






import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
   import torch_xla.core.xla_model as xm

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__ = False						) ->       int:






  if device_specific:
    seed += AcceleratorState().process_index
  random.seed(UpperCamelCase_						)
  np.random.seed(UpperCamelCase_						)
  torch.manual_seed(UpperCamelCase_						)
  if is_xpu_available():
    torch.xpu.manual_seed_all(UpperCamelCase_						)
  elif is_npu_available():
    torch.npu.manual_seed_all(UpperCamelCase_						)
  else:
    torch.cuda.manual_seed_all(UpperCamelCase_						)
  # ^^ safe to call this function even if cuda is not available
  if is_tpu_available():
    xm.set_rng_state(UpperCamelCase_						)

def 						snake_case      (UpperCAmelCase__ = None			,					UpperCAmelCase__ = None						) ->       Union[str, Any]:






  if rng_type == RNGType.TORCH:
    UpperCamelCase_:      List[Any]					   =       torch.get_rng_state()
  elif rng_type == RNGType.CUDA:
    UpperCamelCase_:      Dict					   =       torch.cuda.get_rng_state()
  elif rng_type == RNGType.XLA:
    assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
    UpperCamelCase_:      List[Any]					   =       torch.tensor(xm.get_rng_state()						)
  elif rng_type == RNGType.NPU:
    assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
    UpperCamelCase_:      Union[str, Any]					   =       torch.npu.get_rng_state()
  elif rng_type == RNGType.XPU:
    assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
    UpperCamelCase_:      Optional[int]					   =       torch.xpu.get_rng_state()
  elif rng_type == RNGType.GENERATOR:
    assert generator is not None, "Need a generator to synchronize its seed."
    UpperCamelCase_:      Optional[Any]					   =       generator.get_state()

  # Broadcast the rng state from device 0 to other devices
  UpperCamelCase_:      Union[str, Any]					   =       AcceleratorState()
  if state.distributed_type == DistributedType.TPU:
    UpperCamelCase_:      Any					   =       rng_state.to(xm.xla_device()						)
    xm.collective_broadcast([rng_state]						)
    xm.mark_step()
    UpperCamelCase_:      Optional[Any]					   =       rng_state.cpu()
  elif (
      state.distributed_type in CUDA_DISTRIBUTED_TYPES
      or state.distributed_type == DistributedType.MULTI_NPU
      or state.distributed_type == DistributedType.MULTI_XPU
  ):
    UpperCamelCase_:      int					   =       rng_state.to(state.device						)
    torch.distributed.broadcast(UpperCamelCase_			,					0						)
    UpperCamelCase_:      Dict					   =       rng_state.cpu()
  elif state.distributed_type == DistributedType.MULTI_CPU:
    torch.distributed.broadcast(UpperCamelCase_			,					0						)

  # Set the broadcast rng state
  if rng_type == RNGType.TORCH:
    torch.set_rng_state(UpperCamelCase_						)
  elif rng_type == RNGType.CUDA:
    torch.cuda.set_rng_state(UpperCamelCase_						)
  elif rng_type == RNGType.NPU:
    torch.npu.set_rng_state(UpperCamelCase_						)
  elif rng_type == RNGType.XPU:
    torch.xpu.set_rng_state(UpperCamelCase_						)
  elif rng_type == RNGType.XLA:
    xm.set_rng_state(rng_state.item()						)
  elif rng_type == RNGType.GENERATOR:
    generator.set_state(UpperCamelCase_						)

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__ = None						) ->       str:






  for rng_type in rng_types:
    synchronize_rng_state(RNGType(UpperCamelCase_						)			,					generator=UpperCamelCase_						)







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

   A_	:	Dict				=				True
except ImportError:
   A_	:	Optional[int]				=				False


try:
   from torch.hub import _get_torch_home

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

A_	:	Optional[Any]				=				os.path.join(torch_cache_home, 'transformers')

