






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

       UpperCamelCase    							=      True
except ImportError:
       UpperCamelCase    							=      False


try:
       from torch.hub import _get_torch_home

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

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

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


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Any=OBJECTS					,							__lowerCAmelCase							:					Tuple=ATTRIBUTES							)     ->  Any:
   __UpperCamelCase	:			List[Any]					=					[]
   with open(__lowerCAmelCase							) as f:
      for object in f.readlines():
         vg_classes.append(object.split(""","""							)[0].lower().strip()							)

   __UpperCamelCase	:			Optional[Any]					=					[]
   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     (							__lowerCAmelCase							:					Optional[Any]							)     ->  Dict:
   __UpperCamelCase	:			Any					=					OrderedDict()
   with open(__lowerCAmelCase					,							"""rb"""							) as f:
      __UpperCamelCase	:			Optional[int]					=					pkl.load(__lowerCAmelCase							)["""model"""]
   for k in copy.deepcopy(list(ckp.keys()							)							):
      __UpperCamelCase	:			Optional[Any]					=					ckp.pop(__lowerCAmelCase							)
      if isinstance(__lowerCAmelCase					,							np.ndarray							):
         __UpperCamelCase	:			Any					=					torch.tensor(__lowerCAmelCase							)
      else:
         assert isinstance(__lowerCAmelCase					,							torch.tensor							), type(__lowerCAmelCase							)
      __UpperCamelCase	:			Optional[int]					=					v
   return r







class 		_A						:
  lowercase_						:			Tuple						=					{}

  def __init__(    self						:	List[str]   ,				lowerCamelCase__						:	dict   ,				lowerCamelCase__						:	str = "root"   ,				lowerCamelCase__						:	Any=0			):
     """simple docstring"""

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

     __UpperCamelCase	:			Optional[Any]					=					d

  def __repr__(    self						:	str			):
     """simple docstring"""

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

  def __setattr__(    self						:	List[str]   ,				lowerCamelCase__						:	Tuple   ,				lowerCamelCase__						:	List[Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					val
     __UpperCamelCase	:			Optional[int]					=					val
     __UpperCamelCase	:			Dict					=					key.split("""."""			)
     __UpperCamelCase	:			int					=					len(lowerCamelCase__			) - 1
     __UpperCamelCase	:			Tuple					=					self._pointer
     if len(lowerCamelCase__			) > 1:
        for i, l in enumerate(lowerCamelCase__			):
           if hasattr(self   ,				lowerCamelCase__			) and isinstance(getattr(self   ,				lowerCamelCase__			)   ,				lowerCamelCase__			):
              setattr(getattr(self   ,				lowerCamelCase__			)   ,				""".""".join(levels[i:]			)   ,				lowerCamelCase__			)
           if l == last_level:
              __UpperCamelCase	:			Optional[int]					=					val
           else:
              __UpperCamelCase	:			Optional[Any]					=					pointer[l]

  def    a					(    self						:	Dict			):
     """simple docstring"""

     return self._pointer

  def    a					(    self						:	List[str]   ,				lowerCamelCase__						:	Tuple   ,				lowerCamelCase__						:	Union[str, Any]			):
     """simple docstring"""

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

  def    a					(    self						:	int   ,				lowerCamelCase__						:	Dict   ,				lowerCamelCase__						:	Union[str, Any]			):
     """simple docstring"""

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

  @staticmethod
  def    a					(    lowerCamelCase__						:	str			):
     """simple docstring"""

     with open(lowerCamelCase__			) as stream:
        __UpperCamelCase	:			Optional[Any]					=					load(lowerCamelCase__   ,				Loader=lowerCamelCase__			)
     return data

  def __str__(    self						:	Optional[int]			):
     """simple docstring"""

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

  @classmethod
  def    a					(    cls						:	Dict   ,				lowerCamelCase__						:	str   ,				**lowerCamelCase__						:	str			):
     """simple docstring"""

