iU2dddlZddlZddlZddlmZmZddlmZddlm Z ddl m Z m Z m Z mZddlmZddlZddlmZddlmZeej,j/d d Zeej,j/d dZd ed edefdZd ed eddfdZ d*d ed ededeeeffdZ d+ded ed edede edeeeeff dZ d*d ed ededeeeffdZ d,de de de!defdZ"eddgddfdede e#e$e fde e#e$e fd eed!eddf d"Z%d#e$d$e&d%e&defd&Z'efd'e d(edefd)Z(y)-N)MappingSequence)partial) perf_counter)AnyCallableOptional no_type_check)Mock)Tensor)MetricDOCTEST_DOWNLOAD_TIMEOUTxSKIP_SLOW_DOCTESTpredstargetreturncZ|j|jcxk(xrdk(ScS)Nr)numelrrs r/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/utilities/checks.py_check_for_empty_tensorsr s$ ;;=FLLN /a // //c|j|jk7r&td|jd|jdy)zHCheck that predictions and target have the same shape, else raise error.zEPredictions and targets are expected to have the same shape, but got z and .N)shape RuntimeErrorrs r_check_same_shaper$sH {{fll"STYT_T_S``eflfrfresst u  #rallow_non_binary_targetc|j|jk7r td|jr|js tdt |||S)a9Check ``preds`` and ``target`` tensors are of the same shape and of the correct data type. Args: preds: either tensor with scores/logits target: tensor with ground true labels allow_non_binary_target: whether to allow target to contain non-binary values Raises: ValueError: If ``preds`` and ``target`` don't have the same shape, if they are empty or not of the correct ``dtypes``. Returns: preds: as torch.float32 target: as torch.long if not floating point else torch.float32 z.`preds` and `target` must be of the same shapez=`preds` and `target` must be non-empty and non-scalar tensorsr)r ValueErrorrsize,_check_retrieval_target_and_prediction_typesrrrs r"_check_retrieval_functional_inputsr&,sM, {{fll"IJJ ;;= XYY 7v_v wwrindexes ignore_indexc|j|jk7s|j|jk7r td|jtjur td|||k7}||||||}}}|j r|j s tdt|||\}}|j j||fS)aCheck ``indexes``, ``preds`` and ``target`` tensors are of the same shape and of the correct data type. Args: indexes: tensor with queries indexes preds: tensor with scores/logits target: tensor with ground true labels allow_non_binary_target: whether to allow target to contain non-binary values ignore_index: ignore predictions where targets are equal to this number Raises: ValueError: If ``preds`` and ``target`` don't have the same shape, if they are empty or not of the correct ``dtypes``. Returns: indexes: as ``torch.long`` preds: as ``torch.float32`` target: as ``torch.long`` z9`indexes`, `preds` and `target` must be of the same shapez+`indexes` must be a tensor of long integerszH`indexes`, `preds` and `target` must be non-empty and non-scalar tensorsr!) rr"dtypetorchlongrr#r$flatten)r'rrrr(valid_positionss r_check_retrieval_inputsr/Ks4}} #u{{fll'BTUU}}EJJ&FGG L0!(!95;QSYZiSj ==?',,. V  A v/FME6 <<> ! ! #UF 22rc|jtjtjtjfvr tj |s t d|j s t d|s1|jdkDs|jdkr t d|j r|jn|j}|j}|j|jfS)aCheck ``preds`` and ``target`` tensors are of the same shape and of the correct data type. Args: preds: either tensor with scores/logits target: tensor with ground true labels allow_non_binary_target: whether to allow target to contain non-binary values Raises: ValueError: If ``preds`` and ``target`` don't have the same shape, if they are empty or not of the correct ``dtypes``. z9`target` must be a tensor of booleans, integers or floatsz"`preds` must be a tensor of floatsrz%`target` must contain `binary` values) r*r+boolr,intis_floating_pointr"maxminfloatr-r%s rr$r$|s"||EJJ EII>>uG^G^_eGfTUU  " " $=>> " q(8FJJL1z&_allclose_recursive..sM_62r&r2._sc3BK|]}t||ywr=r>)r@kr8r9s rrCz&_allclose_recursive..s"G$Q&tAwQ8$s) isinstancer r+allclosestrrallzipr)r8r9r:s`` rr?r?s|$~~dDt44$t|$!MSt_MMM$ G$GGG 4<r) di metric_class init_args input_argsnum_update_to_comparerepsc 4|xsi}|xsi}Gdd|}Gdd|}|di|}|di|}d} t|dD]} | t|di||di|z}  |j} |j} | t|  z} | s t dy t j d t||} t||gD]o\}}t|D]\\}}t|D]I}t}t|D] } |di|} t}||z | |||f<|jK^qt j| d }t j| d }tt|D]G}t d ||d |d|fd|d|fdt d||d |d|fdd|d|fdI|d|dkj}t d| dy #t$rd} YwxYw#t$rd} YwxYw)aCheck if the new ``full_state_update`` property works as intended. This function checks if the property can safely be set to ``False`` which will for most metrics results in a speedup when using ``forward``. Args: metric_class: metric class object that should be checked init_args: dict containing arguments for initializing the metric class input_args: dict containing arguments to pass to ``forward`` num_update_to_compare: if we successfully detect that the flag is safe to set to ``False`` we will run some speedup test. This arg should be a list of integers for how many steps to compare over. reps: number of repetitions of speedup test Example (states in ``update`` are independent, save to set ``full_state_update=False``) >>> from torchmetrics.classification import MulticlassConfusionMatrix >>> check_forward_full_state_property( # doctest: +SKIP ... MulticlassConfusionMatrix, ... init_args = {'num_classes': 3}, ... input_args = {'preds': torch.randint(3, (100,)), 'target': torch.randint(3, (100,))}, ... ) Full state for 10 steps took: ... Partial state for 10 steps took: ... Full state for 100 steps took: ... Partial state for 100 steps took: ... Full state for 1000 steps took: ... Partial state for 1000 steps took: ... Recommended setting `full_state_update=False` Example (states in ``update`` are dependent meaning that ``full_state_update=True``): >>> from torchmetrics.classification import MulticlassConfusionMatrix >>> class MyMetric(MulticlassConfusionMatrix): ... def update(self, preds, target): ... super().update(preds, target) ... # by construction make future states dependent on prior states ... if self.confmat.sum() > 20: ... self.reset() >>> check_forward_full_state_property( ... MyMetric, ... init_args = {'num_classes': 3}, ... input_args = {'preds': torch.randint(3, (10,)), 'target': torch.randint(3, (10,))}, ... ) Recommended setting `full_state_update=True` ceZdZdZy)4check_forward_full_state_property..FullStateTN__name__ __module__ __qualname__full_state_updaterr FullStaterUs rr\ceZdZdZy)4check_forward_full_state_property..PartStateFNrVr[rr PartStater^s!rr_TrFz,Recommended setting `full_state_update=True`NzFull state for z steps took: z+-z0.3fzPartial state for r1)r1ra)rraz'Recommended setting `full_state_update=`r[)ranger?rcomputeprintr+zeroslen enumeraterresetmeanstditem)rNrOrPrQrRr\r_ fullstate partstateequal_r8r9resimetricjtrstartendrjrkfasters r!check_forward_full_state_propertyrzsjRI!rJ!L!"L"&I&I&I&I E,Q/0A/ 0GJ0GI`U_I`aaE1    D  " 'd3 3E  <= ++a23T :C 956 634DAq4[$qA,,A""n"U{Aq!G   !57 ::c2 D ))C C 3,- .  5a 89tAqDzlRTUXYZ\]Y]U^_cTdef "#8#;"??  ! ![%9%9 99rfntimeoutctjjrytj|}t d|j d|dt j|j|j||jsyt d|j d|d t j|jy ) aYCheck if a certain function is taking too long to execute. Function will only be executed if running inside a doctest context. Currently, does not support Windows. Args: fn: function to check timeout: timeout for function Returns: Bool indicating if the function finished within the specified timeout T)rzTrying to run `z` for zs...)filerbz` did not complete with z%, killing process and returning FalseF) multiprocessingcurrent_processdaemonProcessrerWsysstderrrwjoinis_alivekill)rrprocs r_try_proceed_with_timeoutr-s&&(//  " "" -D OBKK=wit <3::NJJLIIg ==? Abkk]27);` ahkhrhrs  IIK r)F)FN)gư>))rosrcollections.abcrr functoolsrtimertypingrrr r unittest.mockr r+r torchmetrics.metricr r3environget_DOCTEST_DOWNLOAD_TIMEOUTr2_SKIP_SLOW_DOCTESTrrtupler&r/r$r7r?dictrHrzobjectrrr[rrrsD -99 & /I3 OP"**..) xF%*"& .3 .3 .3 .3" .3 3- .3  666 !" .3h%*- - -"- 66> -@ c  E T +/+/+: f f S#X'f c3h(f $C= f  f  f f R:s:f:f::2