

"""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

       __SCREAMING_SNAKE_CASE								=True
except ImportError:
       __SCREAMING_SNAKE_CASE								=False


try:
       from torch.hub import _get_torch_home

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

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

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



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Any=OBJECTS  ,    __SCREAMING_SNAKE_CASE							:  Any=ATTRIBUTES   ):
     lowercase_			:       List[Any]									=			[]
     with open(__SCREAMING_SNAKE_CASE   ) as f:
          for object in f.readlines():
               vg_classes.append(object.split(','   )[0].lower().strip()   )

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



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







class  UpperCamelCase	:
   lowercase							=						{}
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase = "root"  ,__UpperCamelCase=0    )			->   Optional[int]:


        '''simple docstring'''


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

        lowercase_			:       Dict									=			d
   def __repr__(    self    )			->   List[Any]:


        '''simple docstring'''


        return str(list((self._pointer.keys())    )    )
   def __setattr__(    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   str:


        '''simple docstring'''


        lowercase_			:       Any									=			val
        lowercase_			:       Optional[Any]									=			val
        lowercase_			:       Optional[int]									=			key.split('.'    )
        lowercase_			:       Dict									=			len(__UpperCamelCase    ) - 1
        lowercase_			:       List[Any]									=			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:
                       lowercase_			:       Any									=			val
                  else:
                       lowercase_			:       Any									=			pointer[l]
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        return self._pointer
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Any:


        '''simple docstring'''


        with open(f'''{file_name}'''  ,'w'    ) as stream:
             dump(__UpperCamelCase  ,__UpperCamelCase    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Optional[Any]:


        '''simple docstring'''


        with open(f'''{file_name}'''  ,'w'    ) as stream:
             json.dump(__UpperCamelCase  ,__UpperCamelCase    )
   @staticmethod
   def     _UpperCAmelCase     (    __UpperCamelCase    )			->   Tuple:


        '''simple docstring'''


        with open(__UpperCamelCase    ) as stream:
             lowercase_			:       Union[str, Any]									=			load(__UpperCamelCase  ,Loader=__UpperCamelCase    )
        return data
   def __str__(    self    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       List[str]									=			'    '
        if self._name != "root":
             lowercase_			:       List[Any]									=			f'''{t * (self._level-1)}{self._name}:\n'''
        else:
             lowercase_			:       List[str]									=			''
        lowercase_			:       Tuple									=			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'''
             lowercase_			:       Any									=			level
        return r[:-1]
   @classmethod
   def     _UpperCAmelCase     (    cls  ,__UpperCamelCase  ,**__UpperCamelCase    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       str									=			cls.get_config_dict(__UpperCamelCase  ,**__UpperCamelCase    )
        return cls(__UpperCamelCase    )







   @classmethod
   def     _UpperCAmelCase     (    cls  ,__UpperCamelCase  ,**__UpperCamelCase    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			kwargs.pop('cache_dir'  ,__UpperCamelCase    )
        lowercase_			:       Optional[Any]									=			kwargs.pop('force_download'  ,__UpperCamelCase    )
        lowercase_			:       Tuple									=			kwargs.pop('resume_download'  ,__UpperCamelCase    )
        lowercase_			:       Tuple									=			kwargs.pop('proxies'  ,__UpperCamelCase    )
        lowercase_			:       Dict									=			kwargs.pop('local_files_only'  ,__UpperCamelCase    )

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

        try:
             # Load from URL or cache if already cached
             lowercase_			:       Optional[int]									=			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

             lowercase_			:       str									=			Config.load_yaml(__UpperCamelCase    )

        except EnvironmentError:
             lowercase_			:       Optional[int]									=			'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     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]   ):
     lowercase_			:       Any									=			torch.load('dump.pt'  ,    map_location=in_tensor.device   )
     lowercase_			:       Any									=			in_tensor.numpy()
     lowercase_			:       Tuple									=			out_tensor.numpy()[0]
     print(na.shape  ,    na[0, 0, :5]   )
     print(na.shape  ,    na[0, 0, :5]   )
     assert np.allclose(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    rtol=0.01  ,    atol=0.1   ), (
         F'''{sum([1 for x in np.isclose(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    rtol=0.01  ,    atol=0.1   ).flatten() if x is False]   )/len(na.flatten()   )*1_00:.4f} %'''
         " element-wise mismatch"
     )
     raise Exception('tensors are all good'   )