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

def 						snake_case      (UpperCAmelCase__=OBJECTS			,					UpperCAmelCase__=ATTRIBUTES						) ->       Dict:
  UpperCamelCase_:      Any					   =       []
  with open(UpperCAmelCase__						) as f:
    for object in f.readlines():
      vg_classes.append(object.split(','						)[0].lower().strip()						)

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

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




class        _lowerCAmelCase:





       """simple docstring"""







       a       :	Dict								={}





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

         UpperCamelCase_:      Union[str, Any]					   =       d





       def __repr__(      self							):
         return str(list((self._pointer.keys())							)							)





       def __setattr__(      self						,    _lowerCamelCase						,    _lowerCamelCase							):
         UpperCamelCase_:      Tuple					   =       val
         UpperCamelCase_:      List[Any]					   =       val
         UpperCamelCase_:      Any					   =       key.split('.'							)
         UpperCamelCase_:      str					   =       len(_lowerCamelCase							) - 1
         UpperCamelCase_:      List[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:
               UpperCamelCase_:      int					   =       val
             else:
               UpperCamelCase_:      str					   =       pointer[l]





       def     _a					(      self							):
         return self._pointer





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase							):
         with open(f'''{file_name}'''						,    'w'							) as stream:
           dump(_lowerCamelCase						,    _lowerCamelCase							)





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase							):
         with open(f'''{file_name}'''						,    'w'							) as stream:
           json.dump(_lowerCamelCase						,    _lowerCamelCase							)





       @staticmethod
       def     _a					(      _lowerCamelCase							):
         with open(_lowerCamelCase							) as stream:
           UpperCamelCase_:      Any					   =       load(_lowerCamelCase						,    Loader=_lowerCamelCase							)
         return data





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





       @classmethod
       def     _a					(      cls						,    _lowerCamelCase						,    **_lowerCamelCase							):
         UpperCamelCase_   ,UpperCamelCase_:      str					   =       cls.get_config_dict(_lowerCamelCase						,    **_lowerCamelCase							)
         return cls(_lowerCamelCase							)





       @classmethod
       def     _a					(      cls						,    _lowerCamelCase						,    **_lowerCamelCase							):
         UpperCamelCase_:      int					   =       kwargs.pop('cache_dir'						,    _lowerCamelCase							)
         UpperCamelCase_:      Optional[Any]					   =       kwargs.pop('force_download'						,    _lowerCamelCase							)
         UpperCamelCase_:      Optional[Any]					   =       kwargs.pop('resume_download'						,    _lowerCamelCase							)
         UpperCamelCase_:      Tuple					   =       kwargs.pop('proxies'						,    _lowerCamelCase							)
         UpperCamelCase_:      Dict					   =       kwargs.pop('local_files_only'						,    _lowerCamelCase							)

         if os.path.isdir(_lowerCamelCase							):
           UpperCamelCase_:      Optional[Any]					   =       os.path.join(_lowerCamelCase						,    _lowerCamelCase							)
         elif os.path.isfile(_lowerCamelCase							) or is_remote_url(_lowerCamelCase							):
           UpperCamelCase_:      Any					   =       pretrained_model_name_or_path
         else:
           UpperCamelCase_:      Any					   =       hf_bucket_url(_lowerCamelCase						,    filename=_lowerCamelCase						,    use_cdn=_lowerCamelCase							)

         try:
           # Load from URL or cache if already cached
           UpperCamelCase_:      Tuple					   =       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

           UpperCamelCase_:      Dict					   =       Config.load_yaml(_lowerCamelCase							)

         except EnvironmentError:
           UpperCamelCase_:      Tuple					   =       '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 						snake_case      (UpperCAmelCase__						) ->       Tuple:
  UpperCamelCase_:      Optional[Any]					   =       torch.load('dump.pt'			,					map_location=in_tensor.device						)
  UpperCamelCase_:      Optional[Any]					   =       in_tensor.numpy()
  UpperCamelCase_:      Optional[int]					   =       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()						)*1_0_0:.4f} %'''
      " element-wise mismatch"
  )
  raise Exception('tensors are all good'						)

