
"""simple docstring"""





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

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


try:
    import torch

    _a									=				True
except ImportError:
    _a									=				False


try:
    from torch.hub import _get_torch_home

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

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

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







def       __a	(						__lowerCamelCase=OBJECTS,						__lowerCamelCase=ATTRIBUTES    ):
   UpperCAmelCase_   :   Optional[Any]     	=       []
   with open(__lowerCamelCase    ) as f:
      for object in f.readlines():
         vg_classes.append(object.split(","    )[0].lower().strip()    )

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


class A_      :




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Optional[Any]    	=						{}







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

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

     UpperCAmelCase_   :   List[Any]     	=       d







  def __repr__(						self    ):

     """simple docstring"""
     return str(list((self._pointer.keys())    )    )







  def __setattr__(						self							,  lowercase_							,  lowercase_    ):

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







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self._pointer







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     with open(F"""{file_name}"""							,  "w"    ) as stream:
        dump(lowercase_							,  lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     with open(F"""{file_name}"""							,  "w"    ) as stream:
        json.dump(lowercase_							,  lowercase_    )







  @staticmethod
  def 			UpperCamelCase__   (						lowercase_    ):

     """simple docstring"""
     with open(lowercase_    ) as stream:
        UpperCAmelCase_   :   Optional[Any]     	=       load(lowercase_							,  Loader=lowercase_    )
     return data







  def __str__(						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       "    "
     if self._name != "root":
        UpperCAmelCase_   :   Optional[int]     	=       F"""{t * (self._level-1)}{self._name}:\n"""
     else:
        UpperCAmelCase_   :   List[str]     	=       ""
     UpperCAmelCase_   :   Dict     	=       self._level
     for i, (k, v) in enumerate(self._pointer.items()    ):
        if isinstance(lowercase_							,  lowercase_    ):
           r += F"""{t * (self._level)}{v}\n"""
           self._level += 1
        else:
           r += F"""{t * (self._level)}{k}: {v} ({type(lowercase_    ).__name__})\n"""
        UpperCAmelCase_   :   str     	=       level
     return r[:-1]







  @classmethod
  def 			UpperCamelCase__   (						cls							,  lowercase_							,  **lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   List[Any]     	=       cls.get_config_dict(lowercase_							,  **lowercase_    )
     return cls(lowercase_    )







  @classmethod
  def 			UpperCamelCase__   (						cls							,  lowercase_							,  **lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       kwargs.pop("cache_dir"							,  lowercase_    )
     UpperCAmelCase_   :   Dict     	=       kwargs.pop("force_download"							,  lowercase_    )
     UpperCAmelCase_   :   int     	=       kwargs.pop("resume_download"							,  lowercase_    )
     UpperCAmelCase_   :   Dict     	=       kwargs.pop("proxies"							,  lowercase_    )
     UpperCAmelCase_   :   List[str]     	=       kwargs.pop("local_files_only"							,  lowercase_    )

     if os.path.isdir(lowercase_    ):
        UpperCAmelCase_   :   str     	=       os.path.join(lowercase_							,  lowercase_    )
     elif os.path.isfile(lowercase_    ) or is_remote_url(lowercase_    ):
        UpperCAmelCase_   :   Any     	=       pretrained_model_name_or_path
     else:
        UpperCAmelCase_   :   str     	=       hf_bucket_url(lowercase_							,  filename=lowercase_							,  use_cdn=lowercase_    )

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

        UpperCAmelCase_   :   Union[str, Any]     	=       Config.load_yaml(lowercase_    )

     except EnvironmentError:
        UpperCAmelCase_   :   Any     	=       "Can't load config for"
        raise EnvironmentError(lowercase_    )

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

     return Config.load_yaml(lowercase_    ), kwargs







def       __a	(						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       torch.load("dump.pt",						map_location=in_tensor.device    )
   UpperCAmelCase_   :   List[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       __a	(						__lowerCamelCase    ):
   UpperCAmelCase_   :   List[Any]     	=       urlparse(__lowerCamelCase    )
   return parsed.scheme in ("http", "https")







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase=True    ):
   UpperCAmelCase_   :   str     	=       CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
   UpperCAmelCase_   :   Optional[int]     	=       "/" not in model_id
   if legacy_format:
      return f"""{endpoint}/{model_id}-{filename}"""
   else:
      return f"""{endpoint}/{model_id}/{filename}"""







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase=None,						__lowerCamelCase=0,						__lowerCamelCase=None,						):
   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_   :   Optional[Any]     	=       {"user-agent": ua}
   if resume_size > 0:
      UpperCAmelCase_   :   Any     	=       "bytes=%d-" % (resume_size,)
   UpperCAmelCase_   :   Optional[int]     	=       requests.get(__lowerCamelCase,						stream=__lowerCamelCase,						proxies=__lowerCamelCase,						headers=__lowerCamelCase    )
   if response.status_code == 416:  # Range not satisfiable
      return
   UpperCAmelCase_   :   Optional[Any]     	=       response.headers.get("Content-Length"    )
   UpperCAmelCase_   :   Union[str, Any]     	=       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       __a	(						__lowerCamelCase,						__lowerCamelCase=None,						__lowerCamelCase=False,						__lowerCamelCase=None,						__lowerCamelCase=10,						__lowerCamelCase=False,						__lowerCamelCase=None,						__lowerCamelCase=False,						):
   if cache_dir is None:
      UpperCAmelCase_   :   Union[str, Any]     	=       TRANSFORMERS_CACHE
   if isinstance(__lowerCamelCase,						__lowerCamelCase    ):
      UpperCAmelCase_   :   str     	=       str(__lowerCamelCase    )

   os.makedirs(__lowerCamelCase,						exist_ok=__lowerCamelCase    )

