`i^ddlZddlZddlZddlZddlmZmZddlZddlZ ddl m Z ddl m Z mZmZddlmZddlmZddlmZddlmZdd lmZd d lmZd d lmZmZgd Zej e!Z"de#de$fdZ%de$de#fdZ&dej'de(e$fdZ)dej'de(e#fdZ*dej'de(efdZ+iej,dde)fej-dde)fej.dde)fej/dde+fej#dde+fej0dde+fej1dde+fej2dde+fej3d de+fej4d de+fej5d!de+fej6d!de+fej$d"de+fej7d"de+fej8d"de+fej9d#d$e+fej:d%d&e*fej;d%d&e*fej<d'd&e*fej=d'd&e*fej>d(d)e+fej?d*d+e*fej@d*d+e*fej3d d,e+fejAd d,e+fejBd d,e+fi ZCd-ZD dLd0ZEdMd1ZFdNd3ZGd4ZHd5ZIdOd6ZJdOd7ZKdPd9ZL dQd;ZMdOd<ZNdRd=ZOdSd?ZPd@ZQdTdBZRdCZSdDZT dUdFZUdUdGZVdVdHZWdIZXdJZY dVdKZZdS)WN)AnyOptional) struct_pb2)HistogramProtoSummarySummaryMetadata) TensorProto)TensorShapeProto) layout_pb2)PrCurvePluginData)TextPluginData)make_np)_prepare_videoconvert_to_HWC) half_to_int int_to_halfhparamsscalar histogram_raw histogrammake_histogramimage image_boxes draw_boxes make_imagevideo make_videoaudiocustom_scalarstext tensor_proto pr_curve_rawpr_curve compute_curvemeshfreturncbtjd|}tjd|dS)aACasts a half-precision float value into an integer. Converts a half precision floating point value, such as `torch.half` or `torch.bfloat16`, into an integer value which can be written into the half_val field of a TensorProto for storage. To undo the effects of this conversion, use int_to_half(). r'irstructpackunpack)r'bufs s/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/torch/utils/tensorboard/summary.pyrr6s, +c1  C =c " "1 %%r*cbtjd|}tjd|dS)zCasts an integer value to a half-precision float. Converts an integer value obtained from half_to_int back into a floating point value. r*r'rr+)r*r/s r0rrCs, +c1  C =c " "1 %%r1tcbd|DS)Nc,g|]}t|S)r.0xs r0 z'_tensor_to_half_val..Ns 9 9 9qKNN 9 9 9r1flattentolistr3s r0_tensor_to_half_valr?Ms* 9 9AIIKK$6$6$8$8 9 9 99r1crtj|SN)torch view_as_realr<r=r>s r0_tensor_to_complex_valrDPs+  a ( ( * * 1 1 3 33r1cN|SrAr;r>s r0_tensor_to_listrFSs 99;;    r1DT_HALFhalf_val DT_BFLOAT16DT_FLOAT float_val DT_DOUBLE double_valDT_INT8int_valDT_UINT8DT_INT16DT_INT32DT_INT64 int64_val DT_COMPLEX32 scomplex_val DT_COMPLEX64DT_BOOLbool_val DT_COMPLEX128 dcomplex_val uint32_valct|tjs|n|}|jtjkrdndS)Nr) isinstancenpndarraynumpydtypeuint8)tensor converteds r0_calc_scale_factorrgus<&0&D&DP &I28++114r1blackc ddlm} m} | } | |} ||||f\} }}}| | |f| |f||f||f| |fg|||r|}| |\}}}}||z ||z }}tjd|z}| | ||z d|zz f| |z|fg|| | |z||z |z f||| |S)Nr) ImageDraw ImageFont)widthfillg?ri)rn)rnfont) PILrkrl load_defaultDrawlinegetbboxr`ceil rectangler!)rxminyminxmaxymax display_strcolor color_text thicknessrkrlrodrawleftrighttopbottom text_bottom_left_top_right_bottom text_width text_heightmargins r0_draw_single_boxrzsq)(((((((  ! ! # #D >>% D"&dD!9T5#vII tVnufos|dC[Q    '+||K'@'@$tVW"(5.'D.K  +,, {[01v:=> "K0      F]K+5> ?     Lr1c  ddl}ddlm}m}m}mmm}ddlm }m } m } m } m } ddlm} m}m}t%t&s)t(dt-dt%|t&s)t(dt-d|pi}t%|t&st-d|D]P\}vs1t%|t0rt3fd |Dst-d d d Qg}|}D](\}| t%|t4t6frs||j_|vr&t=jd |D}nd}| ||!d|t%|tDrt||j_#|vr&t=jd|D}nd}| ||!d|!t%|tHrt||j_%|vr&t=jd|D}nd}| ||!d|t%||j&r\tO|d}||j_| ||!dtQd| || }tStSj*| |+}tYtYj!