Spf{n UdZddlmZddlZddlmZmZddlZddlZddl Z ddl Z ddl Z ddl Z ddl Z ddlmZmZmZddlZddlZddlmZejZejZejZdZd Zd d d Ze jeZ ee!ee!e"e#e"e$e%ffZ& dddZ' ddZ(dddZ)ej*Z*ej+Z+ dddZ, dddZ-dd Z.dd"Z/dd$Z0dd%Z1dd&Z2 ddd)Z3 ddd+Z4dd-Z5Gd.d/Z6dd0Z7ej8Z8ej9Z9ej:Z:ej;Z;ej<ZZ>ie8j?ej@de8jAej@d1e8jBej@d2e8jCej@d3e8jDej@d4e8jEej@d5e8jFej@d6e8jGej@d7e8jHej@d8e8jIej@d9e8jJej@d:e8jKej@e:e8jLej@e;e8jMej@e=e8jNej@e<e8jOej@e>e8jPej@e9e8jQej@d;e8jRej@d<e8jSej@d=e8jTej@d>e8jUej@d?e8jVej@ejWe8jXej@ejWiZYd@eYZDZ[dAZ\ej]Z]dBe]_ej^Z^dCe^_ej_Z_dDe__dddGZ`ejaZadHea_ejbZbejcZcejdZddIZedJZfGdKdLejgZhdMZiejjZjejkZkejlZlejmZmejnZnejoZoejpZpejqZqejrZrejsZsejtZtejuZuejvZvejwZwejxZxejyZyejzZzddNZ{ddOZ|e{eq_}e|eq_~GdPdQejZGdRdSeZiZdTedU<iZdVedW<e jZdXdejfdd_ZddaZejZejZejZGdbdcZGdddeZddhZGdidjZddmZGdndoZddrZGdsdtZGdudvZGdwdxZGdydzZd{Zd|ZejZejZejdd}ZddZejZejejejZejZejZejZejZejZejZejZdS)zAn XLA client in Python.) annotationsN)MappingSequence)AnyProtocolUnion) xla_extensioni9HostCUDA)cpugpuTreturn...chtdtjtj|||||S)Nr asynchronousdistributed_clientnode_id num_nodes collectives)register_custom_call_handler_xlaregister_custom_call_targetget_tfrt_cpu_clientrs Q/var/www/html/nettyfy-visnx/env/lib/python3.11/site-packages/jaxlib/xla_client.pymake_cpu_clientrDs@ud&FGGG !+    Fc bt}|d}tj}|dkrtjjj|_|dkrtjjj|_|dkrtjjj|_|dkrtjjj|_d|vr |d|_ d|vr |d|_ d|vr |d|_ td tj td tj tjd ||||||| S) z7Returns a GPU client. BFC allocator is used by default. allocatordefaultplatformbfc cuda_asyncmemory_fraction preallocatecollective_memory_sizer ROCMT)rallocator_configrrr platform_nameallowed_devicesmock) generate_pjrt_gpu_plugin_optionsrGpuAllocatorConfigKindDEFAULTkindPLATFORMBFC CUDA_ASYNCr&r'r(rrget_gpu_client) rrrr+r,r-optionsr!configs rmake_gpu_clientr9Us5 - . .'k")  " $ $&)).6FK*).7FK%).2FK,).9FK'!!$%67Fg /F(($+,D$EF!vt'GHHHvt'GHHH +!%     rr7_NameValueMapping | NonectdsJtdstd|i}tjd|S)Ntpu)pjrt_plugin_loadedpjrt_plugin_initializedinitialize_pjrt_pluginrget_c_api_client)r7s rmake_tfrt_tpu_c_api_clientrA~sQ E " """"  ' '"5!!! _G ug . ..r topology_namestrDeviceTopologyc Btjd|tdi|S))Creates a PJRT C API TopologyDescription.r<)rget_default_c_api_topologydict)rCkwargss r#make_tfrt_tpu_c_api_device_topologyrLs#  ( t~~f~~ N NNrc_apirc Btj||tdi|S)rGrH)rget_c_api_topologyrJ)rMrCrKs rmake_c_api_device_topologyrPs#   t~~f~~ F FFr plugin_nameboolc*tj|SN)rr=rQs rr=r=s  - --r library_pathc0tj||dS)N)rMrload_pjrt_plugin)rQrVs rload_pjrt_plugin_dynamicallyrZs {L E E EErNonec.tj|d|SrTrX)rQrMs rload_pjrt_plugin_with_c_apir]s {D% 8 88rc*tj|SrT)rr>rUs rr>r>s %k 2 22rc.tj|dS)zInitializes a PJRT plugin. The plugin needs to be loaded first (through load_pjrt_plugin_dynamically or static linking) before this method is called. Args: plugin_name: the name of the PJRT plugin. N)rr?rUs rr?r?sk*****rr$_xla.DistributedRuntimeClient | Nonec6|i}tj|||S)axCreates a PJRT C API client for a PJRT plugin. It is required that load_pjrt_plugin_dynamically is called once with the same plugin_name before this method is called. Args: plugin_name: the name of the PJRT plugin. options: extra platform-specific options. distributed_client: distributed client. Returns: A PJRT C API client for plugin_name. )rr@)rQr7rs