code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  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
  
         lowercase__			=	True
  except ImportError:
         lowercase__			=	False
  
  
  try:
         from torch.hub import _get_torch_home
  
         lowercase__			=	_get_torch_home()
  except ImportError:
         lowercase__			=	os.path.expanduser(
             os.getenv('''TORCH_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''torch'''))
         )
  
  lowercase__			=	os.path.join(torch_cache_home, '''transformers''')
  
  lowercase__			=	'''https://cdn.huggingface.co'''
  lowercase__			=	'''https://s3.amazonaws.com/models.huggingface.co/bert'''
  lowercase__			=	'''/'''.join(str(Path(__file__).resolve()).split('''/''')[:-1])
  lowercase__			=	os.path.join(PATH, '''config.yaml''')
  lowercase__			=	os.path.join(PATH, '''attributes.txt''')
  lowercase__			=	os.path.join(PATH, '''objects.txt''')
  lowercase__			=	os.getenv('''PYTORCH_PRETRAINED_BERT_CACHE''', default_cache_path)
  lowercase__			=	os.getenv('''PYTORCH_TRANSFORMERS_CACHE''', PYTORCH_PRETRAINED_BERT_CACHE)
  lowercase__			=	os.getenv('''TRANSFORMERS_CACHE''', PYTORCH_TRANSFORMERS_CACHE)
  lowercase__			=	'''pytorch_model.bin'''
  lowercase__			=	'''config.yaml'''
  
  
  def 				UpperCamelCase	(  snake_case=OBJECTS	,     snake_case=ATTRIBUTES   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Tuple       						= []
     with open(snake_case   ) as f:
        for object in f.readlines():
           vg_classes.append(object.split(''','''   )[0].lower().strip()   )
  
     SCREAMING_SNAKE_CASE__			:				List[str]       						= []
     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'''
  
     SCREAMING_SNAKE_CASE__			:				Any       						= OrderedDict()
     with open(snake_case	,     '''rb'''   ) as f:
        SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= pkl.load(snake_case   )['''model''']
     for k in copy.deepcopy(list(ckp.keys()   )   ):
        SCREAMING_SNAKE_CASE__			:				List[str]       						= ckp.pop(snake_case   )
        if isinstance(snake_case	,     np.ndarray   ):
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= torch.tensor(snake_case   )
        else:
           assert isinstance(snake_case	,     torch.tensor   ), type(snake_case   )
        SCREAMING_SNAKE_CASE__			:				List[Any]       						= v
     return r
  
  
  
  
  
  
  class 						lowercase     :
        __lowercase 		=  {}
  
  
  
  
  
  
        def __init__(self ,  a__ ,  a__ = "root" ,  a__=0					)       ->   str:
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= name
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= level
           SCREAMING_SNAKE_CASE__			:				Tuple       						= {}
           for k, v in dictionary.items():
              if v is None:
                 raise ValueError()
              SCREAMING_SNAKE_CASE__			:				str       						= copy.deepcopy(a__					)
              SCREAMING_SNAKE_CASE__			:				Any       						= copy.deepcopy(a__					)
              if isinstance(a__ ,  a__					):
                 SCREAMING_SNAKE_CASE__			:				Optional[int]       						= Config(a__ ,  name=a__ ,  level=level + 1					)
              SCREAMING_SNAKE_CASE__			:				List[Any]       						= v
              setattr(self ,  a__ ,  a__					)
  
           SCREAMING_SNAKE_CASE__			:				Any       						= d
  
  
  
  
  
  
        def __repr__(self					)       ->   List[Any]:
           return str(list((self._pointer.keys())					)					)
  
  
  
