
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

     lowerCamelCase__     :		Optional[Any]        = True
except ImportError:
     lowerCamelCase__     :		str        = False


try:
     from torch.hub import _get_torch_home

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

lowerCamelCase__     :		Optional[int]        = os.path.join(torch_cache_home, """transformers""")

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

def 		UpperCamelCase						(   lowercase_=OBJECTS			,			lowercase_=ATTRIBUTES )		->						Tuple:


  '''simple docstring'''






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

  lowercase__	: List[str]     		=   []
  with open(lowercase_ ) as f:
    for object in f.readlines():
      vg_attrs.append(object.split(""",""" )[0].lower().strip() )
  return vg_classes, vg_attrs

def 		UpperCamelCase						(   lowercase_ )		->						Tuple:


  '''simple docstring'''






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



class    _snake_case :
 __lowerCAmelCase	:  str	   =      {}


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = "root"       ,	SCREAMING_SNAKE_CASE_=0):


   '''simple docstring'''




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

   lowercase__	: Dict     		=   d


 def __repr__(       self):


   '''simple docstring'''




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


 def __setattr__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




   return self._pointer


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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


 @staticmethod
 def   lowercase__  (       SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   with open(SCREAMING_SNAKE_CASE_) as stream:
     lowercase__	: int     		=   load(SCREAMING_SNAKE_CASE_       ,	Loader=SCREAMING_SNAKE_CASE_)
   return data


 def __str__(       self):


   '''simple docstring'''




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


 @classmethod
 def   lowercase__  (       cls       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__		,     lowercase__	: Optional[Any]     		=   cls.get_config_dict(SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
   return cls(SCREAMING_SNAKE_CASE_)



 @classmethod
 def   lowercase__  (       cls       ,	SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   kwargs.pop("""cache_dir"""       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Any     		=   kwargs.pop("""force_download"""       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: List[str]     		=   kwargs.pop("""resume_download"""       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: List[str]     		=   kwargs.pop("""proxies"""       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Dict     		=   kwargs.pop("""local_files_only"""       ,	SCREAMING_SNAKE_CASE_)

   if os.path.isdir(SCREAMING_SNAKE_CASE_):
     lowercase__	: Tuple     		=   os.path.join(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   elif os.path.isfile(SCREAMING_SNAKE_CASE_) or is_remote_url(SCREAMING_SNAKE_CASE_):
     lowercase__	: Optional[int]     		=   pretrained_model_name_or_path
   else:
     lowercase__	: int     		=   hf_bucket_url(SCREAMING_SNAKE_CASE_       ,	filename=SCREAMING_SNAKE_CASE_       ,	use_cdn=SCREAMING_SNAKE_CASE_)

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

     lowercase__	: Union[str, Any]     		=   Config.load_yaml(SCREAMING_SNAKE_CASE_)

   except EnvironmentError:
     lowercase__	: Optional[Any]     		=   """Can't load config for"""
     raise EnvironmentError(SCREAMING_SNAKE_CASE_)

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

   return Config.load_yaml(SCREAMING_SNAKE_CASE_), kwargs

def 		UpperCamelCase						(   lowercase_ )		->						List[Any]:


  '''simple docstring'''






  lowercase__	: str     		=   torch.load("""dump.pt"""			,			map_location=in_tensor.device )
  lowercase__	: int     		=   in_tensor.numpy()
  lowercase__	: Optional[Any]     		=   out_tensor.numpy()[0]
  print(na.shape			,			na[0, 0, :5] )
  print(na.shape			,			na[0, 0, :5] )
  assert np.allclose(lowercase_			,			lowercase_			,			rtol=0.01			,			atol=0.1 ), (
      F'{sum([1 for x in np.isclose(lowercase_			,			lowercase_			,			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 		UpperCamelCase						(   lowercase_ )		->						Dict:


  '''simple docstring'''






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

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_=True )		->						str:


  '''simple docstring'''






  lowercase__	: Optional[Any]     		=   CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
  lowercase__	: Dict     		=   """/""" not in model_id
  if legacy_format:
    return F'{endpoint}/{model_id}-{filename}'
  else:
    return F'{endpoint}/{model_id}/{filename}'

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_=None			,			lowercase_=0			,			lowercase_=None			,			)		->						Optional[int]:


  '''simple docstring'''