rmake_c_api_clientrbs$$ _G {G5G H HHr str | Nonectds,td|pd}t|t |S)zEReturns a TPU client. Defaults to allowing 32 in-flight computations.r<z libtpu.so)r=rZprofilerregister_plugin_profilerrA)rVr7rMs rmake_tpu_clientrgsJ E " "- ( 0K L LE %%e,,, #G , ,,r_NameValueMappingc~i}d|d<tjdd}tjdd}tjdd}tjdd}|d vrtd |z||d <|rt ||d <|r|d v|d<|rt |dz|d<|S)zYGenerates the PjRt GPU plugin options. Returns: A dictionary of plugin options. cudar+XLA_PYTHON_CLIENT_ALLOCATORr"XLA_PYTHON_CLIENT_MEM_FRACTIONrBXLA_PYTHON_CLIENT_PREALLOCATE(XLA_PYTHON_CLIENT_COLLECTIVE_MEM_SIZE_MB)r"r#r$r%zcXLA_PYTHON_CLIENT_ALLOCATOR env var must be "default", "platform", "bfc", or "cuda_async", got "%s"r!r&)falseFalse0r'ir()osgetenvlower ValueErrorfloatint)r7r!r&r'r(s rr.r.s '#'/i5yAAGGII)I>CC/ 92>>+90"BBB  +-6 7  #'+8!&!7!7G H(0GGGMP(+,B(C(Cw(OG $% .rceZdZdZdZddZdS) OpMetadataz3Python representation of a xla.OpMetadata protobuf.op_typeop_name source_file source_linerBrc>||_||_||_||_dSrTrz)selfr{r|r}r~s r__init__zOpMetadata.__init__s&DLDL"D"DrN)rBrBrBr__name__ __module__ __qualname____doc__ __slots__rrHrrryrys3;;B)######rryctj|dd\}}tj|}t ||||S)zCHelper for use in source mapping that returns an OpMetadata object.r rz)inspectstackrrpathbasenamery)r{r| skip_frames full_filenamelinenofilenames rCurrentSourceInfoMetadatarsU!-//+6qs;- W  m , ,( wH&   rint4int8int16int32int64uint4uint8uint16uint32uint64float16float32float64 complex64 complex128c4i|]\}}t||SrH)rD).0etdts r r/s1BCGGRrcZtttj|S)z;Convenience function for reading DTYPE_TO_XLA_ELEMENT_TYPE.)DTYPE_TO_XLA_ELEMENT_TYPErDnpdtype)rs rdtype_to_etyper4s "3rx#7#7 88ra A Shape is an object defined in C++ that duck types like the following class: class Shape: '''Represents an XLA shape. A shape is either an array shape, having rank-many integer dimensions and an element type (represented by a Numpy dtype), or it is a tuple shape, having a shape for every tuple component: type shape = TupleShape of shape list | ArrayShape of { dimensions: int list; element_type: dtype } ''' @staticmethod def tuple_shape(tuple_shapes) -> Shape: "Construct a tuple shape." @staticmethod def array_shape(element_type, dimensions, minor_to_major=None) -> Shape: @staticmethod def from_pyval(pyval) -> Shape: "Returns a Shape that describes a tuple-tree of Numpy arrays." def __init__(self, str) -> Shape: "Parses a shape string." def __eq__(self, other: Shape) -> bool: def __ne__(self, other: Shape) -> bool: def __hash__(self): def __repr__(self): def is_tuple(self) -> bool: def is_array(self) -> bool: def tuple_shapes(self) -> [Shape]: def numpy_dtype(self) -> np.dtype: "Like element_type(), but returns dtype('O') for a tuple shape." def xla_element_type(self) -> PrimitiveType: def element_type(self) -> np.dtype: def dimensions(self) -> (int, int, ...): def rank(self) -> int: def with_major_to_minor_layout_if_absent(self) -> Shape: "Returns a copy with missing layouts set to major-to-minor." def to_serialized_proto(self) -> bytes: "Returns 'shape' as a serialized proto." z A ProgramShape is a C++ object that duck types like the following class. class ProgramShape: def __init__(self, parameter_shapes, result_shape): def