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

					__UpperCamelCase	: Optional[int]    		=  True
except ImportError:
					__UpperCamelCase	: str    		=  False


try:
					from torch.hub import _get_torch_home

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

__UpperCamelCase	: List[Any]    		=  os.path.join(torch_cache_home, 'transformers')

__UpperCamelCase	: Optional[int]    		=  'https://cdn.huggingface.co'
__UpperCamelCase	: Tuple    		=  'https://s3.amazonaws.com/models.huggingface.co/bert'
__UpperCamelCase	: List[Any]    		=  '/'.join(str(Path(__file__).resolve()).split('/')[:-1])
__UpperCamelCase	: str    		=  os.path.join(PATH, 'config.yaml')
__UpperCamelCase	: Optional[Any]    		=  os.path.join(PATH, 'attributes.txt')
__UpperCamelCase	: Any    		=  os.path.join(PATH, 'objects.txt')
__UpperCamelCase	: str    		=  os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', default_cache_path)
__UpperCamelCase	: List[Any]    		=  os.getenv('PYTORCH_TRANSFORMERS_CACHE', PYTORCH_PRETRAINED_BERT_CACHE)
__UpperCamelCase	: str    		=  os.getenv('TRANSFORMERS_CACHE', PYTORCH_TRANSFORMERS_CACHE)
__UpperCamelCase	: int    		=  'pytorch_model.bin'
__UpperCamelCase	: Any    		=  'config.yaml'
def    A			(					_lowercase=OBJECTS	,						_lowercase=ATTRIBUTES		):
						SCREAMING_SNAKE_CASE							:					Tuple           =							[]
						with open(_lowercase		) as f:
												for object in f.readlines():
																		vg_classes.append(object.split(''','''		)[0].lower().strip()		)

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[]
						with open(_lowercase		) as f:
												for object in f.readlines():
																		vg_attrs.append(object.split(''','''		)[0].lower().strip()		)
						return vg_classes, vg_attrs
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							OrderedDict()
						with open(_lowercase	,						'''rb'''		) as f:
												SCREAMING_SNAKE_CASE							:					List[Any]           =							pkl.load(_lowercase		)['''model''']
						for k in copy.deepcopy(list(ckp.keys()		)		):
												SCREAMING_SNAKE_CASE							:					Dict           =							ckp.pop(_lowercase		)
												if isinstance(_lowercase	,						np.ndarray		):
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.tensor(_lowercase		)
												else:
																		assert isinstance(_lowercase	,						torch.tensor		), type(_lowercase		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							v
						return r





class       lowercase__		:
						UpperCamelCase_  =	{}


						def __init__( self			:       List[Any]					,	UpperCamelCase__			:       dict					,	UpperCamelCase__			:       str = "root"					,	UpperCamelCase__			:       Dict=0		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:					Tuple           =							d


						def __repr__( self			:       List[Any]		):





												'''simple docstring'''





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


						def __setattr__( self			:       int					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





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


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												return self._pointer


						def  __A ( self			:       str					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





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


						def  __A ( self			:       Any					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





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


						@staticmethod
						def  __A ( UpperCamelCase__			:       Dict		):





												'''simple docstring'''





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


						def __str__( self			:       Tuple		):





												'''simple docstring'''





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


						@classmethod
						def  __A ( cls			:       Dict					,	UpperCamelCase__			:       str					,	**UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					List[Any]           =							cls.get_config_dict(UpperCamelCase__					,	**UpperCamelCase__		)
												return cls(UpperCamelCase__		)



						@classmethod
						def  __A ( cls			:       Union[str, Any]					,	UpperCamelCase__			:       str					,	**UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							kwargs.pop('''cache_dir'''					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					str           =							kwargs.pop('''force_download'''					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							kwargs.pop('''resume_download'''					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							kwargs.pop('''proxies'''					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							kwargs.pop('''local_files_only'''					,	UpperCamelCase__		)

												if os.path.isdir(UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							os.path.join(UpperCamelCase__					,	UpperCamelCase__		)
												elif os.path.isfile(UpperCamelCase__		) or is_remote_url(UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Any           =							pretrained_model_name_or_path
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							hf_bucket_url(UpperCamelCase__					,	filename=UpperCamelCase__					,	use_cdn=UpperCamelCase__		)

