



'''simple docstring'''





import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
  import torch

  lowercase_			=  True
except ImportError:
  lowercase_			=  False


try:
  from torch.hub import _get_torch_home

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

lowercase_			=  os.path.join(torch_cache_home, "transformers")

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




def lowerCAmelCase  (__A=OBJECTS  ,     __A=ATTRIBUTES):



    """simple docstring"""



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

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




def lowerCAmelCase  (__A):



    """simple docstring"""



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






class __A      :



 '''simple docstring'''







 __lowerCamelCase   :	List[str]										=	{}



 def __init__(self			,       A			,       A = "root"			,       A=0	)   ->					Optional[Any]:


     """simple docstring"""



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

     _a     =  d



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


     """simple docstring"""



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



 def __setattr__(self			,       A			,       A	)   ->					Any:


     """simple docstring"""



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



 def  a__  (self	)   ->					str:


     """simple docstring"""



     return self._pointer



 def  a__  (self			,       A			,       A	)   ->					Optional[int]:


     """simple docstring"""



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



 def  a__  (self			,       A			,       A	)   ->					List[Any]:


     """simple docstring"""



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



 @staticmethod
 def  a__  (A	)   ->					int:


     """simple docstring"""



     with open(__snake_case	) as stream:
         _a     =  load(__snake_case			,       Loader=__snake_case	)
     return data



 def __str__(self	)   ->					str:


     """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(__snake_case			,       __snake_case	):
             r += f'''{t * (self._level)}{v}\n'''
             self._level += 1
         else:
             r += f'''{t * (self._level)}{k}: {v} ({type(__snake_case	).__name__})\n'''
         _a     =  level
     return r[:-1]



 @classmethod
 def  a__  (cls			,       A			,       **A	)   ->					Optional[Any]:


     """simple docstring"""



     _a     =  cls.get_config_dict(__snake_case			,       **__snake_case	)
     return cls(__snake_case	)



 @classmethod
 def  a__  (cls			,       A			,       **A	)   ->					Dict:


     """simple docstring"""



     _a     =  kwargs.pop('''cache_dir'''			,       __snake_case	)
     _a     =  kwargs.pop('''force_download'''			,       __snake_case	)
     _a     =  kwargs.pop('''resume_download'''			,       __snake_case	)
     _a     =  kwargs.pop('''proxies'''			,       __snake_case	)
     _a     =  kwargs.pop('''local_files_only'''			,       __snake_case	)

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

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

         _a     =  Config.load_yaml(__snake_case	)

     except EnvironmentError:
         _a     =  '''Can\'t load config for'''
         raise EnvironmentError(__snake_case	)

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

     return Config.load_yaml(__snake_case	), kwargs





def lowerCAmelCase  (__A):



    """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(_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 lowerCAmelCase  (__A):



    """simple docstring"""



    _a     =  urlparse(_lowerCAmelCase)
    return parsed.scheme in ("http", "https")




def lowerCAmelCase  (__A  ,     __A  ,     __A=True):



    """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 lowerCAmelCase  (__A  ,     __A  ,     __A=None  ,     __A=0  ,     __A=None  ,     ):



    """simple docstring"""



    _a     =  '''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
    _a     =  {'''user-agent''': ua}
    if resume_size > 0:
        _a     =  '''bytes=%d-''' % (resume_size,)
    _a     =  requests.get(_lowerCAmelCase  ,     stream=_lowerCAmelCase  ,     proxies=_lowerCAmelCase  ,     headers=_lowerCAmelCase)
    if response.status_code == 416:  # Range not satisfiable
        return
    _a     =  response.headers.get('''Content-Length''')
    _a     =  resume_size + int(_lowerCAmelCase) if content_length is not None else None
    _a     =  tqdm(
        unit='''B'''  ,     unit_scale=_lowerCAmelCase  ,     total=_lowerCAmelCase  ,     initial=_lowerCAmelCase  ,     desc='''Downloading'''  ,     )
    for chunk in response.iter_content(chunk_size=1_024):
        if chunk:  # filter out keep-alive new chunks
            progress.update(len(_lowerCAmelCase))
            temp_file.write(_lowerCAmelCase)
    progress.close()




def lowerCAmelCase  (__A  ,     __A=None  ,     __A=False  ,     __A=None  ,     __A=10  ,     __A=False  ,     __A=None  ,     __A=False  ,     ):



    """simple docstring"""



    if cache_dir is None:
        _a     =  TRANSFORMERS_CACHE
    if isinstance(_lowerCAmelCase  ,     _lowerCAmelCase):
        _a     =  str(_lowerCAmelCase)

    os.makedirs(_lowerCAmelCase  ,     exist_ok=_lowerCAmelCase)

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

    _a     =  url_to_filename(_lowerCAmelCase  ,     _lowerCAmelCase)