parameter_shapes(self) -> [Shape]: def result_shape(self) -> Shape: def __repr__(self): a A Shape is an object defined in C++ that duck types like the following class: class ShapeIndex: '''Represents an XLA ShapeIndex. An index for specifying a particular nested subshape within a shape. Used in ShapeUtil::GetSubshape and other interfaces. ShapeIndex defines a path through the Shape tree where each element of ShapeIndex indexes into a tuple (or nested tuple) within the shape. For a non-nested tuple, an index has a single element. ''' def __init__(self, List[int]) -> ShapeIndex: def __eq__(self, other: Shape) -> bool: def __ne__(self, other: Shape) -> bool: def __hash__(self): def __repr__(self): layoutSequence[int] | Nonec(fd|S)zz4shape_from_pyval..convert..s+$C$CcWWS\\$C$C$C$C$C$Cr) isinstancetupleNotImplementedErrorShape tuple_shape array_shaperrshape)pyvalrrs rrz!shape_from_pyval..converts%E  ! H     u$C$C$C$CU$C$C$CCC D DD   u{BHUOOV D DDrrH)rrrs `@rshape_from_pyvalrs8EEEEEE ra A DeviceAssignment is a C++ object with the following signature. def create(assignment): '''Builds a device assignment. Args: assignment: a 2D numpy array of device ordinal integers, indexed by [replica][computation_in_replica]. Returns: A device assignment. ''' def replica_count(): '''Returns the number of replicas.''' def computation_count(): '''Returns the number of computations per replica.''' crfdfd|D}|}d|DS)zBExecute on one replica with Python values as arguments and output.cb|dS)Nr)device)buffer_from_pyval local_devices)argbackend executables rputz'execute_with_python_values..puts-  $ $S1I1I1K1KA1N $ O OOrc&g|] }|SrHrH)rrrs r z.execute_with_python_values..s!---Css3xx---rc6g|]}tj|SrHrasarrayrxs rrz.execute_with_python_values..s ) ) )A"*Q-- ) ) )r)execute)r argumentsroutputsrs` ` @rexecute_with_python_valuesrskPPPPPP.---9---)   y ) )' ) ) ) ) ))rc|fdfdt|D}||}dt|DS)aBExecute on many replicas with Python values as arguments and output. Args: executable: the program to run. arguments: a list of lists of Python values indexed by `[replica][arg_num]` to pass as inputs. backend: the backend we are targeting. Returns: A list of python values, one per replica. c<fdt|DS)NcBg|]\}}||SrH)r)rvdrs rrzRexecute_with_python_values_replicated..copy_to_devices..s- M M M1G % %a + + M M Mr)zip)pyvalsrdevicess rcopy_to_devicesz>execute_with_python_values_replicated..copy_to_devicess' M M M MFG8L8L M M MMrc&g|] }|SrHrH)rrrs rrz9execute_with_python_values_replicated..s# B B BOOF # # B B Brc&g|]}d|DS)c6g|]}tj|SrHrrs rrzDexecute_with_python_values_replicated...s % % %Q2:a== % % %rrH)rxss rrz9execute_with_python_values_replicated..s' > > >" % %" % % % > > >r)rr execute_sharded_on_local_devices)rrrinputsrrrs ` @@r%execute_with_python_values_replicatedrs  $ $ & &'NNNNNN C B B B#y/ B B B&  7 7 ? ?' > >W > > >>rceZdZdZdZdS) PaddingTyper N)rrrVALIDSAMErHrrrrs % $$$rrct|ttfs1d}t|t |t|trn|dkr tj}nI|dkr tj}n$d}t|||tjkrdgt|zS|tjkrgtj tj ||t}dt!