ó V¾÷Xc@`sôdZddlmZddlmZddlmZddlZddlZddlmZddlm Z ddlm Z dd lm Z dd l m Z dd l mZdd l mZdd l mZddl mZddlmZddlmZddlmZdefd„ƒYZdefd„ƒYZdefd„ƒYZdefd„ƒYZdefd„ƒYZdefd„ƒYZdefd„ƒYZd efd!„ƒYZ d"efd#„ƒYZ!d$ej"d$d%d&gƒfd'„ƒYZ#e#d(dƒZ%d)e#fd*„ƒYZ&d+efd,„ƒYZ'dddd-e%dd.„Z(dd-e%dd/„Z)ed0d1ƒd-dd2„ƒZ*dd3dd4„Z+d3dd5„Z,ed0d6ƒd3dd7„ƒZ-d8efd9„ƒYZ.d:efd;„ƒYZ/dS(<sLookup table Operations.i(tabsolute_import(tdivision(tprint_functionN(tdtypes(tops(t sparse_tensor(t tensor_shape(t array_ops(tcontrol_flow_ops(tgen_data_flow_ops(tmath_ops(t string_ops(tBaseSaverBuilder(tcompat(t deprecatedtLookupInterfacecB`steZdZd„Zed„ƒZed„ƒZed„ƒZed„ƒZd d„Z d d„Z d„Z RS( s>Represent a lookup table that persists across different steps.cC`s1tj|ƒ|_tj|ƒ|_||_dS(s²Construct a lookup table interface. Args: key_dtype: The table key type. value_dtype: The table value type. name: A name for the operation (optional). N(Rtas_dtypet _key_dtypet _value_dtypet_name(tselft key_dtypet value_dtypetname((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyt__init__)scC`s|jS(sThe table key dtype.(R(R((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR5scC`s|jS(sThe table value dtype.(R(R((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR:scC`s|jS(sThe name of the table.(R(R((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR?scC`s t‚dS(sThe table initialization op.N(tNotImplementedError(R((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pytinitDscC`s t‚dS(s-Compute the number of elements in this table.N(R(RR((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pytsizeIscC`s t‚dS(s=Looks up `keys` in a table, outputs the corresponding values.N(R(RtkeysR((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pytlookupMscC`sZ||jkr+td|j|fƒ‚n||jkrVtd|j|fƒ‚ndS(s"Check that the given key_dtype and value_dtype matches the table dtypes. Args: key_dtype: The key data type to check. value_dtype: The value data type to check. Raises: TypeError: when 'key_dtype' or 'value_dtype' doesn't match the table data types. s*Invalid key dtype, expected %s but got %s.s,Invalid value dtype, expected %s but got %s.N(Rt TypeErrorR(RRR((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pytcheck_table_dtypesQs N( t__name__t __module__t__doc__RtpropertyRRRRtNoneRRR(((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR&s   tInitializableLookupTableBasecB`s\eZdZd„Zed„ƒZed„ƒZed„ƒZdd„Z dd„Z RS(sjInitializable lookup table interface. An initializable lookup tables persist across different steps. cC`s‹tt|ƒj|j|j|jjjdƒdƒ||_t j |d|j ƒ|_ |j j ƒjtjƒƒ|j|ƒ|_dS(sôConstruct a table object from a table reference. If requires a table initializer object (subclass of `TableInitializerBase`). It provides the table key and value types, as well as the op to initialize the table. The caller is responsible to execute the initialization op. Args: table_ref: The table reference, i.e. the output of the lookup table ops. default_value: The value to use if a key is missing in the table. initializer: The table initializer to use. t/iÿÿÿÿtdtypeN(tsuperR%RRRtopRtsplitt _table_refRtconvert_to_tensorRt_default_valuet get_shapet merge_withRtscalart initializet_init(Rt table_reft default_valuet initializer((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRjs    cC`s|jS(s#Get the underlying table reference.(R+(R((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR3scC`s|jS(sThe default value of the table.(R-(R((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR4„scC`s|jS(sThe table initialization op.(R2(R((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR‰scC`sEtj|d|j|jgƒ}tj|jd|ƒSWdQXdS(sÃCompute the number of elements in this table. Args: name: A name for the operation (optional). Returns: A scalar tensor containing the number of elements in this table. s%s_SizeRN(Rt name_scopeRR+R t_lookup_table_size(RRtscope((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRŽs c C`sç|}t|tjƒr$|j}n|j|jkrUtd|j|jfƒ‚ntj|d|j |j gƒ(}t j |j ||j d|ƒ}WdQX|j|jƒƒt|tjƒrßtj|j||jƒS|SdS(sÏLooks up `keys` in a table, outputs the corresponding values. The `default_value` is used for keys not present in the table. Args: keys: Keys to look up. May be either a `SparseTensor` or dense `Tensor`. name: A name for the operation (optional). Returns: A `SparseTensor` if keys are sparse, otherwise a dense `Tensor`. Raises: TypeError: when `keys` or `default_value` doesn't match the table data