     # Hugging face functions below



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Dict   ):
     lowercase_			:       Dict									=			urlparse(__SCREAMING_SNAKE_CASE   )
     return parsed.scheme in ("http", "https")



def     lowercase__( __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Any=True   ):
     lowercase_			:       int									=			CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
     lowercase_			:       Optional[int]									=			'/' not in model_id
     if legacy_format:
          return F'''{endpoint}/{model_id}-{filename}'''
     else:
          return F'''{endpoint}/{model_id}/{filename}'''



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]  ,    __SCREAMING_SNAKE_CASE							:  List[Any]=None  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]=0  ,    __SCREAMING_SNAKE_CASE							:  List[Any]=None  ,    ):
     lowercase_			:       int									=			'python/{}'.format(sys.version.split()[0]   )
     if _torch_available:
          ua += "; torch/{}".format(torch.__version__   )
     if isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          ua += "; " + "; ".join('{}/{}'.format(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ) for k, v in user_agent.items()   )
     elif isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          ua += "; " + user_agent
     lowercase_			:       Optional[int]									=			{'user-agent': ua}
     if resume_size > 0:
          lowercase_			:       Dict									=			'bytes=%d-' % (resume_size,)
     lowercase_			:       Optional[int]									=			requests.get(__SCREAMING_SNAKE_CASE  ,    stream=__SCREAMING_SNAKE_CASE  ,    proxies=__SCREAMING_SNAKE_CASE  ,    headers=__SCREAMING_SNAKE_CASE   )
     if response.status_code == 4_16:  # Range not satisfiable
          return
     lowercase_			:       Union[str, Any]									=			response.headers.get('Content-Length'   )
     lowercase_			:       Optional[int]									=			resume_size + int(__SCREAMING_SNAKE_CASE   ) if content_length is not None else None
     lowercase_			:       List[str]									=			tqdm(
         unit='B'  ,    unit_scale=__SCREAMING_SNAKE_CASE  ,    total=__SCREAMING_SNAKE_CASE  ,    initial=__SCREAMING_SNAKE_CASE  ,    desc='Downloading'  ,    )
     for chunk in response.iter_content(chunk_size=10_24   ):
          if chunk:  # filter out keep-alive new chunks
               progress.update(len(__SCREAMING_SNAKE_CASE   )   )
               temp_file.write(__SCREAMING_SNAKE_CASE   )
     progress.close()



def     lowercase__( __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Any=None  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]=False  ,    __SCREAMING_SNAKE_CASE							:  int=None  ,    __SCREAMING_SNAKE_CASE							:  int=10  ,    __SCREAMING_SNAKE_CASE							:  int=False  ,    __SCREAMING_SNAKE_CASE							:  Any=None  ,    __SCREAMING_SNAKE_CASE							:  Any=False  ,    ):
     if cache_dir is None:
          lowercase_			:       Optional[Any]									=			TRANSFORMERS_CACHE
     if isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Dict									=			str(__SCREAMING_SNAKE_CASE   )

     os.makedirs(__SCREAMING_SNAKE_CASE  ,    exist_ok=__SCREAMING_SNAKE_CASE   )

     lowercase_			:       Union[str, Any]									=			None
     if not local_files_only:
          try:
               lowercase_			:       Union[str, Any]									=			requests.head(__SCREAMING_SNAKE_CASE  ,    allow_redirects=__SCREAMING_SNAKE_CASE  ,    proxies=__SCREAMING_SNAKE_CASE  ,    timeout=__SCREAMING_SNAKE_CASE   )
               if response.status_code == 2_00:
                    lowercase_			:       int									=			response.headers.get('ETag'   )
          except (EnvironmentError, requests.exceptions.Timeout):
               # etag is already None
               pass

     lowercase_			:       int									=			url_to_filename(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )

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

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

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

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

          if resume_download:
               lowercase_			:       Dict									=			cache_path + '.incomplete'

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

               lowercase_			:       str									=			_resumable_file_manager
               if os.path.exists(__SCREAMING_SNAKE_CASE   ):
                    lowercase_			:       str									=			os.stat(__SCREAMING_SNAKE_CASE   ).st_size
               else:
                    lowercase_			:       Dict									=			0
          else:
               lowercase_			:       Tuple									=			partial(tempfile.NamedTemporaryFile  ,    dir=__SCREAMING_SNAKE_CASE  ,    delete=__SCREAMING_SNAKE_CASE   )
               lowercase_			:       Union[str, Any]									=			0