  
  
  
        def __setattr__(self ,  a__ ,  a__					)       ->   str:
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= val
           SCREAMING_SNAKE_CASE__			:				Dict       						= val
           SCREAMING_SNAKE_CASE__			:				Dict       						= key.split('''.'''					)
           SCREAMING_SNAKE_CASE__			:				int       						= len(a__					) - 1
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self._pointer
           if len(a__					) > 1:
              for i, l in enumerate(a__					):
                 if hasattr(self ,  a__					) and isinstance(getattr(self ,  a__					) ,  a__					):
                    setattr(getattr(self ,  a__					) ,  '''.'''.join(levels[i:]					) ,  a__					)
                 if l == last_level:
                    SCREAMING_SNAKE_CASE__			:				Optional[int]       						= val
                 else:
                    SCREAMING_SNAKE_CASE__			:				Tuple       						= pointer[l]
  
  
  
  
  
  
        def    A__(self					)       ->   Dict:
           return self._pointer
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__					)       ->   Union[str, Any]:
           with open(f"""{file_name}""" ,  '''w'''					) as stream:
              dump(a__ ,  a__					)
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__					)       ->   str:
           with open(f"""{file_name}""" ,  '''w'''					) as stream:
              json.dump(a__ ,  a__					)
  
  
  
  
  
  
        @staticmethod
        def    A__(a__					)       ->   List[str]:
           with open(a__					) as stream:
              SCREAMING_SNAKE_CASE__			:				int       						= load(a__ ,  Loader=a__					)
           return data
  
  
  
  
  
  
        def __str__(self					)       ->   Optional[int]:
           SCREAMING_SNAKE_CASE__			:				Any       						= '''    '''
           if self._name != "root":
              SCREAMING_SNAKE_CASE__			:				int       						= f"""{t * (self._level-1)}{self._name}:\n"""
           else:
              SCREAMING_SNAKE_CASE__			:				Tuple       						= ''''''
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self._level
           for i, (k, v) in enumerate(self._pointer.items()					):
              if isinstance(a__ ,  a__					):
                 r += f"""{t * (self._level)}{v}\n"""
                 self._level += 1
              else:
                 r += f"""{t * (self._level)}{k}: {v} ({type(a__					).__name__})\n"""
              SCREAMING_SNAKE_CASE__			:				List[Any]       						= level
           return r[:-1]
  
  
  
  
  
  
        @classmethod
        def    A__(cls ,  a__ ,  **a__					)       ->   Any:
           SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Tuple       						= cls.get_config_dict(a__ ,  **a__					)
           return cls(a__					)
  
  
  
  
  
  
        @classmethod
        def    A__(cls ,  a__ ,  **a__					)       ->   List[Any]:
           SCREAMING_SNAKE_CASE__			:				Any       						= kwargs.pop('''cache_dir''' ,  a__					)
           SCREAMING_SNAKE_CASE__			:				Any       						= kwargs.pop('''force_download''' ,  a__					)
           SCREAMING_SNAKE_CASE__			:				Dict       						= kwargs.pop('''resume_download''' ,  a__					)
           SCREAMING_SNAKE_CASE__			:				str       						= kwargs.pop('''proxies''' ,  a__					)
           SCREAMING_SNAKE_CASE__			:				Dict       						= kwargs.pop('''local_files_only''' ,  a__					)
  
           if os.path.isdir(a__					):
              SCREAMING_SNAKE_CASE__			:				int       						= os.path.join(a__ ,  a__					)
           elif os.path.isfile(a__					) or is_remote_url(a__					):
              SCREAMING_SNAKE_CASE__			:				Optional[int]       						= pretrained_model_name_or_path
           else:
              SCREAMING_SNAKE_CASE__			:				Tuple       						= hf_bucket_url(a__ ,  filename=a__ ,  use_cdn=a__					)
  
           try:
              # Load from URL or cache if already cached
              SCREAMING_SNAKE_CASE__			:				Tuple       						= cached_path(
                  a__ ,  cache_dir=a__ ,  force_download=a__ ,  proxies=a__ ,  resume_download=a__ ,  local_files_only=a__ ,  )
              # Load config dict
              if resolved_config_file is None:
                 raise EnvironmentError
  
