
"""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 			A_			(					lowercase=OBJECTS						, lowercase=ATTRIBUTES      )     ->							Any:


     """simple docstring"""







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

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




def 			A_			(					lowercase      )     ->							Union[str, Any]:


     """simple docstring"""







     UpperCAmelCase_						:    Optional[int]    					=  OrderedDict()
     with open(UpperCAmelCase_						, """rb"""      ) as f:
          UpperCAmelCase_						:    Union[str, Any]    					=  pkl.load(UpperCAmelCase_      )['''model''']
     for k in copy.deepcopy(list(ckp.keys()      )      ):
          UpperCAmelCase_						:    Union[str, Any]    					=  ckp.pop(UpperCAmelCase_      )
          if isinstance(UpperCAmelCase_						, np.ndarray      ):
               UpperCAmelCase_						:    Dict    					=  torch.tensor(UpperCAmelCase_      )
          else:
               assert isinstance(UpperCAmelCase_						, torch.tensor      ), type(UpperCAmelCase_      )
          UpperCAmelCase_						:    Dict    					=  v
     return r



class 		UpperCAmelCase_	:
 """simple docstring"""




 UpperCamelCase_						:     Union[str, Any]				   =    {}




 def __init__( self       : Union[str, Any]     ,   a_       : dict     ,   a_       : str = "root"     ,   a_       : Optional[int]=0 )->					List[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Any    					=  name
      UpperCAmelCase_						:    Tuple    					=  level
      UpperCAmelCase_						:    str    					=  {}
      for k, v in dictionary.items():
           if v is None:
                raise ValueError()
           UpperCAmelCase_						:    Union[str, Any]    					=  copy.deepcopy(a_ )
           UpperCAmelCase_						:    List[Any]    					=  copy.deepcopy(a_ )
           if isinstance(a_     ,   a_ ):
                UpperCAmelCase_						:    Any    					=  Config(a_     ,   name=a_     ,   level=level + 1 )
           UpperCAmelCase_						:    Tuple    					=  v
           setattr(self     ,   a_     ,   a_ )

      UpperCAmelCase_						:    Union[str, Any]    					=  d




 def __repr__( self       : Tuple )->					Optional[int]:



      """simple docstring"""

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




 def __setattr__( self       : Union[str, Any]     ,   a_       : Optional[Any]     ,   a_       : Tuple )->					int:



      """simple docstring"""

      UpperCAmelCase_						:    Any    					=  val
      UpperCAmelCase_						:    Optional[int]    					=  val
      UpperCAmelCase_						:    List[Any]    					=  key.split(""".""" )
      UpperCAmelCase_						:    Dict    					=  len(a_ ) - 1
      UpperCAmelCase_						:    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:
                     UpperCAmelCase_						:    Union[str, Any]    					=  val
                else:
                     UpperCAmelCase_						:    Tuple    					=  pointer[l]




 def   a      ( self       : int )->					List[Any]:



      """simple docstring"""

      return self._pointer




 def   a      ( self       : Dict     ,   a_       : List[Any]     ,   a_       : int )->					Optional[int]:



      """simple docstring"""

      with open(f'''{file_name}'''     ,   """w""" ) as stream:
           dump(a_     ,   a_ )




 def   a      ( self       : int     ,   a_       : str     ,   a_       : Dict )->					Tuple:



      """simple docstring"""

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




 @staticmethod
 def   a      ( a_       : List[str] )->					Union[str, Any]:



      """simple docstring"""

      with open(a_ ) as stream:
           UpperCAmelCase_						:    Union[str, Any]    					=  load(a_     ,   Loader=a_ )
      return data




 def __str__( self       : int )->					Any:



      """simple docstring"""

      UpperCAmelCase_						:    Dict    					=  '''    '''
      if self._name != "root":
           UpperCAmelCase_						:    int    					=  f'''{t * (self._level-1)}{self._name}:\n'''
      else:
           UpperCAmelCase_						:    int    					=  ''''''
      UpperCAmelCase_						:    List[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'''
           UpperCAmelCase_						:    Any    					=  level
      return r[:-1]




 @classmethod
 def   a      ( cls       : List[str]     ,   a_       : str     ,   **a_       : str )->					Any:



      """simple docstring"""

      UpperCAmelCase_						:    Dict    					=  cls.get_config_dict(a_     ,   **a_ )
      return cls(a_ )






 @classmethod
 def   a      ( cls       : List[str]     ,   a_       : str     ,   **a_       : int )->					List[str]:



      """simple docstring"""

