

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_    :						Optional[int]    		=      True
except ImportError:
      a_    :						Optional[int]    		=      False


try:
      from torch.hub import _get_torch_home

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

a_    :						str    		=      os.path.join(torch_cache_home, "transformers")

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


def        __lowerCAmelCase    (       _UpperCamelCase		:  Tuple=OBJECTS		,							_UpperCamelCase		:  Any=ATTRIBUTES	)		->							str:
 '''simple docstring'''


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

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


def        __lowerCAmelCase    (       _UpperCamelCase		:  Union[str, Any]	)		->							Any:
 '''simple docstring'''


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




class      UpperCamelCase :
    __UpperCamelCase         ={}

    def __init__(			self :      Tuple							,    snake_case__ :      dict							,    snake_case__ :      str = "root"							,    snake_case__ :      Dict=0    ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE								=			d

    def __repr__(			self :      Optional[Any]    ):

     """simple docstring"""






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

    def __setattr__(			self :      Any							,    snake_case__ :      List[str]							,    snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			val
     SCREAMING_SNAKE_CASE								=			val
     SCREAMING_SNAKE_CASE								=			key.split('.'    )
     SCREAMING_SNAKE_CASE								=			len(snake_case__    ) - 1
     SCREAMING_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:
        SCREAMING_SNAKE_CASE								=			val
       else:
        SCREAMING_SNAKE_CASE								=			pointer[l]

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     return self._pointer

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      List[str]    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Tuple							,    snake_case__ :      Tuple							,    snake_case__ :      str    ):

     """simple docstring"""






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

    @staticmethod
    def UpperCamelCase			(			snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






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

    def __str__(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'    '
     if self._name != "root":
      SCREAMING_SNAKE_CASE								=			F"""{t * (self._level-1)}{self._name}:\n"""
     else:
      SCREAMING_SNAKE_CASE								=			''
     SCREAMING_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"""
      SCREAMING_SNAKE_CASE								=			level
     return r[:-1]

    @classmethod
    def UpperCamelCase			(			cls :      int							,    snake_case__ :      str							,    **snake_case__ :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			cls.get_config_dict(snake_case__							,    **snake_case__    )
     return cls(snake_case__    )




    @classmethod
    def UpperCamelCase			(			cls :      int							,    snake_case__ :      str							,    **snake_case__ :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			kwargs.pop('cache_dir'							,    snake_case__    )
     SCREAMING_SNAKE_CASE								=			kwargs.pop('force_download'							,    snake_case__    )
     SCREAMING_SNAKE_CASE								=			kwargs.pop('resume_download'							,    snake_case__    )
     SCREAMING_SNAKE_CASE								=			kwargs.pop('proxies'							,    snake_case__    )
     SCREAMING_SNAKE_CASE								=			kwargs.pop('local_files_only'							,    snake_case__    )

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

     try:
      # Load from URL or cache if already cached
      SCREAMING_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

      SCREAMING_SNAKE_CASE								=			Config.load_yaml(snake_case__    )

     except EnvironmentError:
      SCREAMING_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        __lowerCAmelCase    (       _UpperCamelCase		:  Any	)		->							List[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			torch.load('dump.pt'		,							map_location=in_tensor.device	)
 SCREAMING_SNAKE_CASE								=			in_tensor.numpy()
 SCREAMING_SNAKE_CASE								=			out_tensor.numpy()[0]
 print(na.shape		,							na[0, 0, :5]	)
 print(na.shape		,							na[0, 0, :5]	)
 assert np.allclose(_UpperCamelCase		,							_UpperCamelCase		,							rtol=0.01		,							atol=0.1	), (
     f"""{sum([1 for x in np.isclose(_UpperCamelCase		,							_UpperCamelCase		,							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        __lowerCAmelCase    (       _UpperCamelCase		:  Union[str, Any]	)		->							Optional[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			urlparse(_UpperCamelCase	)
 return parsed.scheme in ("http", "https")


def        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  str		,							_UpperCamelCase		:  List[Any]=True	)		->							str:
 '''simple docstring'''


