



'''simple docstring'''





import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_  ,  unittest.TestCase	):
  """simple docstring"""




  a_				   =PhobertTokenizer
  a_				   =False




  def 		_lowercase	(					self :      Any  )   ->   Tuple:
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __lowerCamelCase			:			Tuple       			=					['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
    __lowerCamelCase			:			Optional[int]       			=					dict(zip(_a     ,      range(len(_a  )  )  )  )
    __lowerCamelCase			:			Union[str, Any]       			=					['#version: 0.2', 'l à</w>']
    __lowerCamelCase			:			Optional[int]       			=					{'unk_token': '<unk>'}

    __lowerCamelCase			:			int       			=					os.path.join(self.tmpdirname     ,      VOCAB_FILES_NAMES['vocab_file']  )
    __lowerCamelCase			:			int       			=					os.path.join(self.tmpdirname     ,      VOCAB_FILES_NAMES['merges_file']  )

    with open(self.vocab_file     ,      'w'     ,      encoding='utf-8'  ) as fp:
      for token in vocab_tokens:
        fp.write(f'{token} {vocab_tokens[token]}\n'  )
    with open(self.merges_file     ,      'w'     ,      encoding='utf-8'  ) as fp:
      fp.write('\n'.join(_a  )  )




  def 		_lowercase	(					self :      Dict     ,      **_a :      List[Any]  )   ->   Any:
    kwargs.update(self.special_tokens_map  )
    return PhobertTokenizer.from_pretrained(self.tmpdirname     ,      **_a  )




  def 		_lowercase	(					self :      List[str]     ,      _a :      Any  )   ->   List[Any]:
    __lowerCamelCase			:			Dict       			=					'Tôi là VinAI Research'
    __lowerCamelCase			:			List[Any]       			=					'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
    return input_text, output_text




  def 		_lowercase	(					self :      str  )   ->   str:
    __lowerCamelCase			:			Tuple       			=					PhobertTokenizer(self.vocab_file     ,      self.merges_file     ,      **self.special_tokens_map  )
    __lowerCamelCase			:			Any       			=					'Tôi là VinAI Research'
    __lowerCamelCase			:			Optional[Any]       			=					'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
    __lowerCamelCase			:			Optional[Any]       			=					tokenizer.tokenize(_a  )
    print(_a  )
    self.assertListEqual(_a     ,      _a  )

    __lowerCamelCase			:			List[Any]       			=					tokens + [tokenizer.unk_token]

    __lowerCamelCase			:			Any       			=					[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(_a  )     ,      _a  )











'''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

   _UpperCamelCase											=		True
except ImportError:
   _UpperCamelCase											=		False


try:
   from torch.hub import _get_torch_home

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

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

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

def 			a_	(				_lowerCAmelCase=OBJECTS			,_lowerCAmelCase=ATTRIBUTES			) -> Union[str, Any]:
  __lowerCamelCase			:			Dict       			=					[]
  with open(_lowerCAmelCase			) as f:
    for object in f.readlines():
      vg_classes.append(object.split(','			)[0].lower().strip()			)

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

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






class    lowerCamelCase_       :
  """simple docstring"""




  a_				   ={}




  def __init__(					self :      int     ,      _a :      dict     ,      _a :      str = "root"     ,      _a :      int=0  )   ->   Union[str, Any]:
    __lowerCamelCase			:			Dict       			=					name
    __lowerCamelCase			:			Union[str, Any]       			=					level
    __lowerCamelCase			:			Tuple       			=					{}
    for k, v in dictionary.items():
      if v is None:
        raise ValueError()
      __lowerCamelCase			:			int       			=					copy.deepcopy(_a  )
      __lowerCamelCase			:			List[str]       			=					copy.deepcopy(_a  )
      if isinstance(_a     ,      _a  ):
        __lowerCamelCase			:			str       			=					Config(_a     ,      name=_a     ,      level=level + 1  )
      __lowerCamelCase			:			Union[str, Any]       			=					v
      setattr(self     ,      _a     ,      _a  )