     __UpperCamelCase	,     __UpperCamelCase	:			Optional[int]					=					cls.get_config_dict(lowerCamelCase__   ,				**lowerCamelCase__			)
     return cls(lowerCamelCase__			)

  @classmethod
  def    a					(    cls						:	str   ,				lowerCamelCase__						:	str   ,				**lowerCamelCase__						:	Any			):
     """simple docstring"""

     __UpperCamelCase	:			int					=					kwargs.pop("""cache_dir"""   ,				lowerCamelCase__			)
     __UpperCamelCase	:			Any					=					kwargs.pop("""force_download"""   ,				lowerCamelCase__			)
     __UpperCamelCase	:			List[Any]					=					kwargs.pop("""resume_download"""   ,				lowerCamelCase__			)
     __UpperCamelCase	:			str					=					kwargs.pop("""proxies"""   ,				lowerCamelCase__			)
     __UpperCamelCase	:			str					=					kwargs.pop("""local_files_only"""   ,				lowerCamelCase__			)

     if os.path.isdir(lowerCamelCase__			):
        __UpperCamelCase	:			Union[str, Any]					=					os.path.join(lowerCamelCase__   ,				lowerCamelCase__			)
     elif os.path.isfile(lowerCamelCase__			) or is_remote_url(lowerCamelCase__			):
        __UpperCamelCase	:			Dict					=					pretrained_model_name_or_path
     else:
        __UpperCamelCase	:			Union[str, Any]					=					hf_bucket_url(lowerCamelCase__   ,				filename=lowerCamelCase__   ,				use_cdn=lowerCamelCase__			)

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

        __UpperCamelCase	:			List[str]					=					Config.load_yaml(lowerCamelCase__			)

     except EnvironmentError:
        __UpperCamelCase	:			Any					=					"""Can't load config for"""
        raise EnvironmentError(lowerCamelCase__			)

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

     return Config.load_yaml(lowerCamelCase__			), kwargs







def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[str]							)     ->  Optional[int]:
   __UpperCamelCase	:			Optional[Any]					=					torch.load("""dump.pt"""					,							map_location=in_tensor.device							)
   __UpperCamelCase	:			Optional[Any]					=					in_tensor.numpy()
   __UpperCamelCase	:			Dict					=					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     (							__lowerCAmelCase							:					int							)     ->  Dict:
   __UpperCamelCase	:			Any					=					urlparse(__lowerCAmelCase							)
   return parsed.scheme in ("http", "https")


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					Any=True							)     ->  str:
   __UpperCamelCase	:			int					=					CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
   __UpperCamelCase	:			Dict					=					"""/""" not in model_id
   if legacy_format:
      return f'{endpoint}/{model_id}-{filename}'
   else:
      return f'{endpoint}/{model_id}/{filename}'


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					Dict					,							__lowerCAmelCase							:					Union[str, Any]=None					,							__lowerCAmelCase							:					Tuple=0					,							__lowerCAmelCase							:					Tuple=None					,							)     ->  Any:
   __UpperCamelCase	:			Tuple					=					"""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
   __UpperCamelCase	:			Tuple					=					{"""user-agent""": ua}
   if resume_size > 0:
      __UpperCamelCase	:			List[str]					=					"""bytes=%d-""" % (resume_size,)
   __UpperCamelCase	:			Union[str, Any]					=					requests.get(__lowerCAmelCase					,							stream=__lowerCAmelCase					,							proxies=__lowerCAmelCase					,							headers=__lowerCAmelCase							)
   if response.status_code == 416:  # Range not satisfiable
      return
   __UpperCamelCase	:			int					=					response.headers.get("""Content-Length"""							)
   __UpperCamelCase	:			List[str]					=					resume_size + int(__lowerCAmelCase							) if content_length is not None else None
   __UpperCamelCase	:			List[str]					=					tqdm(
       unit="""B"""					,							unit_scale=__lowerCAmelCase					,							total=__lowerCAmelCase					,							initial=__lowerCAmelCase					,							desc="""Downloading"""					,							)
   for chunk in response.iter_content(chunk_size=1024							):
      if chunk:  # filter out keep-alive new chunks
         progress.update(len(__lowerCAmelCase							)							)
         temp_file.write(__lowerCAmelCase							)
   progress.close()