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

																		SCREAMING_SNAKE_CASE							:					List[Any]           =							Config.load_yaml(UpperCamelCase__		)

												except EnvironmentError:
																		SCREAMING_SNAKE_CASE							:					Dict           =							'''Can\'t load config for'''
																		raise EnvironmentError(UpperCamelCase__		)

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

												return Config.load_yaml(UpperCamelCase__		), kwargs






def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.load('''dump.pt'''	,						map_location=in_tensor.device		)
						SCREAMING_SNAKE_CASE							:					Tuple           =							in_tensor.numpy()
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							out_tensor.numpy()[0]
						print(na.shape	,						na[0, 0, :5]		)
						print(na.shape	,						na[0, 0, :5]		)
						assert np.allclose(_lowercase	,						_lowercase	,						rtol=0.01	,						atol=0.1		), (
						    f"""{sum([1 for x in np.isclose(_lowercase	,						_lowercase	,						rtol=0.01	,						atol=0.1		).flatten() if x is False]		)/len(na.flatten()		)*100:.4f} %"""
						    " element-wise mismatch"
						)
						raise Exception('''tensors are all good'''		)

						# Hugging face functions below
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					List[str]           =							urlparse(_lowercase		)
						return parsed.scheme in ("http", "https")
def    A			(					_lowercase	,						_lowercase	,						_lowercase=True		):
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							'''/''' not in model_id
						if legacy_format:
												return f"""{endpoint}/{model_id}-{filename}"""
						else:
												return f"""{endpoint}/{model_id}/{filename}"""
def    A			(					_lowercase	,						_lowercase	,						_lowercase=None	,						_lowercase=0	,						_lowercase=None	,						):
						SCREAMING_SNAKE_CASE							:					List[str]           =							'''python/{}'''.format(sys.version.split()[0]		)
						if _torch_available:
												ua += "; torch/{}".format(torch.__version__		)
						if isinstance(_lowercase	,						_lowercase		):
												ua += "; " + "; ".join('''{}/{}'''.format(_lowercase	,						_lowercase		) for k, v in user_agent.items()		)
						elif isinstance(_lowercase	,						_lowercase		):
												ua += "; " + user_agent
						SCREAMING_SNAKE_CASE							:					List[str]           =							{'''user-agent''': ua}
						if resume_size > 0:
												SCREAMING_SNAKE_CASE							:					Dict           =							'''bytes=%d-''' % (resume_size,)
						SCREAMING_SNAKE_CASE							:					Tuple           =							requests.get(_lowercase	,						stream=_lowercase	,						proxies=_lowercase	,						headers=_lowercase		)
						if response.status_code == 416:  # Range not satisfiable
												return
						SCREAMING_SNAKE_CASE							:					Dict           =							response.headers.get('''Content-Length'''		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							resume_size + int(_lowercase		) if content_length is not None else None
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tqdm(
						    unit='''B'''	,						unit_scale=_lowercase	,						total=_lowercase	,						initial=_lowercase	,						desc='''Downloading'''	,						)
						for chunk in response.iter_content(chunk_size=1_024		):
												if chunk:  # filter out keep-alive new chunks
																		progress.update(len(_lowercase		)		)
																		temp_file.write(_lowercase		)
						progress.close()
def    A			(					_lowercase	,						_lowercase=None	,						_lowercase=False	,						_lowercase=None	,						_lowercase=10	,						_lowercase=False	,						_lowercase=None	,						_lowercase=False	,						):
						if cache_dir is None:
												SCREAMING_SNAKE_CASE							:					List[Any]           =							TRANSFORMERS_CACHE
						if isinstance(_lowercase	,						_lowercase		):
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							str(_lowercase		)

						os.makedirs(_lowercase	,						exist_ok=_lowercase		)

						SCREAMING_SNAKE_CASE							:					str           =							None
						if not local_files_only:
												try:
																		SCREAMING_SNAKE_CASE							:					int           =							requests.head(_lowercase	,						allow_redirects=_lowercase	,						proxies=_lowercase	,						timeout=_lowercase		)
																		if response.status_code == 200:
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							response.headers.get('''ETag'''		)
												except (EnvironmentError, requests.exceptions.Timeout):
																		# etag is already None
																		pass

