
"""simple docstring"""




import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
	import torch

snake_case					  =						logging.get_logger(__name__)

def     UpperCamelCase_    (						):
					return importlib.util.find_spec('deepspeed'     ) is not None


if is_accelerate_available() and is_deepspeed_available():
	from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
	# Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
	# Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
	from builtins import object as DeepSpeedConfig




class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				def __init__(							self						,      lowercase__     )       ->       List[Any]:
									"""simple docstring"""







									set_hf_deepspeed_config(self     )
									dep_version_check('accelerate'     )
									dep_version_check('deepspeed'     )
									super().__init__(lowercase__     )






class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				def __init__(							self						,      lowercase__     )       ->       Dict:
									"""simple docstring"""







									super().__init__(lowercase__     )
									SCREAMING_SNAKE_CASE           =      None
									SCREAMING_SNAKE_CASE           =      []


				def 				A   (							self     )       ->       Dict:
									"""simple docstring"""







									if self._dtype is None:
														raise ValueError('trainer_config_process() wasn\'t called yet to tell dtype'     )
									return self._dtype


				def 				A   (							self						,      lowercase__     )       ->       List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.get_value(lowercase__     )
									if val is None:
														return False
									else:
														return val == "auto"


				def 				A   (							self						,      lowercase__						,      lowercase__						,      lowercase__=None						,      lowercase__=True     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      self.find_config_node(lowercase__     )
									if config is None:
														return

									if config.get(lowercase__     ) == "auto":
														SCREAMING_SNAKE_CASE           =      hf_val
														return

									if not must_match:
														return

									SCREAMING_SNAKE_CASE           =      config.get(lowercase__     )
									if ds_val is not None and ds_val != hf_val:
														self.mismatches.append(f'''- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}'''     )

				UpperCAmelCase_							: Optional[int]		    =							partialmethod(__magic_name__    , must_match=__magic_name__ )


				def 				A   (							self						,      lowercase__     )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
									self.fill_match(
									    'train_micro_batch_size_per_gpu'						,      args.per_device_train_batch_size						,      'per_device_train_batch_size'     )
									self.fill_match('gradient_accumulation_steps'						,      args.gradient_accumulation_steps						,      'gradient_accumulation_steps'     )
									self.fill_match('train_batch_size'						,      lowercase__						,      'train_batch_size (calculated)'     )
									self.fill_match('gradient_clipping'						,      args.max_grad_norm						,      'max_grad_norm'     )

									self.fill_match('optimizer.params.lr'						,      args.learning_rate						,      'learning_rate'     )
									self.fill_match('optimizer.params.betas'						,      [args.adam_betaa, args.adam_betaa]						,      'adam_beta1+adam_beta2'     )
									self.fill_match('optimizer.params.eps'						,      args.adam_epsilon						,      'adam_epsilon'     )
									self.fill_match('optimizer.params.weight_decay'						,      args.weight_decay						,      'weight_decay'     )

									self.fill_only('scheduler.params.warmup_min_lr'						,      0     )  # not a trainer arg
									self.fill_match('scheduler.params.warmup_max_lr'						,      args.learning_rate						,      'learning_rate'     )
									# total_num_steps - will get set in trainer_config_finalize

									# fp16
									if args.fpaa or args.fpaa_full_eval:
														SCREAMING_SNAKE_CASE           =      'apex' if args.fpaa_backend == 'apex' else 'amp'
									else:
														SCREAMING_SNAKE_CASE           =      None

									if args.save_on_each_node:
														# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
														SCREAMING_SNAKE_CASE           =      self.config.get('checkpoint'						,      {}     )
														SCREAMING_SNAKE_CASE           =      args.save_on_each_node

									# amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
									# any here unless the user did the work
									self.fill_match(
									    'fp16.enabled'						,      ((args.fpaa or args.fpaa_full_eval) and fpaa_backend == 'amp')						,      'fp16|fp16_full_eval+fp16_backend(amp)'						,      )

									# apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
									# ZeRO features
									self.fill_match('amp.enabled'						,      fpaa_backend == 'apex'						,      'fp16+fp16_backend(apex)'     )
									self.fill_match('amp.opt_level'						,      args.fpaa_opt_level						,      'fp16_opt_level'     )

									self.fill_match('bf16.enabled'						,      (args.bfaa or args.bfaa_full_eval)						,      'bf16|bf16_full_eval'     )