              SCREAMING_SNAKE_CASE__			:				str       						= Config.load_yaml(a__					)
  
           except EnvironmentError:
              SCREAMING_SNAKE_CASE__			:				Any       						= '''Can\'t load config for'''
              raise EnvironmentError(a__					)
  
           if resolved_config_file == config_file:
              print('''loading configuration file from path'''					)
           else:
              print('''loading configuration file cache'''					)
  
           return Config.load_yaml(a__					), kwargs
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				str       						= torch.load('''dump.pt'''	,     map_location=in_tensor.device   )
     SCREAMING_SNAKE_CASE__			:				str       						= in_tensor.numpy()
     SCREAMING_SNAKE_CASE__			:				Any       						= 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'''
  
     SCREAMING_SNAKE_CASE__			:				str       						= urlparse(snake_case   )
     return parsed.scheme in ("http", "https")
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case=True   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				int       						= CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= '''/''' 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'''
  
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= '''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
     SCREAMING_SNAKE_CASE__			:				List[str]       						= {'''user-agent''': ua}
     if resume_size > 0:
        SCREAMING_SNAKE_CASE__			:				int       						= '''bytes=%d-''' % (resume_size,)
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= requests.get(snake_case	,     stream=snake_case	,     proxies=snake_case	,     headers=snake_case   )
     if response.status_code == 4_16:  # Range not satisfiable
        return
     SCREAMING_SNAKE_CASE__			:				str       						= response.headers.get('''Content-Length'''   )
     SCREAMING_SNAKE_CASE__			:				Any       						= resume_size + int(snake_case   ) if content_length is not None else None
     SCREAMING_SNAKE_CASE__			:				List[str]       						= 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:
        SCREAMING_SNAKE_CASE__			:				List[Any]       						= TRANSFORMERS_CACHE
     if isinstance(snake_case	,     snake_case   ):
        SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= str(snake_case   )
  
     os.makedirs(snake_case	,     exist_ok=snake_case   )
  
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= None
     if not local_files_only:
        try:
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= requests.head(snake_case	,     allow_redirects=snake_case	,     proxies=snake_case	,     timeout=snake_case   )
           if response.status_code == 2_00:
              SCREAMING_SNAKE_CASE__			:				int       						= response.headers.get('''ETag'''   )
        except (EnvironmentError, requests.exceptions.Timeout):
           # etag is already None
           pass
  
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= url_to_filename(snake_case	,     snake_case   )
  
     # get cache path to put the file
     SCREAMING_SNAKE_CASE__			:				Dict       						= 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:
           SCREAMING_SNAKE_CASE__			:				Dict       						= [
               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.
     SCREAMING_SNAKE_CASE__			:				List[str]       						= 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:
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= cache_path + '''.incomplete'''
  
           @contextmanager
           def _resumable_file_manager():
              with open(snake_case	,     '''a+b'''   ) as f:
                 yield f
  
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= _resumable_file_manager
           if os.path.exists(snake_case   ):
              SCREAMING_SNAKE_CASE__			:				Tuple       						= os.stat(snake_case   ).st_size
           else:
              SCREAMING_SNAKE_CASE__			:				Dict       						= 0
        else:
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= partial(tempfile.NamedTemporaryFile	,     dir=snake_case	,     delete=snake_case   )
           SCREAMING_SNAKE_CASE__			:				Dict       						= 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   )
  
        SCREAMING_SNAKE_CASE__			:				int       						= {'''url''': url, '''etag''': etag}
        SCREAMING_SNAKE_CASE__			:				Optional[int]       						= 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'''
  
     SCREAMING_SNAKE_CASE__			:				Tuple       						= url.encode('''utf-8'''   )
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= shaaaa(snake_case   )
     SCREAMING_SNAKE_CASE__			:				List[str]       						= url_hash.hexdigest()
  
     if etag:
        SCREAMING_SNAKE_CASE__			:				str       						= etag.encode('''utf-8'''   )
        SCREAMING_SNAKE_CASE__			:				Any       						= 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:
        SCREAMING_SNAKE_CASE__			:				int       						= TRANSFORMERS_CACHE
     if isinstance(snake_case	,     snake_case   ):
        SCREAMING_SNAKE_CASE__			:				Optional[int]       						= str(snake_case   )
     if isinstance(snake_case	,     snake_case   ):
        SCREAMING_SNAKE_CASE__			:				Optional[int]       						= str(snake_case   )
  