						SCREAMING_SNAKE_CASE							:					Optional[int]           =							url_to_filename(_lowercase	,						_lowercase		)

						# get cache path to put the file
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							os.path.join(_lowercase	,						_lowercase		)

						# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
						# try to get the last downloaded one
						if etag is None:
												if os.path.exists(_lowercase		):
																		return cache_path
												else:
																		SCREAMING_SNAKE_CASE							:					Any           =							[
																		    file
																		    for file in fnmatch.filter(os.listdir(_lowercase		)	,						filename + '''.*'''		)
																		    if not file.endswith('''.json'''		) and not file.endswith('''.lock'''		)
																		]
																		if len(_lowercase		) > 0:
																								return os.path.join(_lowercase	,						matching_files[-1]		)
																		else:
																								# If files cannot be found and local_files_only=True,
																								# the models might've been found if local_files_only=False
																								# Notify the user about that
																								if local_files_only:
																														raise ValueError(
																														    '''Cannot find the requested files in the cached path and outgoing traffic has been'''
																														    ''' disabled. To enable model look-ups and downloads online, set \'local_files_only\''''
																														    ''' to False.'''		)
																								return None

    # From now on, etag is not None.
						if os.path.exists(_lowercase		) and not force_download:
												return cache_path

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

												if resume_download:
																		SCREAMING_SNAKE_CASE							:					int           =							cache_path + '''.incomplete'''

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

																		SCREAMING_SNAKE_CASE							:					List[Any]           =							_resumable_file_manager
																		if os.path.exists(_lowercase		):
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							os.stat(_lowercase		).st_size
																		else:
																								SCREAMING_SNAKE_CASE							:					Dict           =							0
												else:
																		SCREAMING_SNAKE_CASE							:					Any           =							partial(tempfile.NamedTemporaryFile	,						dir=_lowercase	,						delete=_lowercase		)
																		SCREAMING_SNAKE_CASE							:					List[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'''	,						_lowercase	,						temp_file.name	,						)

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

												os.replace(temp_file.name	,						_lowercase		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							{'''url''': url, '''etag''': etag}
												SCREAMING_SNAKE_CASE							:					int           =							cache_path + '''.json'''
												with open(_lowercase	,						'''w'''		) as meta_file:
																		json.dump(_lowercase	,						_lowercase		)

						return cache_path
def    A			(					_lowercase	,						_lowercase=None		):
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							url.encode('''utf-8'''		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							shaaaa(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							url_hash.hexdigest()

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

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

						return filename
def    A			(					_lowercase	,						_lowercase=None	,						_lowercase=False	,						_lowercase=None	,						_lowercase=False	,						_lowercase=None	,						_lowercase=False	,						_lowercase=False	,						_lowercase=False	,						):
						if cache_dir is None:
												SCREAMING_SNAKE_CASE							:					List[Any]           =							TRANSFORMERS_CACHE
						if isinstance(_lowercase	,						_lowercase		):
												SCREAMING_SNAKE_CASE							:					int           =							str(_lowercase		)
						if isinstance(_lowercase	,						_lowercase		):
												SCREAMING_SNAKE_CASE							:					int           =							str(_lowercase		)

