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

		lowerCamelCase__			    =				True
except ImportError:
		lowerCamelCase__			    =				False


try:
		from torch.hub import _get_torch_home

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

lowerCamelCase__			    =				os.path.join(torch_cache_home, 'transformers')

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



def 							_lowerCamelCase(				__snake_case=OBJECTS						,						__snake_case=ATTRIBUTES	)  ->   str:
		__snake_case		       =     []
		with open(__lowerCamelCase	) as f:
				for object in f.readlines():
						vg_classes.append(object.split(","	)[0].lower().strip()	)

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



def 							_lowerCamelCase(				__snake_case	)  ->   Optional[Any]:
		__snake_case		       =     OrderedDict()
		with open(__lowerCamelCase						,						"rb"	) as f:
				__snake_case		       =     pkl.load(__lowerCamelCase	)["model"]
		for k in copy.deepcopy(list(ckp.keys()	)	):
				__snake_case		       =     ckp.pop(__lowerCamelCase	)
				if isinstance(__lowerCamelCase						,						np.ndarray	):
						__snake_case		       =     torch.tensor(__lowerCamelCase	)
				else:
						assert isinstance(__lowerCamelCase						,						torch.tensor	), type(__lowerCamelCase	)
				__snake_case		       =     v
		return r




class 					UpperCamelCase	:
					__UpperCamelCase						=   {}




					def __init__( self :					Optional[Any]      ,_lowerCAmelCase :					Optional[Any]      ,_lowerCAmelCase :					List[Any] = "root"      ,_lowerCAmelCase :					Optional[Any]=0						):


							"""simple docstring"""




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

							__snake_case		       =     d




					def __repr__( self :					List[str]						):


							"""simple docstring"""




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




					def __setattr__( self :					Optional[int]      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					str						):


							"""simple docstring"""




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




					def 							UpperCamelCase_						( self :					Tuple						):


							"""simple docstring"""




							return self._pointer




					def 							UpperCamelCase_						( self :					List[Any]      ,_lowerCAmelCase :					Optional[int]      ,_lowerCAmelCase :					Optional[Any]						):


							"""simple docstring"""




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




					def 							UpperCamelCase_						( self :					List[Any]      ,_lowerCAmelCase :					int      ,_lowerCAmelCase :					Optional[int]						):


							"""simple docstring"""




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




					@staticmethod
					def 							UpperCamelCase_						( _lowerCAmelCase :					List[Any]						):


							"""simple docstring"""




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




					def __str__( self :					Any						):


							"""simple docstring"""




							__snake_case		       =     "    "
							if self._name != "root":
									__snake_case		       =     F"""{t * (self._level-1)}{self._name}:\n"""
							else:
									__snake_case		       =     ""
							__snake_case		       =     self._level
							for i, (k, v) in enumerate(self._pointer.items()						):
									if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						):
											r += F"""{t * (self._level)}{v}\n"""
											self._level += 1
									else:
											r += F"""{t * (self._level)}{k}: {v} ({type(_lowerCAmelCase						).__name__})\n"""
									__snake_case		       =     level
							return r[:-1]




					@classmethod
					def 							UpperCamelCase_						( cls :					Any      ,_lowerCAmelCase :					Optional[Any]      ,**_lowerCAmelCase :					Dict						):


							"""simple docstring"""




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







					@classmethod
					def 							UpperCamelCase_						( cls :					List[str]      ,_lowerCAmelCase :					int      ,**_lowerCAmelCase :					List[Any]						):


							"""simple docstring"""




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

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

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

									__snake_case		       =     Config.load_yaml(_lowerCAmelCase						)

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

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

							return Config.load_yaml(_lowerCAmelCase						), kwargs




def 							_lowerCamelCase(				__snake_case	)  ->   List[str]:
		__snake_case		       =     torch.load("dump.pt"						,						map_location=in_tensor.device	)
		__snake_case		       =     in_tensor.numpy()
		__snake_case		       =     out_tensor.numpy()[0]
		print(na.shape						,						na[0, 0, :5]	)
		print(na.shape						,						na[0, 0, :5]	)
		assert np.allclose(__lowerCamelCase						,						__lowerCamelCase						,						rtol=0.0_1						,						atol=0.1	), (
		    f"""{sum([1 for x in np.isclose(__lowerCamelCase						,						__lowerCamelCase						,						rtol=0.0_1						,						atol=0.1	).flatten() if x is False]	)/len(na.flatten()	)*100:.4f} %"""
		    " element-wise mismatch"
		)
		raise Exception("tensors are all good"	)