  # Hugging face functions below

def 						snake_case      (UpperCAmelCase__						) ->       List[str]:
  UpperCamelCase_:      List[str]					   =       urlparse(UpperCAmelCase__						)
  return parsed.scheme in ("http", "https")

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__=True						) ->       str:
  UpperCamelCase_:      Tuple					   =       CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
  UpperCamelCase_:      Any					   =       '/' not in model_id
  if legacy_format:
    return F'''{endpoint}/{model_id}-{filename}'''
  else:
    return F'''{endpoint}/{model_id}/{filename}'''

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__=None			,					UpperCAmelCase__=0			,					UpperCAmelCase__=None			,					) ->       List[Any]:
  UpperCamelCase_:      int					   =       '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_:      int					   =       {'user-agent': ua}
  if resume_size > 0:
    UpperCamelCase_:      int					   =       'bytes=%d-' % (resume_size,)
  UpperCamelCase_:      List[Any]					   =       requests.get(UpperCAmelCase__			,					stream=UpperCAmelCase__			,					proxies=UpperCAmelCase__			,					headers=UpperCAmelCase__						)
  if response.status_code == 4_1_6:  # Range not satisfiable
    return
  UpperCamelCase_:      Tuple					   =       response.headers.get('Content-Length'						)
  UpperCamelCase_:      Union[str, Any]					   =       resume_size + int(UpperCAmelCase__						) if content_length is not None else None
  UpperCamelCase_:      Any					   =       tqdm(
      unit='B'			,					unit_scale=UpperCAmelCase__			,					total=UpperCAmelCase__			,					initial=UpperCAmelCase__			,					desc='Downloading'			,					)
  for chunk in response.iter_content(chunk_size=1_0_2_4						):
    if chunk:  # filter out keep-alive new chunks
      progress.update(len(UpperCAmelCase__						)						)
      temp_file.write(UpperCAmelCase__						)
  progress.close()

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__=None			,					UpperCAmelCase__=False			,					UpperCAmelCase__=None			,					UpperCAmelCase__=1_0			,					UpperCAmelCase__=False			,					UpperCAmelCase__=None			,					UpperCAmelCase__=False			,					) ->       Optional[Any]:
  if cache_dir is None:
    UpperCamelCase_:      int					   =       TRANSFORMERS_CACHE
  if isinstance(UpperCAmelCase__			,					UpperCAmelCase__						):
    UpperCamelCase_:      Any					   =       str(UpperCAmelCase__						)

  os.makedirs(UpperCAmelCase__			,					exist_ok=UpperCAmelCase__						)

  UpperCamelCase_:      Any					   =       None
  if not local_files_only:
    try:
      UpperCamelCase_:      Dict					   =       requests.head(UpperCAmelCase__			,					allow_redirects=UpperCAmelCase__			,					proxies=UpperCAmelCase__			,					timeout=UpperCAmelCase__						)
      if response.status_code == 2_0_0:
        UpperCamelCase_:      Optional[int]					   =       response.headers.get('ETag'						)
    except (EnvironmentError, requests.exceptions.Timeout):
      # etag is already None
      pass

  UpperCamelCase_:      str					   =       url_to_filename(UpperCAmelCase__			,					UpperCAmelCase__						)

  # get cache path to put the file
  UpperCamelCase_:      str					   =       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_:      Union[str, 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_:      int					   =       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_:      Any					   =       cache_path + '.incomplete'

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

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

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

    os.replace(temp_file.name			,					UpperCAmelCase__						)