| |g}fd|-D}|||}| || }tStSj*| |+}tYtYj!||g}||!d }| || !}tStSj*| |+}tYtYj!| |g}|||fS)"a2Output three `Summary` protocol buffers needed by hparams plugin. `Experiment` keeps the metadata of an experiment, such as the name of the hyperparameters and the name of the metrics. `SessionStartInfo` keeps key-value pairs of the hyperparameters `SessionEndInfo` describes status of the experiment e.g. STATUS_SUCCESS Args: hparam_dict: A dictionary that contains names of the hyperparameters and their values. metric_dict: A dictionary that contains names of the metrics and their values. hparam_domain_discrete: (Optional[Dict[str, List[Any]]]) A dictionary that contains names of the hyperparameters and all discrete values they can hold Returns: The `Summary` protobufs for Experiment, SessionStartInfo and SessionEndInfo rN)DataType Experiment HParamInfo MetricInfo MetricNameStatus)EXPERIMENT_TAGPLUGIN_DATA_VERSION PLUGIN_NAMESESSION_END_INFO_TAGSESSION_START_INFO_TAG)HParamsPluginDataSessionEndInfoSessionStartInfoz>parameter: hparam_dict should be a dictionary, nothing logged.z>parameter: metric_dict should be a dictionary, nothing logged.zIparameter: hparam_domain_discrete should be a dictionary, nothing logged.c3^K|]'}t|tV(dSrA)r_type)r8d hparam_dictks r0 zhparams..s9FFqz!T+a.%9%9::FFFFFFr1z"parameter: hparam_domain_discrete[z/] should be a list of same type as hparam_dict[z].c8g|]}tj|S)) number_valuerValuer8rs r0r:zhparams..6#(a888r1)valuesDATA_TYPE_FLOAT64)namerdomain_discretec8g|]}tj|S)) string_valuerrs r0r:zhparams..rr1DATA_TYPE_STRINGc8g|]}tj|S)) bool_valuerrs r0r:zhparams..(s6#(A666r1DATA_TYPE_BOOL)rrz=value should be one of int, float, str, bool, or torch.Tensor)session_start_infoversion plugin_namecontent plugin_data)tagmetadatavaluec<g|]}|S)r)rr6)r8rrrs r0r:zhparams..Js3 J J J!::::!,,, - - - J J Jr1) hparam_infos metric_infos) experimentrSTATUS_SUCCESS)status)session_end_infor).rB#tensorboard.plugins.hparams.api_pb2rrrrrr$tensorboard.plugins.hparams.metadatarrrrr+tensorboard.plugins.hparams.plugin_data_pb2rrrr_dictloggerwarning TypeErroritemslistallintfloatrrr ListValueappendrstrrboolrTensorr ValueErrorr PluginDataSerializeToStringrkeys)r metric_dicthparam_domain_discreterBrrrrrrrrrrrrvhpsssirrsmdmtsexpseirrrs` @@@r0rrs(LLL k4 ( ( WXXX L    k4 ( ( WXXX L   49r ,d 3 3  W   ',,..1 [ a&& !FFFFFAFFFFF !lQllghlll  ! C    C!!##L L 1 9  a#u & & *+CKN '***BLBV!7!:CCC#' JJ !(;<<$3     a   *+CKN '***","6!7!:####' JJ !(:;;$3     a   ()CKN %***","6!7!:####' JJ !(899$3     a & &  1 A*+CKN ' JJzzqx~~>Q/R/RSSS T T T  K    3@STTTG #.#W-F-F-H-H      C +ACPPPQ R R RC J J J J J{7G7G7I7I J J JC *#C 8 8 8C38KLLLG #.#W-F-F-H-H      C >CHHHI J J JC . -= > > ? ? ?C>QRRRG #.#W-F-F-H-H      C +?