		# Hugging face functions below



def 							_lowerCamelCase(				__snake_case	)  ->   List[str]:
		__snake_case		       =     urlparse(__lowerCamelCase	)
		return parsed.scheme in ("http", "https")



def 							_lowerCamelCase(				__snake_case						,						__snake_case						,						__snake_case=True	)  ->   str:
		__snake_case		       =     CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
		__snake_case		       =     "/" not in model_id
		if legacy_format:
				return f"""{endpoint}/{model_id}-{filename}"""
		else:
				return f"""{endpoint}/{model_id}/{filename}"""



def 							_lowerCamelCase(				__snake_case						,						__snake_case						,						__snake_case=None						,						__snake_case=0						,						__snake_case=None						,						)  ->   Any:
		__snake_case		       =     "python/{}".format(sys.version.split()[0]	)
		if _torch_available:
				ua += "; torch/{}".format(torch.__version__	)
		if isinstance(__lowerCamelCase						,						__lowerCamelCase	):
				ua += "; " + "; ".join("{}/{}".format(__lowerCamelCase						,						__lowerCamelCase	) for k, v in user_agent.items()	)
		elif isinstance(__lowerCamelCase						,						__lowerCamelCase	):
				ua += "; " + user_agent
		__snake_case		       =     {"user-agent": ua}
		if resume_size > 0:
				__snake_case		       =     "bytes=%d-" % (resume_size,)
		__snake_case		       =     requests.get(__lowerCamelCase						,						stream=__lowerCamelCase						,						proxies=__lowerCamelCase						,						headers=__lowerCamelCase	)
		if response.status_code == 416:  # Range not satisfiable
				return
		__snake_case		       =     response.headers.get("Content-Length"	)
		__snake_case		       =     resume_size + int(__lowerCamelCase	) if content_length is not None else None
		__snake_case		       =     tqdm(
		    unit="B"						,						unit_scale=__lowerCamelCase						,						total=__lowerCamelCase						,						initial=__lowerCamelCase						,						desc="Downloading"						,						)
		for chunk in response.iter_content(chunk_size=1024	):
				if chunk:  # filter out keep-alive new chunks
						progress.update(len(__lowerCamelCase	)	)
						temp_file.write(__lowerCamelCase	)
		progress.close()



def 							_lowerCamelCase(				__snake_case						,						__snake_case=None						,						__snake_case=False						,						__snake_case=None						,						__snake_case=10						,						__snake_case=False						,						__snake_case=None						,						__snake_case=False						,						)  ->   List[str]:
		if cache_dir is None:
				__snake_case		       =     TRANSFORMERS_CACHE
		if isinstance(__lowerCamelCase						,						__lowerCamelCase	):
				__snake_case		       =     str(__lowerCamelCase	)

		os.makedirs(__lowerCamelCase						,						exist_ok=__lowerCamelCase	)

		__snake_case		       =     None
		if not local_files_only:
				try:
						__snake_case		       =     requests.head(__lowerCamelCase						,						allow_redirects=__lowerCamelCase						,						proxies=__lowerCamelCase						,						timeout=__lowerCamelCase	)
						if response.status_code == 200:
								__snake_case		       =     response.headers.get("ETag"	)
				except (EnvironmentError, requests.exceptions.Timeout):
						# etag is already None
						pass

		__snake_case		       =     url_to_filename(__lowerCamelCase						,						__lowerCamelCase	)

		# get cache path to put the file
		__snake_case		       =     os.path.join(__lowerCamelCase						,						__lowerCamelCase	)

		# 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(__lowerCamelCase	):
						return cache_path
				else:
						__snake_case		       =     [
						    file
						    for file in fnmatch.filter(os.listdir(__lowerCamelCase	)						,						filename + ".*"	)
						    if not file.endswith(".json"	) and not file.endswith(".lock"	)
						]
						if len(__lowerCamelCase	) > 0:
								return os.path.join(__lowerCamelCase						,						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(__lowerCamelCase	) and not force_download:
				return cache_path

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

				if resume_download:
						__snake_case		       =     cache_path + ".incomplete"

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

						__snake_case		       =     _resumable_file_manager
						if os.path.exists(__lowerCamelCase	):
								__snake_case		       =     os.stat(__lowerCamelCase	).st_size
						else:
								__snake_case		       =     0
				else:
						__snake_case		       =     partial(tempfile.NamedTemporaryFile						,						dir=__lowerCamelCase						,						delete=__lowerCamelCase	)
						__snake_case		       =     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"						,						__lowerCamelCase						,						temp_file.name						,						)

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

				os.replace(temp_file.name						,						__lowerCamelCase	)