     if is_remote_url(snake_case   ):
        # URL, so get it from the cache (downloading if necessary)
        SCREAMING_SNAKE_CASE__			:				int       						= 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.
        SCREAMING_SNAKE_CASE__			:				Any       						= 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/"
        SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Any       						= os.path.split(snake_case   )
        SCREAMING_SNAKE_CASE__			:				int       						= output_file.replace('''.'''	,     '''-'''   ) + '''-extracted'''
        SCREAMING_SNAKE_CASE__			:				Optional[int]       						= 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
        SCREAMING_SNAKE_CASE__			:				Optional[int]       						= 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   ):
              SCREAMING_SNAKE_CASE__			:				str       						= 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:
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= eval(f.read()   )
     else:
        SCREAMING_SNAKE_CASE__			:				str       						= requests.get(snake_case   )
        try:
           SCREAMING_SNAKE_CASE__			:				List[str]       						= requests.json()
        except Exception:
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= req.content.decode()
           assert data is not None, "could not connect"
           try:
              SCREAMING_SNAKE_CASE__			:				Tuple       						= eval(snake_case   )
           except Exception:
              SCREAMING_SNAKE_CASE__			:				Tuple       						= data.split('''\n'''   )
        req.close()
     return data
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Optional[int]       						= requests.get(snake_case   )
     SCREAMING_SNAKE_CASE__			:				Dict       						= np.array(Image.open(BytesIO(response.content   )   )   )
     return img
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Tuple       						= url.split('''/'''   )[-1]
     if fn not in os.listdir(os.getcwd()   ):
        wget.download(snake_case   )
     with open(snake_case	,     '''rb'''   ) as stream:
        SCREAMING_SNAKE_CASE__			:				List[Any]       						= pkl.load(snake_case   )
     SCREAMING_SNAKE_CASE__			:				int       						= weights.pop('''model'''   )
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= {}
     for k, v in model.items():
        SCREAMING_SNAKE_CASE__			:				Any       						= torch.from_numpy(snake_case   )
        if "running_var" in k:
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= torch.tensor([0]   )
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= k.replace('''running_var'''	,     '''num_batches_tracked'''   )
           SCREAMING_SNAKE_CASE__			:				Any       						= 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   ):
        SCREAMING_SNAKE_CASE__			:				Optional[int]       						= cva.imread(snake_case   )
     else:
        SCREAMING_SNAKE_CASE__			:				str       						= get_image_from_url(snake_case   )
        assert img is not None, f"""could not connect to: {im}"""
     SCREAMING_SNAKE_CASE__			:				Optional[int]       						= cva.cvtColor(snake_case	,     cva.COLOR_BGR2RGB   )
     if input_format == "RGB":
        SCREAMING_SNAKE_CASE__			:				Tuple       						= 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   ))
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  import argparse
  import json
  from pathlib import Path
  
  import requests
  import torch
  from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
  from PIL import Image
  
  from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  lowercase__			=	logging.get_logger(__name__)
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Tuple       						= DetaConfig(
         num_queries=9_00	,     encoder_ffn_dim=20_48	,     decoder_ffn_dim=20_48	,     num_feature_levels=5	,     assign_first_stage=snake_case	,     with_box_refine=snake_case	,     two_stage=snake_case	,     )
  
     # set labels
     SCREAMING_SNAKE_CASE__			:				str       						= 91
     SCREAMING_SNAKE_CASE__			:				int       						= '''huggingface/label-files'''
     SCREAMING_SNAKE_CASE__			:				Optional[int]       						= '''coco-detection-id2label.json'''
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= json.load(open(cached_download(hf_hub_url(snake_case	,     snake_case	,     repo_type='''dataset'''   )   )	,     '''r'''   )   )
     SCREAMING_SNAKE_CASE__			:				int       						= {int(snake_case   ): v for k, v in idalabel.items()}
     SCREAMING_SNAKE_CASE__			:				str       						= idalabel
     SCREAMING_SNAKE_CASE__			:				List[str]       						= {v: k for k, v in idalabel.items()}
  
     return config
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= []
  
