






'''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							=True
except ImportError:
						__lowerCAmelCase							=False


try:
						from torch.hub import _get_torch_home

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

__lowerCAmelCase							=os.path.join(torch_cache_home, "transformers")

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

def 							a	(   _UpperCAmelCase=OBJECTS					,  _UpperCAmelCase=ATTRIBUTES )				->				Optional[Any]:






					"""simple docstring"""







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

					a_									=   []
					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 )				->				Optional[Any]:






					"""simple docstring"""







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


class    _snake_case   :

					"""simple docstring"""





					_UpperCamelCase		 =      {}




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

										a_									=   d




					def __repr__(     self     )      ->	int:
										return str(list((self._pointer.keys())     )     )




					def __setattr__(     self   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	Dict:
										a_									=   val
										a_									=   val
										a_									=   key.split('.'     )
										a_									=   len(UpperCAmelCase__     ) - 1
										a_									=   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:
																									a_									=   val
																				else:
																									a_									=   pointer[l]




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										return self._pointer




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	Optional[int]:
										with open(F'''{file_name}'''   , 'w'     ) as stream:
															dump(UpperCAmelCase__   , UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	List[str]:
										with open(F'''{file_name}'''   , 'w'     ) as stream:
															json.dump(UpperCAmelCase__   , UpperCAmelCase__     )




					@staticmethod
					def  __SCREAMING_SNAKE_CASE (     UpperCAmelCase__     )      ->	Any:
										with open(UpperCAmelCase__     ) as stream:
															a_									=   load(UpperCAmelCase__   , Loader=UpperCAmelCase__     )
										return data




					def __str__(     self     )      ->	Optional[int]:
										a_									=   '    '
										if self._name != "root":
															a_									=   F'''{t * (self._level-1)}{self._name}:\n'''
										else:
															a_									=   ''
										a_									=   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'''
															a_									=   level
										return r[:-1]




					@classmethod
					def  __SCREAMING_SNAKE_CASE (     cls   , UpperCAmelCase__   , **UpperCAmelCase__     )      ->	str:
										a_							,					a_									=   cls.get_config_dict(UpperCAmelCase__   , **UpperCAmelCase__     )
										return cls(UpperCAmelCase__     )




					@classmethod
					def  __SCREAMING_SNAKE_CASE (     cls   , UpperCAmelCase__   , **UpperCAmelCase__     )      ->	Dict:
										a_									=   kwargs.pop('cache_dir'   , UpperCAmelCase__     )
										a_									=   kwargs.pop('force_download'   , UpperCAmelCase__     )
										a_									=   kwargs.pop('resume_download'   , UpperCAmelCase__     )
										a_									=   kwargs.pop('proxies'   , UpperCAmelCase__     )
										a_									=   kwargs.pop('local_files_only'   , UpperCAmelCase__     )

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

										try:
															# Load from URL or cache if already cached
															a_									=   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

															a_									=   Config.load_yaml(UpperCAmelCase__     )

										except EnvironmentError:
															a_									=   '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	(   _UpperCAmelCase )				->				Tuple:






					"""simple docstring"""







					a_									=   torch.load('dump.pt'					,  map_location=in_tensor.device )
					a_									=   in_tensor.numpy()
					a_									=   out_tensor.numpy()[0]
					print(na.shape					,  na[0, 0, :5] )
					print(na.shape					,  na[0, 0, :5] )
					assert np.allclose(_UpperCAmelCase					,  _UpperCAmelCase					,  rtol=0.0_1					,  atol=0.1 ), (
					    F'''{sum([1 for x in np.isclose(_UpperCAmelCase					,  _UpperCAmelCase					,  rtol=0.0_1					,  atol=0.1 ).flatten() if x is False] )/len(na.flatten() )*1_0_0:.4f} %'''
					    " element-wise mismatch"
					)
					raise Exception('tensors are all good' )

					# Hugging face functions below

def 							a	(   _UpperCAmelCase )				->				Tuple:






					"""simple docstring"""







					a_									=   urlparse(_UpperCAmelCase )
					return parsed.scheme in ("http", "https")

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase=True )				->				str:






					"""simple docstring"""







					a_									=   CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
					a_									=   '/' 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					,  )				->				Tuple:






					"""simple docstring"""







					a_									=   '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
					a_									=   {'user-agent': ua}
					if resume_size > 0:
										a_									=   'bytes=%d-' % (resume_size,)
					a_									=   requests.get(_UpperCAmelCase					,  stream=_UpperCAmelCase					,  proxies=_UpperCAmelCase					,  headers=_UpperCAmelCase )
					if response.status_code == 4_1_6:  # Range not satisfiable
										return
					a_									=   response.headers.get('Content-Length' )
					a_									=   resume_size + int(_UpperCAmelCase ) if content_length is not None else None
					a_									=   tqdm(
					    unit='B'					,  unit_scale=_UpperCAmelCase					,  total=_UpperCAmelCase					,  initial=_UpperCAmelCase					,  desc='Downloading'					,  )
					for chunk in response.iter_content(chunk_size=1_0_2_4 ):
										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=1_0					,  _UpperCAmelCase=False					,  _UpperCAmelCase=None					,  _UpperCAmelCase=False					,  )				->				Any:






					"""simple docstring"""







					if cache_dir is None:
										a_									=   TRANSFORMERS_CACHE
					if isinstance(_UpperCAmelCase					,  _UpperCAmelCase ):
										a_									=   str(_UpperCAmelCase )

					os.makedirs(_UpperCAmelCase					,  exist_ok=_UpperCAmelCase )

					a_									=   None
					if not local_files_only:
										try:
															a_									=   requests.head(_UpperCAmelCase					,  allow_redirects=_UpperCAmelCase					,  proxies=_UpperCAmelCase					,  timeout=_UpperCAmelCase )
															if response.status_code == 2_0_0:
																				a_									=   response.headers.get('ETag' )
										except (EnvironmentError, requests.exceptions.Timeout):
															# etag is already None
															pass

					a_									=   url_to_filename(_UpperCAmelCase					,  _UpperCAmelCase )

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

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

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

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

					return cache_path

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase=None )				->				List[str]:






					"""simple docstring"""







					a_									=   url.encode('utf-8' )
					a_									=   shaaaa(_UpperCAmelCase )
					a_									=   url_hash.hexdigest()

					if etag:
										a_									=   etag.encode('utf-8' )
										a_									=   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					,  )				->				Any:






					"""simple docstring"""







					if cache_dir is None:
										a_									=   TRANSFORMERS_CACHE
					if isinstance(_UpperCAmelCase					,  _UpperCAmelCase ):
										a_									=   str(_UpperCAmelCase )
					if isinstance(_UpperCAmelCase					,  _UpperCAmelCase ):
										a_									=   str(_UpperCAmelCase )

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






					"""simple docstring"""







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

def 							a	(   _UpperCAmelCase )				->				List[str]:






					"""simple docstring"""







					a_									=   requests.get(_UpperCAmelCase )
					a_									=   np.array(Image.open(BytesIO(response.content ) ) )
					return img

def 							a	(   _UpperCAmelCase )				->				Dict:






					"""simple docstring"""







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

def 							a	(   )				->				Optional[int]:






					"""simple docstring"""







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

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase="RGB" )				->				Optional[int]:






					"""simple docstring"""







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





def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase=1 )				->				Dict:






					"""simple docstring"""







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









'''simple docstring'''
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = False )				->				List[str]:






					"""simple docstring"""







					if device_specific:
										seed += AcceleratorState().process_index
					random.seed(_UpperCAmelCase )
					np.random.seed(_UpperCAmelCase )
					torch.manual_seed(_UpperCAmelCase )
					if is_xpu_available():
										torch.xpu.manual_seed_all(_UpperCAmelCase )
					elif is_npu_available():
										torch.npu.manual_seed_all(_UpperCAmelCase )
					else:
										torch.cuda.manual_seed_all(_UpperCAmelCase )
					# ^^ safe to call this function even if cuda is not available
					if is_tpu_available():
										xm.set_rng_state(_UpperCAmelCase )

def 							a	(   _UpperCAmelCase = None					,  _UpperCAmelCase = None )				->				Tuple:






					"""simple docstring"""







					if rng_type == RNGType.TORCH:
										a_									=   torch.get_rng_state()
					elif rng_type == RNGType.CUDA:
										a_									=   torch.cuda.get_rng_state()
					elif rng_type == RNGType.XLA:
										assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
										a_									=   torch.tensor(xm.get_rng_state() )
					elif rng_type == RNGType.NPU:
										assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
										a_									=   torch.npu.get_rng_state()
					elif rng_type == RNGType.XPU:
										assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
										a_									=   torch.xpu.get_rng_state()
					elif rng_type == RNGType.GENERATOR:
										assert generator is not None, "Need a generator to synchronize its seed."
										a_									=   generator.get_state()

					# Broadcast the rng state from device 0 to other devices
					a_									=   AcceleratorState()
					if state.distributed_type == DistributedType.TPU:
										a_									=   rng_state.to(xm.xla_device() )
										xm.collective_broadcast([rng_state] )
										xm.mark_step()
										a_									=   rng_state.cpu()
					elif (
					    state.distributed_type in CUDA_DISTRIBUTED_TYPES
					    or state.distributed_type == DistributedType.MULTI_NPU
					    or state.distributed_type == DistributedType.MULTI_XPU
					):
										a_									=   rng_state.to(state.device )
										torch.distributed.broadcast(_UpperCAmelCase					,  0 )
										a_									=   rng_state.cpu()
					elif state.distributed_type == DistributedType.MULTI_CPU:
										torch.distributed.broadcast(_UpperCAmelCase					,  0 )

					# Set the broadcast rng state
					if rng_type == RNGType.TORCH:
										torch.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.CUDA:
										torch.cuda.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.NPU:
										torch.npu.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.XPU:
										torch.xpu.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.XLA:
										xm.set_rng_state(rng_state.item() )
					elif rng_type == RNGType.GENERATOR:
										generator.set_state(_UpperCAmelCase )





def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = None )				->				Any:






					"""simple docstring"""







					for rng_type in rng_types:
										synchronize_rng_state(RNGType(_UpperCAmelCase )					,  generator=_UpperCAmelCase )