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


def        __lowerCAmelCase    (       _UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  Dict		,							_UpperCamelCase		:  int=None		,							_UpperCamelCase		:  List[Any]=0		,							_UpperCamelCase		:  Tuple=None		,							)		->							int:
 '''simple docstring'''


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


def        __lowerCAmelCase    (       _UpperCamelCase		:  Dict		,							_UpperCamelCase		:  Union[str, Any]=None		,							_UpperCamelCase		:  Dict=False		,							_UpperCamelCase		:  int=None		,							_UpperCamelCase		:  Union[str, Any]=10		,							_UpperCamelCase		:  Union[str, Any]=False		,							_UpperCamelCase		:  Tuple=None		,							_UpperCamelCase		:  List[str]=False		,							)		->							Optional[int]:
 '''simple docstring'''


 if cache_dir is None:
  SCREAMING_SNAKE_CASE								=			TRANSFORMERS_CACHE
 if isinstance(_UpperCamelCase		,							_UpperCamelCase	):
  SCREAMING_SNAKE_CASE								=			str(_UpperCamelCase	)

 os.makedirs(_UpperCamelCase		,							exist_ok=_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			None
 if not local_files_only:
  try:
   SCREAMING_SNAKE_CASE								=			requests.head(_UpperCamelCase		,							allow_redirects=_UpperCamelCase		,							proxies=_UpperCamelCase		,							timeout=_UpperCamelCase	)
   if response.status_code == 2_00:
    SCREAMING_SNAKE_CASE								=			response.headers.get('ETag'	)
  except (EnvironmentError, requests.exceptions.Timeout):
   # etag is already None
   pass

 SCREAMING_SNAKE_CASE								=			url_to_filename(_UpperCamelCase		,							_UpperCamelCase	)

 # get cache path to put the file
 SCREAMING_SNAKE_CASE								=			os.path.join(_UpperCamelCase		,							_UpperCamelCase	)

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

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

  if resume_download:
   SCREAMING_SNAKE_CASE								=			cache_path + '.incomplete'

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

   SCREAMING_SNAKE_CASE								=			_resumable_file_manager
   if os.path.exists(_UpperCamelCase	):
    SCREAMING_SNAKE_CASE								=			os.stat(_UpperCamelCase	).st_size
   else:
    SCREAMING_SNAKE_CASE								=			0
  else:
   SCREAMING_SNAKE_CASE								=			partial(tempfile.NamedTemporaryFile		,							dir=_UpperCamelCase		,							delete=_UpperCamelCase	)
   SCREAMING_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'		,							_UpperCamelCase		,							temp_file.name		,							)

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

  os.replace(temp_file.name		,							_UpperCamelCase	)

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

 return cache_path


def        __lowerCAmelCase    (       _UpperCamelCase		:  Union[str, Any]		,							_UpperCamelCase		:  List[str]=None	)		->							Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			url.encode('utf-8'	)
 SCREAMING_SNAKE_CASE								=			shaaaa(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			url_hash.hexdigest()

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

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

 return filename


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[str]		,							_UpperCamelCase		:  Dict=None		,							_UpperCamelCase		:  Dict=False		,							_UpperCamelCase		:  Tuple=None		,							_UpperCamelCase		:  str=False		,							_UpperCamelCase		:  int=None		,							_UpperCamelCase		:  Optional[int]=False		,							_UpperCamelCase		:  str=False		,							_UpperCamelCase		:  str=False		,							)		->							List[Any]:
 '''simple docstring'''


 if cache_dir is None:
  SCREAMING_SNAKE_CASE								=			TRANSFORMERS_CACHE
 if isinstance(_UpperCamelCase		,							_UpperCamelCase	):
  SCREAMING_SNAKE_CASE								=			str(_UpperCamelCase	)
 if isinstance(_UpperCamelCase		,							_UpperCamelCase	):
  SCREAMING_SNAKE_CASE								=			str(_UpperCamelCase	)