#NNNO P P PC S=r1Fct|}|jdksJd|jd|jdt |}|rqt |gd}|rt |gd}t jd }t | }ttj ||| g Sttj ||g S)aOutput a `Summary` protocol buffer containing a single scalar value. The generated Summary has a Tensor.proto containing the input Tensor. Args: name: A name for the generated node. Will also serve as the series name in TensorBoard. tensor: A real numeric Tensor containing a single value. collections: Optional list of graph collections keys. The new summary op is added to these collections. Defaults to `[GraphKeys.SUMMARIES]`. new_style: Whether to use new style (tensor field) or old style (simple_value field). New style could lead to faster data loading. Returns: A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf. Raises: ValueError: If tensor has the wrong shape or type. rzBTensor should contain one element (0 dimensions). Was given size: z and z dimensions.rJ)rKrcrL)rMrcscalarsrrrrerr)r simple_value) rsqueezendimsizerr rrrr) rre collections new_styledouble_precisionrr"rrs r0rrbs"V__ $ $ & &F qwFKww^d^iwww 6]]FM"fXZHHH  O&6(+NNNL%0YGGG +666 '     gm6JJJKLLLLr1c ||jzdkrtd|jtvrOt|j\}}}t d id|dt d|jD|||i}nt|d|jtj d }t| }ttj ||| g S)aOutputs a `Summary` protocol buffer containing the full tensor. The generated Summary has a Tensor.proto containing the input Tensor. Args: tag: A name for the generated node. Will also serve as the series name in TensorBoard. tensor: Tensor to be converted to protobuf Returns: A tensor protobuf in a `Summary` protobuf. Raises: ValueError: If tensor is too big to be converted to protobuf, or tensor data type is not supported lzAtensor is bigger than protocol buffer's hard limit of 2GB in sizerc tensor_shapec8g|]}tj|S)r)r Dimr7s r0r:z tensor_proto..s'LLL!)-1555LLLr1dimz has unsupported tensor dtype rerrrrrerr6) numelitemsizerrc_TENSOR_TYPE_MAPr r shaperrrr)rrerc field_name conversion_fnr"rrs r0r"r"s||~~'G44 O   |'''+;FL+I(z="   0LLv|LLL!!!MM&11   CMMv|MMNNN!,BBBK k 2 2 2C '-C#lSSST U U UUr1c xt|||||||}ttj||gS)aOutput a `Summary` protocol buffer with a histogram. The generated [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) has one summary value containing a histogram for `values`. Args: name: A name for the generated node. Will also serve as a series name in TensorBoard. min: A float or int min value max: A float or int max value num: Int number of values sum: Float or int sum of all values sum_squares: Float or int sum of squares for all values bucket_limits: A numeric `Tensor` with upper value per bucket bucket_counts: A numeric `Tensor` with number of values per bucket Returns: A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. minmaxnumsum sum_squares bucket_limitbucketrhistor)rrr) rrrrrr bucket_limits bucket_countshists r0rrsR*     "   D '-D===> ? ? ??r1ct|}t|t||}t t j||gS)adOutput a `Summary` protocol buffer with a histogram. The generated [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) has one summary value containing a histogram for `values`. This op reports an `InvalidArgument` error if any value is not finite. Args: name: A name for the generated node. Will also serve as a series name in TensorBoard. values: A real numeric `Tensor`. Any shape. Values to use to build the histogram. Returns: A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. rr)rrastyperrr)rrbinsmax_binsr s r0rrsO"V__F &--..h ? ?D '-D===> ? ? ??r1c |jdkrtd|d}tj||\}}t |}|||kr||z}||z}|dkrtj|d||z ggdd}|d|d}tj|jd zf|j }|dd||dd<|d|d<|}tj tj |d} tj | d| dd z gd \} } t| } t| d z} ~ | dkr || d z | ntjdg|d| g}|| | d z}|jdks |jdkrtd ||} t!||t ||| || S)zDConvert values into a histogram proto using logic from histogram.cc.rzThe input has no element.)r Nconstant) pad_widthmodeconstant_values)axisrr)sidez1The histogram is empty, please file a bug report.r)rrreshaper`rlenpadremptyrccumsumgreater searchsortedr concatenatedotrrrr=) rr r countslimitsnum_bins subsamplingsubsampling_remainder new_limits cum_countsstartendsum_sqs r0rrsd {a4555 ^^B  F\&t444NFF6{{H8 3 3(* (; 6 A % %V{-BBCD ! F K0044"4==Xv{Q0&,??  "[1 3B3 22:fa0011JaB!1C-D7SSSJE3 JJE c((Q,C$)199uqy3".1#vdsd|AT2U2U EC!GO $F {a6;!++LMMM ZZ  F  JJLL JJLL KK JJLL]]__}}   r1NCHWctt|}t||}t|}|tj}||zddtj}t||}ttj ||gS)aJOutput a `Summary` protocol buffer with images. The summary has up to `max_images` summary values containing images. The images are built from `tensor` which must be 3-D with shape `[height, width, channels]` and where `channels` can be: * 1: `tensor` is interpreted as Grayscale. * 3: `tensor` is interpreted as RGB. * 4: `tensor` is interpreted as RGBA. The `name` in the outputted Summary.Value protobufs is generated based on the name, with a suffix depending on the max_outputs setting: * If `max_outputs` is 1, the summary value tag is '*name*/image'. * If `max_outputs` is greater than 1, the summary value tags are generated sequentially as '*name*/image/0', '*name*/image/1', etc. Args: tag: A name for the generated node. Will also serve as a series name in TensorBoard. tensor: A 3-D `uint8` or `float32` `Tensor` of shape `[height, width, channels]` where `channels` is 1, 3, or 4. 'tensor' can either have values in [0, 1] (float32) or [0, 255] (uint8). The image() function will scale the image values to [0, 255] by applying a scale factor of either 1 (uint8) or 255 (float32). Out-of-range values will be clipped. Returns: A scalar `Tensor` of type `string`. The serialized `Summary` protocol buffer. rr^)rescalerrr) rrrgr r`float32cliprdrrr)rrer+ dataformats scale_factorrs r0rr s6V__F FK 0 0F%f--L ]]2: & &F|# ) )!S 1 1 8 8 B BF vw / / /E '-Cu===> ? ? ??r1CHWct|}t||}t|}|tjt |z}t |ddtj|||}ttj ||gS)z/Output a `Summary` protocol buffer with images.rr^)r+roislabelsr,r) rrr r`r-rgrr.rdrr)r tensor_image tensor_boxesr+r/r4rs r0rrEs<((L!, < ? ? ??r1c |jd}t|}|D]B}t|||df||df||df||df|dn||d}C|S)NrrriRed)r{r|)rranger) disp_imageboxesr4 num_boxeslist_gtr*s r0rrVs AIIG    %  !Q$K !Q$K !Q$K !Q$K &F1I    r1cddlm}|j\}}}t||z}t||z} ||} |t | ||} |jj} | | |f| } ddl } | } | | d| }| tj||||S)z=Convert a numpy representation of an image to Image protobuf.r)ImageN)r4PNG)formatheightrm colorspaceencoded_image_string)rpr@rr fromarrayr ResamplingLANCZOSresizeioBytesIOsavegetvaluecloser)rer+r3r4r@rDrmchannel scaled_height scaled_widthr ANTIALIASrKoutput image_strings r0rrgs#\FE7())Muw''L OOF # #E 5$v666 (I LL, 6 B BE III ZZ\\F JJveJ$$$??