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[Any]					,							__lowerCAmelCase							:					str=None					,							__lowerCAmelCase							:					Union[str, Any]=False					,							__lowerCAmelCase							:					Optional[Any]=None					,							__lowerCAmelCase							:					Tuple=10					,							__lowerCAmelCase							:					Tuple=False					,							__lowerCAmelCase							:					Optional[int]=None					,							__lowerCAmelCase							:					int=False					,							)     ->  List[Any]:
   if cache_dir is None:
      __UpperCamelCase	:			Optional[Any]					=					TRANSFORMERS_CACHE
   if isinstance(__lowerCAmelCase					,							__lowerCAmelCase							):
      __UpperCamelCase	:			int					=					str(__lowerCAmelCase							)

   os.makedirs(__lowerCAmelCase					,							exist_ok=__lowerCAmelCase							)

   __UpperCamelCase	:			Dict					=					None
   if not local_files_only:
      try:
         __UpperCamelCase	:			str					=					requests.head(__lowerCAmelCase					,							allow_redirects=__lowerCAmelCase					,							proxies=__lowerCAmelCase					,							timeout=__lowerCAmelCase							)
         if response.status_code == 200:
            __UpperCamelCase	:			Dict					=					response.headers.get("""ETag"""							)
      except (EnvironmentError, requests.exceptions.Timeout):
         # etag is already None
         pass

   __UpperCamelCase	:			Union[str, Any]					=					url_to_filename(__lowerCAmelCase					,							__lowerCAmelCase							)

   # get cache path to put the file
   __UpperCamelCase	:			Optional[Any]					=					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:
         __UpperCamelCase	:			List[Any]					=					[
             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.
   __UpperCamelCase	:			int					=					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:
         __UpperCamelCase	:			Tuple					=					cache_path + """.incomplete"""

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

         __UpperCamelCase	:			List[Any]					=					_resumable_file_manager
         if os.path.exists(__lowerCAmelCase							):
            __UpperCamelCase	:			Union[str, Any]					=					os.stat(__lowerCAmelCase							).st_size
         else:
            __UpperCamelCase	:			List[Any]					=					0
      else:
         __UpperCamelCase	:			Optional[Any]					=					partial(tempfile.NamedTemporaryFile					,							dir=__lowerCAmelCase					,							delete=__lowerCAmelCase							)
         __UpperCamelCase	:			Tuple					=					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							)

      __UpperCamelCase	:			List[Any]					=					{"""url""": url, """etag""": etag}
      __UpperCamelCase	:			Optional[int]					=					cache_path + """.json"""
      with open(__lowerCAmelCase					,							"""w"""							) as meta_file:
         json.dump(__lowerCAmelCase					,							__lowerCAmelCase							)

   return cache_path


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[Any]					,							__lowerCAmelCase							:					Optional[int]=None							)     ->  Optional[Any]:
   __UpperCamelCase	:			str					=					url.encode("""utf-8"""							)
   __UpperCamelCase	:			int					=					shaaaa(__lowerCAmelCase							)
   __UpperCamelCase	:			List[str]					=					url_hash.hexdigest()

   if etag:
      __UpperCamelCase	:			Dict					=					etag.encode("""utf-8"""							)
      __UpperCamelCase	:			List[str]					=					shaaaa(__lowerCAmelCase							)
      filename += "." + etag_hash.hexdigest()

