
'''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

						lowerCAmelCase :  List[Any]      =      True
except ImportError:
						lowerCAmelCase :  List[str]      =      False


try:
						from torch.hub import _get_torch_home

						lowerCAmelCase :  int      =      _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 :  int      =      os.path.join(torch_cache_home, 'transformers')

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







def 						A_(			A		:						Any=OBJECTS						,			A		:						Optional[Any]=ATTRIBUTES):
							UpperCamelCase					       =			[]
							with open(A) as f:
														for object in f.readlines():
																					vg_classes.append(object.split(',')[0].lower().strip())

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







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


class        SCREAMING_SNAKE_CASE__	:
				lowerCAmelCase_			       =				{}







				def __init__(    self		,							A_		,							A_ = "root"		,							A_=0			)->						List[str]:







											'''simple docstring'''






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

											UpperCamelCase					       =			d







				def __repr__(    self			)->						str:







											'''simple docstring'''






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







				def __setattr__(    self		,							A_		,							A_			)->						List[str]:







											'''simple docstring'''






											UpperCamelCase					       =			val
											UpperCamelCase					       =			val
											UpperCamelCase					       =			key.split('.'			)
											UpperCamelCase					       =			len(A_			) - 1
											UpperCamelCase					       =			self._pointer
											if len(A_			) > 1:
																		for i, l in enumerate(A_			):
																									if hasattr(self		,							A_			) and isinstance(getattr(self		,							A_			)		,							A_			):
																																setattr(getattr(self		,							A_			)		,							'.'.join(levels[i:]			)		,							A_			)
																									if l == last_level:
																																UpperCamelCase					       =			val
																									else:
																																UpperCamelCase					       =			pointer[l]







				def 		UpperCAmelCase_  (    self			)->						Optional[int]:







											'''simple docstring'''






											return self._pointer







				def 		UpperCAmelCase_  (    self		,							A_		,							A_			)->						Optional[Any]:







											'''simple docstring'''






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







				def 		UpperCAmelCase_  (    self		,							A_		,							A_			)->						int:







											'''simple docstring'''






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







				@staticmethod
				def 		UpperCAmelCase_  (    A_			)->						str:







											'''simple docstring'''






											with open(A_			) as stream:
																		UpperCamelCase					       =			load(A_		,							Loader=A_			)
											return data







				def __str__(    self			)->						List[Any]:







											'''simple docstring'''






											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(A_		,							A_			):
																									r += F'''{t * (self._level)}{v}\n'''
																									self._level += 1
																		else:
																									r += F'''{t * (self._level)}{k}: {v} ({type(A_			).__name__})\n'''
																		UpperCamelCase					       =			level
											return r[:-1]







				@classmethod
				def 		UpperCAmelCase_  (    cls		,							A_		,							**A_			)->						List[Any]:







											'''simple docstring'''






											UpperCamelCase       ,     UpperCamelCase					       =			cls.get_config_dict(A_		,							**A_			)
											return cls(A_			)







				@classmethod
				def 		UpperCAmelCase_  (    cls		,							A_		,							**A_			)->						List[str]:







											'''simple docstring'''






											UpperCamelCase					       =			kwargs.pop('cache_dir'		,							A_			)
											UpperCamelCase					       =			kwargs.pop('force_download'		,							A_			)
											UpperCamelCase					       =			kwargs.pop('resume_download'		,							A_			)
											UpperCamelCase					       =			kwargs.pop('proxies'		,							A_			)
											UpperCamelCase					       =			kwargs.pop('local_files_only'		,							A_			)

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

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

																		UpperCamelCase					       =			Config.load_yaml(A_			)

											except EnvironmentError:
																		UpperCamelCase					       =			'Can\'t load config for'
																		raise EnvironmentError(A_			)