types. s2Signature mismatch. Keys must be dtype %s, got %s.s %s_LookupRN(t isinstanceRt SparseTensortvaluesR'RRRR6RR+R t_lookup_table_findR-t set_shapeR.tindicest dense_shape(RRRt key_tensorR8R;((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRs !N( R R!R"RR#R3R4RR$RR(((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR%ds  t HashTablecB`seZdZddd„ZRS(sA generic hash table implementation. Example usage: ```python table = tf.contrib.lookup.HashTable( tf.contrib.lookup.KeyValueTensorInitializer(keys, values), -1) out = table.lookup(input_tensor). table.init.run() print out.eval() ``` c C`sktj|d|gƒM}tjd|d|jd|jd|ƒ}tt|ƒj|||ƒWdQXdS(sPCreates a non-initialized `HashTable` object. Creates a table, the type of its keys and values are specified by the initializer. Before using the table you will have to initialize it. After initialization the table will be immutable. Args: initializer: The table initializer to use. default_value: The value to use if a key is missing in the table. shared_name: If non-empty, this table will be shared under the given name across multiple sessions. name: A name for the operation (optional). Returns: A `HashTable` object. t hash_tablet shared_nameRRRN( RR6R t _hash_tableRRR(RAR(RR5R4RCRR8R3((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRÑs    N(R R!R"R$R(((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRAÃs tTableInitializerBasecB`s>eZdZd„Zed„ƒZed„ƒZd„ZRS(s)Base class for lookup table initializers.cC`s(tj|ƒ|_tj|ƒ|_dS(s‹Construct a table initializer object. Args: key_dtype: Type of the table keys. value_dtype: Type of the table values. N(RRRR(RRR((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRòscC`s|jS(sThe expected table key dtype.(R(R((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRüscC`s|jS(sThe expected table value dtype.(R(R((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRscC`s t‚dS(s$Returns the table initialization op.N(R(Rttable((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR1s(R R!R"RR#RRR1(((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyREïs  tKeyValueTensorInitializercB`s)eZdZdddd„Zd„ZRS(s5Table initializers given `keys` and `values` tensors.c C`s’tj|d||gƒL}tj|d|ddƒ|_tj|d|ddƒ|_||_WdQXtt|ƒj|jj |jj ƒdS(srConstructs a table initializer object based on keys and values tensors. Args: keys: The tensor for the keys. values: The tensor for the values. key_dtype: The `keys` data type. Used when `keys` is a python array. value_dtype: The `values` data type. Used when `values` is a python array. name: A name for the operation (optional). tkey_value_initR'RRR;N( RR6R,t_keyst_valuesRR(RGRR'(RRR;RRRR8((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRs   c C`s~|j|jj|jjƒtj|jd|gƒ+}tj|j |j|jd|ƒ}WdQXtj tj j |ƒ|S(s,Initializes the given `table` with `keys` and `values` tensors. Args: table: The table to initialize. Returns: The operation that initializes the table. Raises: TypeError: when the keys and values data types do not match the table key and value data types. R;RN( RRIR'RJRR6RR t_initialize_tableR3tadd_to_collectiont GraphKeystTABLE_INITIALIZERS(RRFR8tinit_op((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR1"s   N(R R!R"R$RR1(((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRG st TextFileIndexcB`seZdZdZRS(iþÿÿÿiÿÿÿÿ(R R!t WHOLE_LINEt LINE_NUMBER(((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRP;stTextFileInitializercB`s)eZdZdddd„Zd„ZRS(sÓTable initializers from a text file. This initializer assigns one entry in the table for each line in the file. The key and value type of the table to initialize is given by `key_dtype` and `value_dtype`. The key and value content to get from each line is specified by the `key_index` and `value_index`. - TextFileIndex.LINE_NUMBER means use the line number starting from zero, expects data type int64. - TextFileIndex.WHOLE_LINE means use the whole line content, expects data type string. - A value >=0 means use the index (starting at zero) of the split line based on `delimiter`. For example if we have a file with the following content: ``` emerson 10 lake 20 palmer 30 ``` The following snippet initializes a table with the first column as keys and second column as values: - emerson -> 10 - lake -> 20 - palmer -> 30 ```python table = tf.contrib.lookup.HashTable(tf.contrib.lookup.TextFileInitializer( "test.txt", tf.string, 0, tf.int64, 