||||D}d|DSd}t||) zAMaps PaddingType or string to pad values (list of pairs of ints).z0padding_type must be str or PaddingType, got {}.rrz@Unknown padding type string: expected "VALID" or "SAME", got {}.)rrcPg|]#\}}}}t|dz |z|z|z d$S)r r)max)rout_sizestride filter_sizein_sizes rrz5window_padding_type_to_pad_values.. sM 2Hfk7 X\V #k 1G ;Q??rc(g|]}|dz||dzz fS)rrH)rpad_sizes rrz5window_padding_type_to_pad_values..s* P P P(X]Hx1}4 5 P P Prz Unexpected PaddingType value: {})rrDr TypeErrorformattypeupperrrrulenrceil true_divideastyperwr) padding_typelhs_dimsrhs_dimswindow_stridesmsg out_shape pad_sizess r!window_padding_type_to_pad_valuesrsv L3 "4 5 54 .,s H H H1!A$ H H Hr)execute_sharded%disassemble_into_single_device_arraysrrrresultss rLoadedExecutable_executer)s8   + +' H HEEGG H H HHrc~||d}d|D|dfS)NT) with_tokenscg|] }|d SrrHrs rrz7LoadedExecutable_execute_with_token..3sEEEqtEEErr)rr consume_token get_tokenrs r#LoadedExecutable_execute_with_tokenr /sa   = ='EEWBBDDEEE ''** rceZdZdZdZdS)CustomCallTargetTraitsrr N)rrrr1COMMAND_BUFFER_COMPATIBLErHrrrr<s '  rrceZdZ ddd ZdS)CustomCallHandler. api_versionrwtraitsrnamerDfnrr#rr[cdSrTrH)rrrr#rrs r__call__zCustomCallHandler.__call__Hs CrN)..) rrwrrrrDrrr#rDrr[)rrrrrHrrrrFs5'*       rrzdict[str, CustomCallHandler]_custom_callback_handlerz=dict[str, list[tuple[str, Any, int, CustomCallTargetTraits]]]_custom_callbackrrrr#rrwrc&t||}t5|tvrt||||||n2t|g||||fddddS#1swxYwYdS)aRegisters a custom call target. Args: name: bytes containing the name of the function. fn: a PyCapsule object containing the function pointer. platform: the target platform. api_version: the XLA FFI version to use. Supported versions are: 0 for the untyped FFI and 1 for the typed FFI. traits: custom call traits corresponding to XLA FFI handler traits. N)xla_platform_namesget_custom_callback_lockrr setdefaultappend)rrr#rrxla_platform_names rrr\s&),,Xx@@44401 %{F!!"3R88?? [& ) sABB  B handlerc ^t||}t5|tvr)td| ddddS|t|<|t vr,t |D]\}}}}||||||t |=ddddS#1swxYwYdS)a0Registers a custom handler and use it to register existing custom calls. If a custom call handler for the platform already exist, calling this method is a no-op and it will not register a new handler. Args: platform: the target platform. handler: the function to register a custom call. zKCustom call handler for %s is already register. Will not register a new oneN)rrrrloggerdebugr)r#r!r rrrrs rrr{s:),,Xx@@ . .444 ll     . . . . . . . .3:./,,,+;>)rr0padding_config4Union[PaddingConfig, Sequence[tuple[int, int, int]]]ct|tsV|}t}|D]C\}}}t}||_||_||_|j|D|S)a7Create PaddingConfig proto from list of triples of integers. Args: padding_config: either a PaddingConfig or a list of integer triples (edge_padding_low, edge_padding_high, interior_padding) representing the configuration of the padding operation. Returns: A `PaddingConfig` object. )rr0r&r(r)r*r2r)r4tripleslohiinterior dimensions rmake_padding_configr<s} NM 2 22G"__N#22B(**i#%i $&i!#+i &&y1111 rceZdZdZdZdZdS)DotDimensionNumberszg|_g|_g|_g|_dSrTr?r-s rrzDotDimensionNumbers.