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

 if extract_compressed_file:
  if not is_zipfile(_UpperCamelCase	) and not tarfile.is_tarfile(_UpperCamelCase	):
   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/"
  SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			os.path.split(_UpperCamelCase	)
  SCREAMING_SNAKE_CASE								=			output_file.replace('.'		,							'-'	) + '-extracted'
  SCREAMING_SNAKE_CASE								=			os.path.join(_UpperCamelCase		,							_UpperCamelCase	)

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

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

  return output_path_extracted

 return output_path


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  Dict=","	)		->							Dict:
 '''simple docstring'''


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


def        __lowerCAmelCase    (       _UpperCamelCase		:  Dict	)		->							Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			requests.get(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			np.array(Image.open(BytesIO(response.content	)	)	)
 return img


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[Any]	)		->							Dict:
 '''simple docstring'''


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


def        __lowerCAmelCase    (       )		->							List[str]:
 '''simple docstring'''


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


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  Optional[int]="RGB"	)		->							Tuple:
 '''simple docstring'''


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




def        __lowerCAmelCase    (       _UpperCamelCase		:  List[str]		,							_UpperCamelCase		:  List[str]=1	)		->							int:
 '''simple docstring'''


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




import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			unittest.TestCase   ):
    __UpperCamelCase         =AudioLDMPipeline
    __UpperCamelCase         =TEXT_TO_AUDIO_PARAMS
    __UpperCamelCase         =TEXT_TO_AUDIO_BATCH_PARAMS
    __UpperCamelCase         =frozenset(
        [
            "num_inference_steps",
            "num_waveforms_per_prompt",
            "generator",
            "latents",
            "output_type",
            "return_dict",
            "callback",
            "callback_steps",
        ]   )

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     torch.manual_seed(0    )
     SCREAMING_SNAKE_CASE								=			UNetaDConditionModel(
         block_out_channels=(3_2, 6_4)							,    layers_per_block=2							,    sample_size=3_2							,    in_channels=4							,    out_channels=4							,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')							,    cross_attention_dim=(3_2, 6_4)							,    class_embed_type='simple_projection'							,    projection_class_embeddings_input_dim=3_2							,    class_embeddings_concat=snake_case__							,    )
     SCREAMING_SNAKE_CASE								=			DDIMScheduler(
         beta_start=0.00_085							,    beta_end=0.012							,    beta_schedule='scaled_linear'							,    clip_sample=snake_case__							,    set_alpha_to_one=snake_case__							,    )
     torch.manual_seed(0    )
     SCREAMING_SNAKE_CASE								=			AutoencoderKL(
         block_out_channels=[3_2, 6_4]							,    in_channels=1							,    out_channels=1							,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,    latent_channels=4							,    )
     torch.manual_seed(0    )
     SCREAMING_SNAKE_CASE								=			ClapTextConfig(
         bos_token_id=0							,    eos_token_id=2							,    hidden_size=3_2							,    intermediate_size=3_7							,    layer_norm_eps=1E-05							,    num_attention_heads=4							,    num_hidden_layers=5							,    pad_token_id=1							,    vocab_size=1_0_0_0							,    projection_dim=3_2							,    )
     SCREAMING_SNAKE_CASE								=			ClapTextModelWithProjection(snake_case__    )
     SCREAMING_SNAKE_CASE								=			RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta'							,    model_max_length=7_7    )

     SCREAMING_SNAKE_CASE								=			SpeechTaHifiGanConfig(
         model_in_dim=8							,    sampling_rate=1_6_0_0_0							,    upsample_initial_channel=1_6							,    upsample_rates=[2, 2]							,    upsample_kernel_sizes=[4, 4]							,    resblock_kernel_sizes=[3, 7]							,    resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]							,    normalize_before=snake_case__							,    )

     SCREAMING_SNAKE_CASE								=			SpeechTaHifiGan(snake_case__    )