$$L LLNNN =)    r1cpt|}t|}t|}|tj}||zddtj}t||}ttj ||gS)Nrr^r,r) rrrgr r`r-r.rdrrr)rrefpsr0rs r0rrs V__F F # #F%f--L ]]2: & &F|# ) )!S 1 1 8 8 B BF vs # #E '-Cu===> ? ? ??r1c> ddl}n #t$rtdYdSwxYw ddlm}n!#t$rtddYdSwxYwddl}|j\}}}}|t||} |dd j } | | dd nQ#t$rD | | dd n'#t$r| | d YnwxYwYnwxYwt| d 5} | } dddn #1swxYwY tj| n*#t $rt"dYnwxYwt'j|||| S)Nrzadd_video needs package moviepy)editorz6moviepy is installed, but can't import moviepy.editor.z2Some packages could be missing [imageio, requests])rXz.gifF)suffixdelete)verboser)r] progress_bar)r]rbz5The temporary file used by moviepy cannot be deleted.rC)moviepy ImportErrorprintrZtempfilerImageSequenceCliprNamedTemporaryFiler write_gifropenreadosremoveOSErrorrrrr@) rerXr`mpyrc_thwcr.filenamer' tensor_strings r0rrsh  /000)))))))  D @      OOO,KB1a  f3 7 7D**&*GGLH4 xt<<<< 444 4 NN8UN G G G G 4 4 4 NN8UN 3 3 3 3 3 44 h  ! !!!!!!!!!!!!!!!P ( PPPNOOOOOP =am   su$$/A  A !B:: DCD!D?DDDDD<<EEE$FFDct|}|}t|dkr%t d|dd}|jdks Jd|tjtj jz d}ddl }ddl }| }||d5}|d|d||||jdddn #1swxYwY|}|t-j|d|jd|d } t-t-j|| g S) Nrz4warning: audio amplitude out of range, auto clipped.rz%input tensor should be 1 dimensional.z>LIIKKK Mk"o)    E '-Cu===> ? ? ??s.AEEEc zg}|D]\}}g}|D]\}}|d}|ddkrtt|dksJtjtj|d|d|dg}tj||} n+tj| } tj|| } || |tj|| tj | }tj d } t| } td| gt} ttjd| | gS)NrrMarginr8ri)rlowerupper)series)titlerr)r multiline)rchart)categoryr rr DT_STRINGrc string_valrcustom_scalars__config__rr)rrr MarginChartContentSeriesChartMultilineChartContentrCategoryLayoutrrr rr rr)layout categoriesrrcharts chart_namechart_metadatatagsmgccrmlccrrres r0r r sJ FF1*+'')) ! ! &J!!$Da H,,4yyA~~~~!4"5<<"&q'aQ=#(z$GGG!7DAAA"(zTJJJ MM% *-AVDDDEEEE   3 3 3F!,9IJJJK k 2 2 2C ,,../%''F  M8RU V V V    r1c rtjdtd}t|}t d|dgt t jd g  }ttj |d z||gS)Nr!r)rrrrutf_8)encodingrrrrz /text_summaryrr) rrr rr encoder rrr)rr!rrres r0r!r!s!,N1$=$=$=$O$O$Q$QK k 2 2 2C KKK112%+;+?Q+G+G+G*HIIIF }!6VTTTU   r1c *|dkrd}tj||||||f} td|} t jd| } t | } t d| dttj | j d tj | j d  g  } ttj || | gS)Nrrrnum_thresholds pr_curvesrrrJrrrrrcrKrrr)r`stackr rrrr rr=r rrrr)rtpfptnfn precisionrecallrweightsrpr_curve_plugin_datarrres r0r#r# s' 8RRY7 8 8D,.",)=K k 2 2 2C ,,r""))++% $$*Q-888 $$*Q-888      F '-C#fMMMN O O OOr1c ,t|d}t||||}td|}t jd|}t |}t d|d ttj |j d tj |j d  g  } ttj ||| gS)Nr)rrrrrrrrJrrrrrrr)rr%r rrrr rr=r rrrr) rr4 predictionsrrrrrrres r0r$r$#s'--N  NG   D-.",)=K k 2 2 2C ,,r""))++% $$*Q-888 $$*Q-888      F '-C#fMMMN O O OOr1cd}|d}tjtj||dz z}|tj}d|dz