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

		return cache_path



def 							_lowerCamelCase(				__snake_case						,						__snake_case=None	)  ->   Union[str, Any]:
		__snake_case		       =     url.encode("utf-8"	)
		__snake_case		       =     shaaaa(__lowerCamelCase	)
		__snake_case		       =     url_hash.hexdigest()

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

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

		return filename



def 							_lowerCamelCase(				__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						,						)  ->   Any:
		if cache_dir is None:
				__snake_case		       =     TRANSFORMERS_CACHE
		if isinstance(__lowerCamelCase						,						__lowerCamelCase	):
				__snake_case		       =     str(__lowerCamelCase	)
		if isinstance(__lowerCamelCase						,						__lowerCamelCase	):
				__snake_case		       =     str(__lowerCamelCase	)

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

		if extract_compressed_file:
				if not is_zipfile(__lowerCamelCase	) and not tarfile.is_tarfile(__lowerCamelCase	):
						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/"
				__snake_case       ,       __snake_case		       =     os.path.split(__lowerCamelCase	)
				__snake_case		       =     output_file.replace("."						,						"-"	) + "-extracted"
				__snake_case		       =     os.path.join(__lowerCamelCase						,						__lowerCamelCase	)

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

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

				return output_path_extracted

		return output_path



def 							_lowerCamelCase(				__snake_case						,						__snake_case=","	)  ->   Optional[int]:
		assert isinstance(__lowerCamelCase						,						__lowerCamelCase	)
		if os.path.isfile(__lowerCamelCase	):
				with open(__lowerCamelCase	) as f:
						__snake_case		       =     eval(f.read()	)
		else:
				__snake_case		       =     requests.get(__lowerCamelCase	)
				try:
						__snake_case		       =     requests.json()
				except Exception:
						__snake_case		       =     req.content.decode()
						assert data is not None, "could not connect"
						try:
								__snake_case		       =     eval(__lowerCamelCase	)
						except Exception:
								__snake_case		       =     data.split("\n"	)
				req.close()
		return data



def 							_lowerCamelCase(				__snake_case	)  ->   Any:
		__snake_case		       =     requests.get(__lowerCamelCase	)
		__snake_case		       =     np.array(Image.open(BytesIO(response.content	)	)	)
		return img



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



def 							_lowerCamelCase(				)  ->   Dict:
		print(f"""{os.path.abspath(os.path.join(__lowerCamelCase						,						os.pardir	)	)}/demo.ipynb"""	)



def 							_lowerCamelCase(				__snake_case						,						__snake_case="RGB"	)  ->   Optional[Any]:
		assert isinstance(__lowerCamelCase						,						__lowerCamelCase	)
		if os.path.isfile(__lowerCamelCase	):
				__snake_case		       =     cva.imread(__lowerCamelCase	)
		else:
				__snake_case		       =     get_image_from_url(__lowerCamelCase	)
				assert img is not None, f"""could not connect to: {im}"""
		__snake_case		       =     cva.cvtColor(__lowerCamelCase						,						cva.COLOR_BGR2RGB	)
		if input_format == "RGB":
				__snake_case		       =     img[:, :, ::-1]
		return img





def 							_lowerCamelCase(				__snake_case						,						__snake_case=1	)  ->   int:
		return (images[i : i + batch] for i in range(0						,						len(__lowerCamelCase	)						,						__lowerCamelCase	))






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 					UpperCamelCase	(     snake_case__			,      unittest.TestCase      ):
					__UpperCamelCase						=   PaintByExamplePipeline
					__UpperCamelCase						=   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
					__UpperCamelCase						=   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					__UpperCamelCase						=   frozenset([]      )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess




					def 							UpperCamelCase_						( self :					Tuple						):


							"""simple docstring"""




							torch.manual_seed(0						)
							__snake_case		       =     UNetaDConditionModel(
							    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=9      ,out_channels=4      ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,cross_attention_dim=32      ,)
							__snake_case		       =     PNDMScheduler(skip_prk_steps=_lowerCAmelCase						)
							torch.manual_seed(0						)
							__snake_case		       =     AutoencoderKL(
							    block_out_channels=[32, 64]      ,in_channels=3      ,out_channels=3      ,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,latent_channels=4      ,)
							torch.manual_seed(0						)
							__snake_case		       =     CLIPVisionConfig(
							    hidden_size=32      ,projection_dim=32      ,intermediate_size=37      ,layer_norm_eps=1E-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,image_size=32      ,patch_size=4      ,)
							__snake_case		       =     PaintByExampleImageEncoder(_lowerCAmelCase      ,proj_size=32						)
							__snake_case		       =     CLIPImageProcessor(crop_size=32      ,size=32						)

