





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

     UpperCAmelCase	:	List[str]		     =       True
except ImportError:
     UpperCAmelCase	:	Optional[Any]		     =       False


try:
     from torch.hub import _get_torch_home

     UpperCAmelCase	:	Optional[int]		     =       _get_torch_home()
except ImportError:
     UpperCAmelCase	:	Optional[Any]		     =       os.path.expanduser(
         os.getenv("""TORCH_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """torch"""))
     )

UpperCAmelCase	:	Union[str, Any]		     =       os.path.join(torch_cache_home, """transformers""")

UpperCAmelCase	:	Optional[Any]		     =       """https://cdn.huggingface.co"""
UpperCAmelCase	:	int		     =       """https://s3.amazonaws.com/models.huggingface.co/bert"""
UpperCAmelCase	:	Optional[int]		     =       """/""".join(str(Path(__file__).resolve()).split("""/""")[:-1])
UpperCAmelCase	:	Union[str, Any]		     =       os.path.join(PATH, """config.yaml""")
UpperCAmelCase	:	str		     =       os.path.join(PATH, """attributes.txt""")
UpperCAmelCase	:	int		     =       os.path.join(PATH, """objects.txt""")
UpperCAmelCase	:	int		     =       os.getenv("""PYTORCH_PRETRAINED_BERT_CACHE""", default_cache_path)
UpperCAmelCase	:	List[str]		     =       os.getenv("""PYTORCH_TRANSFORMERS_CACHE""", PYTORCH_PRETRAINED_BERT_CACHE)
UpperCAmelCase	:	Dict		     =       os.getenv("""TRANSFORMERS_CACHE""", PYTORCH_TRANSFORMERS_CACHE)
UpperCAmelCase	:	Union[str, Any]		     =       """pytorch_model.bin"""
UpperCAmelCase	:	List[Any]		     =       """config.yaml"""
def      _A       (		SCREAMING_SNAKE_CASE     :   Tuple=OBJECTS       ,       SCREAMING_SNAKE_CASE     :   Dict=ATTRIBUTES						):





  """simple docstring"""



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

  a__	:					Optional[Any]										=[]
  with open(SCREAMING_SNAKE_CASE						) as f:
    for object in f.readlines():
      vg_attrs.append(object.split(","						)[0].lower().strip()						)
  return vg_classes, vg_attrs
def      _A       (		SCREAMING_SNAKE_CASE     :   Union[str, Any]						):





  """simple docstring"""



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







class 				__lowerCAmelCase       :
      _lowercase				:					List[Any]						=			{}







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = "root"		,      lowerCAmelCase__=0							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Optional[Any]										=name
        a__	:					Optional[Any]										=level
        a__	:					Optional[Any]										={}
        for k, v in dictionary.items():
          if v is None:
            raise ValueError()
          a__	:					int										=copy.deepcopy(lowerCAmelCase__							)
          a__	:					Optional[int]										=copy.deepcopy(lowerCAmelCase__							)
          if isinstance(lowerCAmelCase__		,      lowerCAmelCase__							):
            a__	:					Dict										=Config(lowerCAmelCase__		,      name=lowerCAmelCase__		,      level=level + 1							)
          a__	:					int										=v
          setattr(self		,      lowerCAmelCase__		,      lowerCAmelCase__							)

        a__	:					int										=d







      def __repr__(    self							)   ->						List[Any]:



        '''simple docstring'''




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







      def __setattr__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						str:



        '''simple docstring'''




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







      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        return self._pointer







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




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







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						int:



        '''simple docstring'''




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







      @staticmethod
      def    _lowercase  (    lowerCAmelCase__							)   ->						Optional[Any]:



        '''simple docstring'''




        with open(lowerCAmelCase__							) as stream:
          a__	:					Tuple										=load(lowerCAmelCase__		,      Loader=lowerCAmelCase__							)
        return data







      def __str__(    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					str										="    "
        if self._name != "root":
          a__	:					Optional[int]										=F'''{t * (self._level-1)}{self._name}:\n'''
        else:
          a__	:					Union[str, Any]										=""
        a__	:					List[str]										=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'''
          a__	:					Optional[int]										=level
        return r[:-1]







      @classmethod
      def    _lowercase  (    cls		,      lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						int:



        '''simple docstring'''




        a__					,							a__	:					Dict										=cls.get_config_dict(lowerCAmelCase__		,      **lowerCAmelCase__							)
        return cls(lowerCAmelCase__							)







      @classmethod
      def    _lowercase  (    cls		,      lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					Union[str, Any]										=kwargs.pop("cache_dir"		,      lowerCAmelCase__							)
        a__	:					List[str]										=kwargs.pop("force_download"		,      lowerCAmelCase__							)
        a__	:					int										=kwargs.pop("resume_download"		,      lowerCAmelCase__							)
        a__	:					Optional[Any]										=kwargs.pop("proxies"		,      lowerCAmelCase__							)
        a__	:					Any										=kwargs.pop("local_files_only"		,      lowerCAmelCase__							)

        if os.path.isdir(lowerCAmelCase__							):
          a__	:					Any										=os.path.join(lowerCAmelCase__		,      lowerCAmelCase__							)
        elif os.path.isfile(lowerCAmelCase__							) or is_remote_url(lowerCAmelCase__							):
          a__	:					Dict										=pretrained_model_name_or_path
        else:
          a__	:					int										=hf_bucket_url(lowerCAmelCase__		,      filename=lowerCAmelCase__		,      use_cdn=lowerCAmelCase__							)

        try:
          # Load from URL or cache if already cached
          a__	:					Optional[Any]										=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

          a__	:					Tuple										=Config.load_yaml(lowerCAmelCase__							)

        except EnvironmentError:
          a__	:					Union[str, Any]										="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      _A       (		SCREAMING_SNAKE_CASE     :   Union[str, Any]						):





  """simple docstring"""



  a__	:					List[Any]										=torch.load("dump.pt"       ,       map_location=in_tensor.device						)
  a__	:					Any										=in_tensor.numpy()
  a__	:					Any										=out_tensor.numpy()[0]
  print(na.shape       ,       na[0, 0, :5]						)
  print(na.shape       ,       na[0, 0, :5]						)
  assert np.allclose(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       rtol=0.0_1       ,       atol=0.1						), (
      f'''{sum([1 for x in np.isclose(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       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      _A       (		SCREAMING_SNAKE_CASE     :   Union[str, Any]						):





  """simple docstring"""



  a__	:					Dict										=urlparse(SCREAMING_SNAKE_CASE						)
  return parsed.scheme in ("http", "https")
def      _A       (		SCREAMING_SNAKE_CASE     :   str       ,       SCREAMING_SNAKE_CASE     :   str       ,       SCREAMING_SNAKE_CASE     :   List[str]=True						):





  """simple docstring"""



  a__	:					Optional[int]										=CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
  a__	:					int										="/" not in model_id
  if legacy_format:
    return f'''{endpoint}/{model_id}-{filename}'''
  else:
    return f'''{endpoint}/{model_id}/{filename}'''
def      _A       (		SCREAMING_SNAKE_CASE     :   Any       ,       SCREAMING_SNAKE_CASE     :   str       ,       SCREAMING_SNAKE_CASE     :   Optional[int]=None       ,       SCREAMING_SNAKE_CASE     :   str=0       ,       SCREAMING_SNAKE_CASE     :   List[str]=None       ,       ):





  """simple docstring"""