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

   return filename


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[Any]					,							__lowerCAmelCase							:					List[str]=None					,							__lowerCAmelCase							:					Dict=False					,							__lowerCAmelCase							:					Dict=None					,							__lowerCAmelCase							:					Any=False					,							__lowerCAmelCase							:					Optional[int]=None					,							__lowerCAmelCase							:					Dict=False					,							__lowerCAmelCase							:					Tuple=False					,							__lowerCAmelCase							:					Dict=False					,							)     ->  List[Any]:
   if cache_dir is None:
      __UpperCamelCase	:			str					=					TRANSFORMERS_CACHE
   if isinstance(__lowerCAmelCase					,							__lowerCAmelCase							):
      __UpperCamelCase	:			int					=					str(__lowerCAmelCase							)
   if isinstance(__lowerCAmelCase					,							__lowerCAmelCase							):
      __UpperCamelCase	:			List[str]					=					str(__lowerCAmelCase							)

   if is_remote_url(__lowerCAmelCase							):
      # URL, so get it from the cache (downloading if necessary)
      __UpperCamelCase	:			int					=					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.
      __UpperCamelCase	:			Union[str, Any]					=					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/"
      __UpperCamelCase	,     __UpperCamelCase	:			List[str]					=					os.path.split(__lowerCAmelCase							)
      __UpperCamelCase	:			Optional[int]					=					output_file.replace("""."""					,							"""-"""							) + """-extracted"""
      __UpperCamelCase	:			Union[str, Any]					=					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
      __UpperCamelCase	:			str					=					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							):
            __UpperCamelCase	:			List[str]					=					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     (							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					List[Any]=","							)     ->  List[str]:
   assert isinstance(__lowerCAmelCase					,							__lowerCAmelCase							)
   if os.path.isfile(__lowerCAmelCase							):
      with open(__lowerCAmelCase							) as f:
         __UpperCamelCase	:			Optional[int]					=					eval(f.read()							)
   else:
      __UpperCamelCase	:			Optional[Any]					=					requests.get(__lowerCAmelCase							)
      try:
         __UpperCamelCase	:			str					=					requests.json()
      except Exception:
         __UpperCamelCase	:			Optional[Any]					=					req.content.decode()
         assert data is not None, "could not connect"
         try:
            __UpperCamelCase	:			Union[str, Any]					=					eval(__lowerCAmelCase							)
         except Exception:
            __UpperCamelCase	:			str					=					data.split("""\n"""							)
      req.close()
   return data


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Any							)     ->  List[str]:
   __UpperCamelCase	:			List[Any]					=					requests.get(__lowerCAmelCase							)
   __UpperCamelCase	:			Tuple					=					np.array(Image.open(BytesIO(response.content							)							)							)
   return img


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


def 				__lowerCamelCase     (							)     ->  str:
   print(f'{os.path.abspath(os.path.join(__lowerCAmelCase					,							os.pardir							)							)}/demo.ipynb'							)


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



def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[Any]					,							__lowerCAmelCase							:					List[Any]=1							)     ->  int:
   return (images[i : i + batch] for i in range(0					,							len(__lowerCAmelCase							)					,							__lowerCAmelCase							))











import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase    							=      logging.get_logger(__name__)


UpperCamelCase    							=      ['model.decoder.embed_positions.weights']


