code: 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	))
  
  
  
  
  
  
style_context: 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
  
  
  
  
  
  