          # Download to temporary file, then copy to cache dir once finished.
          # Otherwise you get corrupt cache entries if the download gets interrupted.
          with temp_file_manager() as temp_file:
               print(
                   '%s not found in cache or force_download set to True, downloading to %s'  ,    __SCREAMING_SNAKE_CASE  ,    temp_file.name  ,    )

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

          os.replace(temp_file.name  ,    __SCREAMING_SNAKE_CASE   )

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

     return cache_path



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  Tuple=None   ):
     lowercase_			:       Dict									=			url.encode('utf-8'   )
     lowercase_			:       Optional[Any]									=			shaaaa(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       int									=			url_hash.hexdigest()

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

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

     return filename



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  List[Any]=None  ,    __SCREAMING_SNAKE_CASE							:  List[Any]=False  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]=None  ,    __SCREAMING_SNAKE_CASE							:  Tuple=False  ,    __SCREAMING_SNAKE_CASE							:  List[str]=None  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]=False  ,    __SCREAMING_SNAKE_CASE							:  List[Any]=False  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]=False  ,    ):
     if cache_dir is None:
          lowercase_			:       int									=			TRANSFORMERS_CACHE
     if isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Any									=			str(__SCREAMING_SNAKE_CASE   )
     if isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Dict									=			str(__SCREAMING_SNAKE_CASE   )

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

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

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

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

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

          return output_path_extracted

     return output_path



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



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]   ):
     lowercase_			:       List[str]									=			requests.get(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Optional[int]									=			np.array(Image.open(BytesIO(response.content   )   )   )
     return img



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



def     lowercase__( ):
     print(F'''{os.path.abspath(os.path.join(__SCREAMING_SNAKE_CASE  ,    os.pardir   )   )}/demo.ipynb'''   )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[int]  ,    __SCREAMING_SNAKE_CASE							:  Any="RGB"   ):
     assert isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     if os.path.isfile(__SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Optional[Any]									=			cva.imread(__SCREAMING_SNAKE_CASE   )
     else:
          lowercase_			:       Tuple									=			get_image_from_url(__SCREAMING_SNAKE_CASE   )
          assert img is not None, F'''could not connect to: {im}'''
     lowercase_			:       Optional[Any]									=			cva.cvtColor(__SCREAMING_SNAKE_CASE  ,    cva.COLOR_BGR2RGB   )
     if input_format == "RGB":
          lowercase_			:       Any									=			img[:, :, ::-1]
     return img







def     lowercase__( __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]=1   ):
     return (images[i : i + batch] for i in range(0  ,    len(__SCREAMING_SNAKE_CASE   )  ,    __SCREAMING_SNAKE_CASE   ))


"""simple docstring"""







import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE								={
    "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						'wav2vec2'
   def __init__(    self  ,__UpperCamelCase=32  ,__UpperCamelCase=768  ,__UpperCamelCase=12  ,__UpperCamelCase=12  ,__UpperCamelCase=3072  ,__UpperCamelCase="gelu"  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.0  ,__UpperCamelCase=0.0  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.02  ,__UpperCamelCase=1e-5  ,__UpperCamelCase="group"  ,__UpperCamelCase="gelu"  ,__UpperCamelCase=(512, 512, 512, 512, 512, 512, 512)  ,__UpperCamelCase=(5, 2, 2, 2, 2, 2, 2)  ,__UpperCamelCase=(10, 3, 3, 3, 3, 2, 2)  ,__UpperCamelCase=False  ,__UpperCamelCase=128  ,__UpperCamelCase=16  ,__UpperCamelCase=False  ,__UpperCamelCase=True  ,__UpperCamelCase=0.05  ,__UpperCamelCase=10  ,__UpperCamelCase=2  ,__UpperCamelCase=0.0  ,__UpperCamelCase=10  ,__UpperCamelCase=0  ,__UpperCamelCase=320  ,__UpperCamelCase=2  ,__UpperCamelCase=0.1  ,__UpperCamelCase=100  ,__UpperCamelCase=256  ,__UpperCamelCase=256  ,__UpperCamelCase=0.1  ,__UpperCamelCase="sum"  ,__UpperCamelCase=False  ,__UpperCamelCase=False  ,__UpperCamelCase=256  ,__UpperCamelCase=(512, 512, 512, 512, 1500)  ,__UpperCamelCase=(5, 3, 3, 1, 1)  ,__UpperCamelCase=(1, 2, 3, 1, 1)  ,__UpperCamelCase=512  ,__UpperCamelCase=0  ,__UpperCamelCase=1  ,__UpperCamelCase=2  ,__UpperCamelCase=False  ,__UpperCamelCase=3  ,__UpperCamelCase=2  ,__UpperCamelCase=3  ,__UpperCamelCase=None  ,__UpperCamelCase=None  ,**__UpperCamelCase  ,)			->   Optional[Any]:


        '''simple docstring'''


        super().__init__(**__UpperCamelCase  ,pad_token_id=__UpperCamelCase  ,bos_token_id=__UpperCamelCase  ,eos_token_id=__UpperCamelCase    )
        lowercase_			:       Optional[Any]									=			hidden_size
        lowercase_			:       Tuple									=			feat_extract_norm
        lowercase_			:       Dict									=			feat_extract_activation
        lowercase_			:       List[str]									=			list(__UpperCamelCase    )
        lowercase_			:       str									=			list(__UpperCamelCase    )
        lowercase_			:       Dict									=			list(__UpperCamelCase    )
        lowercase_			:       Optional[Any]									=			conv_bias
        lowercase_			:       Dict									=			num_conv_pos_embeddings
        lowercase_			:       List[str]									=			num_conv_pos_embedding_groups
        lowercase_			:       Optional[Any]									=			len(self.conv_dim    )
        lowercase_			:       Any									=			num_hidden_layers
        lowercase_			:       List[Any]									=			intermediate_size
        lowercase_			:       List[Any]									=			hidden_act
        lowercase_			:       Optional[int]									=			num_attention_heads
        lowercase_			:       int									=			hidden_dropout
        lowercase_			:       Dict									=			attention_dropout
        lowercase_			:       Union[str, Any]									=			activation_dropout
        lowercase_			:       Tuple									=			feat_proj_dropout
        lowercase_			:       List[str]									=			final_dropout
        lowercase_			:       Union[str, Any]									=			layerdrop
        lowercase_			:       List[str]									=			layer_norm_eps
        lowercase_			:       Optional[int]									=			initializer_range
        lowercase_			:       List[Any]									=			vocab_size
        lowercase_			:       Optional[int]									=			do_stable_layer_norm
        lowercase_			:       Union[str, Any]									=			use_weighted_layer_sum

        if (
            (len(self.conv_stride    ) != self.num_feat_extract_layers)
            or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
            or (len(self.conv_dim    ) != self.num_feat_extract_layers)
        ):
             raise ValueError(
                 'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
                 ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
                 f''' {len(self.conv_dim    )}`, `len(config.conv_stride) = {len(self.conv_stride    )}`,'''
                 f''' `len(config.conv_kernel) = {len(self.conv_kernel    )}`.'''    )

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        lowercase_			:       Dict									=			apply_spec_augment
        lowercase_			:       Optional[int]									=			mask_time_prob
        lowercase_			:       Union[str, Any]									=			mask_time_length
        lowercase_			:       List[str]									=			mask_time_min_masks
        lowercase_			:       List[str]									=			mask_feature_prob
        lowercase_			:       Any									=			mask_feature_length
        lowercase_			:       List[Any]									=			mask_feature_min_masks

        # parameters for pretraining with codevector quantized representations
        lowercase_			:       List[Any]									=			num_codevectors_per_group
        lowercase_			:       Optional[int]									=			num_codevector_groups
        lowercase_			:       Dict									=			contrastive_logits_temperature
        lowercase_			:       int									=			feat_quantizer_dropout
        lowercase_			:       Optional[int]									=			num_negatives
        lowercase_			:       str									=			codevector_dim
        lowercase_			:       str									=			proj_codevector_dim
        lowercase_			:       Optional[Any]									=			diversity_loss_weight

        # ctc loss
        lowercase_			:       Tuple									=			ctc_loss_reduction
        lowercase_			:       int									=			ctc_zero_infinity

        # adapter
        lowercase_			:       int									=			add_adapter
        lowercase_			:       Dict									=			adapter_kernel_size
        lowercase_			:       List[str]									=			adapter_stride
        lowercase_			:       Dict									=			num_adapter_layers
        lowercase_			:       Dict									=			output_hidden_size or hidden_size
        lowercase_			:       Optional[Any]									=			adapter_attn_dim

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        lowercase_			:       Dict									=			classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        lowercase_			:       Any									=			list(__UpperCamelCase    )
        lowercase_			:       str									=			list(__UpperCamelCase    )
        lowercase_			:       Any									=			list(__UpperCamelCase    )
        lowercase_			:       Tuple									=			xvector_output_dim







   @property
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        return functools.reduce(operator.mul  ,self.conv_stride  ,1    )