f}tj|||||z\}} tj|||d|z |z\} } tj|dddddd} tj| dddddd} | d| z } | d| z }| tj|| | zz }| tj|| |zz }tj| | | |||fS)NgHz>g?rr)r r:rr) r`int32floorr float64rrmaximumr)r4rrr_MINIMUM_COUNTbucket_indices float_labelshistogram_range tp_buckets_ fp_bucketsrrrrrrs r0r%r%>slNXbh{nq6H'IJJKKN==,,L.1,-OL w& MJ L |#w. MJ :ddd# $ $TTrT *B :ddd# $ $TTrT *B AB ABRZR888I "*^R"W55 5F 8RRY7 8 88r1c ddl}ddlm}|j|}||||||j||} t d|dttj |jdtj |jdtj |jd g  }tj | |||| } | S) aCreate a tensor summary with summary metadata. Args: name: Uniquely identifiable name of the summary op. Could be replaced by combination of name and type to make it unique even outside of this summary. display_name: Will be used as the display name in TensorBoard. Defaults to `name`. description: A longform readable description of the summary data. Markdown is supported. tensor: Tensor to display in summary. content_type: Type of content inside the Tensor. components: Bitmask representing present parts (vertices, colors, etc.) that belong to the summary. json_config: A string, JSON-serialized dictionary of ThreeJS classes configuration. Returns: Tensor summary with metadata. rNr) json_configrJrrrrirrr)rBtensorboard.plugins.meshr as_tensorcreate_summary_metadatarr rr=r rrrget_instance_name) r display_name descriptionrerz componentsrrBrtensor_metadatatensor_summarys r0_get_tensor_summaryr_s*.LLL111111 U_V $ $F66  7O..$$++--% $&,q/::: $&,q/::: $&,q/:::      F]  & &t\ : : N r1c:d}|tj|d}|S)z5Parse and returns JSON string from python dictionary.z{}NT) sort_keys)jsondumps) config_dictrs r0_get_json_configrs'Kj=== r1c Fddlm}ddlm}t |} g} ||jf||jf||jfg} d| D} |d| D} | D]-\} }| t|||| || | .t| S)a'Output a merged `Summary` protocol buffer with a mesh/point cloud. Args: tag: A name for this summary operation. vertices: Tensor of shape `[dim_1, ..., dim_n, 3]` representing the 3D coordinates of vertices. faces: Tensor of shape `[dim_1, ..., dim_n, 3]` containing indices of vertices within each triangle. colors: Tensor of shape `[dim_1, ..., dim_n, 3]` containing colors for each vertex. display_name: If set, will be used as the display name in TensorBoard. Defaults to `name`. description: A longform readable description of the summary data. Markdown is supported. config_dict: Dictionary with ThreeJS classes names and configuration. Returns: Merged summary for mesh/point cloud representation. rr)MeshPluginDatac"g|] }|d | S)rr6)r8res r0r:zmesh..s!EEE&vay/Dv/D/D/Dr1cg|]\}}|Sr6r6)r8rerzs r0r:zmesh..s<<<06<<<() #$ %&G FEGEEEG00<rs  &&&&&& <;;;;;FFFFFF888888JJJJJJCCCCCC 22222222   0  8 $ $ &5 &S & & & &&3&5&&&&:5<:DI::::4el4tE{4444 u| S      JJ(;< MIz+>? N]J0CD MJ _=   K*k?;   MK?  L; o> JI7 K*i9 K*i9 K*i9 K*i9 I I7 K*i9 L:y/:  K*k?;!" Onn6LM#$ K..2HI Onn6LM L>>3IJ JJ8 8NO MO^5KL K*lO< L:|_= NZ?5<555 ((((Vzzzzz(M(M(M(MV"V"V"VJ@@@B@@@@,4444n"@"@"@"@LKO@@@@""4@@@@(((V@@@@<"""J    IMPPPP4PPPP69999B888vOS3$3$3$3$3$3$r1