


"""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__ (       _UpperCamelCase=OBJECTS	,			_UpperCamelCase=ATTRIBUTES ):





						"""simple docstring"""

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

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






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

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


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      {}






							def __init__(						self :     Any		, lowerCamelCase_ :     dict		, lowerCamelCase_ :     str = "root"		, lowerCamelCase_ :     List[str]=0				):
													UpperCAmelCase__		=    name
													UpperCAmelCase__		=    level
													UpperCAmelCase__		=    {}
													for k, v in dictionary.items():
																			if v is None:
																									raise ValueError()
																			UpperCAmelCase__		=    copy.deepcopy(lowerCamelCase_				)
																			UpperCAmelCase__		=    copy.deepcopy(lowerCamelCase_				)
																			if isinstance(lowerCamelCase_		, lowerCamelCase_				):
																									UpperCAmelCase__		=    Config(lowerCamelCase_		, name=lowerCamelCase_		, level=level + 1				)
																			UpperCAmelCase__		=    v
																			setattr(self		, lowerCamelCase_		, lowerCamelCase_				)

													UpperCAmelCase__		=    d






							def __repr__(						self :     Union[str, Any]				):
													return str(list((self._pointer.keys())				)				)






							def __setattr__(						self :     List[str]		, lowerCamelCase_ :     Any		, lowerCamelCase_ :     Union[str, Any]				):
													UpperCAmelCase__		=    val
													UpperCAmelCase__		=    val
													UpperCAmelCase__		=    key.split('''.'''				)
													UpperCAmelCase__		=    len(lowerCamelCase_				) - 1
													UpperCAmelCase__		=    self._pointer
													if len(lowerCamelCase_				) > 1:
																			for i, l in enumerate(lowerCamelCase_				):
																									if hasattr(self		, lowerCamelCase_				) and isinstance(getattr(self		, lowerCamelCase_				)		, lowerCamelCase_				):
																															setattr(getattr(self		, lowerCamelCase_				)		, '''.'''.join(levels[i:]				)		, lowerCamelCase_				)
																									if l == last_level:
																															UpperCAmelCase__		=    val
																									else:
																															UpperCAmelCase__		=    pointer[l]






							def 					lowerCAmelCase     (						self :     Tuple				):
													return self._pointer






							def 					lowerCAmelCase     (						self :     Dict		, lowerCamelCase_ :     Union[str, Any]		, lowerCamelCase_ :     List[Any]				):
													with open(f"""{file_name}"""		, '''w'''				) as stream:
																			dump(lowerCamelCase_		, lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     Optional[int]		, lowerCamelCase_ :     Union[str, Any]		, lowerCamelCase_ :     Optional[int]				):
													with open(f"""{file_name}"""		, '''w'''				) as stream:
																			json.dump(lowerCamelCase_		, lowerCamelCase_				)






							@staticmethod
							def 					lowerCAmelCase     (						lowerCamelCase_ :     Optional[Any]				):
													with open(lowerCamelCase_				) as stream:
																			UpperCAmelCase__		=    load(lowerCamelCase_		, Loader=lowerCamelCase_				)
													return data






							def __str__(						self :     Tuple				):
													UpperCAmelCase__		=    '''    '''
													if self._name != "root":
																			UpperCAmelCase__		=    f"""{t * (self._level-1)}{self._name}:\n"""
													else:
																			UpperCAmelCase__		=    ''''''
													UpperCAmelCase__		=    self._level
													for i, (k, v) in enumerate(self._pointer.items()				):
																			if isinstance(lowerCamelCase_		, lowerCamelCase_				):
																									r += f"""{t * (self._level)}{v}\n"""
																									self._level += 1
																			else:
																									r += f"""{t * (self._level)}{k}: {v} ({type(lowerCamelCase_				).__name__})\n"""
																			UpperCAmelCase__		=    level
													return r[:-1]






							@classmethod
							def 					lowerCAmelCase     (						cls :     List[str]		, lowerCamelCase_ :     str		, **lowerCamelCase_ :     str				):
													UpperCAmelCase__	,				UpperCAmelCase__		=    cls.get_config_dict(lowerCamelCase_		, **lowerCamelCase_				)
													return cls(lowerCamelCase_				)