     SCREAMING_SNAKE_CASE								=			{
         'unet': unet,
         'scheduler': scheduler,
         'vae': vae,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'vocoder': vocoder,
     }
     return components

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      int							,    snake_case__ :      int=0    ):

     """simple docstring"""






     if str(snake_case__    ).startswith('mps'    ):
      SCREAMING_SNAKE_CASE								=			torch.manual_seed(snake_case__    )
     else:
      SCREAMING_SNAKE_CASE								=			torch.Generator(device=snake_case__    ).manual_seed(snake_case__    )
     SCREAMING_SNAKE_CASE								=			{
         'prompt': 'A hammer hitting a wooden surface',
         'generator': generator,
         'num_inference_steps': 2,
         'guidance_scale': 6.0,
     }
     return inputs

    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'cpu'  # ensure determinism for the device-dependent torch.Generator

     SCREAMING_SNAKE_CASE								=			self.get_dummy_components()
     SCREAMING_SNAKE_CASE								=			AudioLDMPipeline(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.to(snake_case__    )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__    )

     SCREAMING_SNAKE_CASE								=			self.get_dummy_inputs(snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			output.audios[0]

     assert audio.ndim == 1
     assert len(snake_case__    ) == 2_5_6

     SCREAMING_SNAKE_CASE								=			audio[:1_0]
     SCREAMING_SNAKE_CASE								=			np.array(
         [-0.0_050, 0.0_050, -0.0_060, 0.0_033, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_033]    )

     assert np.abs(audio_slice - expected_slice    ).max() < 1E-2

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_dummy_components()
     SCREAMING_SNAKE_CASE								=			AudioLDMPipeline(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.to(snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.to(snake_case__    )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__    )

     SCREAMING_SNAKE_CASE								=			self.get_dummy_inputs(snake_case__    )
     SCREAMING_SNAKE_CASE								=			3 * [inputs['prompt']]

     # forward
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			output.audios[0]

     SCREAMING_SNAKE_CASE								=			self.get_dummy_inputs(snake_case__    )
     SCREAMING_SNAKE_CASE								=			3 * [inputs.pop('prompt'    )]

     SCREAMING_SNAKE_CASE								=			audioldm_pipe.tokenizer(
         snake_case__							,    padding='max_length'							,    max_length=audioldm_pipe.tokenizer.model_max_length							,    truncation=snake_case__							,    return_tensors='pt'							,    )
     SCREAMING_SNAKE_CASE								=			text_inputs['input_ids'].to(snake_case__    )

     SCREAMING_SNAKE_CASE								=			audioldm_pipe.text_encoder(
         snake_case__							,    )
     SCREAMING_SNAKE_CASE								=			prompt_embeds.text_embeds
     # additional L_2 normalization over each hidden-state
     SCREAMING_SNAKE_CASE								=			F.normalize(snake_case__							,    dim=-1    )

     SCREAMING_SNAKE_CASE								=			prompt_embeds

     # forward
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			output.audios[0]

     assert np.abs(audio_a - audio_a    ).max() < 1E-2

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_dummy_components()
     SCREAMING_SNAKE_CASE								=			AudioLDMPipeline(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.to(snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.to(snake_case__    )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__    )

     SCREAMING_SNAKE_CASE								=			self.get_dummy_inputs(snake_case__    )
     SCREAMING_SNAKE_CASE								=			3 * ['this is a negative prompt']
     SCREAMING_SNAKE_CASE								=			negative_prompt
     SCREAMING_SNAKE_CASE								=			3 * [inputs['prompt']]

     # forward
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			output.audios[0]

     SCREAMING_SNAKE_CASE								=			self.get_dummy_inputs(snake_case__    )
     SCREAMING_SNAKE_CASE								=			3 * [inputs.pop('prompt'    )]

     SCREAMING_SNAKE_CASE								=			[]
     for p in [prompt, negative_prompt]:
      SCREAMING_SNAKE_CASE								=			audioldm_pipe.tokenizer(
          snake_case__							,    padding='max_length'							,    max_length=audioldm_pipe.tokenizer.model_max_length							,    truncation=snake_case__							,    return_tensors='pt'							,    )
      SCREAMING_SNAKE_CASE								=			text_inputs['input_ids'].to(snake_case__    )