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

											return Config.load_yaml(A_			), kwargs







def 						A_(			A		:						List[str]):
							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(A						,			A						,			rtol=0.01						,			atol=0.1), (
							    f'''{sum([1 for x in np.isclose(A						,			A						,			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_(			A		:						str):
							UpperCamelCase					       =			urlparse(A)
							return parsed.scheme in ("http", "https")







def 						A_(			A		:						str						,			A		:						str						,			A		:						str=True):
							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_(			A		:						Optional[int]						,			A		:						Optional[Any]						,			A		:						List[str]=None						,			A		:						List[Any]=0						,			A		:						str=None						,			):
							UpperCamelCase					       =			'python/{}'.format(sys.version.split()[0])
							if _torch_available:
														ua += "; torch/{}".format(torch.__version__)
							if isinstance(A						,			A):
														ua += "; " + "; ".join('{}/{}'.format(A						,			A) for k, v in user_agent.items())
							elif isinstance(A						,			A):
														ua += "; " + user_agent
							UpperCamelCase					       =			{'user-agent': ua}
							if resume_size > 0:
														UpperCamelCase					       =			'bytes=%d-' % (resume_size,)
							UpperCamelCase					       =			requests.get(A						,			stream=A						,			proxies=A						,			headers=A)
							if response.status_code == 416:  # Range not satisfiable
														return
							UpperCamelCase					       =			response.headers.get('Content-Length')
							UpperCamelCase					       =			resume_size + int(A) if content_length is not None else None
							UpperCamelCase					       =			tqdm(
							    unit='B'						,			unit_scale=A						,			total=A						,			initial=A						,			desc='Downloading'						,			)
							for chunk in response.iter_content(chunk_size=1024):
														if chunk:  # filter out keep-alive new chunks
																					progress.update(len(A))
																					temp_file.write(A)
							progress.close()







def 						A_(			A		:						List[str]						,			A		:						str=None						,			A		:						Union[str, Any]=False						,			A		:						List[Any]=None						,			A		:						Union[str, Any]=10						,			A		:						Optional[Any]=False						,			A		:						List[str]=None						,			A		:						Any=False						,			):
							if cache_dir is None:
														UpperCamelCase					       =			TRANSFORMERS_CACHE
							if isinstance(A						,			A):
														UpperCamelCase					       =			str(A)

							os.makedirs(A						,			exist_ok=A)

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

							UpperCamelCase					       =			url_to_filename(A						,			A)

							# get cache path to put the file
							UpperCamelCase					       =			os.path.join(A						,			A)

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

							# Prevent parallel downloads of the same file with a lock.
							UpperCamelCase					       =			cache_path + '.lock'
							with FileLock(A):
														# If the download just completed while the lock was activated.
														if os.path.exists(A) 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(A						,			'a+b') as f:
																																			yield f

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

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

														os.replace(temp_file.name						,			A)

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

							return cache_path







def 						A_(			A		:						Tuple						,			A		:						int=None):
							UpperCamelCase					       =			url.encode('utf-8')
							UpperCamelCase					       =			shaaaa(A)
							UpperCamelCase					       =			url_hash.hexdigest()

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

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

							return filename







def 						A_(			A		:						List[str]						,			A		:						Dict=None						,			A		:						List[Any]=False						,			A		:						str=None						,			A		:						Tuple=False						,			A		:						Union[str, Any]=None						,			A		:						Optional[Any]=False						,			A		:						Tuple=False						,			A		:						List[str]=False						,			):
							if cache_dir is None:
														UpperCamelCase					       =			TRANSFORMERS_CACHE
							if isinstance(A						,			A):
														UpperCamelCase					       =			str(A)
							if isinstance(A						,			A):
														UpperCamelCase					       =			str(A)

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

							if extract_compressed_file:
														if not is_zipfile(A) and not tarfile.is_tarfile(A):
																					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(A)
														UpperCamelCase					       =			output_file.replace('.'						,			'-') + '-extracted'
														UpperCamelCase					       =			os.path.join(A						,			A)

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

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

														return output_path_extracted

							return output_path







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







def 						A_(			A		:						List[Any]):
							UpperCamelCase					       =			requests.get(A)
							UpperCamelCase					       =			np.array(Image.open(BytesIO(response.content)))
							return img







