file, then copy to cache dir once finished.
										# Otherwise you get corrupt cache entries if the download gets interrupted.
										with temp_file_manager() as temp_file:
															print(
															    """%s not found in cache or force_download set to True, downloading to %s"""      ,		snake_case      ,		temp_file.name      ,		)

															http_get(
															    snake_case      ,		snake_case      ,		proxies=snake_case      ,		resume_size=snake_case      ,		user_agent=snake_case      ,		)

										os.replace(temp_file.name      ,		snake_case     )

										_lowerCAmelCase         =       {"""url""": url, """etag""": etag}
										_lowerCAmelCase         =       cache_path + """.json"""
										with open(snake_case      ,		"""w"""     ) as meta_file:
															json.dump(snake_case      ,		snake_case     )

					return cache_path


def 						_UpperCAmelCase							( snake_case      ,		snake_case=None     ):





					"""simple docstring"""

					_lowerCAmelCase         =       url.encode("""utf-8"""     )
					_lowerCAmelCase         =       shaaaa(snake_case     )
					_lowerCAmelCase         =       url_hash.hexdigest()

					if etag:
										_lowerCAmelCase         =       etag.encode("""utf-8"""     )
										_lowerCAmelCase         =       shaaaa(snake_case     )
										filename += "." + etag_hash.hexdigest()

					if url.endswith(""".h5"""     ):
										filename += ".h5"

					return filename


def 						_UpperCAmelCase							( snake_case      ,		snake_case=None      ,		snake_case=False      ,		snake_case=None      ,		snake_case=False      ,		snake_case=None      ,		snake_case=False      ,		snake_case=False      ,		snake_case=False      ,		):





					"""simple docstring"""

					if cache_dir is None:
										_lowerCAmelCase         =       TRANSFORMERS_CACHE
					if isinstance(snake_case      ,		snake_case     ):
										_lowerCAmelCase         =       str(snake_case     )
					if isinstance(snake_case      ,		snake_case     ):
										_lowerCAmelCase         =       str(snake_case     )

					if is_remote_url(snake_case     ):
										# URL, so get it from the cache (downloading if necessary)
										_lowerCAmelCase         =       get_from_cache(
										    snake_case      ,		cache_dir=snake_case      ,		force_download=snake_case      ,		proxies=snake_case      ,		resume_download=snake_case      ,		user_agent=snake_case      ,		local_files_only=snake_case      ,		)
					elif os.path.exists(snake_case     ):
										# File, and it exists.
										_lowerCAmelCase         =       url_or_filename
					elif urlparse(snake_case     ).scheme == "":
										# File, but it doesn't exist.
										raise EnvironmentError("""file {} not found""".format(snake_case     )     )
					else:
										# Something unknown
										raise ValueError("""unable to parse {} as a URL or as a local path""".format(snake_case     )     )

					if extract_compressed_file:
										if not is_zipfile(snake_case     ) and not tarfile.is_tarfile(snake_case     ):
															return output_path

										# Path where we extract compressed archives
										# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
										_lowerCAmelCase      ,      _lowerCAmelCase         =       os.path.split(snake_case     )
										_lowerCAmelCase         =       output_file.replace("""."""      ,		"""-"""     ) + """-extracted"""
										_lowerCAmelCase         =       os.path.join(snake_case      ,		snake_case     )

										if os.path.isdir(snake_case     ) and os.listdir(snake_case     ) and not force_extract:
															return output_path_extracted

										# Prevent parallel extractions
										_lowerCAmelCase         =       output_path + """.lock"""
										with FileLock(snake_case     ):
															shutil.rmtree(snake_case      ,		ignore_errors=snake_case     )
															os.makedirs(snake_case     )
															if is_zipfile(snake_case     ):
																				with ZipFile(snake_case      ,		"""r"""     ) as zip_file:
																									zip_file.extractall(snake_case     )
																									zip_file.close()
															elif tarfile.is_tarfile(snake_case     ):
																				_lowerCAmelCase         =       tarfile.open(snake_case     )
																				tar_file.extractall(snake_case     )
																				tar_file.close()
															else:
																				raise EnvironmentError("""Archive format of {} could not be identified""".format(snake_case     )     )

										return output_path_extracted

					return output_path


def 						_UpperCAmelCase							( snake_case      ,		snake_case=","     ):





