U 9%eZ4@sZddlZddlZddlZddlmZddlZddlZddlm Z ddl m Z m Z m Z ddlmZddlmZddlmZddlmZdd lmZd d lmZd d lmZmZd ddddddddddddddddddgZeeZ ej!d ej"d ej#d!ej$d"ej%d"ej&d#ej'd#ej(d$ej)d%ej*d%ej+d&ej,d&ej-d'ej.d'ej/d'ej0d(ej1d)ej2d)ej3d*ej4d*ej5d+ej6d,ej7d,ej)d-ej8d-ej9d-iZ:d.d/Z;dQd2d3Zd7dZ?d8dZ@dTd9dZAdUd:dZBdVddZDdXd?dZEdYd@dZFdZdBdZGdCdZHd[dEdZIdFdZJdGdZKd\dIdZLd]dJdZMd^dKdZNdLdMZOdNdOZPd_dPdZQdS)`N)Optional) struct_pb2)HistogramProtoSummarySummaryMetadata) TensorProto)TensorShapeProto) layout_pb2)PrCurvePluginData)TextPluginData)make_np)_prepare_videoconvert_to_HWChparamsscalar histogram_raw histogrammake_histogramimage image_boxes draw_boxes make_imagevideo make_videoaudiocustom_scalarstext tensor_proto pr_curve_rawpr_curve compute_curvemesh)ZDT_HALFZhalf_val)Z DT_BFLOAT float_val)DT_FLOATr#) DT_DOUBLE double_val)ZDT_INT8int_val)DT_UINT8r')ZDT_INT16r')ZDT_INT32r')ZDT_INT64Z int64_val)Z DT_COMPLEX32 scomplex_val)Z DT_COMPLEX64r))ZDT_BOOLZbool_val)Z DT_COMPLEX128 dcomplex_val)r(Z uint32_valcCs,t|tjs|n|}|jtjkr(dSdS)Nr ) isinstancenpZndarraynumpydtypeuint8)tensorZ convertedr2^/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/torch/utils/tensorboard/summary.py_calc_scale_factorRsr4blackc Csddlm} m} | } | |} ||||f\} }}}| j| |f| |f||f||f| |fg||d|r|}| |\}}td|}| j | ||d|f| ||fg|d| j | ||||f||| d|S)Nr) ImageDraw ImageFont)widthfillg?r6)r:)r:font) PILr7r8Z load_defaultZDrawlinegetsizer-ceilZ rectangler)rZxminZyminZxmaxZymax display_strcolorZ color_textZ thicknessr7r8r;ZdrawleftrighttopbottomZ text_bottom text_widthZ text_heightmarginr2r2r3_draw_single_boxWs4    rHcsddl}ddlm}m}m}mmm}ddlm }m } m } m } m } ddlm} m}m}ttsttdtdt|tstdtd|pi}t|tstd|D]H\}kst|trtfd d |Dstd d d qg}|}D]\}|dkr&qt|ttfr||j_|krhtjdd|Dd}nd}| ||!d|dqt|t"r||j_#|krtjdd|Dd}nd}| ||!d|dqt|t$rJ||j_%|kr(tjdd|Dd}nd}| ||!d|dqt||j&rt'|d}||j_| ||!ddqt(dq| || d}t)t)j*| |+dd}t,t,j!| |dgd}fdd|-D}|||d}| || d }t)t)j*| |+dd}t,t,j!||dgd}||!d!d"}| || d#}t)t)j*| |+dd}t,t,j!| |dgd}|||fS)$a2Outputs 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.c3s |]}t|tVqdS)N)r,type.0d) hparam_dictkr2r3 szhparams..z"parameter: hparam_domain_discrete[z/] should be a list of same type as hparam_dict[z].cSsg|]}tj|dqS)) number_valuerValuerXr2r2r3 szhparams..)valuesZDATA_TYPE_FLOAT64)namerWdomain_discretecSsg|]}tj|dqS)) string_valuer_rXr2r2r3rasZDATA_TYPE_STRINGcSsg|]}tj|dqS)) bool_valuer_rXr2r2r3rasZDATA_TYPE_BOOL)rcrWz=value should be one of int, float, str, bool, or torch.Tensor)Zsession_start_infoversion plugin_namecontent plugin_data)tagmetadatavaluecsg|]}|ddqS)rm)rcr2)rYr\)rLrMr2r3ra%s)Z hparam_infosZ metric_infos)Z experimentrgZSTATUS_SUCCESS)status)Zsession_end_inforg).torchZ#tensorboard.plugins.hparams.api_pb2rIrJrKrLrMrNZ$tensorboard.plugins.hparams.metadatarOrPrQrRrSZ+tensorboard.plugins.hparams.plugin_data_pb2rTrUrVr,dictloggerwarning TypeErroritemslistallintfloatrr^rZ ListValueappendr`strreboolrfZTensorr ValueErrorr PluginDataSerializeToStringrkeys)r[Z metric_dictZhparam_domain_discretersrIrJrKrNrOrPrQrRrSrTrUrVvZhpsZssirdrjsmdZmtsexpZseir2)rLrMr[r\r3rs                     Fc Cst|}|jdks0td|jd|jdt|}|rt|gdd}|r\t|gdd}tjd d }t|d }t t j |||d gd St t j ||dgd SdS)aOutputs a `Summary` protocol buffer containing a single scalar value. The generated Summary has a Tensor.proto containing the input Tensor. Args: name: A name for the generated node. Will also serve as the series name in TensorBoard. tensor: A real numeric Tensor containing a single value. collections: Optional list of graph collections keys. The new summary op is added to these collections. Defaults to `[GraphKeys.SUMMARIES]`. new_style: Whether to use new style (tensor field) or old style (simple_value field). New style could lead to faster data loading. Returns: A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf. Raises: ValueError: If tensor has the wrong shape or type. rzBTensor should contain one element (0 dimensions). Was given size: z and z dimensions.r$)r#r/r%)r&r/Zscalarsrirkrmr1rnro)rmZ simple_valueN) r squeezendimAssertionErrorsizer|rrrrr`) rcr1 collections new_styleZdouble_precisionrrrlrr2r2r3r=s*    csjdkrtdjtkrtjd}|dksB|dkrVt}n6dkrn g}ndkrg}n }dtjddt fd d t Dd ||i}t f|}nt|d jtjd d}t|d}ttj|||dgdS)aOutputs a `Summary` protocol buffer containing the full tensor. 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: 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 sizer r)r*rr/ tensor_shapecsg|]}tjj|dqS)r)rDimshape)rYir1r2r3rasz tensor_proto..dimz has unsupported tensor dtype r1rrkrmrnr1ro)Znumelitemsizerr/_TENSOR_TYPE_MAPrsZ view_as_realflattentolistitemrrangerrrrrr`)rmr1Zproto_val_fieldZproto_val_contentsZtensor_proto_argsrrlrr2rr3rgs:            c Cs,t|||||||d}ttj||dgdS)aOutputs 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_squaresZ bucket_limitZbucketrmZhistoro)rrr`) rcrrrrrZ bucket_limitsZ bucket_countshistr2r2r3rs cCs0t|}t|t||}ttj||dgdS)adOutputs 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. rro)r rastyper|rr`)rcrbbinsmax_binsrr2r2r3rsc Cs|jdkrtd|d}tj||d\}}t|}|dk r||kr||}||}|dkrztj|d||ggddd}|d|jdd}t|jd f|j }|dd||dd<|d|d<|}t t |d} tj | d| dd gd d \} } t | } t | d } ~ | dkr,|| d | ntdg|d| g}|| | d }|jdksl|jdkrttd ||} t||t||| ||d S)zDConvert values into a histogram proto using logic from histogram.cc.rzThe input has no element.)rNZconstant)Z pad_widthmodeZconstant_values)Zaxisr rC)Zsidez1The histogram is empty, please file a bug report.r)rrreshaper-rlenpadremptyr/cumsumZgreaterZ searchsortedr{Z concatenatedotrrrr) rbrrcountsZlimitsZnum_binsZ subsamplingZsubsampling_remainderZ new_limitsZ cum_countsstartendZsum_sqr2r2r3rsP      0 NCHWcCs`t|}t||}t|}|tj}||ddtj}t||d}t t j ||dgdS)aJOutputs 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). 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