





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      :       Optional[int]									=  True
except ImportError:
    lowercase      :       int									=  False


try:
    from torch.hub import _get_torch_home

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

lowercase      :       List[str]									=  os.path.join(torch_cache_home, """transformers""")

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



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

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



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





class  A__    :




   """simple docstring"""



   __A   :				Optional[Any]         =  {}

   def __init__(					self					,  lowercase					,  lowercase = "root"					,  lowercase=0)			->						Union[str, Any]:







          '''simple docstring'''

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

          a__						:	List[Any] =				d

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







          '''simple docstring'''

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

   def __setattr__(					self					,  lowercase					,  lowercase)			->						Optional[int]:







          '''simple docstring'''

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

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          return self._pointer

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						str:







          '''simple docstring'''

          with open(F'{file_name}'					,  'w') as stream:
                 dump(lowercase					,  lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						Optional[int]:







          '''simple docstring'''

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

   @staticmethod
   def 							__lowercase		(					lowercase)			->						Optional[int]:







          '''simple docstring'''

          with open(lowercase) as stream:
                 a__						:	Tuple =				load(lowercase					,  Loader=lowercase)
          return data

   def __str__(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Optional[int] =				'    '
          if self._name != "root":
                 a__						:	Any =				F'{t * (self._level-1)}{self._name}:\n'
          else:
                 a__						:	Dict =				''
          a__						:	Tuple =				self._level
          for i, (k, v) in enumerate(self._pointer.items()):
                 if isinstance(lowercase					,  lowercase):
                        r += F'{t * (self._level)}{v}\n'
                        self._level += 1
                 else:
                        r += F'{t * (self._level)}{k}: {v} ({type(lowercase).__name__})\n'
                 a__						:	Tuple =				level
          return r[:-1]

   @classmethod
   def 							__lowercase		(					cls					,  lowercase					,  **lowercase)			->						Tuple:







          '''simple docstring'''

          a__ ,							a__						:	Optional[Any] =				cls.get_config_dict(lowercase					,  **lowercase)
          return cls(lowercase)

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







          '''simple docstring'''

          a__						:	Union[str, Any] =				kwargs.pop('cache_dir'					,  lowercase)
          a__						:	Tuple =				kwargs.pop('force_download'					,  lowercase)
          a__						:	str =				kwargs.pop('resume_download'					,  lowercase)
          a__						:	Tuple =				kwargs.pop('proxies'					,  lowercase)
          a__						:	List[str] =				kwargs.pop('local_files_only'					,  lowercase)

          if os.path.isdir(lowercase):
                 a__						:	int =				os.path.join(lowercase					,  lowercase)
          elif os.path.isfile(lowercase) or is_remote_url(lowercase):
                 a__						:	Union[str, Any] =				pretrained_model_name_or_path
          else:
                 a__						:	Tuple =				hf_bucket_url(lowercase					,  filename=lowercase					,  use_cdn=lowercase)

          try:
                 # Load from URL or cache if already cached
                 a__						:	Optional[Any] =				cached_path(
                     lowercase					,  cache_dir=lowercase					,  force_download=lowercase					,  proxies=lowercase					,  resume_download=lowercase					,  local_files_only=lowercase					,  )
                 # Load config dict
                 if resolved_config_file is None:
                        raise EnvironmentError

                 a__						:	Optional[int] =				Config.load_yaml(lowercase)

          except EnvironmentError:
                 a__						:	List[str] =				'Can\'t load config for'
                 raise EnvironmentError(lowercase)

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

          return Config.load_yaml(lowercase), kwargs



def A_	(		A__				)  ->		Union[str, Any]:
       a__						:	str =				torch.load('dump.pt'		,      map_location=in_tensor.device				)
       a__						:	Optional[Any] =				in_tensor.numpy()
       a__						:	List[Any] =				out_tensor.numpy()[0]
       print(na.shape		,      na[0, 0, :5]				)
       print(na.shape		,      na[0, 0, :5]				)
       assert np.allclose(A__		,      A__		,      rtol=0.01		,      atol=0.1				), (
           F'{sum([1 for x in np.isclose(A__		,      A__		,      rtol=0.01		,      atol=0.1				).flatten() if x is False]				)/len(na.flatten()				)*100:.4f} %'
           " element-wise mismatch"
       )
       raise Exception('tensors are all good'				)

       # Hugging face functions below



def A_	(		A__				)  ->		str:
       a__						:	Dict =				urlparse(A__				)
       return parsed.scheme in ("http", "https")



def A_	(		A__		,      A__		,      A__=True				)  ->		str:
       a__						:	int =				CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
       a__						:	Any =				'/' not in model_id
       if legacy_format:
              return F'{endpoint}/{model_id}-{filename}'
       else:
              return F'{endpoint}/{model_id}/{filename}'



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



def A_	(		A__		,      A__=None		,      A__=False		,      A__=None		,      A__=10		,      A__=False		,      A__=None		,      A__=False		,      )  ->		List[str]:
       if cache_dir is None:
              a__						:	str =				TRANSFORMERS_CACHE
       if isinstance(A__		,      A__				):
              a__						:	Dict =				str(A__				)

       os.makedirs(A__		,      exist_ok=A__				)