      UpperCAmelCase_						:    Any    					=  kwargs.pop("""cache_dir"""     ,   a_ )
      UpperCAmelCase_						:    str    					=  kwargs.pop("""force_download"""     ,   a_ )
      UpperCAmelCase_						:    List[Any]    					=  kwargs.pop("""resume_download"""     ,   a_ )
      UpperCAmelCase_						:    str    					=  kwargs.pop("""proxies"""     ,   a_ )
      UpperCAmelCase_						:    List[Any]    					=  kwargs.pop("""local_files_only"""     ,   a_ )

      if os.path.isdir(a_ ):
           UpperCAmelCase_						:    List[Any]    					=  os.path.join(a_     ,   a_ )
      elif os.path.isfile(a_ ) or is_remote_url(a_ ):
           UpperCAmelCase_						:    Dict    					=  pretrained_model_name_or_path
      else:
           UpperCAmelCase_						:    List[str]    					=  hf_bucket_url(a_     ,   filename=a_     ,   use_cdn=a_ )

      try:
           # Load from URL or cache if already cached
           UpperCAmelCase_						:    str    					=  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

           UpperCAmelCase_						:    Dict    					=  Config.load_yaml(a_ )

      except EnvironmentError:
           UpperCAmelCase_						:    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 			A_			(					lowercase      )     ->							Union[str, Any]:


     """simple docstring"""







     UpperCAmelCase_						:    Optional[Any]    					=  torch.load("""dump.pt"""						, map_location=in_tensor.device      )
     UpperCAmelCase_						:    Optional[Any]    					=  in_tensor.numpy()
     UpperCAmelCase_						:    Tuple    					=  out_tensor.numpy()[0]
     print(na.shape						, na[0, 0, :5]      )
     print(na.shape						, na[0, 0, :5]      )
     assert np.allclose(UpperCAmelCase_						, UpperCAmelCase_						, rtol=0.01						, atol=0.1      ), (
         f'''{sum([1 for x in np.isclose(UpperCAmelCase_						, UpperCAmelCase_						, rtol=0.01						, 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 			A_			(					lowercase      )     ->							Optional[Any]:


     """simple docstring"""







     UpperCAmelCase_						:    Dict    					=  urlparse(UpperCAmelCase_      )
     return parsed.scheme in ("http", "https")




def 			A_			(					lowercase						, lowercase						, lowercase=True      )     ->							str:


     """simple docstring"""







     UpperCAmelCase_						:    Tuple    					=  CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
     UpperCAmelCase_						:    Optional[Any]    					=  '''/''' not in model_id
     if legacy_format:
          return f'''{endpoint}/{model_id}-{filename}'''
     else:
          return f'''{endpoint}/{model_id}/{filename}'''




def 			A_			(					lowercase						, lowercase						, lowercase=None						, lowercase=0						, lowercase=None						, )     ->							int:


     """simple docstring"""







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




def 			A_			(					lowercase						, lowercase=None						, lowercase=False						, lowercase=None						, lowercase=10						, lowercase=False						, lowercase=None						, lowercase=False						, )     ->							List[str]:


     """simple docstring"""







     if cache_dir is None:
          UpperCAmelCase_						:    Dict    					=  TRANSFORMERS_CACHE
     if isinstance(UpperCAmelCase_						, UpperCAmelCase_      ):
          UpperCAmelCase_						:    int    					=  str(UpperCAmelCase_      )

     os.makedirs(UpperCAmelCase_						, exist_ok=UpperCAmelCase_      )

     UpperCAmelCase_						:    Union[str, Any]    					=  None
     if not local_files_only:
          try:
               UpperCAmelCase_						:    Union[str, Any]    					=  requests.head(UpperCAmelCase_						, allow_redirects=UpperCAmelCase_						, proxies=UpperCAmelCase_						, timeout=UpperCAmelCase_      )
               if response.status_code == 200:
                    UpperCAmelCase_						:    Tuple    					=  response.headers.get("""ETag"""      )
          except (EnvironmentError, requests.exceptions.Timeout):
               # etag is already None
               pass

     UpperCAmelCase_						:    Any    					=  url_to_filename(UpperCAmelCase_						, UpperCAmelCase_      )

     # get cache path to put the file
     UpperCAmelCase_						:    Any    					=  os.path.join(UpperCAmelCase_						, UpperCAmelCase_      )

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

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

          if resume_download:
               UpperCAmelCase_						:    Dict    					=  cache_path + '''.incomplete'''

