AVh9dZddlZddlmZddlmZddlmZddlm Z ddl m Z ddlmZdd l mZdd l mZdd l mZdd lmZdd lmZddlmZddlmZm Z m!Z!ddl"m#Z#ejHdddgZ%d(de#e!ejLfde#e!ejLfde'de'de'f dZ(edejRe(Z*de#e!ejLfde#e!ejLfde'de'de'f dZ+d(de#e!ejLfde#e!ejLfde#e!ejXfde#e!ejXfd e#e!ejZfd!e'd"e'd#e'd$e#e!ejZffd%Z.ed&ejRe.Z/de#e!ejLfde#e!ejLfde#e!ejXfde#e!ejXfd e#e!ejZfd!e'd"e'd#e'd$e#e!ejZffd'Z0y))zUPython wrappers around TensorFlow ops. This file is MACHINE GENERATED! Do not edit. N) pywrap_tfe)context)core)execute)dtypes)annotation_types)op_def_registry)ops)op_def_library)deprecated_endpoints)dispatch) tf_export)TypeVarListAny) AnnotatedGenerateVocabRemapping remapping num_presentnew_vocab_fileold_vocab_filenew_vocab_offset num_new_vocabold_vocab_sizec tjxstj}|j}|jr7 t j |d|||d|d|d| }t j|}|Stj |d}tj |d}|d}tj |d}t#j$d||||||\} } } } | dd}tj&rYd| j)dd| j)dd| j)df} | j*}tj,d|| |t j|}|S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t|||||||S#tj$rYcwxYw) a Given a path to new and old vocabulary files, returns a remapping Tensor of length `num_new_vocab`, where `remapping[i]` contains the row number in the old vocabulary that corresponds to row `i` in the new vocabulary (starting at line `new_vocab_offset` and up to `num_new_vocab` entities), or `-1` if entry `i` in the new vocabulary is not in the old vocabulary. The old vocabulary is constrained to the first `old_vocab_size` entries if `old_vocab_size` is not the default value of -1. `num_vocab_offset` enables use in the partitioned variable case, and should generally be set through examining partitioning info. The format of the files should be a text file, with each line containing a single entity within the vocabulary. For example, with `new_vocab_file` a text file containing each of the following elements on a single line: `[f0, f1, f2, f3]`, old_vocab_file = [f1, f0, f3], `num_new_vocab = 3, new_vocab_offset = 1`, the returned remapping would be `[0, -1, 2]`. The op also returns a count of how many entries in the new vocabulary were present in the old vocabulary, which is used to calculate the number of values to initialize in a weight matrix remapping This functionality can be used to remap both row vocabularies (typically, features) and column vocabularies (typically, classes) from TensorFlow checkpoints. Note that the partitioning logic relies on contiguous vocabularies corresponding to div-partitioned variables. Moreover, the underlying remapping uses an IndexTable (as opposed to an inexact CuckooTable), so client code should use the corresponding index_table_from_file() as the FeatureColumn framework does (as opposed to tf.feature_to_id(), which uses a CuckooTable). Args: new_vocab_file: A `Tensor` of type `string`. Path to the new vocab file. old_vocab_file: A `Tensor` of type `string`. Path to the old vocab file. new_vocab_offset: An `int` that is `>= 0`. How many entries into the new vocab file to start reading. num_new_vocab: An `int` that is `>= 0`. Number of entries in the new vocab file to remap. old_vocab_size: An optional `int` that is `>= -1`. Defaults to `-1`. Number of entries in the old vocab file to consider. If -1, use the entire old vocabulary. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (remapping, num_present). remapping: A `Tensor` of type `int64`. num_present: A `Tensor` of type `int32`. rrrrN)rrrnamectx)rrrrrr)_contextr_thread_local_datais_eagerrTFE_Py_FastPathExecute_GenerateVocabRemappingOutput_make_core_NotOkStatusException_opsraise_from_not_ok_status_FallbackException'generate_vocab_remapping_eager_fallback_SymbolicException_executemake_int_op_def_library_apply_op_helpermust_record_gradient _get_attr_intinputsrecord_gradient)rrrrrr_ctxtld_resulte__op_outputs_attrs _inputs_flats X/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/ops/gen_checkpoint_ops.pygenerate_vocab_remappingr>sd    0h..0$ #\\  11 &nn,o}.