							@classmethod
							def 					lowerCAmelCase     (						cls :     Tuple		, lowerCamelCase_ :     str		, **lowerCamelCase_ :     Dict				):
													UpperCAmelCase__		=    kwargs.pop('''cache_dir'''		, lowerCamelCase_				)
													UpperCAmelCase__		=    kwargs.pop('''force_download'''		, lowerCamelCase_				)
													UpperCAmelCase__		=    kwargs.pop('''resume_download'''		, lowerCamelCase_				)
													UpperCAmelCase__		=    kwargs.pop('''proxies'''		, lowerCamelCase_				)
													UpperCAmelCase__		=    kwargs.pop('''local_files_only'''		, lowerCamelCase_				)

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

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

																			UpperCAmelCase__		=    Config.load_yaml(lowerCamelCase_				)

													except EnvironmentError:
																			UpperCAmelCase__		=    '''Can\'t load config for'''
																			raise EnvironmentError(lowerCamelCase_				)

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

													return Config.load_yaml(lowerCamelCase_				), kwargs






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    torch.load('''dump.pt'''	,			map_location=in_tensor.device )
						UpperCAmelCase__		=    in_tensor.numpy()
						UpperCAmelCase__		=    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() )*100:.4f} %"""
						    " element-wise mismatch"
						)
						raise Exception('''tensors are all good''' )

						# Hugging face functions below






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase=True ):





						"""simple docstring"""

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase=None	,			_UpperCamelCase=0	,			_UpperCamelCase=None	,			):





						"""simple docstring"""

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=None	,			_UpperCamelCase=False	,			_UpperCamelCase=None	,			_UpperCamelCase=10	,			_UpperCamelCase=False	,			_UpperCamelCase=None	,			_UpperCamelCase=False	,			):





						"""simple docstring"""

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

						os.makedirs(_UpperCamelCase	,			exist_ok=_UpperCamelCase )

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

						UpperCAmelCase__		=    url_to_filename(_UpperCamelCase	,			_UpperCamelCase )

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

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

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

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

						return cache_path






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=None ):





						"""simple docstring"""

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

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

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

						return filename






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=None	,			_UpperCamelCase=False	,			_UpperCamelCase=None	,			_UpperCamelCase=False	,			_UpperCamelCase=None	,			_UpperCamelCase=False	,			_UpperCamelCase=False	,			_UpperCamelCase=False	,			):





						"""simple docstring"""

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

						if is_remote_url(_UpperCamelCase ):
												# URL, so get it from the cache (downloading if necessary)
												UpperCAmelCase__		=    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.
												UpperCAmelCase__		=    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/"
												UpperCAmelCase__	,				UpperCAmelCase__		=    os.path.split(_UpperCamelCase )
												UpperCAmelCase__		=    output_file.replace('''.'''	,			'''-''' ) + '''-extracted'''
												UpperCAmelCase__		=    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
												UpperCAmelCase__		=    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 ):
																								UpperCAmelCase__		=    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 					a__ (       _UpperCamelCase	,			_UpperCamelCase="," ):





						"""simple docstring"""

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






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

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






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

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






def 					a__ (       ):





						"""simple docstring"""

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase="RGB" ):





						"""simple docstring"""

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=1 ):





						"""simple docstring"""

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



"""simple docstring"""







import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


__a			 =   logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      42
							SCREAMING_SNAKE_CASE										=      None






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=0.999	,			_UpperCamelCase="cosine"	,			):





						"""simple docstring"""

						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_UpperCamelCase ):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(_UpperCamelCase ):
																		return math.exp(t * -12.0 )

						else:
												raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )

						UpperCAmelCase__		=    []
						for i in range(_UpperCamelCase ):
												UpperCAmelCase__		=    i / num_diffusion_timesteps
												UpperCAmelCase__		=    (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(_UpperCamelCase ) / alpha_bar_fn(_UpperCamelCase )	,			_UpperCamelCase ) )
						return torch.tensor(_UpperCamelCase	,			dtype=torch.floataa )


class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      [e.name for e in KarrasDiffusionSchedulers]
							SCREAMING_SNAKE_CASE										=      1