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

               UpperCAmelCase_						:    str    					=  _resumable_file_manager
               if os.path.exists(UpperCAmelCase_      ):
                    UpperCAmelCase_						:    List[Any]    					=  os.stat(UpperCAmelCase_      ).st_size
               else:
                    UpperCAmelCase_						:    List[Any]    					=  0
          else:
               UpperCAmelCase_						:    Optional[Any]    					=  partial(tempfile.NamedTemporaryFile						, dir=UpperCAmelCase_						, delete=UpperCAmelCase_      )
               UpperCAmelCase_						:    List[str]    					=  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"""						, UpperCAmelCase_						, temp_file.name						, )

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

          os.replace(temp_file.name						, UpperCAmelCase_      )

          UpperCAmelCase_						:    int    					=  {'''url''': url, '''etag''': etag}
          UpperCAmelCase_						:    Union[str, Any]    					=  cache_path + '''.json'''
          with open(UpperCAmelCase_						, """w"""      ) as meta_file:
               json.dump(UpperCAmelCase_						, UpperCAmelCase_      )

     return cache_path




def 			A_			(					lowercase						, lowercase=None      )     ->							List[Any]:


     """simple docstring"""







     UpperCAmelCase_						:    Dict    					=  url.encode("""utf-8"""      )
     UpperCAmelCase_						:    List[str]    					=  shaaaa(UpperCAmelCase_      )
     UpperCAmelCase_						:    str    					=  url_hash.hexdigest()

     if etag:
          UpperCAmelCase_						:    Dict    					=  etag.encode("""utf-8"""      )
          UpperCAmelCase_						:    Tuple    					=  shaaaa(UpperCAmelCase_      )
          filename += "." + etag_hash.hexdigest()

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

     return filename




def 			A_			(					lowercase						, lowercase=None						, lowercase=False						, lowercase=None						, lowercase=False						, lowercase=None						, lowercase=False						, lowercase=False						, lowercase=False						, )     ->							int:


     """simple docstring"""







     if cache_dir is None:
          UpperCAmelCase_						:    Union[str, Any]    					=  TRANSFORMERS_CACHE
     if isinstance(UpperCAmelCase_						, UpperCAmelCase_      ):
          UpperCAmelCase_						:    Tuple    					=  str(UpperCAmelCase_      )
     if isinstance(UpperCAmelCase_						, UpperCAmelCase_      ):
          UpperCAmelCase_						:    List[str]    					=  str(UpperCAmelCase_      )

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

     if extract_compressed_file:
          if not is_zipfile(UpperCAmelCase_      ) and not tarfile.is_tarfile(UpperCAmelCase_      ):
               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/"
          UpperCAmelCase_						:    Any    					=  os.path.split(UpperCAmelCase_      )
          UpperCAmelCase_						:    str    					=  output_file.replace("""."""						, """-"""      ) + '''-extracted'''
          UpperCAmelCase_						:    Union[str, Any]    					=  os.path.join(UpperCAmelCase_						, UpperCAmelCase_      )

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

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

          return output_path_extracted

     return output_path




def 			A_			(					lowercase						, lowercase=","      )     ->							Union[str, Any]:


     """simple docstring"""







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




def 			A_			(					lowercase      )     ->							str:


     """simple docstring"""







     UpperCAmelCase_						:    List[Any]    					=  requests.get(UpperCAmelCase_      )
     UpperCAmelCase_						:    Optional[Any]    					=  np.array(Image.open(BytesIO(response.content      )      )      )
     return img




def 			A_			(					lowercase      )     ->							Optional[int]:


     """simple docstring"""







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




def 			A_			(					)     ->							int:


     """simple docstring"""







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




def 			A_			(					lowercase						, lowercase="RGB"      )     ->							List[str]:


     """simple docstring"""







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




def 			A_			(					lowercase						, lowercase=1      )     ->							Dict:


     """simple docstring"""







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



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_						    =					{"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_						    =					[
           "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "VisualBertForMultipleChoice",
           "VisualBertForPreTraining",
           "VisualBertForQuestionAnswering",
           "VisualBertForRegionToPhraseAlignment",
           "VisualBertForVisualReasoning",
           "VisualBertLayer",
           "VisualBertModel",
           "VisualBertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_visual_bert import (
                  VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VisualBertForMultipleChoice,
                  VisualBertForPreTraining,
                  VisualBertForQuestionAnswering,
                  VisualBertForRegionToPhraseAlignment,
                  VisualBertForVisualReasoning,
                  VisualBertLayer,
                  VisualBertModel,
                  VisualBertPreTrainedModel,
              )


else:
       import sys

       lowercase_						    =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