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

def 		UpperCamelCase						(   lowercase_			,			lowercase_=None			,			lowercase_=False			,			lowercase_=None			,			lowercase_=10			,			lowercase_=False			,			lowercase_=None			,			lowercase_=False			,			)		->						Tuple:


  '''simple docstring'''






  if cache_dir is None:
    lowercase__	: int     		=   TRANSFORMERS_CACHE
  if isinstance(lowercase_			,			lowercase_ ):
    lowercase__	: str     		=   str(lowercase_ )

  os.makedirs(lowercase_			,			exist_ok=lowercase_ )

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

  lowercase__	: List[Any]     		=   url_to_filename(lowercase_			,			lowercase_ )

  # get cache path to put the file
  lowercase__	: Optional[Any]     		=   os.path.join(lowercase_			,			lowercase_ )

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

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

    if resume_download:
      lowercase__	: Any     		=   cache_path + """.incomplete"""

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

      lowercase__	: List[Any]     		=   _resumable_file_manager
      if os.path.exists(lowercase_ ):
        lowercase__	: str     		=   os.stat(lowercase_ ).st_size
      else:
        lowercase__	: Union[str, Any]     		=   0
    else:
      lowercase__	: int     		=   partial(tempfile.NamedTemporaryFile			,			dir=lowercase_			,			delete=lowercase_ )
      lowercase__	: Dict     		=   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"""			,			lowercase_			,			temp_file.name			,			)

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

    os.replace(temp_file.name			,			lowercase_ )

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

  return cache_path

def 		UpperCamelCase						(   lowercase_			,			lowercase_=None )		->						int:


  '''simple docstring'''






  lowercase__	: Optional[Any]     		=   url.encode("""utf-8""" )
  lowercase__	: Optional[int]     		=   shaaaa(lowercase_ )
  lowercase__	: int     		=   url_hash.hexdigest()

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

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

  return filename

def 		UpperCamelCase						(   lowercase_			,			lowercase_=None			,			lowercase_=False			,			lowercase_=None			,			lowercase_=False			,			lowercase_=None			,			lowercase_=False			,			lowercase_=False			,			lowercase_=False			,			)		->						int:


  '''simple docstring'''






  if cache_dir is None:
    lowercase__	: Union[str, Any]     		=   TRANSFORMERS_CACHE
  if isinstance(lowercase_			,			lowercase_ ):
    lowercase__	: int     		=   str(lowercase_ )
  if isinstance(lowercase_			,			lowercase_ ):
    lowercase__	: int     		=   str(lowercase_ )

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

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

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

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

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

    return output_path_extracted

  return output_path

def 		UpperCamelCase						(   lowercase_			,			lowercase_="," )		->						str:


  '''simple docstring'''






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

def 		UpperCamelCase						(   lowercase_ )		->						str:


  '''simple docstring'''






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

def 		UpperCamelCase						(   lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






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

def 		UpperCamelCase						(   )		->						List[str]:


  '''simple docstring'''






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

def 		UpperCamelCase						(   lowercase_			,			lowercase_="RGB" )		->						Tuple:


  '''simple docstring'''






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



def 		UpperCamelCase						(   lowercase_			,			lowercase_=1 )		->						str:


  '''simple docstring'''






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








import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  int	   =      (DDPMScheduler,)


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   {
       """num_train_timesteps""": 10_00,
       """beta_start""": 0.0_0_0_1,
       """beta_end""": 0.0_2,
       """beta_schedule""": """linear""",
       """variance_type""": """fixed_small""",
       """clip_sample""": True,
   }

   config.update(**SCREAMING_SNAKE_CASE_)
   return config


 def   lowercase__  (       self):


   '''simple docstring'''




   for timesteps in [1, 5, 1_00, 10_00]:
     self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]       ,	[0.0_0_2, 0.0_2, 0.2, 2]):
     self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_       ,	beta_end=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for schedule in ["linear", "squaredcos_cap_v2"]:
     self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for variance in ["fixed_small", "fixed_large", "other"]:
     self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for clip_sample in [True, False]:
     self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_)
   for threshold in [0.5, 1.0, 2.0]:
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
       self.check_over_configs(
           thresholding=SCREAMING_SNAKE_CASE_       ,	prediction_type=SCREAMING_SNAKE_CASE_       ,	sample_max_value=SCREAMING_SNAKE_CASE_       ,	)


 def   lowercase__  (       self):