							__snake_case		       =     {
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							    "image_encoder": image_encoder,
							    "safety_checker": None,
							    "feature_extractor": feature_extractor,
							}
							return components




					def 							UpperCamelCase_						( self :					Union[str, Any]      ,_lowerCAmelCase :					Any						):


							"""simple docstring"""




							__snake_case		       =     np.array(image.convert("RGB"						)						)
							__snake_case		       =     image[None].transpose(0      ,3      ,1      ,2						)
							__snake_case		       =     torch.from_numpy(_lowerCAmelCase						).to(dtype=torch.floataa						) / 1_2_7.5 - 1.0
							return image




					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Any="cpu"      ,_lowerCAmelCase :					List[str]=0						):


							"""simple docstring"""




							__snake_case		       =     floats_tensor((1, 3, 32, 32)      ,rng=random.Random(_lowerCAmelCase						)						).to(_lowerCAmelCase						)
							__snake_case		       =     image.cpu().permute(0      ,2      ,3      ,1						)[0]
							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						).resize((64, 64)						)
							__snake_case		       =     Image.fromarray(np.uinta(image + 4						)						).convert("RGB"						).resize((64, 64)						)
							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						).resize((32, 32)						)

							if str(_lowerCAmelCase						).startswith("mps"						):
									__snake_case		       =     torch.manual_seed(_lowerCAmelCase						)
							else:
									__snake_case		       =     torch.Generator(device=_lowerCAmelCase						).manual_seed(_lowerCAmelCase						)
							__snake_case		       =     {
							    "example_image": example_image,
							    "image": init_image,
							    "mask_image": mask_image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "guidance_scale": 6.0,
							    "output_type": "numpy",
							}
							return inputs




					def 							UpperCamelCase_						( self :					int						):


							"""simple docstring"""




							__snake_case		       =     self.get_dummy_components()

							# make sure here that pndm scheduler skips prk
							__snake_case		       =     PaintByExamplePipeline(**_lowerCAmelCase						)
							__snake_case		       =     pipe.to("cpu"						)
							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

							__snake_case		       =     self.get_dummy_inputs()
							__snake_case		       =     pipe(**_lowerCAmelCase						)
							__snake_case		       =     output.images

							__snake_case		       =     image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							__snake_case		       =     np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]						)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2




					def 							UpperCamelCase_						( self :					int						):


							"""simple docstring"""




							__snake_case		       =     "cpu"
							__snake_case		       =     self.get_dummy_inputs()
							inputs.pop("mask_image"						)
							__snake_case		       =     self.convert_to_pt(inputs.pop("image"						)						)
							__snake_case		       =     image.clamp(0      ,1						) / 2

							# make sure here that pndm scheduler skips prk
							__snake_case		       =     PaintByExamplePipeline(**self.get_dummy_components()						)
							__snake_case		       =     pipe.to(_lowerCAmelCase						)
							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

							__snake_case		       =     pipe(image=_lowerCAmelCase      ,mask_image=mask_image[:, 0]      ,**_lowerCAmelCase						)
							__snake_case		       =     output.images

							__snake_case		       =     image.cpu().permute(0      ,2      ,3      ,1						)[0]
							__snake_case		       =     mask_image.cpu().permute(0      ,2      ,3      ,1						)[0]

							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						)
							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						)

							__snake_case		       =     pipe(**self.get_dummy_inputs()						)
							__snake_case		       =     output.images

							assert out_a.shape == (1, 64, 64, 3)
							assert np.abs(out_a.flatten() - out_a.flatten()						).max() < 5E-2







					def 							UpperCamelCase_						( self :					Union[str, Any]						):


							"""simple docstring"""




							super().test_inference_batch_single_identical(expected_max_diff=3E-3						)




@slow
@require_torch_gpu
class 					UpperCamelCase	(     unittest.TestCase      ):




					def 							UpperCamelCase_						( self :					Any						):


							"""simple docstring"""




							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()







					def 							UpperCamelCase_						( self :					Dict						):


							"""simple docstring"""




							__snake_case		       =     load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/dog_in_bucket.png"						)
							__snake_case		       =     load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/mask.png"						)
							__snake_case		       =     load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/panda.jpg"						)

							__snake_case		       =     PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"						)
							__snake_case		       =     pipe.to(_lowerCAmelCase						)
							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

							__snake_case		       =     torch.manual_seed(321						)
							__snake_case		       =     pipe(
							    image=_lowerCAmelCase      ,mask_image=_lowerCAmelCase      ,example_image=_lowerCAmelCase      ,generator=_lowerCAmelCase      ,guidance_scale=5.0      ,num_inference_steps=50      ,output_type="np"      ,)

							__snake_case		       =     output.images

							__snake_case		       =     image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							__snake_case		       =     np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]						)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2