      SCREAMING_SNAKE_CASE								=			audioldm_pipe.text_encoder(
          snake_case__							,    )
      SCREAMING_SNAKE_CASE								=			text_embeds.text_embeds
      # additional L_2 normalization over each hidden-state
      SCREAMING_SNAKE_CASE								=			F.normalize(snake_case__							,    dim=-1    )

      embeds.append(snake_case__    )

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			embeds

     # forward
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			output.audios[0]

     assert np.abs(audio_a - audio_a    ).max() < 1E-2

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'cpu'  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE								=			self.get_dummy_components()
     SCREAMING_SNAKE_CASE								=			PNDMScheduler(skip_prk_steps=snake_case__    )
     SCREAMING_SNAKE_CASE								=			AudioLDMPipeline(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.to(snake_case__    )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__    )

     SCREAMING_SNAKE_CASE								=			self.get_dummy_inputs(snake_case__    )
     SCREAMING_SNAKE_CASE								=			'egg cracking'
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(**snake_case__							,    negative_prompt=snake_case__    )
     SCREAMING_SNAKE_CASE								=			output.audios[0]

     assert audio.ndim == 1
     assert len(snake_case__    ) == 2_5_6

     SCREAMING_SNAKE_CASE								=			audio[:1_0]
     SCREAMING_SNAKE_CASE								=			np.array(
         [-0.0_051, 0.0_050, -0.0_060, 0.0_034, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_032]    )

     assert np.abs(audio_slice - expected_slice    ).max() < 1E-2

    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'cpu'  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE								=			self.get_dummy_components()
     SCREAMING_SNAKE_CASE								=			PNDMScheduler(skip_prk_steps=snake_case__    )
     SCREAMING_SNAKE_CASE								=			AudioLDMPipeline(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.to(snake_case__    )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__    )

     SCREAMING_SNAKE_CASE								=			'A hammer hitting a wooden surface'

     # test num_waveforms_per_prompt=1 (default)
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(snake_case__							,    num_inference_steps=2    ).audios

     assert audios.shape == (1, 2_5_6)

     # test num_waveforms_per_prompt=1 (default) for batch of prompts
     SCREAMING_SNAKE_CASE								=			2
     SCREAMING_SNAKE_CASE								=			audioldm_pipe([prompt] * batch_size							,    num_inference_steps=2    ).audios

     assert audios.shape == (batch_size, 2_5_6)

     # test num_waveforms_per_prompt for single prompt
     SCREAMING_SNAKE_CASE								=			2
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(snake_case__							,    num_inference_steps=2							,    num_waveforms_per_prompt=snake_case__    ).audios

     assert audios.shape == (num_waveforms_per_prompt, 2_5_6)

     # test num_waveforms_per_prompt for batch of prompts
     SCREAMING_SNAKE_CASE								=			2
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(
         [prompt] * batch_size							,    num_inference_steps=2							,    num_waveforms_per_prompt=snake_case__    ).audios

     assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_5_6)

    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'cpu'  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE								=			self.get_dummy_components()
     SCREAMING_SNAKE_CASE								=			AudioLDMPipeline(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.to(snake_case__    )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.vocoder.config.sampling_rate

     SCREAMING_SNAKE_CASE								=			self.get_dummy_inputs(snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(audio_length_in_s=0.016							,    **snake_case__    )
     SCREAMING_SNAKE_CASE								=			output.audios[0]

     assert audio.ndim == 1
     assert len(snake_case__    ) / vocoder_sampling_rate == 0.016

     SCREAMING_SNAKE_CASE								=			audioldm_pipe(audio_length_in_s=0.032							,    **snake_case__    )
     SCREAMING_SNAKE_CASE								=			output.audios[0]

     assert audio.ndim == 1
     assert len(snake_case__    ) / vocoder_sampling_rate == 0.032