   '''simple docstring'''




   for prediction_type in ["epsilon", "sample", "v_prediction"]:
     self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for t in [0, 5_00, 9_99]:
     self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   self.scheduler_classes[0]
   lowercase__	: Union[str, Any]     		=   self.get_scheduler_config()
   lowercase__	: List[Any]     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0_9_7_9)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.0_2)) < 1E-5


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.scheduler_classes[0]
   lowercase__	: str     		=   self.get_scheduler_config()
   lowercase__	: Tuple     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: int     		=   len(SCREAMING_SNAKE_CASE_)

   lowercase__	: Any     		=   self.dummy_model()
   lowercase__	: List[Any]     		=   self.dummy_sample_deter
   lowercase__	: str     		=   torch.manual_seed(0)

   for t in reversed(range(SCREAMING_SNAKE_CASE_)):
     # 1. predict noise residual
     lowercase__	: Dict     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     # 2. predict previous mean of sample x_t-1
     lowercase__	: List[str]     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_).prev_sample

     # if t > 0:
     #     noise = self.dummy_sample_deter
     #     variance = scheduler.get_variance(t) ** (0.5) * noise
     #
     # sample = pred_prev_sample + variance
     lowercase__	: str     		=   pred_prev_sample

   lowercase__	: Optional[int]     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: Optional[Any]     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 2_5_8.9_6_0_6) < 1E-2
   assert abs(result_mean.item() - 0.3_3_7_2) < 1E-3


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   self.scheduler_classes[0]
   lowercase__	: Tuple     		=   self.get_scheduler_config(prediction_type="""v_prediction""")
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   len(SCREAMING_SNAKE_CASE_)

   lowercase__	: Tuple     		=   self.dummy_model()
   lowercase__	: Union[str, Any]     		=   self.dummy_sample_deter
   lowercase__	: int     		=   torch.manual_seed(0)

   for t in reversed(range(SCREAMING_SNAKE_CASE_)):
     # 1. predict noise residual
     lowercase__	: List[Any]     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     # 2. predict previous mean of sample x_t-1
     lowercase__	: int     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_).prev_sample

     # if t > 0:
     #     noise = self.dummy_sample_deter
     #     variance = scheduler.get_variance(t) ** (0.5) * noise
     #
     # sample = pred_prev_sample + variance
     lowercase__	: Tuple     		=   pred_prev_sample

   lowercase__	: Union[str, Any]     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: int     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 2_0_2.0_2_9_6) < 1E-2
   assert abs(result_mean.item() - 0.2_6_3_1) < 1E-3


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   self.scheduler_classes[0]
   lowercase__	: int     		=   self.get_scheduler_config()
   lowercase__	: str     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   [1_00, 87, 50, 1, 0]

   scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)

   lowercase__	: List[Any]     		=   scheduler.timesteps

   for i, timestep in enumerate(SCREAMING_SNAKE_CASE_):
     if i == len(SCREAMING_SNAKE_CASE_) - 1:
       lowercase__	: Optional[int]     		=   -1
     else:
       lowercase__	: Tuple     		=   timesteps[i + 1]

     lowercase__	: Any     		=   scheduler.previous_timestep(SCREAMING_SNAKE_CASE_)
     lowercase__	: int     		=   prev_t.item()

     self.assertEqual(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.scheduler_classes[0]
   lowercase__	: List[Any]     		=   self.get_scheduler_config()
   lowercase__	: int     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[Any]     		=   [1_00, 87, 50, 51, 0]

   with self.assertRaises(SCREAMING_SNAKE_CASE_       ,	msg="""`custom_timesteps` must be in descending order."""):
     scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   self.scheduler_classes[0]
   lowercase__	: List[Any]     		=   self.get_scheduler_config()
   lowercase__	: int     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: int     		=   [1_00, 87, 50, 1, 0]
   lowercase__	: Union[str, Any]     		=   len(SCREAMING_SNAKE_CASE_)

   with self.assertRaises(SCREAMING_SNAKE_CASE_       ,	msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""):
     scheduler.set_timesteps(num_inference_steps=SCREAMING_SNAKE_CASE_       ,	timesteps=SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.scheduler_classes[0]
   lowercase__	: int     		=   self.get_scheduler_config()
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: str     		=   [scheduler.config.num_train_timesteps]

   with self.assertRaises(
       SCREAMING_SNAKE_CASE_       ,	msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""       ,	):
     scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)