*g.33G 3&   6$0C"& ('""$  ,A ) / / 8' . ckpt_pathold_tensor_name row_remapping col_remappinginitializing_valuesnum_rowsnum_colsmax_rows_in_memoryreturnc tjxstj} | j} | jr% t j | d||||||d|d|d|} | Stj|d}tj|d}|d}tj|d}tj d||||||||| \} } }}|dd} tj"rYd|j%dd|j%dd|j%df}|j&}tj(d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||||  S#t j$rYXwxYw) a Loads a 2-D (matrix) `Tensor` with name `old_tensor_name` from the checkpoint at `ckpt_path` and potentially reorders its rows and columns using the specified remappings. Most users should use one of the wrapper initializers (such as `tf.contrib.framework.load_and_remap_matrix_initializer`) instead of this function directly. The remappings are 1-D tensors with the following properties: * `row_remapping` must have exactly `num_rows` entries. Row `i` of the output matrix will be initialized from the row corresponding to index `row_remapping[i]` in the old `Tensor` from the checkpoint. * `col_remapping` must have either 0 entries (indicating that no column reordering is needed) or `num_cols` entries. If specified, column `j` of the output matrix will be initialized from the column corresponding to index `col_remapping[j]` in the old `Tensor` from the checkpoint. * A value of -1 in either of the remappings signifies a "missing" entry. In that case, values from the `initializing_values` tensor will be used to fill that missing row or column. If `row_remapping` has `r` missing entries and `col_remapping` has `c` missing entries, then the following condition must be true: `(r * num_cols) + (c * num_rows) - (r * c) == len(initializing_values)` The remapping tensors can be generated using the GenerateVocabRemapping op. As an example, with row_remapping = [1, 0, -1], col_remapping = [0, 2, -1], initializing_values = [0.5, -0.5, 0.25, -0.25, 42], and w(i, j) representing the value from row i, column j of the old tensor in the checkpoint, the output matrix will look like the following: [[w(1, 0), w(1, 2), 0.5], [w(0, 0), w(0, 2), -0.5], [0.25, -0.25, 42]] Args: ckpt_path: A `Tensor` of type `string`. Path to the TensorFlow checkpoint (version 2, `TensorBundle`) from which the old matrix `Tensor` will be loaded. old_tensor_name: A `Tensor` of type `string`. Name of the 2-D `Tensor` to load from checkpoint. row_remapping: A `Tensor` of type `int64`. An int `Tensor` of row remappings (generally created by `generate_vocab_remapping`). Even if no row remapping is needed, this must still be an index-valued Tensor (e.g. [0, 1, 2, ...]), or a shifted index-valued `Tensor` (e.g. [8, 9, 10, ...], for partitioned `Variables`). col_remapping: A `Tensor` of type `int64`. An int `Tensor` of column remappings (generally created by `generate_vocab_remapping`). May be a size-0 `Tensor` if only row remapping is to be done (e.g. column ordering is the same). initializing_values: A `Tensor` of type `float32`. A float `Tensor` containing values to fill in for cells in the output matrix that are not loaded from the checkpoint. Length must be exactly the same as the number of missing / new cells. num_rows: An `int` that is `>= 0`. Number of rows (length of the 1st dimension) in the output matrix. num_cols: An `int` that is `>= 1`. Number of columns (length of the 2nd dimension) in the output matrix. max_rows_in_memory: An optional `int`. Defaults to `-1`. The maximum number of rows to load from the checkpoint at once. If less than or equal to 0, the entire matrix will be loaded into memory. Setting this arg trades increased disk reads for lower memory usage. name: A name for the operation (optional). Returns: A `Tensor` of type `float32`. LoadAndRemapMatrixrLrMrNN)rLrMrNrrr) rGrHrIrJrKrLrMrNr)rrr r!rr"r%r&r'r(r)$load_and_remap_matrix_eager_fallbackr+r,r-r.r/r0r1r2r3)rGrHrIrJrKrLrMrNrr4r5r6r7r8r9r:r;r<s r=load_and_remap_matrixrSsL    0h..0$ #\\  11 "D)_}&9:*h(< g n  x 4(   x 4((();=QR'88 .=,9,92E'/(1C#')!QX QK' ""$#++J7 +-A 457F::L lFG= (' .G  & &- ##At,,  # #    1 _m] 8/dFF  # #   s0#EF E44F  F F&&F=<F=zraw_ops.LoadAndRemapMatrixc tj|d}tj|d}|d}tj|d}tj|tj }tj|tj }tj|tj }tj|tj }tj|tj}|||||g} d|d|d|f} tjdd| | | |} tjrtjd| | | | \} | S) NrLrMrrNsLoadAndRemapMatrixrArQ) r,r-r'rCrDrEint64float32rr0r3) rGrHrIrJrKrLrMrNrrr<r;r6s r=rRrR s+   x 4(   x 4((();=QR$$Y?)**?GNNK/(( F-(( F-../BGOOT_m]L_`, *h8L &   2Al#)s ?' ""$ lFG= (' .rF)rN)1__doc__ collectionstensorflow.pythonrtensorflow.python.eagerrrrr%rr,tensorflow.python.frameworkrrDtensorflow.security.fuzzing.pyr_atypesr _op_def_registryr r'r r."tensorflow.python.util.deprecationr tensorflow.python.utilr _dispatch tensorflow.python.util.tf_exportrtypingrrrtyping_extensionsr namedtupler#Stringintr> to_raw_oprr*Int64Float32rSrQrRrFr=rms 67179FK3IC86%%' 6 6 6- !" \YsGNN7J-K\]fgjlslzlzgz]{\PS\dg\x{\|E#CD^T^^TlEmnIc7>>FY>>Q4Renort{uCuCpCfDU^_bdkdqdq_qUrCLMPRYR_R_M_C`w@ADFMFUFUAUwVbeqtJM]fgjlsl{l{g{]|rF