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







def 						A_(			):
							print(f'''{os.path.abspath(os.path.join(A						,			os.pardir))}/demo.ipynb''')







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







def 						A_(			A		:						str						,			A		:						Union[str, Any]=1):
							return (images[i : i + batch] for i in range(0						,			len(A)						,			A))






'''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


lowerCAmelCase :  Any      =      logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class        SCREAMING_SNAKE_CASE__	(			snake_case_):
				lowerCAmelCase_			       =				42
				lowerCAmelCase_			       =				None







def 						A_(			A		:						Any						,			A		:						Optional[Any]=0.999						,			A		:						Tuple="cosine"						,			):
							if alpha_transform_type == "cosine":

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

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(A		:						Optional[Any]):
																					return math.exp(t * -12.0)

							else:
														raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''')

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





class        SCREAMING_SNAKE_CASE__	(			snake_case_				,    snake_case_):
				lowerCAmelCase_			       =				[e.name for e in KarrasDiffusionSchedulers]
				lowerCAmelCase_			       =				1







				@register_to_config
				def __init__(    self		,							A_ = 1000		,							A_ = 0.0_001		,							A_ = 0.02		,							A_ = "linear"		,							A_ = None		,							A_ = "epsilon"		,							A_ = "linear"		,							A_ = False		,							A_ = "linspace"		,							A_ = 0		,							)->						Optional[Any]:







											'''simple docstring'''






											if trained_betas is not None:
																		UpperCamelCase					       =			torch.tensor(A_		,							dtype=torch.floataa			)
											elif beta_schedule == "linear":
																		UpperCamelCase					       =			torch.linspace(A_		,							A_		,							A_		,							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		,							A_		,							dtype=torch.floataa			) ** 2
																		)
											elif beta_schedule == "squaredcos_cap_v2":
																		# Glide cosine schedule
																		UpperCamelCase					       =			betas_for_alpha_bar(A_			)
											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(A_			)

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







				@property
				def 		UpperCAmelCase_  (    self			)->						Any:







											'''simple docstring'''






											if self.config.timestep_spacing in ["linspace", "trailing"]:
																		return self.sigmas.max()

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







				def 		UpperCAmelCase_  (    self		,							A_		,							A_			)->						torch.FloatTensor:







											'''simple docstring'''






											if isinstance(A_		,							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 		UpperCAmelCase_  (    self		,							A_		,							A_ = None			)->						List[str]:







											'''simple docstring'''






											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		,							A_		,							dtype=A_			)[
																		    ::-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		,							A_			) * step_ratio).round()[::-1].copy().astype(A_			)
																		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(A_			)
																		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.log(A_			)

											if self.config.interpolation_type == "linear":
																		UpperCamelCase					       =			np.interp(A_		,							np.arange(0		,							len(A_			)			)		,							A_			)
											elif self.config.interpolation_type == "log_linear":
																		UpperCamelCase					       =			torch.linspace(np.log(sigmas[-1]			)		,							np.log(sigmas[0]			)		,							num_inference_steps + 1			).exp()
											else:
																		raise ValueError(
																		    F'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
																		    ' \'linear\' or \'log_linear\''			)

											if self.use_karras_sigmas:
																		UpperCamelCase					       =			self._convert_to_karras(in_sigmas=A_		,							num_inference_steps=self.num_inference_steps			)
																		UpperCamelCase					       =			np.array([self._sigma_to_t(A_		,							A_			) for sigma in sigmas]			)

											UpperCamelCase					       =			np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
											UpperCamelCase					       =			torch.from_numpy(A_			).to(device=A_			)
											if str(A_			).startswith('mps'			):
																		# mps does not support float64
																		UpperCamelCase					       =			torch.from_numpy(A_			).to(A_		,							dtype=torch.floataa			)
											else:
																		UpperCamelCase					       =			torch.from_numpy(A_			).to(device=A_			)







				def 		UpperCAmelCase_  (    self		,							A_		,							A_			)->						Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase					       =			np.log(A_			)