  a__	:					List[str]										="python/{}".format(sys.version.split()[0]						)
  if _torch_available:
    ua += "; torch/{}".format(torch.__version__						)
  if isinstance(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						):
    ua += "; " + "; ".join("{}/{}".format(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						) for k, v in user_agent.items()						)
  elif isinstance(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						):
    ua += "; " + user_agent
  a__	:					List[str]										={"user-agent": ua}
  if resume_size > 0:
    a__	:					str										="bytes=%d-" % (resume_size,)
  a__	:					int										=requests.get(SCREAMING_SNAKE_CASE       ,       stream=SCREAMING_SNAKE_CASE       ,       proxies=SCREAMING_SNAKE_CASE       ,       headers=SCREAMING_SNAKE_CASE						)
  if response.status_code == 416:  # Range not satisfiable
    return
  a__	:					Optional[int]										=response.headers.get("Content-Length"						)
  a__	:					Dict										=resume_size + int(SCREAMING_SNAKE_CASE						) if content_length is not None else None
  a__	:					Any										=tqdm(
      unit="B"       ,       unit_scale=SCREAMING_SNAKE_CASE       ,       total=SCREAMING_SNAKE_CASE       ,       initial=SCREAMING_SNAKE_CASE       ,       desc="Downloading"       ,       )
  for chunk in response.iter_content(chunk_size=1_024						):
    if chunk:  # filter out keep-alive new chunks
      progress.update(len(SCREAMING_SNAKE_CASE						)						)
      temp_file.write(SCREAMING_SNAKE_CASE						)
  progress.close()
def      _A       (		SCREAMING_SNAKE_CASE     :   List[Any]       ,       SCREAMING_SNAKE_CASE     :   Optional[int]=None       ,       SCREAMING_SNAKE_CASE     :   Dict=False       ,       SCREAMING_SNAKE_CASE     :   List[Any]=None       ,       SCREAMING_SNAKE_CASE     :   Optional[int]=10       ,       SCREAMING_SNAKE_CASE     :   List[str]=False       ,       SCREAMING_SNAKE_CASE     :   Optional[Any]=None       ,       SCREAMING_SNAKE_CASE     :   List[Any]=False       ,       ):





  """simple docstring"""



  if cache_dir is None:
    a__	:					int										=TRANSFORMERS_CACHE
  if isinstance(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						):
    a__	:					Tuple										=str(SCREAMING_SNAKE_CASE						)

  os.makedirs(SCREAMING_SNAKE_CASE       ,       exist_ok=SCREAMING_SNAKE_CASE						)

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

  a__	:					List[Any]										=url_to_filename(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

  # get cache path to put the file
  a__	:					str										=os.path.join(SCREAMING_SNAKE_CASE       ,       SCREAMING_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(SCREAMING_SNAKE_CASE						):
      return cache_path
    else:
      a__	:					Dict										=[
          file
          for file in fnmatch.filter(os.listdir(SCREAMING_SNAKE_CASE						)       ,       filename + ".*"						)
          if not file.endswith(".json"						) and not file.endswith(".lock"						)
      ]
      if len(SCREAMING_SNAKE_CASE						) > 0:
        return os.path.join(SCREAMING_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(SCREAMING_SNAKE_CASE						) and not force_download:
    return cache_path

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

    if resume_download:
      a__	:					Any										=cache_path + ".incomplete"

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

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

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

    os.replace(temp_file.name       ,       SCREAMING_SNAKE_CASE						)

    a__	:					str										={"url": url, "etag": etag}
    a__	:					List[Any]										=cache_path + ".json"
    with open(SCREAMING_SNAKE_CASE       ,       "w"						) as meta_file:
      json.dump(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

  return cache_path
def      _A       (		SCREAMING_SNAKE_CASE     :   Optional[int]       ,       SCREAMING_SNAKE_CASE     :   Dict=None						):





  """simple docstring"""



  a__	:					Dict										=url.encode("utf-8"						)
  a__	:					Dict										=shaaaa(SCREAMING_SNAKE_CASE						)
  a__	:					int										=url_hash.hexdigest()

  if etag:
    a__	:					Optional[int]										=etag.encode("utf-8"						)
    a__	:					List[str]										=shaaaa(SCREAMING_SNAKE_CASE						)
    filename += "." + etag_hash.hexdigest()

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

  return filename
def      _A       (		SCREAMING_SNAKE_CASE     :   Optional[Any]       ,       SCREAMING_SNAKE_CASE     :   List[Any]=None       ,       SCREAMING_SNAKE_CASE     :   str=False       ,       SCREAMING_SNAKE_CASE     :   Any=None       ,       SCREAMING_SNAKE_CASE     :   Dict=False       ,       SCREAMING_SNAKE_CASE     :   List[Any]=None       ,       SCREAMING_SNAKE_CASE     :   Optional[int]=False       ,       SCREAMING_SNAKE_CASE     :   Any=False       ,       SCREAMING_SNAKE_CASE     :   Optional[int]=False       ,       ):





  """simple docstring"""



  if cache_dir is None:
    a__	:					Union[str, Any]										=TRANSFORMERS_CACHE
  if isinstance(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						):
    a__	:					int										=str(SCREAMING_SNAKE_CASE						)
  if isinstance(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						):
    a__	:					Dict										=str(SCREAMING_SNAKE_CASE						)

