


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

  _lowerCamelCase     :		List[str]								=       True
except ImportError:
  _lowerCamelCase     :		Dict								=       False


try:
  from torch.hub import _get_torch_home

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

_lowerCamelCase     :		str								=       os.path.join(torch_cache_home, """transformers""")

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







def SCREAMING_SNAKE_CASE    (	lowercase_=OBJECTS			,    lowercase_=ATTRIBUTES						)       ->	Tuple:



       """simple docstring"""

       A__							=   []
       with open(lowercase_						) as f:
              for object in f.readlines():
                     vg_classes.append(object.split(''','''						)[0].lower().strip()						)

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







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Optional[Any]:



       """simple docstring"""

       A__							=   OrderedDict()
       with open(lowercase_			,    '''rb'''						) as f:
              A__							=   pkl.load(lowercase_						)['''model''']
       for k in copy.deepcopy(list(ckp.keys()						)						):
              A__							=   ckp.pop(lowercase_						)
              if isinstance(lowercase_			,    np.ndarray						):
                     A__							=   torch.tensor(lowercase_						)
              else:
                     assert isinstance(lowercase_			,    torch.tensor						), type(lowercase_						)
              A__							=   v
       return r


class     UpperCamelCase_   :

    '''simple docstring'''






    UpperCAmelCase__							  =				{}


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





           '''simple docstring'''

           A__							=   name
           A__							=   level
           A__							=   {}
           for k, v in dictionary.items():
                  if v is None:
                         raise ValueError()
                  A__							=   copy.deepcopy(UpperCAmelCase__)
                  A__							=   copy.deepcopy(UpperCAmelCase__)
                  if isinstance(UpperCAmelCase__ ,			UpperCAmelCase__):
                         A__							=   Config(UpperCAmelCase__ ,			name=UpperCAmelCase__ ,			level=level + 1)
                  A__							=   v
                  setattr(self ,			UpperCAmelCase__ ,			UpperCAmelCase__)

           A__							=   d


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





           '''simple docstring'''

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


    def __setattr__(      self				:     Tuple ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Tuple)						->int:





           '''simple docstring'''

           A__							=   val
           A__							=   val
           A__							=   key.split('''.''')
           A__							=   len(UpperCAmelCase__) - 1
           A__							=   self._pointer
           if len(UpperCAmelCase__) > 1:
                  for i, l in enumerate(UpperCAmelCase__):
                         if hasattr(self ,			UpperCAmelCase__) and isinstance(getattr(self ,			UpperCAmelCase__) ,			UpperCAmelCase__):
                                setattr(getattr(self ,			UpperCAmelCase__) ,			'''.'''.join(levels[i:]) ,			UpperCAmelCase__)
                         if l == last_level:
                                A__							=   val
                         else:
                                A__							=   pointer[l]


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->List[Any]:





           '''simple docstring'''

           return self._pointer


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     List[str])						->str:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Optional[Any])						->List[Any]:





           '''simple docstring'''

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


    @staticmethod
    def 	SCREAMING_SNAKE_CASE							(      UpperCAmelCase__				:     Tuple)						->Union[str, Any]:





           '''simple docstring'''

           with open(UpperCAmelCase__) as stream:
                  A__							=   load(UpperCAmelCase__ ,			Loader=UpperCAmelCase__)
           return data


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





           '''simple docstring'''

           A__							=   '''    '''
           if self._name != "root":
                  A__							=   f"""{t * (self._level-1)}{self._name}:\n"""
           else:
                  A__							=   ''''''
           A__							=   self._level
           for i, (k, v) in enumerate(self._pointer.items()):
                  if isinstance(UpperCAmelCase__ ,			UpperCAmelCase__):
                         r += f"""{t * (self._level)}{v}\n"""
                         self._level += 1
                  else:
                         r += f"""{t * (self._level)}{k}: {v} ({type(UpperCAmelCase__).__name__})\n"""
                  A__							=   level
           return r[:-1]