					"""simple docstring"""

					assert isinstance(snake_case      ,		snake_case     )
					if os.path.isfile(snake_case     ):
										with open(snake_case     ) as f:
															_lowerCAmelCase         =       eval(f.read()     )
					else:
										_lowerCAmelCase         =       requests.get(snake_case     )
										try:
															_lowerCAmelCase         =       requests.json()
										except Exception:
															_lowerCAmelCase         =       req.content.decode()
															assert data is not None, "could not connect"
															try:
																				_lowerCAmelCase         =       eval(snake_case     )
															except Exception:
																				_lowerCAmelCase         =       data.split("""\n"""     )
										req.close()
					return data


def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       requests.get(snake_case     )
					_lowerCAmelCase         =       np.array(Image.open(BytesIO(response.content     )     )     )
					return img


def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       url.split("""/"""     )[-1]
					if fn not in os.listdir(os.getcwd()     ):
										wget.download(snake_case     )
					with open(snake_case      ,		"""rb"""     ) as stream:
										_lowerCAmelCase         =       pkl.load(snake_case     )
					_lowerCAmelCase         =       weights.pop("""model"""     )
					_lowerCAmelCase         =       {}
					for k, v in model.items():
										_lowerCAmelCase         =       torch.from_numpy(snake_case     )
										if "running_var" in k:
															_lowerCAmelCase         =       torch.tensor([0]     )
															_lowerCAmelCase         =       k.replace("""running_var"""      ,		"""num_batches_tracked"""     )
															_lowerCAmelCase         =       zero
					return new


def 						_UpperCAmelCase							( ):





					"""simple docstring"""

					print(F'{os.path.abspath(os.path.join(snake_case      ,		os.pardir     )     )}/demo.ipynb'     )


def 						_UpperCAmelCase							( snake_case      ,		snake_case="RGB"     ):





					"""simple docstring"""

					assert isinstance(snake_case      ,		snake_case     )
					if os.path.isfile(snake_case     ):
										_lowerCAmelCase         =       cva.imread(snake_case     )
					else:
										_lowerCAmelCase         =       get_image_from_url(snake_case     )
										assert img is not None, F'could not connect to: {im}'
					_lowerCAmelCase         =       cva.cvtColor(snake_case      ,		cva.COLOR_BGR2RGB     )
					if input_format == "RGB":
										_lowerCAmelCase         =       img[:, :, ::-1]
					return img


def 						_UpperCAmelCase							( snake_case      ,		snake_case=1     ):





					"""simple docstring"""

					return (images[i : i + batch] for i in range(0      ,		len(snake_case     )      ,		snake_case     ))









from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


UpperCamelCase__            =     logging.get_logger(__name__)  # pylint: disable=invalid-name
class      a__       (   snake_case__ ):
						_a				:       Tuple   =       ["""safety_checker""", """feature_extractor"""]






						def __init__(  self						,     _A						,     _A						,     _A						,     _A						,     _A						,     _A						,     _A						,     _A = True						,     ):




											"""simple docstring"""



											super().__init__()
											if hasattr(scheduler.config						,     "steps_offset"     ) and scheduler.config.steps_offset != 1:
																__lowerCAmelCase							  =			(
																    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     )
																__lowerCAmelCase							  =			dict(scheduler.config     )
																__lowerCAmelCase							  =			1
																__lowerCAmelCase							  =			FrozenDict(_A     )

											self.register_modules(
											    vae=_A						,     text_encoder=_A						,     tokenizer=_A						,     unet=_A						,     scheduler=_A						,     safety_checker=_A						,     feature_extractor=_A						,     )
											self.register_to_config(requires_safety_checker=_A     )






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return {k: getattr(self						,     _A     ) for k in self.config.keys() if not k.startswith("_"     )}






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A = "auto"     ):




											"""simple docstring"""



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






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											self.enable_attention_slicing(_A     )






						@torch.no_grad()
						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A						,     _A = 0.8						,     _A = 5_0						,     _A = 7.5						,     _A = None						,     _A = 1						,     _A = 0.0						,     _A = None						,     _A = "pil"						,     _A = True						,     _A = None						,     _A = 1						,     ):




											"""simple docstring"""



											return StableDiffusionInpaintPipelineLegacy(**self.components     )(
											    prompt=_A						,     image=_A						,     mask_image=_A						,     strength=_A						,     num_inference_steps=_A						,     guidance_scale=_A						,     negative_prompt=_A						,     num_images_per_prompt=_A						,     eta=_A						,     generator=_A						,     output_type=_A						,     return_dict=_A						,     callback=_A						,     )






						@torch.no_grad()
						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A = 0.8						,     _A = 5_0						,     _A = 7.5						,     _A = None						,     _A = 1						,     _A = 0.0						,     _A = None						,     _A = "pil"						,     _A = True						,     _A = None						,     _A = 1						,     **_A						,     ):




											"""simple docstring"""



											return StableDiffusionImgaImgPipeline(**self.components     )(
											    prompt=_A						,     image=_A						,     strength=_A						,     num_inference_steps=_A						,     guidance_scale=_A						,     negative_prompt=_A						,     num_images_per_prompt=_A						,     eta=_A						,     generator=_A						,     output_type=_A						,     return_dict=_A						,     callback=_A						,     callback_steps=_A						,     )






						@torch.no_grad()
						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A = 5_1_2						,     _A = 5_1_2						,     _A = 5_0						,     _A = 7.5						,     _A = None						,     _A = 1						,     _A = 0.0						,     _A = None						,     _A = None						,     _A = "pil"						,     _A = True						,     _A = None						,     _A = 1						,     ):




											"""simple docstring"""



											return StableDiffusionPipeline(**self.components     )(
											    prompt=_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						,     )