__init__s(&(D#&(D# "D "DrNrrHrrr>r>s0DD)#####rr>dimension_numbers[Union[DotDimensionNumbers, tuple[tuple[list[int], list[int]], tuple[list[int], list[int]]]]cDt|ttfr|\\}}\}}t}|j||j||j||j||S|S)aBuilds a DotDimensionNumbers object from a specification. Args: dimension_numbers: either a `DotDimensionNumbers` or a nested tuple `((lhs_contract, rhs_contract), (lhs_batch, rhs_batch))` of lists of integers representing the dimensions to treat as contracting dimensions and batch dimensions on each input operand. Returns: A `DotDimensionNumbers` object. ) rlistrr>r@extendrArBrC)rE lhs_contract rhs_contract lhs_batch rhs_batchdot_dims_protos rmake_dot_dimension_numbersrOs"!D%=11 ;L8 \<"89i(**N-44\BBB-44\BBB'..y999'..y999  rceZdZdZdZdZdS)ConvolutionDimensionNumberszDPython representation of a xla.ConvolutionDimensionNumbers protobuf. input_batch_dimensioninput_feature_dimensioninput_spatial_dimensionskernel_input_feature_dimensionkernel_output_feature_dimensionkernel_spatial_dimensionsoutput_batch_dimensionoutput_feature_dimensionoutput_spatial_dimensionscd|_d|_g|_d|_d|_g|_d|_d|_g|_dSr,rRr-s rrz$ConvolutionDimensionNumbers.__init__ sP!"D#$D $&D!*+D'+,D(%'D""#D$%D!%'D"""rNrrHrrrQrQs0LL ) ( ( ( ( (rrQ>Union[None, ConvolutionDimensionNumbers, tuple[str, str, str]]num_spatial_dimensionsc||}t}d|_d|_d|_d|_d|_d|_|jtdd|z|j tdd|z|j tdd|znt|tri|\t}d|_d|_d|_d|_d|_d|_|j d tD|jt!d tDfd  |j t!d tDfd |S)aBuilds a ConvolutionDimensionNumbers object from a specification. Args: dimension_numbers: optional, either a ConvolutionDimensionNumbers object or a tuple (lhs_spec, rhs_spec, out_spec). Each element is a string of length N+2 identifying by position: (1) batch dimensions in lhs, rhs, and the output with the character 'N', (2) feature dimensions in lhs and the output with the character 'C', (3) input and output feature dimensions in rhs with the characters 'I' and 'O' respectively, and (4) spatial dimension correspondences between lhs, rhs, and the output using any distinct characters. For example, to indicate dimension numbers consistent with the Conv operation with two spatial dimensions, one could use ('NCHW', 'OIHW', 'NCHW'). As another example, to indicate dimension numbers consistent with the TensorFlow Conv2D operation, one could use ('NHWC', 'HWIO', 'NHWC'). When using the latter form of convolution dimension specification, window strides are associated with spatial dimension character labels according to the order in which the labels appear in the rhs_spec string, so that window_strides[0] is matched with the dimension corresponding to the first character appearing in rhs_spec that is not 'I' or 'O'. By default, use the same dimension numbering as Conv and ConvWithGeneralPadding. num_spatial_dimensions: the number of spatial dimensions. Returns: A `ConvolutionDimensionNumbers` object. Nrr rNCOIc3(K|] \}}|dv |VdS)>rcrbNrHrics rrz5make_convolution_dimension_numbers..Ls977aQj-@-@-@-@-@-@77rc3(K|] \}}|dv |VdS>rar`NrHres rrz5make_convolution_dimension_numbers..Q0 F F41a!:2E2EQ2E2E2E2E F Frc:|SrTindex)rflhs_specrhs_specs rz4make_convolution_dimension_numbers..R(..!55r)keyc3(K|] \}}|dv |VdSrirHres rrz5make_convolution_dimension_numbers..Wrjrc:|SrTrl)rfout_specros rrpz4make_convolution_dimension_numbers..Xrqr)rQrSrTrYrZrWrVrUrIrangerXr[rrrm enumeratesorted)rEr^ndrnruros @@@r"make_convolution_dimension_numbersrzse@ B355./+01-/0,12.895784.55eAq2v6F6FGGG/66uQB7G7GHHH/66uQB7G7GHHHH#U++#4 Hh355.6nnS.A.A+08s0C0C-/7~~c/B/B,191D1D.8@s8K8K57?