									# deepspeed's default mode is fp16 unless there is a config that says differently
									if self.is_true('bf16.enabled'     ):
														SCREAMING_SNAKE_CASE           =      torch.bfloataa
									elif self.is_false('fp16.enabled'     ):
														SCREAMING_SNAKE_CASE           =      torch.floataa
									else:
														SCREAMING_SNAKE_CASE           =      torch.floataa




				def 				A   (							self						,      lowercase__						,      lowercase__						,      lowercase__     )       ->       Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      [
									    'zero_optimization.reduce_bucket_size',
									    'zero_optimization.stage3_prefetch_bucket_size',
									    'zero_optimization.stage3_param_persistence_threshold',
									]
									SCREAMING_SNAKE_CASE           =      [x for x in hidden_size_based_keys if self.is_auto(lowercase__     )]

									if len(lowercase__     ) > 0:
														if hasattr(model.config						,      'hidden_size'     ):
																			SCREAMING_SNAKE_CASE           =      model.config.hidden_size
														elif hasattr(model.config						,      'hidden_sizes'     ):
																			# if there are many hidden sizes pick the largest one
																			SCREAMING_SNAKE_CASE           =      max(model.config.hidden_sizes     )
														else:
																			raise ValueError(
																			    'The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '
																			    'therefore it\'s not possible to automatically fill out the following `auto` entries '
																			    f'''in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing '''
																			    '`auto` values for these keys with an integer value of your choice.'     )

														self.fill_only('zero_optimization.reduce_bucket_size'						,      hidden_size * hidden_size     )
														if self.is_zeroa():
																			# automatically assign the optimal config values based on model config
																			self.fill_only('zero_optimization.stage3_prefetch_bucket_size'						,      0.9 * hidden_size * hidden_size     )
																			self.fill_only('zero_optimization.stage3_param_persistence_threshold'						,      10 * hidden_size     )

        # scheduler
									self.fill_match('scheduler.params.total_num_steps'						,      lowercase__						,      'num_training_steps (calculated)'     )
									self.fill_match('scheduler.params.warmup_num_steps'						,      args.get_warmup_steps(lowercase__     )						,      'warmup_steps'     )

									if len(self.mismatches     ) > 0:
														SCREAMING_SNAKE_CASE           =      '\n'.join(self.mismatches     )
														raise ValueError(
														    'Please correct the following DeepSpeed config values that mismatch TrainingArguments'
														    f''' values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to \'auto\'.'''     )






snake_case					  =						None

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					# this is a special weakref global object to allow us to get to Deepspeed config from APIs
					# that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
					global _hf_deepspeed_config_weak_ref
					# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
					SCREAMING_SNAKE_CASE           =      weakref.ref(SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						):
					# useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
					global _hf_deepspeed_config_weak_ref
					SCREAMING_SNAKE_CASE           =      None

def     UpperCamelCase_    (						):
					if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
										return _hf_deepspeed_config_weak_ref().is_zeroa()
					else:
										return False

def     UpperCamelCase_    (						):
					if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
										return _hf_deepspeed_config_weak_ref().config
					else:
										return None

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					from accelerate.utils import DummyOptim, DummyScheduler

					SCREAMING_SNAKE_CASE           =      hf_deepspeed_config.config

					# Optimizer + Scheduler
					# Currently supported combos:
					# 1. DS scheduler + DS optimizer: Yes
					# 2. HF scheduler + HF optimizer: Yes
					# 3. DS scheduler + HF optimizer: Yes
					# 4. HF scheduler + DS optimizer: No
					#
					# Unless Offload is enabled in which case it's:
					# 1. DS scheduler + DS optimizer: Yes
					# 2. HF scheduler + HF optimizer: Mostly*
					# 3. DS scheduler + HF optimizer: Mostly*
					# 4. HF scheduler + DS optimizer: No
					#
					# Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

					SCREAMING_SNAKE_CASE           =      None
					if "optimizer" in config:
										if args.adafactor:
															raise ValueError(
															    '--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '
															    'Only one optimizer can be configured.'     )
										SCREAMING_SNAKE_CASE           =      DummyOptim(params=SCREAMING_SNAKE_CASE_     )
					else:
										if hf_deepspeed_config.is_offload():
															logger.info(
															    'Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'
															    ' custom optimizer has both CPU and GPU implementation (except LAMB)'     )

										# ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
										# But trainer uses AdamW by default.
										SCREAMING_SNAKE_CASE           =      trainer.create_optimizer()
										# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
										SCREAMING_SNAKE_CASE           =      True

					SCREAMING_SNAKE_CASE           =      None
					if "scheduler" in config:
										SCREAMING_SNAKE_CASE           =      DummyScheduler(SCREAMING_SNAKE_CASE_     )
					else:
										if isinstance(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
															raise ValueError(
															    'Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '
															    'Please configure a scheduler in the DeepSpeed config.'     )
										SCREAMING_SNAKE_CASE           =      trainer.create_scheduler(num_training_steps=SCREAMING_SNAKE_CASE_,	optimizer=SCREAMING_SNAKE_CASE_     )

					return optimizer, lr_scheduler

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_=False     ):
					from deepspeed.utils import logger as ds_logger