    # get cache path to put the file
    _a     =  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:
            _a     =  [
                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.
    _a     =  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:
            _a     =  cache_path + '''.incomplete'''

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

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

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

        os.replace(temp_file.name  ,     _lowerCAmelCase)

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

    return cache_path




def lowerCAmelCase  (__A  ,     __A=None):



    """simple docstring"""



    _a     =  url.encode('''utf-8''')
    _a     =  shaaaa(_lowerCAmelCase)
    _a     =  url_hash.hexdigest()

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

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

    return filename




def lowerCAmelCase  (__A  ,     __A=None  ,     __A=False  ,     __A=None  ,     __A=False  ,     __A=None  ,     __A=False  ,     __A=False  ,     __A=False  ,     ):



    """simple docstring"""



    if cache_dir is None:
        _a     =  TRANSFORMERS_CACHE
    if isinstance(_lowerCAmelCase  ,     _lowerCAmelCase):
        _a     =  str(_lowerCAmelCase)
    if isinstance(_lowerCAmelCase  ,     _lowerCAmelCase):
        _a     =  str(_lowerCAmelCase)

    if is_remote_url(_lowerCAmelCase):
        # URL, so get it from the cache (downloading if necessary)
        _a     =  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.
        _a     =  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/"
        _a     =  os.path.split(_lowerCAmelCase)
        _a     =  output_file.replace('''.'''  ,     '''-''') + '''-extracted'''
        _a     =  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
        _a     =  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):
                _a     =  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 lowerCAmelCase  (__A  ,     __A=","):



    """simple docstring"""



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




def lowerCAmelCase  (__A):



    """simple docstring"""



    _a     =  requests.get(_lowerCAmelCase)
    _a     =  np.array(Image.open(BytesIO(response.content)))
    return img




def lowerCAmelCase  (__A):



    """simple docstring"""



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




def lowerCAmelCase  ():



    """simple docstring"""



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




def lowerCAmelCase  (__A  ,     __A="RGB"):



    """simple docstring"""



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




def lowerCAmelCase  (__A  ,     __A=1):



    """simple docstring"""



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








'''simple docstring'''





import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


lowercase_			=  importlib.metadata.version("numpy")
lowercase_			=  ".".join([str(x) for x in sys.version_info[:3]])






class __A      (  A						):



 '''simple docstring'''







 def  a__  (self	)   ->					Any:


      """simple docstring"""



      require_version_core('''numpy<1000.4.5'''	)
      require_version_core('''numpy<1000.4'''	)
      require_version_core('''numpy<1000'''	)

      # le
      require_version_core('''numpy<=1000.4.5'''	)
      require_version_core(f'''numpy<={numpy_ver}'''	)

      # eq
      require_version_core(f'''numpy=={numpy_ver}'''	)

      # ne
      require_version_core('''numpy!=1000.4.5'''	)

      # ge
      require_version_core('''numpy>=1.0'''	)
      require_version_core('''numpy>=1.0.0'''	)
      require_version_core(f'''numpy>={numpy_ver}'''	)

      # gt
      require_version_core('''numpy>1.0.0'''	)

      # mix
      require_version_core('''numpy>1.0.0,<1000'''	)

      # requirement w/o version
      require_version_core('''numpy'''	)

      # unmet requirements due to version conflict
      for req in ["numpy==1.0.0", "numpy>=1000.0.0", f'''numpy<{numpy_ver}''']:
           try:
                require_version_core(A	)
           except ImportError as e:
                self.assertIn(f'''{req} is required'''			,       str(A	)	)
                self.assertIn('''but found'''			,       str(A	)	)

        # unmet requirements due to missing module
      for req in ["numpipypie>1", "numpipypie2"]:
           try:
                require_version_core(A	)
           except importlib.metadata.PackageNotFoundError as e:
                self.assertIn(f'''The \'{req}\' distribution was not found and is required by this application'''			,       str(A	)	)
                self.assertIn('''Try: pip install transformers -U'''			,       str(A	)	)

        # bogus requirements formats:
        # 1. whole thing
      for req in ["numpy??1.0.0", "numpy1.0.0"]:
           try:
                require_version_core(A	)
           except ValueError as e:
                self.assertIn('''requirement needs to be in the pip package format'''			,       str(A	)	)
        # 2. only operators
      for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
           try:
                require_version_core(A	)
           except ValueError as e:
                self.assertIn('''need one of '''			,       str(A	)	)



 def  a__  (self	)   ->					int:


      """simple docstring"""



      require_version('''python>=3.6.0'''	)

      # not matching requirements
      for req in ["python>9.9.9", "python<3.0.0"]:
           try:
                require_version_core(A	)
           except ImportError as e:
                self.assertIn(f'''{req} is required'''			,       str(A	)	)
                self.assertIn(f'''but found python=={python_ver}'''			,       str(A	)	)