     # stem
     # fmt: off
     rename_keys.append(('''backbone.0.body.conv1.weight''', '''model.backbone.model.embedder.embedder.convolution.weight''')   )
     rename_keys.append(('''backbone.0.body.bn1.weight''', '''model.backbone.model.embedder.embedder.normalization.weight''')   )
     rename_keys.append(('''backbone.0.body.bn1.bias''', '''model.backbone.model.embedder.embedder.normalization.bias''')   )
     rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''model.backbone.model.embedder.embedder.normalization.running_mean''')   )
     rename_keys.append(('''backbone.0.body.bn1.running_var''', '''model.backbone.model.embedder.embedder.normalization.running_var''')   )
     # stages
     for stage_idx in range(len(config.backbone_config.depths   )   ):
        for layer_idx in range(config.backbone_config.depths[stage_idx]   ):
           # shortcut
           if layer_idx == 0:
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
                  )   )
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
                  )   )
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
                  )   )
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
                  )   )
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
                  )   )
           # 3 convs
           for i in range(3   ):
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
                  )   )
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
                  )   )
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
                  )   )
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
                  )   )
              rename_keys.append(
                  (
                      f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
                      f"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                  )   )
      # transformer encoder
     for i in range(config.encoder_layers   ):
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", f"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", f"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", f"""model.encoder.layers.{i}.self_attn.value_proj.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", f"""model.encoder.layers.{i}.self_attn.value_proj.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", f"""model.encoder.layers.{i}.self_attn.output_proj.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", f"""model.encoder.layers.{i}.self_attn.output_proj.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.weight""", f"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""model.encoder.layers.{i}.fc1.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""model.encoder.layers.{i}.fc1.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""model.encoder.layers.{i}.fc2.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""model.encoder.layers.{i}.fc2.bias""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""model.encoder.layers.{i}.final_layer_norm.weight""")   )
        rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""model.encoder.layers.{i}.final_layer_norm.bias""")   )
  
     # transformer decoder
     for i in range(config.decoder_layers   ):
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.weight""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""model.decoder.layers.{i}.self_attn.out_proj.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""model.decoder.layers.{i}.self_attn.out_proj.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.weight""", f"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.bias""", f"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""model.decoder.layers.{i}.fc1.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""model.decoder.layers.{i}.fc1.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""model.decoder.layers.{i}.fc2.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""model.decoder.layers.{i}.fc2.bias""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""model.decoder.layers.{i}.final_layer_norm.weight""")   )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""model.decoder.layers.{i}.final_layer_norm.bias""")   )
  
     # fmt: on
  
     return rename_keys
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= dct.pop(snake_case   )
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= val
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= config.d_model
     for i in range(config.decoder_layers   ):
        # read in weights + bias of input projection layer of self-attention
        SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""   )
        SCREAMING_SNAKE_CASE__			:				Any       						= state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= in_proj_weight[:hidden_size, :]
        SCREAMING_SNAKE_CASE__			:				Optional[int]       						= in_proj_bias[:hidden_size]
        SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= in_proj_weight[
            hidden_size : hidden_size * 2, :
        ]
        SCREAMING_SNAKE_CASE__			:				List[Any]       						= in_proj_bias[hidden_size : hidden_size * 2]
        SCREAMING_SNAKE_CASE__			:				List[Any]       						= in_proj_weight[-hidden_size:, :]
        SCREAMING_SNAKE_CASE__			:				str       						= in_proj_bias[-hidden_size:]
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Dict       						= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= Image.open(requests.get(snake_case	,     stream=snake_case   ).raw   )
  
     return im
  
  
  @torch.no_grad()
  def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= get_deta_config()
  