					SCREAMING_SNAKE_CASE           =      trainer.model
					SCREAMING_SNAKE_CASE           =      trainer.args

					SCREAMING_SNAKE_CASE           =      trainer.accelerator.state.deepspeed_plugin.hf_ds_config

					# resume config update - some bits like `model` and `num_training_steps` only become available during train
					hf_deepspeed_config.trainer_config_finalize(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )

					# set the Deepspeed log level consistent with the Trainer
					ds_logger.setLevel(args.get_process_log_level()     )

					if inference:
										# only Z3 makes sense for the inference
										if not hf_deepspeed_config.is_zeroa():
															raise ValueError('ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'     )

										# in case the training config is re-used for inference
										hf_deepspeed_config.del_config_sub_tree('optimizer'     )
										hf_deepspeed_config.del_config_sub_tree('lr_scheduler'     )
										SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      None, None
										SCREAMING_SNAKE_CASE           =      None
					else:
										SCREAMING_SNAKE_CASE           =      None  # important for when deepspeed_init is used as re-init
										SCREAMING_SNAKE_CASE           =      list(filter(lambda SCREAMING_SNAKE_CASE_     : p.requires_grad,	model.parameters()     )     )
										SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      deepspeed_optim_sched(
										    SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )

					# keep for quick debug:
					# from pprint import pprint; pprint(config)

					return optimizer, lr_scheduler



def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					# it's possible that the user is trying to resume from model_path, which doesn't necessarily
					# contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
					# a resume from a checkpoint and not just a local pretrained weight. So we check here if the
					# path contains what looks like a deepspeed checkpoint
					import glob

					SCREAMING_SNAKE_CASE           =      sorted(glob.glob(f'''{checkpoint_path}/global_step*'''     )     )

					if len(SCREAMING_SNAKE_CASE_     ) > 0:
										logger.info(f'''Attempting to resume from {checkpoint_path}'''     )
										# this magically updates self.optimizer and self.lr_scheduler
										SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      deepspeed_engine.load_checkpoint(
										    SCREAMING_SNAKE_CASE_,	load_optimizer_states=SCREAMING_SNAKE_CASE_,	load_lr_scheduler_states=SCREAMING_SNAKE_CASE_     )
										if load_path is None:
															raise ValueError(f'''[deepspeed] failed to resume from checkpoint {checkpoint_path}'''     )
					else:
										raise ValueError(f'''Can\'t find a valid checkpoint at {checkpoint_path}'''     )


"""simple docstring"""




import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


snake_case					  =						[nn.LayerNorm]

snake_case					  =						logging.get_logger(__name__)

snake_case					  =						version.parse(version.parse(torch.__version__).base_version)

snake_case					  =						parsed_torch_version_base >= version.parse('2.0')
snake_case					  =						parsed_torch_version_base >= version.parse('1.12')
snake_case					  =						parsed_torch_version_base >= version.parse('1.11')
snake_case					  =						parsed_torch_version_base < version.parse('1.11')

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	self     ):
					from torch import _softmax_backward_data

					if is_torch_less_than_1_11:
										return _softmax_backward_data(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	parent.dim,	self     )
					else:
										return _softmax_backward_data(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	parent.dim,	self.dtype     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = 0     ):
					SCREAMING_SNAKE_CASE           =      index.to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      layer.weight.index_select(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ).clone().detach()
					if layer.bias is not None:
										if dim == 1:
															SCREAMING_SNAKE_CASE           =      layer.bias.clone().detach()
										else:
															SCREAMING_SNAKE_CASE           =      layer.bias[index].clone().detach()
					SCREAMING_SNAKE_CASE           =      list(layer.weight.size()     )
					SCREAMING_SNAKE_CASE           =      len(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      nn.Linear(new_size[1],	new_size[0],	bias=layer.bias is not None     ).to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      False
					new_layer.weight.copy_(W.contiguous()     )
					SCREAMING_SNAKE_CASE           =      True
					if layer.bias is not None:
										SCREAMING_SNAKE_CASE           =      False
										new_layer.bias.copy_(b.contiguous()     )
										SCREAMING_SNAKE_CASE           =      True
					return new_layer




class 	UpperCamelCase      (			nn.Module ):

