

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

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


try:
 import torch

 __A     						=      True
except ImportError:
 __A     						=      False


try:
 from torch.hub import _get_torch_home

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

__A     						=      os.path.join(torch_cache_home, '''transformers''')

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



def  snake_case_(_UpperCamelCase=OBJECTS					,			_UpperCamelCase=ATTRIBUTES	)				->      List[Any]:



  """simple docstring"""






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

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



def  snake_case_(_UpperCamelCase	)				->      Optional[int]:



  """simple docstring"""






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



class     lowercase_       :
      UpperCamelCase_     :						Tuple		 =							{}



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

        _snake_case		=				d



      def __repr__(     self :							Optional[Any]						)       ->	Tuple:
        return str(list((self._pointer.keys())						)						)



      def __setattr__(     self :							int     ,   A__ :							Optional[Any]     ,   A__ :							List[str]						)       ->	Dict:
        _snake_case		=				val
        _snake_case		=				val
        _snake_case		=				key.split('''.'''						)
        _snake_case		=				len(snake_case__						) - 1
        _snake_case		=				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:
              _snake_case		=				val
            else:
              _snake_case		=				pointer[l]



      def 		UpperCamelCase_						(     self :							Dict						)       ->	Any:
        return self._pointer



      def 		UpperCamelCase_						(     self :							int     ,   A__ :							Any     ,   A__ :							Union[str, Any]						)       ->	Optional[Any]:


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



      def 		UpperCamelCase_						(     self :							Union[str, Any]     ,   A__ :							Optional[Any]     ,   A__ :							Optional[Any]						)       ->	Tuple:


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



      @staticmethod
      def 		UpperCamelCase_						(     A__ :							List[str]						)       ->	Union[str, Any]:


        with open(snake_case__						) as stream:
          _snake_case		=				load(snake_case__     ,   Loader=snake_case__						)
        return data



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



      @classmethod
      def 		UpperCamelCase_						(     cls :							Tuple     ,   A__ :							str     ,   **A__ :							Optional[int]						)       ->	Optional[int]:
        _snake_case,	_snake_case		=				cls.get_config_dict(snake_case__     ,   **snake_case__						)
        return cls(snake_case__						)







      @classmethod
      def 		UpperCamelCase_						(     cls :							List[str]     ,   A__ :							Any     ,   **A__ :							Tuple						)       ->	Tuple:
        _snake_case		=				kwargs.pop('''cache_dir'''     ,   snake_case__						)
        _snake_case		=				kwargs.pop('''force_download'''     ,   snake_case__						)
        _snake_case		=				kwargs.pop('''resume_download'''     ,   snake_case__						)
        _snake_case		=				kwargs.pop('''proxies'''     ,   snake_case__						)
        _snake_case		=				kwargs.pop('''local_files_only'''     ,   snake_case__						)

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

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

          _snake_case		=				Config.load_yaml(snake_case__						)

        except EnvironmentError:
          _snake_case		=				'''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  snake_case_(_UpperCamelCase	)				->      Union[str, Any]:



  """simple docstring"""






  _snake_case		=				torch.load('''dump.pt'''					,			map_location=in_tensor.device	)
  _snake_case		=				in_tensor.numpy()
  _snake_case		=				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  snake_case_(_UpperCamelCase	)				->      Optional[int]:



  """simple docstring"""






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



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase=True	)				->      List[str]:



  """simple docstring"""






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



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase=None					,			_UpperCamelCase=0					,			_UpperCamelCase=None					,			)				->      List[str]:



  """simple docstring"""






  _snake_case		=				'''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
  _snake_case		=				{'''user-agent''': ua}
  if resume_size > 0:
    _snake_case		=				'''bytes=%d-''' % (resume_size,)
  _snake_case		=				requests.get(_lowerCAmelCase					,			stream=_lowerCAmelCase					,			proxies=_lowerCAmelCase					,			headers=_lowerCAmelCase	)
  if response.status_code == 416:  # Range not satisfiable
    return
  _snake_case		=				response.headers.get('''Content-Length'''	)
  _snake_case		=				resume_size + int(_lowerCAmelCase	) if content_length is not None else None
  _snake_case		=				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  snake_case_(_UpperCamelCase					,			_UpperCamelCase=None					,			_UpperCamelCase=False					,			_UpperCamelCase=None					,			_UpperCamelCase=10					,			_UpperCamelCase=False					,			_UpperCamelCase=None					,			_UpperCamelCase=False					,			)				->      int:



  """simple docstring"""






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

  os.makedirs(_lowerCAmelCase					,			exist_ok=_lowerCAmelCase	)

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

  _snake_case		=				url_to_filename(_lowerCAmelCase					,			_lowerCAmelCase	)

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

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

      _snake_case		=				_resumable_file_manager
      if os.path.exists(_lowerCAmelCase	):
        _snake_case		=				os.stat(_lowerCAmelCase	).st_size
      else:
        _snake_case		=				0
    else:
      _snake_case		=				partial(tempfile.NamedTemporaryFile					,			dir=_lowerCAmelCase					,			delete=_lowerCAmelCase	)
      _snake_case		=				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	)

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

  return cache_path



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase=None	)				->      List[Any]:



  """simple docstring"""






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

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

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

  return filename



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase=None					,			_UpperCamelCase=False					,			_UpperCamelCase=None					,			_UpperCamelCase=False					,			_UpperCamelCase=None					,			_UpperCamelCase=False					,			_UpperCamelCase=False					,			_UpperCamelCase=False					,			)				->      Optional[int]:



  """simple docstring"""






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

  if is_remote_url(_lowerCAmelCase	):
    # URL, so get it from the cache (downloading if necessary)
    _snake_case		=				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.
    _snake_case		=				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/"
    _snake_case,	_snake_case		=				os.path.split(_lowerCAmelCase	)
    _snake_case		=				output_file.replace('''.'''					,			'''-'''	) + '''-extracted'''
    _snake_case		=				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
    _snake_case		=				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	):
        _snake_case		=				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  snake_case_(_UpperCamelCase					,			_UpperCamelCase=","	)				->      List[Any]:



  """simple docstring"""






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



def  snake_case_(_UpperCamelCase	)				->      int:



  """simple docstring"""






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



def  snake_case_(_UpperCamelCase	)				->      Optional[Any]:



  """simple docstring"""






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



def  snake_case_()				->      List[str]:



  """simple docstring"""






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



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase="RGB"	)				->      Dict:



  """simple docstring"""






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



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase=1	)				->      int:



  """simple docstring"""






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






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


UpperCAmelCase :				Union[str, Any]							=logging.get_logger(__name__)


class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    """speech-encoder-decoder"""
				lowercase__     =    True

				def __init__(			self  ,			**snake_case__     ):

									'''simple docstring'''


									super().__init__(**snake_case__     )
									if "encoder" not in kwargs or "decoder" not in kwargs:
														raise ValueError(
														    F"""A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and"""
														    F""" `decoder` sub-configurations are passed, but only {kwargs}"""     )

									UpperCamelCase_			= kwargs.pop("encoder"     )
									UpperCamelCase_			= encoder_config.pop("model_type"     )
									UpperCamelCase_			= kwargs.pop("decoder"     )
									UpperCamelCase_			= decoder_config.pop("model_type"     )

									UpperCamelCase_			= AutoConfig.for_model(snake_case__  ,			**snake_case__     )
									UpperCamelCase_			= AutoConfig.for_model(snake_case__  ,			**snake_case__     )
									UpperCamelCase_			= True

				@classmethod
				def    _lowerCamelCase       (			cls  ,			snake_case__  ,			snake_case__  ,			**snake_case__     ):

									'''simple docstring'''


									logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"     )
									UpperCamelCase_			= True
									UpperCamelCase_			= True

									return cls(encoder=encoder_config.to_dict()  ,			decoder=decoder_config.to_dict()  ,			**snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= copy.deepcopy(self.__dict__     )
									UpperCamelCase_			= self.encoder.to_dict()
									UpperCamelCase_			= self.decoder.to_dict()
									UpperCamelCase_			= self.__class__.model_type
									return output