~~c7J7J4/6677))777.55 F F9X.. F F F55555    /66 F F9X.. F F F55555    rc*eZdZdZdZejZdZdS)PrecisionConfigz8Python representation of a xla.PrecisionConfig protobuf.operand_precisioncg|_dSrTr}r-s rrzPrecisionConfig.__init__esDrN) rrrrrrPrecisionConfig_Precision PrecisionrrHrrr|r|^s6@@$),)     rr|ceZdZdZdZdZdS)GatherDimensionNumbersz?Python representation of a xla.GatherDimensionNumbers protobuf. offset_dimscollapsed_slice_dimsstart_index_mapindex_vector_dimc>g|_g|_g|_d|_dSr,rr-s rrzGatherDimensionNumbers.__init__ss(D "DDDrNrrHrrrris0GG)rrceZdZdZdZdZdS)ScatterDimensionNumbersz@Python representation of a xla.ScatterDimensionNumbers protobuf.update_window_dimsinserted_window_dimsscatter_dims_to_operand_dimsrc>g|_g|_g|_d|_dSr,rr-s rrz ScatterDimensionNumbers.__init__s( D "D(*D%DrNrrHrrrrzs0HH)rrceZdZdZdZdZdS) ReplicaGroupz5Python representation of a xla.ReplicaGroup protobuf. replica_idscg|_dSrTrr-s rrzReplicaGroup.__init__sDrNrrHrrrrs.==)rrcVt}|j||SrT)rrrI) replica_groupreplica_group_protos r_make_replica_group_protors)$!((777 rcF|g}nt|}d|D}|S)Nc,g|]}t|SrH)r)rgroups rrz'make_replica_groups..s.-2!%((r)rH)replica_groupsreplica_groups_protoss rmake_replica_groupsrsE.))N6D rc#~Ktj}|t_ dV|t_dS#|t_wxYw)z>Context manager that enables or disables traceback collection.N) Tracebackenabled)rsaveds r tracebacksrsH  %) EEEIIs.<clientClientbytescNtj|S)zBReturns a gzipped pprof protocol buffer containing a heap profile.)gzipcompress heap_profile)rs rrrs v**,, - --r)TNrr N)rr)Nrr NNFrT)r7r:)rB)rCrDrrE)rMrrCrDrrE)rQrDrrR)rQrDrVrDrr)rQrDrMrrr[)rQrDrr[)NN)rQrDr7r:rr`)rVrcr7r:)rrh)NNr )rr) rrDrrr#rDrrwrrrr[)r#rDr!rrr[)r4r5rr0)rErFrr>)rEr]r^rwrrQ)T)rrrr)r __future__ratexitcollections.abcrr contextlibenumrrloggingrr threadingtypingrrr ml_dtypesnumpyrrBr r ifrt_programsopsre_versionmlir_api_versionr getLoggerrr#rDrwrHrvrRrhrr9rArEget_topology_for_devicesrLrPr=rZr]r>r?rbrgr.ryr PrimitiveTypebfloat16 float8_e4m3fnfloat8_e4m3b11fnuzfloat8_e4m3fnuz float8_e5m2float8_e5m2fnuzPREDrS4S8S16S32S64U4U8U16U32U64F8E4M3FN F8E4M3B11FNUZF8E5M2 F8E4M3FNUZ F8E5M2FNUZBF16F16F32F64C64C128TUPLEobject_TOKENXLA_ELEMENT_TYPE_TO_DTYPEitemsrrr ProgramShape ShapeIndexrDeviceAssignmentDeviceCompileOptionsHostBufferSemanticsrrEnumrr XlaBuilderXlaComputationXlaOpFftTyperMemory ArrayImplLoadedExecutable DeviceList OpSharding HloShardingSharding NamedShardingSingleDeviceSharding PmapSharding GSPMDSharding PjRtLayoutrr rexecute_with_tokenIntFlagrrrr.rLockrr1rr register_custom_call_partitioner encode_inspect_sharding_callbackhlo_sharding_utilr&r0r<r>rOrQrzr|rrrrrrFramecontextmanagerrrXlaRuntimeErrorregistercollect_garbageweakref_lru_cachearray_result_handler+batched_copy_array_to_devices_with_shardingbatched_device_putbatched_block_until_ready"check_and_canonicalize_memory_kindLayoutcustom_call_targetsrHrrrs """""" --------  ''''''''''######"  h =     8 $ $CsCcE4'G!HHI  $   &&&&R/////$8OOOOO&(GGGGG....FFFF99993333++++)-?CIIIII0JN-----< # # # # # # # #"  ' 1+# +((hbhv&&hbhv&&xrx((  xrx((  xrx(( hbhw''hbhw''xrx))xrx))xrx))HBH]33*....6$(#H #'#H *        2########":((((((((6FFFFR        ""  N   .... &$%%%*04,, :%)%L" .r