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

  if extract_compressed_file:
    if not is_zipfile(SCREAMING_SNAKE_CASE						) and not tarfile.is_tarfile(SCREAMING_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/"
    a__					,							a__	:					List[Any]										=os.path.split(SCREAMING_SNAKE_CASE						)
    a__	:					int										=output_file.replace("."       ,       "-"						) + "-extracted"
    a__	:					str										=os.path.join(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

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

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

    return output_path_extracted

  return output_path
def      _A       (		SCREAMING_SNAKE_CASE     :   Optional[int]       ,       SCREAMING_SNAKE_CASE     :   Union[str, Any]=","						):





  """simple docstring"""



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





  """simple docstring"""



  a__	:					Optional[int]										=requests.get(SCREAMING_SNAKE_CASE						)
  a__	:					Optional[int]										=np.array(Image.open(BytesIO(response.content						)						)						)
  return img
def      _A       (		SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



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





  """simple docstring"""



  print(f'''{os.path.abspath(os.path.join(SCREAMING_SNAKE_CASE       ,       os.pardir						)						)}/demo.ipynb'''						)
def      _A       (		SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   List[Any]="RGB"						):





  """simple docstring"""



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





def      _A       (		SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   Union[str, Any]=1						):





  """simple docstring"""



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













import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import CLIPSegProcessor, ViTImageProcessor







@require_vision
class 				__lowerCAmelCase       (    unittest.TestCase):







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Any										=tempfile.mkdtemp()

        # fmt: off
        a__	:					List[Any]										=["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
        # fmt: on
        a__	:					str										=dict(zip(lowerCAmelCase__		,      range(len(lowerCAmelCase__							)							)							)							)
        a__	:					List[Any]										=["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
        a__	:					Optional[int]										={"unk_token": "<unk>"}

        a__	:					Optional[Any]										=os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["vocab_file"]							)
        a__	:					Tuple										=os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["merges_file"]							)
        with open(self.vocab_file		,      "w"		,      encoding="utf-8"							) as fp:
          fp.write(json.dumps(lowerCAmelCase__							) + "\n"							)
        with open(self.merges_file		,      "w"		,      encoding="utf-8"							) as fp:
          fp.write("\n".join(lowerCAmelCase__							)							)

        a__	:					Optional[Any]										={
            "do_resize": True,
            "size": 2_0,
            "do_center_crop": True,
            "crop_size": 1_8,
            "do_normalize": True,
            "image_mean": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
            "image_std": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
        }
        a__	:					Dict										=os.path.join(self.tmpdirname		,      lowerCAmelCase__							)
        with open(self.image_processor_file		,      "w"		,      encoding="utf-8"							) as fp:
          json.dump(lowerCAmelCase__		,      lowerCAmelCase__							)







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        return CLIPTokenizer.from_pretrained(self.tmpdirname		,      **lowerCAmelCase__							)







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						List[Any]:



        '''simple docstring'''




        return CLIPTokenizerFast.from_pretrained(self.tmpdirname		,      **lowerCAmelCase__							)







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




        return ViTImageProcessor.from_pretrained(self.tmpdirname		,      **lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        shutil.rmtree(self.tmpdirname							)







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					Optional[Any]										=[np.random.randint(2_5_5		,      size=(3, 3_0, 4_0_0)		,      dtype=np.uinta							)]

        a__	:					List[Any]										=[Image.fromarray(np.moveaxis(lowerCAmelCase__		,      0		,      -1							)							) for x in image_inputs]

        return image_inputs







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Union[str, Any]										=self.get_tokenizer()
        a__	:					int										=self.get_rust_tokenizer()
        a__	:					List[str]										=self.get_image_processor()

        a__	:					Dict										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)
        processor_slow.save_pretrained(self.tmpdirname							)
        a__	:					Optional[Any]										=CLIPSegProcessor.from_pretrained(self.tmpdirname		,      use_fast=lowerCAmelCase__							)

        a__	:					Tuple										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)
        processor_fast.save_pretrained(self.tmpdirname							)
        a__	:					Dict										=CLIPSegProcessor.from_pretrained(self.tmpdirname							)