     # load original state dict
     if model_name == "deta-resnet-50":
        SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= '''adet_checkpoint0011.pth'''
     elif model_name == "deta-resnet-50-24-epochs":
        SCREAMING_SNAKE_CASE__			:				Any       						= '''adet_2x_checkpoint0023.pth'''
     else:
        raise ValueError(f"""Model name {model_name} not supported"""   )
     SCREAMING_SNAKE_CASE__			:				Optional[int]       						= hf_hub_download(repo_id='''nielsr/deta-checkpoints'''	,     filename=snake_case   )
     SCREAMING_SNAKE_CASE__			:				int       						= torch.load(snake_case	,     map_location='''cpu'''   )['''model''']
  
     # rename keys
     SCREAMING_SNAKE_CASE__			:				Dict       						= create_rename_keys(snake_case   )
     for src, dest in rename_keys:
        rename_key(snake_case	,     snake_case	,     snake_case   )
     read_in_decoder_q_k_v(snake_case	,     snake_case   )
  
     # fix some prefixes
     for key in state_dict.copy().keys():
        if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= state_dict.pop(snake_case   )
           SCREAMING_SNAKE_CASE__			:				Dict       						= val
        if "input_proj" in key:
           SCREAMING_SNAKE_CASE__			:				Any       						= state_dict.pop(snake_case   )
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= val
        if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
           SCREAMING_SNAKE_CASE__			:				List[str]       						= state_dict.pop(snake_case   )
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= val
  
      # finally, create HuggingFace model and load state dict
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= DetaForObjectDetection(snake_case   )
     model.load_state_dict(snake_case   )
     model.eval()
  
     SCREAMING_SNAKE_CASE__			:				Tuple       						= '''cuda''' if torch.cuda.is_available() else '''cpu'''
     model.to(snake_case   )
  
     # load image processor
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= DetaImageProcessor(format='''coco_detection'''   )
  
     # verify our conversion on image
     SCREAMING_SNAKE_CASE__			:				int       						= prepare_img()
     SCREAMING_SNAKE_CASE__			:				int       						= processor(images=snake_case	,     return_tensors='''pt'''   )
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= encoding['''pixel_values''']
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= model(pixel_values.to(snake_case   )   )
  
     # verify logits
     if model_name == "deta-resnet-50":
        SCREAMING_SNAKE_CASE__			:				str       						= torch.tensor(
            [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]   )
        SCREAMING_SNAKE_CASE__			:				List[Any]       						= torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]   )
     elif model_name == "deta-resnet-50-24-epochs":
        SCREAMING_SNAKE_CASE__			:				List[str]       						= torch.tensor(
            [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]   )
        SCREAMING_SNAKE_CASE__			:				Tuple       						= torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]   )
  
     assert torch.allclose(outputs.logits[0, :3, :3]	,     expected_logits.to(snake_case   )	,     atol=1e-4   )
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,     expected_boxes.to(snake_case   )	,     atol=1e-4   )
     print('''Everything ok!'''   )
  
     if pytorch_dump_folder_path:
        # Save model and processor
        logger.info(f"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""   )
        Path(snake_case   ).mkdir(exist_ok=snake_case   )
        model.save_pretrained(snake_case   )
        processor.save_pretrained(snake_case   )
  
     # Push to hub
     if push_to_hub:
        print('''Pushing model and processor to hub...'''   )
        model.push_to_hub(f"""jozhang97/{model_name}"""   )
        processor.push_to_hub(f"""jozhang97/{model_name}"""   )
  
  
  if __name__ == "__main__":
         lowercase__			=	argparse.ArgumentParser()
  
         parser.add_argument(
             '''--model_name''',
             type=str,
             default='''deta-resnet-50''',
             choices=['''deta-resnet-50''', '''deta-resnet-50-24-epochs'''],
             help='''Name of the model you\'d like to convert.''',
         )
         parser.add_argument(
             '''--pytorch_dump_folder_path''',
             default=None,
             type=str,
             help='''Path to the folder to output PyTorch model.''',
         )
         parser.add_argument(
             '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
         )
         lowercase__			=	parser.parse_args()
         convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
  
  