    __lowerCamelCase			:			Optional[int]       			=					d




  def __repr__(					self :      int  )   ->   Any:
    return str(list((self._pointer.keys())  )  )




  def __setattr__(					self :      int     ,      _a :      Tuple     ,      _a :      Optional[int]  )   ->   Optional[Any]:
    __lowerCamelCase			:			Union[str, Any]       			=					val
    __lowerCamelCase			:			Optional[Any]       			=					val
    __lowerCamelCase			:			List[str]       			=					key.split('.'  )
    __lowerCamelCase			:			str       			=					len(_a  ) - 1
    __lowerCamelCase			:			str       			=					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:
          __lowerCamelCase			:			Union[str, Any]       			=					val
        else:
          __lowerCamelCase			:			List[Any]       			=					pointer[l]




  def 		_lowercase	(					self :      Dict  )   ->   Optional[Any]:
    return self._pointer




  def 		_lowercase	(					self :      Any     ,      _a :      List[str]     ,      _a :      List[str]  )   ->   Dict:
    with open(f'{file_name}'     ,      'w'  ) as stream:
      dump(_a     ,      _a  )




  def 		_lowercase	(					self :      Any     ,      _a :      Union[str, Any]     ,      _a :      List[Any]  )   ->   List[Any]:
    with open(f'{file_name}'     ,      'w'  ) as stream:
      json.dump(_a     ,      _a  )




  @staticmethod
  def 		_lowercase	(					_a :      Any  )   ->   List[str]:
    with open(_a  ) as stream:
      __lowerCamelCase			:			Dict       			=					load(_a     ,      Loader=_a  )
    return data




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




  @classmethod
  def 		_lowercase	(					cls :      int     ,      _a :      str     ,      **_a :      List[str]  )   ->   Union[str, Any]:
    __lowerCamelCase			,__lowerCamelCase			:			str       			=					cls.get_config_dict(_a     ,      **_a  )
    return cls(_a  )




  @classmethod
  def 		_lowercase	(					cls :      Dict     ,      _a :      str     ,      **_a :      List[str]  )   ->   List[Any]:
    __lowerCamelCase			:			str       			=					kwargs.pop('cache_dir'     ,      _a  )
    __lowerCamelCase			:			List[Any]       			=					kwargs.pop('force_download'     ,      _a  )
    __lowerCamelCase			:			List[Any]       			=					kwargs.pop('resume_download'     ,      _a  )
    __lowerCamelCase			:			List[Any]       			=					kwargs.pop('proxies'     ,      _a  )
    __lowerCamelCase			:			Optional[Any]       			=					kwargs.pop('local_files_only'     ,      _a  )

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

    try:
      # Load from URL or cache if already cached
      __lowerCamelCase			:			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

      __lowerCamelCase			:			Optional[Any]       			=					Config.load_yaml(_a  )

    except EnvironmentError:
      __lowerCamelCase			:			Optional[int]       			=					'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_	(				_lowerCAmelCase			) -> Dict:
  __lowerCamelCase			:			Dict       			=					torch.load('dump.pt'			,map_location=in_tensor.device			)
  __lowerCamelCase			:			Optional[int]       			=					in_tensor.numpy()
  __lowerCamelCase			:			int       			=					out_tensor.numpy()[0]
  print(na.shape			,na[0, 0, :5]			)
  print(na.shape			,na[0, 0, :5]			)
  assert np.allclose(_lowerCAmelCase			,_lowerCAmelCase			,rtol=0.01			,atol=0.1			), (
      F'{sum([1 for x in np.isclose(_lowerCAmelCase			,_lowerCAmelCase			,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_	(				_lowerCAmelCase			) -> int:
  __lowerCamelCase			:			List[str]       			=					urlparse(_lowerCAmelCase			)
  return parsed.scheme in ("http", "https")

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase=True			) -> str:
  __lowerCamelCase			:			Any       			=					CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
  __lowerCamelCase			:			List[str]       			=					'/' not in model_id
  if legacy_format:
    return F'{endpoint}/{model_id}-{filename}'
  else:
    return F'{endpoint}/{model_id}/{filename}'

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

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase=None			,_lowerCAmelCase=False			,_lowerCAmelCase=None			,_lowerCAmelCase=10			,_lowerCAmelCase=False			,_lowerCAmelCase=None			,_lowerCAmelCase=False			,) -> List[str]:
  if cache_dir is None:
    __lowerCamelCase			:			Any       			=					TRANSFORMERS_CACHE
  if isinstance(_lowerCAmelCase			,_lowerCAmelCase			):
    __lowerCamelCase			:			Tuple       			=					str(_lowerCAmelCase			)

  os.makedirs(_lowerCAmelCase			,exist_ok=_lowerCAmelCase			)

  __lowerCamelCase			:			int       			=					None
  if not local_files_only:
    try:
      __lowerCamelCase			:			Optional[int]       			=					requests.head(_lowerCAmelCase			,allow_redirects=_lowerCAmelCase			,proxies=_lowerCAmelCase			,timeout=_lowerCAmelCase			)
      if response.status_code == 200:
        __lowerCamelCase			:			List[Any]       			=					response.headers.get('ETag'			)
    except (EnvironmentError, requests.exceptions.Timeout):
      # etag is already None
      pass