							@register_to_config
							def __init__(						self :     Dict		, lowerCamelCase_ :     int = 1000		, lowerCamelCase_ :     float = 0.0001		, lowerCamelCase_ :     float = 0.02		, lowerCamelCase_ :     str = "linear"		, lowerCamelCase_ :     Optional[Union[np.ndarray, List[float]]] = None		, lowerCamelCase_ :     str = "epsilon"		, lowerCamelCase_ :     str = "linspace"		, lowerCamelCase_ :     int = 0		, ):
													if trained_betas is not None:
																			UpperCAmelCase__		=    torch.tensor(lowerCamelCase_		, dtype=torch.floataa				)
													elif beta_schedule == "linear":
																			UpperCAmelCase__		=    torch.linspace(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, dtype=torch.floataa				)
													elif beta_schedule == "scaled_linear":
																			# this schedule is very specific to the latent diffusion model.
																			UpperCAmelCase__		=    (
																			    torch.linspace(beta_start**0.5		, beta_end**0.5		, lowerCamelCase_		, dtype=torch.floataa				) ** 2
																			)
													elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			UpperCAmelCase__		=    betas_for_alpha_bar(lowerCamelCase_				)
													else:
																			raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""				)

													UpperCAmelCase__		=    1.0 - self.betas
													UpperCAmelCase__		=    torch.cumprod(self.alphas		, dim=0				)

													UpperCAmelCase__		=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
													UpperCAmelCase__		=    np.concatenate([sigmas[::-1], [0.0]]				).astype(np.floataa				)
													UpperCAmelCase__		=    torch.from_numpy(lowerCamelCase_				)

													# setable values
													UpperCAmelCase__		=    None
													UpperCAmelCase__		=    np.linspace(0		, num_train_timesteps - 1		, lowerCamelCase_		, dtype=lowerCamelCase_				)[::-1].copy()
													UpperCAmelCase__		=    torch.from_numpy(lowerCamelCase_				)
													UpperCAmelCase__		=    False






							@property
							def 					lowerCAmelCase     (						self :     Optional[int]				):
													# standard deviation of the initial noise distribution
													if self.config.timestep_spacing in ["linspace", "trailing"]:
																			return self.sigmas.max()

													return (self.sigmas.max() ** 2 + 1) ** 0.5






							def 					lowerCAmelCase     (						self :     Union[str, Any]		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     Union[float, torch.FloatTensor]				):
													if isinstance(lowerCamelCase_		, torch.Tensor				):
																			UpperCAmelCase__		=    timestep.to(self.timesteps.device				)
													UpperCAmelCase__		=    (self.timesteps == timestep).nonzero().item()
													UpperCAmelCase__		=    self.sigmas[step_index]
													UpperCAmelCase__		=    sample / ((sigma**2 + 1) ** 0.5)
													UpperCAmelCase__		=    True
													return sample






							def 					lowerCAmelCase     (						self :     List[Any]		, lowerCamelCase_ :     int		, lowerCamelCase_ :     Union[str, torch.device] = None				):
													UpperCAmelCase__		=    num_inference_steps

													# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
													if self.config.timestep_spacing == "linspace":
																			UpperCAmelCase__		=    np.linspace(0		, self.config.num_train_timesteps - 1		, lowerCamelCase_		, dtype=lowerCamelCase_				)[
																			    ::-1
																			].copy()
													elif self.config.timestep_spacing == "leading":
																			UpperCAmelCase__		=    self.config.num_train_timesteps // self.num_inference_steps
																			# creates integer timesteps by multiplying by ratio
																			# casting to int to avoid issues when num_inference_step is power of 3
																			UpperCAmelCase__		=    (np.arange(0		, lowerCamelCase_				) * step_ratio).round()[::-1].copy().astype(lowerCamelCase_				)
																			timesteps += self.config.steps_offset
													elif self.config.timestep_spacing == "trailing":
																			UpperCAmelCase__		=    self.config.num_train_timesteps / self.num_inference_steps
																			# creates integer timesteps by multiplying by ratio
																			# casting to int to avoid issues when num_inference_step is power of 3
																			UpperCAmelCase__		=    (np.arange(self.config.num_train_timesteps		, 0		, -step_ratio				)).round().copy().astype(lowerCamelCase_				)
																			timesteps -= 1
													else:
																			raise ValueError(
																			    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""				)