    UpperCamelCase_:      Dict					   =       {'url': url, 'etag': etag}
    UpperCamelCase_:      Dict					   =       cache_path + '.json'
    with open(UpperCAmelCase__			,					'w'						) as meta_file:
      json.dump(UpperCAmelCase__			,					UpperCAmelCase__						)

  return cache_path

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__=None						) ->       List[str]:
  UpperCamelCase_:      int					   =       url.encode('utf-8'						)
  UpperCamelCase_:      List[Any]					   =       shaaaa(UpperCAmelCase__						)
  UpperCamelCase_:      Union[str, Any]					   =       url_hash.hexdigest()

  if etag:
    UpperCamelCase_:      List[str]					   =       etag.encode('utf-8'						)
    UpperCamelCase_:      Union[str, Any]					   =       shaaaa(UpperCAmelCase__						)
    filename += "." + etag_hash.hexdigest()

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

  return filename

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__=None			,					UpperCAmelCase__=False			,					UpperCAmelCase__=None			,					UpperCAmelCase__=False			,					UpperCAmelCase__=None			,					UpperCAmelCase__=False			,					UpperCAmelCase__=False			,					UpperCAmelCase__=False			,					) ->       str:
  if cache_dir is None:
    UpperCamelCase_:      Any					   =       TRANSFORMERS_CACHE
  if isinstance(UpperCAmelCase__			,					UpperCAmelCase__						):
    UpperCamelCase_:      Optional[int]					   =       str(UpperCAmelCase__						)
  if isinstance(UpperCAmelCase__			,					UpperCAmelCase__						):
    UpperCamelCase_:      List[Any]					   =       str(UpperCAmelCase__						)

  if is_remote_url(UpperCAmelCase__						):
    # URL, so get it from the cache (downloading if necessary)
    UpperCamelCase_:      List[str]					   =       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_:      Tuple					   =       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_   ,UpperCamelCase_:      List[Any]					   =       os.path.split(UpperCAmelCase__						)
    UpperCamelCase_:      Dict					   =       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_:      List[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_:      List[str]					   =       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 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__=","						) ->       Any:
  assert isinstance(UpperCAmelCase__			,					UpperCAmelCase__						)
  if os.path.isfile(UpperCAmelCase__						):
    with open(UpperCAmelCase__						) as f:
      UpperCamelCase_:      Optional[Any]					   =       eval(f.read()						)
  else:
    UpperCamelCase_:      List[str]					   =       requests.get(UpperCAmelCase__						)
    try:
      UpperCamelCase_:      str					   =       requests.json()
    except Exception:
      UpperCamelCase_:      List[str]					   =       req.content.decode()
      assert data is not None, "could not connect"
      try:
        UpperCamelCase_:      str					   =       eval(UpperCAmelCase__						)
      except Exception:
        UpperCamelCase_:      Optional[Any]					   =       data.split('\n'						)
    req.close()
  return data

def 						snake_case      (UpperCAmelCase__						) ->       str:
  UpperCamelCase_:      str					   =       requests.get(UpperCAmelCase__						)
  UpperCamelCase_:      int					   =       np.array(Image.open(BytesIO(response.content						)						)						)
  return img

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

def 						snake_case      () ->       Any:
  print(F'''{os.path.abspath(os.path.join(UpperCAmelCase__			,					os.pardir						)						)}/demo.ipynb'''						)

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__="RGB"						) ->       List[Any]:
  assert isinstance(UpperCAmelCase__			,					UpperCAmelCase__						)
  if os.path.isfile(UpperCAmelCase__						):
    UpperCamelCase_:      Union[str, Any]					   =       cva.imread(UpperCAmelCase__						)
  else:
    UpperCamelCase_:      Any					   =       get_image_from_url(UpperCAmelCase__						)
    assert img is not None, F'''could not connect to: {im}'''
  UpperCamelCase_:      Any					   =       cva.cvtColor(UpperCAmelCase__			,					cva.COLOR_BGR2RGB						)
  if input_format == "RGB":
    UpperCamelCase_:      Tuple					   =       img[:, :, ::-1]
  return img

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__=1						) ->       List[str]:
  return (images[i : i + batch] for i in range(0			,					len(UpperCAmelCase__						)			,					UpperCAmelCase__						))