   UpperCAmelCase_   :   Optional[int]     	=       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_   :   str     	=       response.headers.get("ETag"    )
      except (EnvironmentError, requests.exceptions.Timeout):
         # etag is already None
         pass

   UpperCAmelCase_   :   int     	=       url_to_filename(__lowerCamelCase,						__lowerCamelCase    )

   # get cache path to put the file
   UpperCAmelCase_   :   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_   :   Optional[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_   :   Any     	=       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_   :   List[str]     	=       cache_path + ".incomplete"

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

         UpperCAmelCase_   :   Any     	=       _resumable_file_manager
         if os.path.exists(__lowerCamelCase    ):
            UpperCAmelCase_   :   int     	=       os.stat(__lowerCamelCase    ).st_size
         else:
            UpperCAmelCase_   :   List[str]     	=       0
      else:
         UpperCAmelCase_   :   Any     	=       partial(tempfile.NamedTemporaryFile,						dir=__lowerCamelCase,						delete=__lowerCamelCase    )
         UpperCAmelCase_   :   Any     	=       0

      # Download to temporary file, then copy to cache dir once finished.
      # Otherwise you get corrupt cache entries if the download gets interrupted.
      with temp_file_manager() as temp_file:
         print(
             "%s not found in cache or force_download set to True, downloading to %s",						__lowerCamelCase,						temp_file.name,						)

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

      os.replace(temp_file.name,						__lowerCamelCase    )

      UpperCAmelCase_   :   Dict     	=       {"url": url, "etag": etag}
      UpperCAmelCase_   :   Dict     	=       cache_path + ".json"
      with open(__lowerCamelCase,						"w"    ) as meta_file:
         json.dump(__lowerCamelCase,						__lowerCamelCase    )

   return cache_path







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

   if etag:
      UpperCAmelCase_   :   List[str]     	=       etag.encode("utf-8"    )
      UpperCAmelCase_   :   Union[str, Any]     	=       shaaaa(__lowerCamelCase    )
      filename += "." + etag_hash.hexdigest()

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

   return filename







def       __a	(						__lowerCamelCase,						__lowerCamelCase=None,						__lowerCamelCase=False,						__lowerCamelCase=None,						__lowerCamelCase=False,						__lowerCamelCase=None,						__lowerCamelCase=False,						__lowerCamelCase=False,						__lowerCamelCase=False,						):
   if cache_dir is None:
      UpperCAmelCase_   :   str     	=       TRANSFORMERS_CACHE
   if isinstance(__lowerCamelCase,						__lowerCamelCase    ):
      UpperCAmelCase_   :   List[Any]     	=       str(__lowerCamelCase    )
   if isinstance(__lowerCamelCase,						__lowerCamelCase    ):
      UpperCAmelCase_   :   int     	=       str(__lowerCamelCase    )

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







def       __a	(						__lowerCamelCase    ):
   UpperCAmelCase_   :   Any     	=       requests.get(__lowerCamelCase    )
   UpperCAmelCase_   :   Union[str, Any]     	=       np.array(Image.open(BytesIO(response.content    )    )    )
   return img







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







def       __a	(						):
   print(f"""{os.path.abspath(os.path.join(__lowerCamelCase,						os.pardir    )    )}/demo.ipynb"""    )







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







def       __a	(						__lowerCamelCase,						__lowerCamelCase=1    ):
   return (images[i : i + batch] for i in range(0,						len(__lowerCamelCase    ),						__lowerCamelCase    ))



import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest







class       __snake_case (  lowerCamelCase__ ):
 __lowerCamelCase     : Dict 	=   (VQDiffusionScheduler,)
 def 						UpperCAmelCase__							(	self		, **snake_case__    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	={
           '''num_vec_classes''': 4097,
           '''num_train_timesteps''': 100,
       }

       config.update(**snake_case__    )
       return config
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			List[str]     	=4
       UpperCAmelCase     :			Optional[int]     	=8
       UpperCAmelCase     :			Optional[Any]     	=8

       UpperCAmelCase     :			str     	=torch.randint(0		, snake_case__		, (batch_size, height * width)    )

       return sample
 @property
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       assert False
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Tuple:
       '''simple docstring'''





       def model(snake_case__		, snake_case__		, *snake_case__    ):
             UpperCAmelCase	,    UpperCAmelCase     :			Union[str, Any]     	=sample.shape
             UpperCAmelCase     :			Tuple     	=torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)    )
             UpperCAmelCase     :			List[str]     	=F.log_softmax(logits.double()		, dim=1    ).float()
             return return_value

       return model
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       for timesteps in [2, 5, 100, 1000]:
             self.check_over_configs(num_train_timesteps=snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





       for num_vec_classes in [5, 100, 1000, 4000]:
             self.check_over_configs(num_vec_classes=snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       for t in [0, 50, 99]:
             self.check_over_forward(time_step=snake_case__    )






 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       pass