													UpperCAmelCase__		=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
													UpperCAmelCase__		=    np.interp(lowerCamelCase_		, np.arange(0		, len(lowerCamelCase_				)				)		, lowerCamelCase_				)
													UpperCAmelCase__		=    np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
													UpperCAmelCase__		=    torch.from_numpy(lowerCamelCase_				).to(device=lowerCamelCase_				)
													if str(lowerCamelCase_				).startswith('''mps'''				):
																			# mps does not support float64
																			UpperCAmelCase__		=    torch.from_numpy(lowerCamelCase_				).to(lowerCamelCase_		, dtype=torch.floataa				)
													else:
																			UpperCAmelCase__		=    torch.from_numpy(lowerCamelCase_				).to(device=lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     List[str]		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     Union[float, torch.FloatTensor]		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     Optional[torch.Generator] = None		, lowerCamelCase_ :     bool = True		, ):

													if (
													    isinstance(lowerCamelCase_		, lowerCamelCase_				)
													    or isinstance(lowerCamelCase_		, torch.IntTensor				)
													    or isinstance(lowerCamelCase_		, torch.LongTensor				)
													):
																			raise ValueError(
																			    (
																			        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
																			        ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
																			        ''' one of the `scheduler.timesteps` as a timestep.'''
																			    )		, )

													if not self.is_scale_input_called:
																			logger.warning(
																			    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
																			    '''See `StableDiffusionPipeline` for a usage example.'''				)

													if isinstance(lowerCamelCase_		, torch.Tensor				):
																			UpperCAmelCase__		=    timestep.to(self.timesteps.device				)

													UpperCAmelCase__		=    (self.timesteps == timestep).nonzero().item()
													UpperCAmelCase__		=    self.sigmas[step_index]

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																			UpperCAmelCase__		=    sample - sigma * model_output
													elif self.config.prediction_type == "v_prediction":
																			# * c_out + input * c_skip
																			UpperCAmelCase__		=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
													elif self.config.prediction_type == "sample":
																			raise NotImplementedError('''prediction_type not implemented yet: sample'''				)
													else:
																			raise ValueError(
																			    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""				)

													UpperCAmelCase__		=    self.sigmas[step_index]
													UpperCAmelCase__		=    self.sigmas[step_index + 1]
													UpperCAmelCase__		=    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
													UpperCAmelCase__		=    (sigma_to**2 - sigma_up**2) ** 0.5

													# 2. Convert to an ODE derivative
													UpperCAmelCase__		=    (sample - pred_original_sample) / sigma

													UpperCAmelCase__		=    sigma_down - sigma

													UpperCAmelCase__		=    sample + derivative * dt

													UpperCAmelCase__		=    model_output.device
													UpperCAmelCase__		=    randn_tensor(model_output.shape		, dtype=model_output.dtype		, device=lowerCamelCase_		, generator=lowerCamelCase_				)

													UpperCAmelCase__		=    prev_sample + noise * sigma_up

													if not return_dict:
																			return (prev_sample,)

													return EulerAncestralDiscreteSchedulerOutput(
													    prev_sample=lowerCamelCase_		, pred_original_sample=lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     int		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     torch.FloatTensor		, ):
													# Make sure sigmas and timesteps have the same device and dtype as original_samples
													UpperCAmelCase__		=    self.sigmas.to(device=original_samples.device		, dtype=original_samples.dtype				)
													if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase_				):
																			# mps does not support float64
																			UpperCAmelCase__		=    self.timesteps.to(original_samples.device		, dtype=torch.floataa				)
																			UpperCAmelCase__		=    timesteps.to(original_samples.device		, dtype=torch.floataa				)
													else:
																			UpperCAmelCase__		=    self.timesteps.to(original_samples.device				)
																			UpperCAmelCase__		=    timesteps.to(original_samples.device				)

													UpperCAmelCase__		=    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

													UpperCAmelCase__		=    sigmas[step_indices].flatten()
													while len(sigma.shape				) < len(original_samples.shape				):
																			UpperCAmelCase__		=    sigma.unsqueeze(-1				)

													UpperCAmelCase__		=    original_samples + noise * sigma
													return noisy_samples






							def __len__(						self :     Optional[int]				):
													return self.config.num_train_timesteps