				"""simple docstring"""



				def __init__(							self						,      lowercase__						,      lowercase__     )       ->       List[Any]:
									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE           =      nf
									SCREAMING_SNAKE_CASE           =      nn.Parameter(torch.empty(lowercase__						,      lowercase__     )     )
									SCREAMING_SNAKE_CASE           =      nn.Parameter(torch.zeros(lowercase__     )     )
									nn.init.normal_(self.weight						,      std=0.02     )




				def 				A   (							self						,      lowercase__     )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      x.size()[:-1] + (self.nf,)
									SCREAMING_SNAKE_CASE           =      torch.addmm(self.bias						,      x.view(-1						,      x.size(-1     )     )						,      self.weight     )
									SCREAMING_SNAKE_CASE           =      x.view(lowercase__     )
									return x






def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = 1     ):
					SCREAMING_SNAKE_CASE           =      index.to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      layer.weight.index_select(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ).clone().detach()
					if dim == 0:
										SCREAMING_SNAKE_CASE           =      layer.bias.clone().detach()
					else:
										SCREAMING_SNAKE_CASE           =      layer.bias[index].clone().detach()
					SCREAMING_SNAKE_CASE           =      list(layer.weight.size()     )
					SCREAMING_SNAKE_CASE           =      len(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      ConvaD(new_size[1],	new_size[0]     ).to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      False
					new_layer.weight.copy_(W.contiguous()     )
					SCREAMING_SNAKE_CASE           =      True
					SCREAMING_SNAKE_CASE           =      False
					new_layer.bias.copy_(b.contiguous()     )
					SCREAMING_SNAKE_CASE           =      True
					return new_layer

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = None     ):
					if isinstance(SCREAMING_SNAKE_CASE_,	nn.Linear     ):
										return prune_linear_layer(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	dim=0 if dim is None else dim     )
					elif isinstance(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
										return prune_convad_layer(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	dim=1 if dim is None else dim     )
					else:
										raise ValueError(f'''Can\'t prune layer of class {layer.__class__}'''     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	*SCREAMING_SNAKE_CASE_     ):
					assert len(SCREAMING_SNAKE_CASE_     ) > 0, f'''{input_tensors} has to be a tuple/list of tensors'''

					# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
					SCREAMING_SNAKE_CASE           =      len(inspect.signature(SCREAMING_SNAKE_CASE_     ).parameters     )
					if num_args_in_forward_chunk_fn != len(SCREAMING_SNAKE_CASE_     ):
										raise ValueError(
										    f'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(SCREAMING_SNAKE_CASE_     )} input '''
										    'tensors are given'     )

					if chunk_size > 0:
										SCREAMING_SNAKE_CASE           =      input_tensors[0].shape[chunk_dim]
										for input_tensor in input_tensors:
															if input_tensor.shape[chunk_dim] != tensor_shape:
																				raise ValueError(
																				    f'''All input tenors have to be of the same shape: {tensor_shape}, '''
																				    f'''found shape {input_tensor.shape[chunk_dim]}'''     )

										if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
															raise ValueError(
															    f'''The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '''
															    f'''size {chunk_size}'''     )

										SCREAMING_SNAKE_CASE           =      input_tensors[0].shape[chunk_dim] // chunk_size

										# chunk input tensor into tuples
										SCREAMING_SNAKE_CASE           =      tuple(input_tensor.chunk(SCREAMING_SNAKE_CASE_,	dim=SCREAMING_SNAKE_CASE_     ) for input_tensor in input_tensors     )
										# apply forward fn to every tuple
										SCREAMING_SNAKE_CASE           =      tuple(forward_fn(*SCREAMING_SNAKE_CASE_     ) for input_tensors_chunk in zip(*SCREAMING_SNAKE_CASE_     )     )
										# concatenate output at same dimension
										return torch.cat(SCREAMING_SNAKE_CASE_,	dim=SCREAMING_SNAKE_CASE_     )

					return forward_fn(*SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      torch.ones(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      set(SCREAMING_SNAKE_CASE_     ) - already_pruned_heads  # Convert to set and remove already pruned heads
					for head in heads:
										# Compute how many pruned heads are before the head and move the index accordingly
										SCREAMING_SNAKE_CASE           =      head - sum(1 if h < head else 0 for h in already_pruned_heads     )
										SCREAMING_SNAKE_CASE           =      0
					SCREAMING_SNAKE_CASE           =      mask.view(-1     ).contiguous().eq(1     )
					SCREAMING_SNAKE_CASE           =      torch.arange(len(SCREAMING_SNAKE_CASE_     )     )[mask].long()
					return heads, index

def     UpperCamelCase_    (						*SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = None     ):
					return torch.meshgrid(*SCREAMING_SNAKE_CASE_,	indexing=SCREAMING_SNAKE_CASE_     )



def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					return tensor.device, storage_ptr(SCREAMING_SNAKE_CASE_     ), storage_size(SCREAMING_SNAKE_CASE_     )