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[int]							)     ->  Union[str, Any]:
   if "emb" in name:
      __UpperCamelCase	:			Any					=					name.replace("""emb"""					,							"""model.decoder.embed_tokens"""							)
   if "transformer" in name:
      __UpperCamelCase	:			str					=					name.replace("""transformer"""					,							"""model.decoder"""							)
   if "cross_attention" in name:
      __UpperCamelCase	:			List[str]					=					name.replace("""cross_attention"""					,							"""encoder_attn"""							)
   if "linear1" in name:
      __UpperCamelCase	:			Optional[int]					=					name.replace("""linear1"""					,							"""fc1"""							)
   if "linear2" in name:
      __UpperCamelCase	:			Optional[Any]					=					name.replace("""linear2"""					,							"""fc2"""							)
   if "norm1" in name:
      __UpperCamelCase	:			Tuple					=					name.replace("""norm1"""					,							"""self_attn_layer_norm"""							)
   if "norm_cross" in name:
      __UpperCamelCase	:			List[str]					=					name.replace("""norm_cross"""					,							"""encoder_attn_layer_norm"""							)
   if "norm2" in name:
      __UpperCamelCase	:			Tuple					=					name.replace("""norm2"""					,							"""final_layer_norm"""							)
   if "out_norm" in name:
      __UpperCamelCase	:			Union[str, Any]					=					name.replace("""out_norm"""					,							"""model.decoder.layer_norm"""							)
   if "linears" in name:
      __UpperCamelCase	:			Optional[int]					=					name.replace("""linears"""					,							"""lm_heads"""							)
   if "condition_provider.conditioners.description.output_proj" in name:
      __UpperCamelCase	:			Tuple					=					name.replace("""condition_provider.conditioners.description.output_proj"""					,							"""enc_to_dec_proj"""							)
   return name


def 				__lowerCamelCase     (							__lowerCAmelCase							:					OrderedDict					,							__lowerCAmelCase							:					int							)     ->  Tuple[Dict, Dict]:
   __UpperCamelCase	:			Tuple					=					list(state_dict.keys()							)
   __UpperCamelCase	:			List[Any]					=					{}
   for key in keys:
      __UpperCamelCase	:			Optional[Any]					=					state_dict.pop(__lowerCAmelCase							)
      __UpperCamelCase	:			Dict					=					rename_keys(__lowerCAmelCase							)
      if "in_proj_weight" in key:
         # split fused qkv proj
         __UpperCamelCase	:			Optional[Any]					=					val[:hidden_size, :]
         __UpperCamelCase	:			Optional[Any]					=					val[hidden_size : 2 * hidden_size, :]
         __UpperCamelCase	:			List[str]					=					val[-hidden_size:, :]
      elif "enc_to_dec_proj" in key:
         __UpperCamelCase	:			Dict					=					val
      else:
         __UpperCamelCase	:			Any					=					val
   return state_dict, enc_dec_proj_state_dict


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  MusicgenDecoderConfig:
   if checkpoint == "small":
      # default config values
      __UpperCamelCase	:			int					=					1024
      __UpperCamelCase	:			Union[str, Any]					=					24
      __UpperCamelCase	:			int					=					16
   elif checkpoint == "medium":
      __UpperCamelCase	:			List[Any]					=					1536
      __UpperCamelCase	:			Dict					=					48
      __UpperCamelCase	:			Dict					=					24
   elif checkpoint == "large":
      __UpperCamelCase	:			List[Any]					=					2048
      __UpperCamelCase	:			str					=					48
      __UpperCamelCase	:			Optional[int]					=					32
   else:
      raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'							)
   __UpperCamelCase	:			Any					=					MusicgenDecoderConfig(
       hidden_size=__lowerCAmelCase					,							ffn_dim=hidden_size * 4					,							num_hidden_layers=__lowerCAmelCase					,							num_attention_heads=__lowerCAmelCase					,							)
   return config


@torch.no_grad()
def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					Optional[Any]=None					,							__lowerCAmelCase							:					Tuple=None					,							__lowerCAmelCase							:					str="cpu"							)     ->  Optional[int]:
   __UpperCamelCase	:			str					=					MusicGen.get_pretrained(__lowerCAmelCase					,							device=__lowerCAmelCase							)
   __UpperCamelCase	:			int					=					decoder_config_from_checkpoint(__lowerCAmelCase							)

   __UpperCamelCase	:			Optional[int]					=					fairseq_model.lm.state_dict()
   __UpperCamelCase	,     __UpperCamelCase	:			Union[str, Any]					=					rename_state_dict(
       __lowerCAmelCase					,							hidden_size=decoder_config.hidden_size							)