											# get distribution
											UpperCamelCase					       =			log_sigma - log_sigmas[:, np.newaxis]

											# get sigmas range
											UpperCamelCase					       =			np.cumsum((dists >= 0)		,							axis=0			).argmax(axis=0			).clip(max=log_sigmas.shape[0] - 2			)
											UpperCamelCase					       =			low_idx + 1

											UpperCamelCase					       =			log_sigmas[low_idx]
											UpperCamelCase					       =			log_sigmas[high_idx]

											# interpolate sigmas
											UpperCamelCase					       =			(low - log_sigma) / (low - high)
											UpperCamelCase					       =			np.clip(A_		,							0		,							1			)

											# transform interpolation to time range
											UpperCamelCase					       =			(1 - w) * low_idx + w * high_idx
											UpperCamelCase					       =			t.reshape(sigma.shape			)
											return t







				def 		UpperCAmelCase_  (    self		,							A_		,							A_			)->						torch.FloatTensor:







											'''simple docstring'''






											UpperCamelCase					       =			in_sigmas[-1].item()
											UpperCamelCase					       =			in_sigmas[0].item()

											UpperCamelCase					       =			7.0  # 7.0 is the value used in the paper
											UpperCamelCase					       =			np.linspace(0		,							1		,							A_			)
											UpperCamelCase					       =			sigma_min ** (1 / rho)
											UpperCamelCase					       =			sigma_max ** (1 / rho)
											UpperCamelCase					       =			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
											return sigmas







				def 		UpperCAmelCase_  (    self		,							A_		,							A_		,							A_		,							A_ = 0.0		,							A_ = 0.0		,							A_ = float('inf'			)		,							A_ = 1.0		,							A_ = None		,							A_ = True		,							)->						Union[EulerDiscreteSchedulerOutput, Tuple]:







											'''simple docstring'''






											if (
											    isinstance(A_		,							A_			)
											    or isinstance(A_		,							torch.IntTensor			)
											    or isinstance(A_		,							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(A_		,							torch.Tensor			):
																		UpperCamelCase					       =			timestep.to(self.timesteps.device			)

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

											UpperCamelCase					       =			min(s_churn / (len(self.sigmas			) - 1)		,							2**0.5 - 1			) if s_tmin <= sigma <= s_tmax else 0.0

											UpperCamelCase					       =			randn_tensor(
											    model_output.shape		,							dtype=model_output.dtype		,							device=model_output.device		,							generator=A_			)

											UpperCamelCase					       =			noise * s_noise
											UpperCamelCase					       =			sigma * (gamma + 1)

											if gamma > 0:
																		UpperCamelCase					       =			sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											# NOTE: "original_sample" should not be an expected prediction_type but is left in for
											# backwards compatibility
											if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
																		UpperCamelCase					       =			model_output
											elif self.config.prediction_type == "epsilon":
																		UpperCamelCase					       =			sample - sigma_hat * 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))
											else:
																		raise ValueError(
																		    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''			)

											# 2. Convert to an ODE derivative
											UpperCamelCase					       =			(sample - pred_original_sample) / sigma_hat

											UpperCamelCase					       =			self.sigmas[step_index + 1] - sigma_hat

											UpperCamelCase					       =			sample + derivative * dt

											if not return_dict:
																		return (prev_sample,)

											return EulerDiscreteSchedulerOutput(prev_sample=A_		,							pred_original_sample=A_			)







				def 		UpperCAmelCase_  (    self		,							A_		,							A_		,							A_		,							)->						torch.FloatTensor:







											'''simple docstring'''






											UpperCamelCase					       =			self.sigmas.to(device=original_samples.device		,							dtype=original_samples.dtype			)
											if original_samples.device.type == "mps" and torch.is_floating_point(A_			):
																		# 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]:







											'''simple docstring'''






											return self.config.num_train_timesteps