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

       a__						:	Optional[Any] =				url_to_filename(A__		,      A__				)

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

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

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

              if resume_download:
                     a__						:	Any =				cache_path + '.incomplete'

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

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

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

              os.replace(temp_file.name		,      A__				)

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

       return cache_path



def A_	(		A__		,      A__=None				)  ->		Union[str, Any]:
       a__						:	int =				url.encode('utf-8'				)
       a__						:	str =				shaaaa(A__				)
       a__						:	Union[str, Any] =				url_hash.hexdigest()

       if etag:
              a__						:	Optional[Any] =				etag.encode('utf-8'				)
              a__						:	Tuple =				shaaaa(A__				)
              filename += "." + etag_hash.hexdigest()

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

       return filename



def A_	(		A__		,      A__=None		,      A__=False		,      A__=None		,      A__=False		,      A__=None		,      A__=False		,      A__=False		,      A__=False		,      )  ->		Any:
       if cache_dir is None:
              a__						:	Tuple =				TRANSFORMERS_CACHE
       if isinstance(A__		,      A__				):
              a__						:	Optional[Any] =				str(A__				)
       if isinstance(A__		,      A__				):
              a__						:	Optional[int] =				str(A__				)

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

       if extract_compressed_file:
              if not is_zipfile(A__				) and not tarfile.is_tarfile(A__				):
                     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__						:	List[Any] =				os.path.split(A__				)
              a__						:	Dict =				output_file.replace('.'		,      '-'				) + '-extracted'
              a__						:	Union[str, Any] =				os.path.join(A__		,      A__				)

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

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

              return output_path_extracted

       return output_path



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



def A_	(		A__				)  ->		List[str]:
       a__						:	str =				requests.get(A__				)
       a__						:	str =				np.array(Image.open(BytesIO(response.content				)				)				)
       return img



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



def A_	(		)  ->		List[Any]:
       print(F'{os.path.abspath(os.path.join(A__		,      os.pardir				)				)}/demo.ipynb'				)



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



def A_	(		A__		,      A__=1				)  ->		Tuple:
       return (images[i : i + batch] for i in range(0		,      len(A__				)		,      A__				))









from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
    import torch

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


lowercase      :       List[str]									=  logging.get_logger(__name__)





@dataclass
class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   __A   :				Optional[Any]         =  [
       '''no_inference''',
       '''no_cuda''',
       '''no_tpu''',
       '''no_speed''',
       '''no_memory''',
       '''no_env_print''',
       '''no_multi_process''',
   ]

   def __init__(					self					,  **lowercase)			->						Tuple:







          '''simple docstring'''

          for deprecated_arg in self.deprecated_args:
                 if deprecated_arg in kwargs:
                        a__						:	Union[str, Any] =				deprecated_arg[3:]
                        setattr(self					,  lowercase					,  not kwargs.pop(lowercase))
                        logger.warning(
                            F'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
                            F' {positive_arg}={kwargs[positive_arg]}')

          a__						:	Union[str, Any] =				kwargs.pop('torchscript'					,  self.torchscript)
          a__						:	Tuple =				kwargs.pop('torch_xla_tpu_print_metrics'					,  self.torch_xla_tpu_print_metrics)
          a__						:	Tuple =				kwargs.pop('fp16_opt_level'					,  self.fpaa_opt_level)
          super().__init__(**lowercase)

   __A   :				bool         =  field(default=__UpperCAmelCase ,  metadata={'''help''': '''Trace the models using torchscript'''}	)
   __A   :				bool         =  field(default=__UpperCAmelCase ,  metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}	)
   __A   :				str         =  field(
       default='''O1''' ,  metadata={
           '''help''': (
               '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
               '''See details at https://nvidia.github.io/apex/amp.html'''
           )
       } ,  )

   @cached_property
   def 							__lowercase		(					self)			->						Tuple["torch.device", int]:







          '''simple docstring'''

          requires_backends(self					,  ['torch'])
          logger.info('PyTorch: setting up devices')
          if not self.cuda:
                 a__						:	List[str] =				torch.device('cpu')
                 a__						:	Optional[Any] =				0
          elif is_torch_tpu_available():
                 a__						:	List[str] =				xm.xla_device()
                 a__						:	Union[str, Any] =				0
          else:
                 a__						:	List[Any] =				torch.device('cuda' if torch.cuda.is_available() else 'cpu')
                 a__						:	Optional[int] =				torch.cuda.device_count()
          return device, n_gpu

   @property
   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          return is_torch_tpu_available() and self.tpu

   @property
   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          requires_backends(self					,  ['torch'])
          # TODO(PVP): currently only single GPU is supported
          return torch.cuda.current_device()

   @property
   def 							__lowercase		(					self)			->						"torch.device":







          '''simple docstring'''

          requires_backends(self					,  ['torch'])
          return self._setup_devices[0]

   @property
   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          requires_backends(self					,  ['torch'])
          return self._setup_devices[1]

   @property
   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          return self.n_gpu > 0