   __UpperCamelCase	:			List[Any]					=					TaEncoderModel.from_pretrained("""t5-base"""							)
   __UpperCamelCase	:			Dict					=					EncodecModel.from_pretrained("""facebook/encodec_32khz"""							)
   __UpperCamelCase	:			List[str]					=					MusicgenForCausalLM(__lowerCAmelCase							).eval()

   # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
   __UpperCamelCase	,     __UpperCamelCase	:			Tuple					=					decoder.load_state_dict(__lowerCAmelCase					,							strict=__lowerCAmelCase							)

   for key in missing_keys.copy():
      if key.startswith(("""text_encoder""", """audio_encoder""")							) or key in EXPECTED_MISSING_KEYS:
         missing_keys.remove(__lowerCAmelCase							)

   if len(__lowerCAmelCase							) > 0:
      raise ValueError(f'Missing key(s) in state_dict: {missing_keys}'							)

   if len(__lowerCAmelCase							) > 0:
      raise ValueError(f'Unexpected key(s) in state_dict: {unexpected_keys}'							)

   # init the composite model
   __UpperCamelCase	:			Tuple					=					MusicgenForConditionalGeneration(text_encoder=__lowerCAmelCase					,							audio_encoder=__lowerCAmelCase					,							decoder=__lowerCAmelCase							)

   # load the pre-trained enc-dec projection (from the decoder state dict)
   model.enc_to_dec_proj.load_state_dict(__lowerCAmelCase							)

   # check we can do a forward pass
   __UpperCamelCase	:			int					=					torch.arange(0					,							8					,							dtype=torch.long							).reshape(2					,							-1							)
   __UpperCamelCase	:			List[Any]					=					input_ids.reshape(2 * 4					,							-1							)

   with torch.no_grad():
      __UpperCamelCase	:			int					=					model(input_ids=__lowerCAmelCase					,							decoder_input_ids=__lowerCAmelCase							).logits

   if logits.shape != (8, 1, 2048):
      raise ValueError("""Incorrect shape for logits"""							)

   # now construct the processor
   __UpperCamelCase	:			Union[str, Any]					=					AutoTokenizer.from_pretrained("""t5-base"""							)
   __UpperCamelCase	:			List[Any]					=					AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""					,							padding_side="""left"""							)

   __UpperCamelCase	:			Any					=					MusicgenProcessor(feature_extractor=__lowerCAmelCase					,							tokenizer=__lowerCAmelCase							)

   # set the appropriate bos/pad token ids
   __UpperCamelCase	:			Tuple					=					2048
   __UpperCamelCase	:			int					=					2048

   # set other default generation config params
   __UpperCamelCase	:			str					=					int(30 * audio_encoder.config.frame_rate							)
   __UpperCamelCase	:			List[str]					=					True
   __UpperCamelCase	:			Tuple					=					3.0

   if pytorch_dump_folder is not None:
      Path(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
      logger.info(f'Saving model {checkpoint} to {pytorch_dump_folder}'							)
      model.save_pretrained(__lowerCAmelCase							)
      processor.save_pretrained(__lowerCAmelCase							)

   if repo_id:
      logger.info(f'Pushing model {checkpoint} to {repo_id}'							)
      model.push_to_hub(__lowerCAmelCase							)
      processor.push_to_hub(__lowerCAmelCase							)


if __name__ == "__main__":
       UpperCamelCase    							=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--checkpoint',
           default='small',
           type=str,
           help='Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.',
       )
       parser.add_argument(
           '--pytorch_dump_folder',
           required=True,
           default=None,
           type=str,
           help='Path to the output PyTorch model directory.',
       )
       parser.add_argument(
           '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
       )
       parser.add_argument(
           '--device', default='cpu', type=str, help='Torch device to run the conversion, either cpu or cuda.'
       )

       UpperCamelCase    							=      parser.parse_args()
       convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)