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_dummy_components()
     SCREAMING_SNAKE_CASE								=			AudioLDMPipeline(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.to(snake_case__    )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__    )

     SCREAMING_SNAKE_CASE								=			['hey']

     SCREAMING_SNAKE_CASE								=			audioldm_pipe(snake_case__							,    num_inference_steps=1    )
     SCREAMING_SNAKE_CASE								=			output.audios.shape
     assert audio_shape == (1, 2_5_6)

     SCREAMING_SNAKE_CASE								=			audioldm_pipe.vocoder.config
     config.model_in_dim *= 2
     SCREAMING_SNAKE_CASE								=			SpeechTaHifiGan(snake_case__    ).to(snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(snake_case__							,    num_inference_steps=1    )
     SCREAMING_SNAKE_CASE								=			output.audios.shape
     # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
     assert audio_shape == (1, 2_5_6)

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     self._test_attention_slicing_forward_pass(test_mean_pixel_difference=snake_case__    )

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     self._test_inference_batch_single_identical(test_mean_pixel_difference=snake_case__    )




    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()							,    reason='XFormers attention is only available with CUDA and `xformers` installed'							,    )
    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=snake_case__    )




@slow
class      UpperCamelCase (     unittest.TestCase   ):

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()

    def UpperCamelCase			(			self :      int							,    snake_case__ :      int							,    snake_case__ :      Tuple="cpu"							,    snake_case__ :      List[str]=torch.floataa							,    snake_case__ :      Optional[Any]=0    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			torch.Generator(device=snake_case__    ).manual_seed(snake_case__    )
     SCREAMING_SNAKE_CASE								=			np.random.RandomState(snake_case__    ).standard_normal((1, 8, 1_2_8, 1_6)    )
     SCREAMING_SNAKE_CASE								=			torch.from_numpy(snake_case__    ).to(device=snake_case__							,    dtype=snake_case__    )
     SCREAMING_SNAKE_CASE								=			{
         'prompt': 'A hammer hitting a wooden surface',
         'latents': latents,
         'generator': generator,
         'num_inference_steps': 3,
         'guidance_scale': 2.5,
     }
     return inputs

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			AudioLDMPipeline.from_pretrained('cvssp/audioldm'    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.to(snake_case__    )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__    )

     SCREAMING_SNAKE_CASE								=			self.get_inputs(snake_case__    )
     SCREAMING_SNAKE_CASE								=			2_5
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(**snake_case__    ).audios[0]

     assert audio.ndim == 1
     assert len(snake_case__    ) == 8_1_9_2_0

     SCREAMING_SNAKE_CASE								=			audio[7_7_2_3_0:7_7_2_4_0]
     SCREAMING_SNAKE_CASE								=			np.array(
         [-0.4_884, -0.4_607, 0.0_023, 0.5_007, 0.5_896, 0.5_151, 0.3_813, -0.0_208, -0.3_687, -0.4_315]    )
     SCREAMING_SNAKE_CASE								=			np.abs(expected_slice - audio_slice    ).max()
     assert max_diff < 1E-2




    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			AudioLDMPipeline.from_pretrained('cvssp/audioldm'    )
     SCREAMING_SNAKE_CASE								=			LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.to(snake_case__    )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__    )

     SCREAMING_SNAKE_CASE								=			self.get_inputs(snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(**snake_case__    ).audios[0]

     assert audio.ndim == 1
     assert len(snake_case__    ) == 8_1_9_2_0

     SCREAMING_SNAKE_CASE								=			audio[2_7_7_8_0:2_7_7_9_0]
     SCREAMING_SNAKE_CASE								=			np.array([-0.2_131, -0.0_873, -0.0_124, -0.0_189, 0.0_569, 0.1_373, 0.1_883, 0.2_886, 0.3_297, 0.2_212]    )
     SCREAMING_SNAKE_CASE								=			np.abs(expected_slice - audio_slice    ).max()
     assert max_diff < 3E-2