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

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

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

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

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

												return output_path_extracted

						return output_path
def    A			(					_lowercase	,						_lowercase=","		):
						assert isinstance(_lowercase	,						_lowercase		)
						if os.path.isfile(_lowercase		):
												with open(_lowercase		) as f:
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							eval(f.read()		)
						else:
												SCREAMING_SNAKE_CASE							:					Tuple           =							requests.get(_lowercase		)
												try:
																		SCREAMING_SNAKE_CASE							:					str           =							requests.json()
												except Exception:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							req.content.decode()
																		assert data is not None, "could not connect"
																		try:
																								SCREAMING_SNAKE_CASE							:					List[str]           =							eval(_lowercase		)
																		except Exception:
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							data.split('''\n'''		)
												req.close()
						return data
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Any           =							requests.get(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							np.array(Image.open(BytesIO(response.content		)		)		)
						return img
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							url.split('''/'''		)[-1]
						if fn not in os.listdir(os.getcwd()		):
												wget.download(_lowercase		)
						with open(_lowercase	,						'''rb'''		) as stream:
												SCREAMING_SNAKE_CASE							:					int           =							pkl.load(_lowercase		)
						SCREAMING_SNAKE_CASE							:					str           =							weights.pop('''model'''		)
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{}
						for k, v in model.items():
												SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.from_numpy(_lowercase		)
												if "running_var" in k:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.tensor([0]		)
																		SCREAMING_SNAKE_CASE							:					int           =							k.replace('''running_var'''	,						'''num_batches_tracked'''		)
																		SCREAMING_SNAKE_CASE							:					int           =							zero
						return new
def    A			(					):
						print(f"""{os.path.abspath(os.path.join(_lowercase	,						os.pardir		)		)}/demo.ipynb"""		)
def    A			(					_lowercase	,						_lowercase="RGB"		):
						assert isinstance(_lowercase	,						_lowercase		)
						if os.path.isfile(_lowercase		):
												SCREAMING_SNAKE_CASE							:					Any           =							cva.imread(_lowercase		)
						else:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							get_image_from_url(_lowercase		)
												assert img is not None, f"""could not connect to: {im}"""
						SCREAMING_SNAKE_CASE							:					int           =							cva.cvtColor(_lowercase	,						cva.COLOR_BGR2RGB		)
						if input_format == "RGB":
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							img[:, :, ::-1]
						return img



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






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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput





class       lowercase__		(     UpperCamelCase_			,							UpperCamelCase_):
						UpperCamelCase_  =	1


						@register_to_config
						def __init__( self			:       List[str]					,	UpperCamelCase__			:       int = 1000					,	UpperCamelCase__			:       Optional[Union[np.ndarray, List[float]]] = None		):





												'''simple docstring'''





												self.set_timesteps(UpperCamelCase__		)

												# standard deviation of the initial noise distribution
												SCREAMING_SNAKE_CASE							:					str           =							1.0

												# For now we only support F-PNDM, i.e. the runge-kutta method
												# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
												# mainly at formula (9), (12), (13) and the Algorithm 2.
												SCREAMING_SNAKE_CASE							:					Tuple           =							4

												# running values
												SCREAMING_SNAKE_CASE							:					int           =							[]


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Union[str, torch.device] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							num_inference_steps
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							torch.linspace(1					,	0					,	num_inference_steps + 1		)[:-1]
												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.cat([steps, torch.tensor([0.0]		)]		)

												if self.config.trained_betas is not None:
																		SCREAMING_SNAKE_CASE							:					int           =							torch.tensor(self.config.trained_betas					,	dtype=torch.floataa		)
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.sin(steps * math.pi / 2		) ** 2

												SCREAMING_SNAKE_CASE							:					Dict           =							(1.0 - self.betas**2) ** 0.5

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(torch.atana(self.betas					,	self.alphas		) / math.pi * 2)[:-1]
												SCREAMING_SNAKE_CASE							:					List[str]           =							timesteps.to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Any           =							[]


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       torch.FloatTensor					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       torch.FloatTensor					,	UpperCamelCase__			:       bool = True					,	):





												'''simple docstring'''





												if self.num_inference_steps is None:
																		raise ValueError(
																		    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							(self.timesteps == timestep).nonzero().item()
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							timestep_index + 1

												SCREAMING_SNAKE_CASE							:					int           =							sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(UpperCamelCase__		)

												if len(self.ets		) == 1:
																		SCREAMING_SNAKE_CASE							:					Dict           =							self.ets[-1]
												elif len(self.ets		) == 2:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							(3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets		) == 3:
																		SCREAMING_SNAKE_CASE							:					str           =							(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self._get_prev_sample(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												if not return_dict:
																		return (prev_sample,)

												return SchedulerOutput(prev_sample=UpperCamelCase__		)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       torch.FloatTensor					,	*UpperCamelCase__			:       List[str]					,	**UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												return sample


						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Any					,	UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							self.alphas[timestep_index]
												SCREAMING_SNAKE_CASE							:					List[str]           =							self.betas[timestep_index]

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.alphas[prev_timestep_index]
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.betas[prev_timestep_index]

												SCREAMING_SNAKE_CASE							:					Dict           =							(sample - sigma * ets) / max(UpperCamelCase__					,	1E-8		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							next_alpha * pred + ets * next_sigma

												return prev_sample



						def __len__( self			:       int		):





												'''simple docstring'''





												return self.config.num_train_timesteps