  __lowerCamelCase			:			Tuple       			=					url_to_filename(_lowerCAmelCase			,_lowerCAmelCase			)

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

  # etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
  # try to get the last downloaded one
  if etag is None:
    if os.path.exists(_lowerCAmelCase			):
      return cache_path
    else:
      __lowerCamelCase			:			int       			=					[
          file
          for file in fnmatch.filter(os.listdir(_lowerCAmelCase			)			,filename + '.*'			)
          if not file.endswith('.json'			) and not file.endswith('.lock'			)
      ]
      if len(_lowerCAmelCase			) > 0:
        return os.path.join(_lowerCAmelCase			,matching_files[-1]			)
      else:
        # If files cannot be found and local_files_only=True,
        # the models might've been found if local_files_only=False
        # Notify the user about that
        if local_files_only:
          raise ValueError(
              'Cannot find the requested files in the cached path and outgoing traffic has been'
              ' disabled. To enable model look-ups and downloads online, set \'local_files_only\''
              ' to False.'			)
        return None

    # From now on, etag is not None.
  if os.path.exists(_lowerCAmelCase			) and not force_download:
    return cache_path

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

    if resume_download:
      __lowerCamelCase			:			Optional[int]       			=					cache_path + '.incomplete'

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

      __lowerCamelCase			:			Optional[int]       			=					_resumable_file_manager
      if os.path.exists(_lowerCAmelCase			):
        __lowerCamelCase			:			int       			=					os.stat(_lowerCAmelCase			).st_size
      else:
        __lowerCamelCase			:			Dict       			=					0
    else:
      __lowerCamelCase			:			str       			=					partial(tempfile.NamedTemporaryFile			,dir=_lowerCAmelCase			,delete=_lowerCAmelCase			)
      __lowerCamelCase			:			List[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'			,_lowerCAmelCase			,temp_file.name			,)

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

    os.replace(temp_file.name			,_lowerCAmelCase			)

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

  return cache_path

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase=None			) -> Dict:
  __lowerCamelCase			:			Any       			=					url.encode('utf-8'			)
  __lowerCamelCase			:			Tuple       			=					shaaaa(_lowerCAmelCase			)
  __lowerCamelCase			:			Dict       			=					url_hash.hexdigest()

  if etag:
    __lowerCamelCase			:			List[str]       			=					etag.encode('utf-8'			)
    __lowerCamelCase			:			Any       			=					shaaaa(_lowerCAmelCase			)
    filename += "." + etag_hash.hexdigest()

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

  return filename

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase=None			,_lowerCAmelCase=False			,_lowerCAmelCase=None			,_lowerCAmelCase=False			,_lowerCAmelCase=None			,_lowerCAmelCase=False			,_lowerCAmelCase=False			,_lowerCAmelCase=False			,) -> str:
  if cache_dir is None:
    __lowerCamelCase			:			Optional[Any]       			=					TRANSFORMERS_CACHE
  if isinstance(_lowerCAmelCase			,_lowerCAmelCase			):
    __lowerCamelCase			:			List[str]       			=					str(_lowerCAmelCase			)
  if isinstance(_lowerCAmelCase			,_lowerCAmelCase			):
    __lowerCamelCase			:			List[str]       			=					str(_lowerCAmelCase			)

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

  if extract_compressed_file:
    if not is_zipfile(_lowerCAmelCase			) and not tarfile.is_tarfile(_lowerCAmelCase			):
      return output_path

    # Path where we extract compressed archives
    # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
    __lowerCamelCase			,__lowerCamelCase			:			Tuple       			=					os.path.split(_lowerCAmelCase			)
    __lowerCamelCase			:			str       			=					output_file.replace('.'			,'-'			) + '-extracted'
    __lowerCamelCase			:			Optional[int]       			=					os.path.join(_lowerCAmelCase			,_lowerCAmelCase			)

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

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

    return output_path_extracted

  return output_path

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

def 			a_	(				_lowerCAmelCase			) -> List[Any]:
  __lowerCamelCase			:			str       			=					requests.get(_lowerCAmelCase			)
  __lowerCamelCase			:			Dict       			=					np.array(Image.open(BytesIO(response.content			)			)			)
  return img

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

def 			a_	(				) -> Dict:
  print(F'{os.path.abspath(os.path.join(_lowerCAmelCase			,os.pardir			)			)}/demo.ipynb'			)

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


def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase=1			) -> Union[str, Any]:
  return (images[i : i + batch] for i in range(0			,len(_lowerCAmelCase			)			,_lowerCAmelCase			))