        self.assertEqual(processor_slow.tokenizer.get_vocab()		,      tokenizer_slow.get_vocab()							)
        self.assertEqual(processor_fast.tokenizer.get_vocab()		,      tokenizer_fast.get_vocab()							)
        self.assertEqual(tokenizer_slow.get_vocab()		,      tokenizer_fast.get_vocab()							)
        self.assertIsInstance(processor_slow.tokenizer		,      lowerCAmelCase__							)
        self.assertIsInstance(processor_fast.tokenizer		,      lowerCAmelCase__							)

        self.assertEqual(processor_slow.image_processor.to_json_string()		,      image_processor.to_json_string()							)
        self.assertEqual(processor_fast.image_processor.to_json_string()		,      image_processor.to_json_string()							)
        self.assertIsInstance(processor_slow.image_processor		,      lowerCAmelCase__							)
        self.assertIsInstance(processor_fast.image_processor		,      lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					List[str]										=CLIPSegProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()							)
        processor.save_pretrained(self.tmpdirname							)

        a__	:					str										=self.get_tokenizer(bos_token="(BOS)"		,      eos_token="(EOS)"							)
        a__	:					int										=self.get_image_processor(do_normalize=lowerCAmelCase__		,      padding_value=1.0							)

        a__	:					Optional[Any]										=CLIPSegProcessor.from_pretrained(
            self.tmpdirname		,      bos_token="(BOS)"		,      eos_token="(EOS)"		,      do_normalize=lowerCAmelCase__		,      padding_value=1.0							)

        self.assertEqual(processor.tokenizer.get_vocab()		,      tokenizer_add_kwargs.get_vocab()							)
        self.assertIsInstance(processor.tokenizer		,      lowerCAmelCase__							)

        self.assertEqual(processor.image_processor.to_json_string()		,      image_processor_add_kwargs.to_json_string()							)
        self.assertIsInstance(processor.image_processor		,      lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					str										=self.get_image_processor()
        a__	:					Optional[int]										=self.get_tokenizer()

        a__	:					Dict										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					str										=self.prepare_image_inputs()

        a__	:					Any										=image_processor(lowerCAmelCase__		,      return_tensors="np"							)
        a__	:					Optional[int]										=processor(images=lowerCAmelCase__		,      return_tensors="np"							)

        for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum()		,      input_processor[key].sum()		,      delta=1E-2							)







      def    _lowercase  (    self							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					Optional[int]										=self.get_image_processor()
        a__	:					List[Any]										=self.get_tokenizer()

        a__	:					Optional[int]										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Union[str, Any]										="lower newer"

        a__	:					List[str]										=processor(text=lowerCAmelCase__							)

        a__	:					str										=tokenizer(lowerCAmelCase__							)

        for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]		,      encoded_processor[key]							)







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					Any										=self.get_image_processor()
        a__	:					Dict										=self.get_tokenizer()

        a__	:					Union[str, Any]										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Dict										="lower newer"
        a__	:					int										=self.prepare_image_inputs()

        a__	:					Any										=processor(text=lowerCAmelCase__		,      images=lowerCAmelCase__							)

        self.assertListEqual(list(inputs.keys()							)		,      ["input_ids", "attention_mask", "pixel_values"]							)

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase__							):
          processor()







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Union[str, Any]										=self.get_image_processor()
        a__	:					Optional[Any]										=self.get_tokenizer()

        a__	:					str										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					int										=self.prepare_image_inputs()
        a__	:					Union[str, Any]										=self.prepare_image_inputs()

        a__	:					Tuple										=processor(images=lowerCAmelCase__		,      visual_prompt=lowerCAmelCase__							)

        self.assertListEqual(list(inputs.keys()							)		,      ["pixel_values", "conditional_pixel_values"]							)

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase__							):
          processor()







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Optional[int]										=self.get_image_processor()
        a__	:					Any										=self.get_tokenizer()

        a__	:					Tuple										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Dict										=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        a__	:					Optional[Any]										=processor.batch_decode(lowerCAmelCase__							)
        a__	:					Dict										=tokenizer.batch_decode(lowerCAmelCase__							)

        self.assertListEqual(lowerCAmelCase__		,      lowerCAmelCase__							)