    @classmethod
    def 	SCREAMING_SNAKE_CASE							(      cls				:     int ,			UpperCAmelCase__				:     str ,			**UpperCAmelCase__				:     List[Any])						->Dict:





           '''simple docstring'''

           A__			,							A__							=   cls.get_config_dict(UpperCAmelCase__ ,			**UpperCAmelCase__)
           return cls(UpperCAmelCase__)





    @classmethod
    def 	SCREAMING_SNAKE_CASE							(      cls				:     List[str] ,			UpperCAmelCase__				:     str ,			**UpperCAmelCase__				:     Tuple)						->Optional[Any]:





           '''simple docstring'''

           A__							=   kwargs.pop('''cache_dir''' ,			UpperCAmelCase__)
           A__							=   kwargs.pop('''force_download''' ,			UpperCAmelCase__)
           A__							=   kwargs.pop('''resume_download''' ,			UpperCAmelCase__)
           A__							=   kwargs.pop('''proxies''' ,			UpperCAmelCase__)
           A__							=   kwargs.pop('''local_files_only''' ,			UpperCAmelCase__)

           if os.path.isdir(UpperCAmelCase__):
                  A__							=   os.path.join(UpperCAmelCase__ ,			UpperCAmelCase__)
           elif os.path.isfile(UpperCAmelCase__) or is_remote_url(UpperCAmelCase__):
                  A__							=   pretrained_model_name_or_path
           else:
                  A__							=   hf_bucket_url(UpperCAmelCase__ ,			filename=UpperCAmelCase__ ,			use_cdn=UpperCAmelCase__)

           try:
                  # Load from URL or cache if already cached
                  A__							=   cached_path(
                      UpperCAmelCase__ ,			cache_dir=UpperCAmelCase__ ,			force_download=UpperCAmelCase__ ,			proxies=UpperCAmelCase__ ,			resume_download=UpperCAmelCase__ ,			local_files_only=UpperCAmelCase__ ,			)
                  # Load config dict
                  if resolved_config_file is None:
                         raise EnvironmentError

                  A__							=   Config.load_yaml(UpperCAmelCase__)

           except EnvironmentError:
                  A__							=   '''Can\'t load config for'''
                  raise EnvironmentError(UpperCAmelCase__)

           if resolved_config_file == config_file:
                  print('''loading configuration file from path''')
           else:
                  print('''loading configuration file cache''')

           return Config.load_yaml(UpperCAmelCase__), kwargs







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Tuple:



       """simple docstring"""

       A__							=   torch.load('''dump.pt'''			,    map_location=in_tensor.device						)
       A__							=   in_tensor.numpy()
       A__							=   out_tensor.numpy()[0]
       print(na.shape			,    na[0, 0, :5]						)
       print(na.shape			,    na[0, 0, :5]						)
       assert np.allclose(lowercase_			,    lowercase_			,    rtol=0.01			,    atol=0.1						), (
           f"""{sum([1 for x in np.isclose(lowercase_			,    lowercase_			,    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 SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	List[str]:



       """simple docstring"""

       A__							=   urlparse(lowercase_						)
       return parsed.scheme in ("http", "https")







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_=True						)       ->	str:



       """simple docstring"""

       A__							=   CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
       A__							=   '''/''' not in model_id
       if legacy_format:
              return f"""{endpoint}/{model_id}-{filename}"""
       else:
              return f"""{endpoint}/{model_id}/{filename}"""







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_=None			,    lowercase_=0			,    lowercase_=None			,    )       ->	List[str]:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_=None			,    lowercase_=False			,    lowercase_=None			,    lowercase_=10			,    lowercase_=False			,    lowercase_=None			,    lowercase_=False			,    )       ->	List[str]:



       """simple docstring"""

       if cache_dir is None:
              A__							=   TRANSFORMERS_CACHE
       if isinstance(lowercase_			,    lowercase_						):
              A__							=   str(lowercase_						)

       os.makedirs(lowercase_			,    exist_ok=lowercase_						)

       A__							=   None
       if not local_files_only:
              try:
                     A__							=   requests.head(lowercase_			,    allow_redirects=lowercase_			,    proxies=lowercase_			,    timeout=lowercase_						)
                     if response.status_code == 200:
                            A__							=   response.headers.get('''ETag'''						)
              except (EnvironmentError, requests.exceptions.Timeout):
                     # etag is already None
                     pass

       A__							=   url_to_filename(lowercase_			,    lowercase_						)

       # get cache path to put the file
       A__							=   os.path.join(lowercase_			,    lowercase_						)

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

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

              if resume_download:
                     A__							=   cache_path + '''.incomplete'''

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

                     A__							=   _resumable_file_manager
                     if os.path.exists(lowercase_						):
                            A__							=   os.stat(lowercase_						).st_size
                     else:
                            A__							=   0
              else:
                     A__							=   partial(tempfile.NamedTemporaryFile			,    dir=lowercase_			,    delete=lowercase_						)
                     A__							=   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'''			,    lowercase_			,    temp_file.name			,    )

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

              os.replace(temp_file.name			,    lowercase_						)

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

       return cache_path







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_=None						)       ->	Any:



       """simple docstring"""

       A__							=   url.encode('''utf-8'''						)
       A__							=   shaaaa(lowercase_						)
       A__							=   url_hash.hexdigest()

       if etag:
              A__							=   etag.encode('''utf-8'''						)
              A__							=   shaaaa(lowercase_						)
              filename += "." + etag_hash.hexdigest()

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

       return filename







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_=None			,    lowercase_=False			,    lowercase_=None			,    lowercase_=False			,    lowercase_=None			,    lowercase_=False			,    lowercase_=False			,    lowercase_=False			,    )       ->	Tuple:



       """simple docstring"""

       if cache_dir is None:
              A__							=   TRANSFORMERS_CACHE
       if isinstance(lowercase_			,    lowercase_						):
              A__							=   str(lowercase_						)
       if isinstance(lowercase_			,    lowercase_						):
              A__							=   str(lowercase_						)

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

       if extract_compressed_file:
              if not is_zipfile(lowercase_						) and not tarfile.is_tarfile(lowercase_						):
                     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__							=   os.path.split(lowercase_						)
              A__							=   output_file.replace('''.'''			,    '''-'''						) + '''-extracted'''
              A__							=   os.path.join(lowercase_			,    lowercase_						)

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

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

              return output_path_extracted

       return output_path







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_=","						)       ->	Union[str, Any]:



       """simple docstring"""

       assert isinstance(lowercase_			,    lowercase_						)
       if os.path.isfile(lowercase_						):
              with open(lowercase_						) as f:
                     A__							=   eval(f.read()						)
       else:
              A__							=   requests.get(lowercase_						)
              try:
                     A__							=   requests.json()
              except Exception:
                     A__							=   req.content.decode()
                     assert data is not None, "could not connect"
                     try:
                            A__							=   eval(lowercase_						)
                     except Exception:
                            A__							=   data.split('''\n'''						)
              req.close()
       return data







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	int:



       """simple docstring"""

       A__							=   requests.get(lowercase_						)
       A__							=   np.array(Image.open(BytesIO(response.content						)						)						)
       return img







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Any:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	)       ->	List[str]:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_="RGB"						)       ->	Optional[Any]:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_=1						)       ->	Tuple:



       """simple docstring"""

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








from __future__ import annotations

from typing import Any







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       create_state_space_tree(lowercase_			,    []			,    0						)







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	None:



       """simple docstring"""

       if index == len(lowercase_						):
              print(lowercase_						)
              return

       create_state_space_tree(lowercase_			,    lowercase_			,    index + 1						)
       current_subsequence.append(sequence[index]						)
       create_state_space_tree(lowercase_			,    lowercase_			,    index + 1						)
       current_subsequence.pop()


if __name__ == "__main__":
  _lowerCamelCase     :		list[Any]								=       [3, 1, 2, 4]
  generate_all_subsequences(seq)

  seq.clear()
  seq.extend(["""A""", """B""", """C"""])
  generate_all_subsequences(seq)