1, delimiter=" "), -1) ... table.init.run() ``` Similarly to initialize the whole line as keys and the line number as values. - emerson 10 -> 0 - lake 20 -> 1 - palmer 30 -> 2 ```python table = tf.contrib.lookup.HashTable(tf.contrib.lookup.TextFileInitializer( "test.txt", tf.string, tf.contrib.lookup.TextFileIndex.WHOLE_LINE, tf.int64, tf.contrib.lookup.TextFileIndex.LINE_NUMBER, delimiter=" "), -1) ... table.init.run() ``` s c C`sït|tjƒ r-| r-td|ƒ‚ntj|ƒ}tj|ƒ}|dkrjtd|ƒ‚n|tjkr¤|tjkr¤tdtj|fƒ‚n|tj krÞ|tj krÞtdtj |fƒ‚n|dkrýtd|ƒ‚n|tjkr7|tjkr7tdtj|fƒ‚n|tj krq|tj krqtdtj |fƒ‚n|d k rœ|dkrœtd|ƒ‚n||_ ||_ ||_||_||_||_tt|ƒj||ƒd S( sÒConstructs a table initializer object to populate from a text file. It generates one key-value pair per line. The type of table key and value are specified by `key_dtype` and `value_dtype`, respectively. Similarly the content of the key and value are specified by the key_index and value_index. - TextFileIndex.LINE_NUMBER means use the line number starting from zero, expects data type int64. - TextFileIndex.WHOLE_LINE means use the whole line content, expects data type string. - A value >=0 means use the index (starting at zero) of the split line based on `delimiter`. Args: filename: The filename of the text file to be used for initialization. The path must be accessible from wherever the graph is initialized (eg. trainer or eval workers). The filename may be a scalar `Tensor`. key_dtype: The `key` data type. key_index: the index that represents information of a line to get the table 'key' values from. value_dtype: The `value` data type. value_index: the index that represents information of a line to get the table 'value' values from.' vocab_size: The number of elements in the file, if known. delimiter: The delimiter to separate fields in a line. name: A name for the operation (optional). Raises: ValueError: when the filename is empty, or when the table key and value data types do not match the expected data types. sFilename required for %s.iþÿÿÿsInvalid key index %s.s2Signature mismatch. Keys must be dtype %s, got %s.sInvalid value index %s.s4Signature mismatch. Values must be dtype %s, got %s.isInvalid vocab_size %s.N(R9RtTensort ValueErrorRRRPRRtint64RQtstringR$t _filenamet _key_indext _value_indext _vocab_sizet _delimiterRR(RSR( RtfilenameRt key_indexRt value_indext vocab_sizet delimiterR((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRts:)        c C`sÐ|j|j|jƒtj|jd|gƒm}tj|jtj ddƒ}t j |j ||j |j|jdkr‚dn|j|jd|ƒ}WdQXtjtjj|ƒtjtjj|ƒ|S(sInitializes the table from a text file. Args: table: The table to be initialized. Returns: The operation that initializes the table. Raises: TypeError: when the keys and values data types do not match the table key and value data types. ttext_file_initRtasset_filepathiÿÿÿÿN(RRRRR6RR,RXRRWR t _initialize_table_from_text_fileR3RYRZR[R$R\RLRMRNtASSET_FILEPATHS(RRFR8R]RO((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR1Âs     N(R R!R"R$RR1(((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRS@s 2 FtTextFileStringTableInitializercB`s,eZdZejejdddd„ZRS(sDTable initializer for `int64` IDs to string tables from a text file.s ttext_file_string_table_initc C`s>tt|ƒj|tj|tj|d|d|d|ƒdS(sÁConstructs an initializer for an id-to-string table from a text file. It populates a table that its key and value types are int64 and string, respectively. It generates one key-value pair per line. The content of the key and value are specified by `key_column_index` and `value_column_index`. - TextFileIndex.LINE_NUMBER means use the line number starting from zero, expects data type int64. - TextFileIndex.WHOLE_LINE means use the whole line content, expects data type string. - A value >=0 means use the index (starting at zero) of the split line based on `delimiter`. Args: filename: The filename of the text file to be used for initialization. The path must be accessible from wherever the graph is initialized (eg. trainer or eval workers). The filename may be a scalar `Tensor`. key_column_index: The column index from the text file to get the keys from. The default is 0 that represents the whole line content. value_column_index: The column index from the text file to get the values from. The default is to use the line number, starting from zero. vocab_size: The number of elements in the file, if known. delimiter: The delimiter to separate fields in a line. name: Optional name for the op. Raises: TypeError: when the filename is empty, or when the table key and value data types do not match the expected data types. R`RaRN(R(RfRRRVRW(RR]tkey_column_indextvalue_column_indexR`RaR((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRæs%N(R R!R"RPRRRQR$R(((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRfãs tTextFileIdTableInitializercB`s,eZdZejejdddd„ZRS(sDTable initializer for string to `int64` IDs tables from a text file.s ttext_file_id_table_initc C`s>tt|ƒj|tj|tj|d|d|d|ƒdS(sÃConstructs an initializer for an string-to-id table from a text file. It populates a table that its key and value types are string and int64, respectively. It generates one key-value pair per line. The content of the key and value are specified by the key_index and value_index. - TextFileIndex.LINE_NUMBER means use the line number starting from zero, expects data type int64. - TextFileIndex.WHOLE_LINE means use the whole line content, expects data type string. - A value >=0 means use the index (starting at zero) of the split line based on `delimiter`. Args: filename: The filename of the text file to be used for initialization. The path must be accessible from wherever the graph is initialized (eg. trainer or eval workers). The filename may be a scalar `Tensor`. key_column_index: The column index from the text file to get the `key` values from. The default is to use the line number, starting from zero. value_column_index: The column index from the text file ro get the `value` values from. The default is 0 that represents the whole line content. vocab_size: The number of elements in the file, if known. delimiter: The delimiter to separate fields in a line. name: Optional name for the op. Raises: TypeError: when the filename is empty, or when the table key and value data types do not match the expected data types. R`RaRN(R(RjRRRWRV(RR]RhRiR`RaR((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRs%N(R R!R"RPRQRRR$R(((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRjs t HasherSpecthashertkeycB`seZdZdZRS(s‘A structure for the spec of the hashing function to use for hash buckets. `hasher` is the name of the hashing function to use (eg. "fasthash", "stronghash"). `key` is optional and specify the key to use for the hash function if supported, currently only used by a strong hash. Fields: hasher: The hasher name to use. key: The key to be used by the hashing function, if required. ((R R!R"t __slots__(((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRlGs tfasthashtStrongHashSpeccB`seZdZdZd„ZRS(s7A structure to specify a key of the strong keyed hash spec. The strong hash requires a `key`, which is a list of 2 unsigned integer numbers. These should be non-zero; random numbers generated from random.org would be a fine choice. Fields: key: The key to be used by the keyed hashing function. cC`sˆt|ƒdkr+tdt|ƒƒ‚nt|dtjƒ sYt|dtjƒ rltd|ƒ‚nt|tƒj|d|ƒS(Niskey must have size 2, got %s.iis0Invalid key %s. Must be unsigned integer values.t stronghash( tlenRUR9R tintegral_typesRR(Rqt__new__(tclsRn((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRues ((R R!R"RoRu(((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRqYs tIdTableWithHashBucketscB`sMeZdZedd„Zed„ƒZdd„Zd„Z dd„Z RS(sYString to Id table wrapper that assigns out-of-vocabulary keys to buckets. For example, if an instance of `IdTableWithHashBuckets` is initialized with a string-to-id table that maps: - emerson -> 0 - lake -> 1 - palmer -> 2 The `IdTableWithHashBuckets` object will performs the following mapping: - emerson -> 0 - lake -> 1 - palmer -> 2 - -> bucket id between 3 and 3 + num_oov_buckets, calculated by: hash() % num_oov_buckets + vocab_size If input_tensor is ["emerson", "lake", "palmer", "king", "crimson"], the lookup result is [0, 1, 2, 4, 7] If `table` is None, only out-of-vocabulary buckets are used. Example usage: ```python num_oov_buckets = 3 input_tensor = tf.constant(["emerson", "lake", "palmer", "king", "crimnson"]) table = tf.IdTableWithHashBuckets( tf.HashTable(tf.TextFileIdTableInitializer(filename), default_value), num_oov_buckets) out = table.lookup(input_tensor). table.init.run() print out.eval() ``` The hash function used for generating out-of-vocabulary buckets ID is handled by `hasher_spec`. cC`sé|r|jdƒ}n|rR|jtjtjƒ||_|pL|jj}n0|dkrmtdƒ‚nd|_|pd}||_ t |t ƒs­t d|ƒ‚n||_ tt|ƒjtjtj|jdƒdƒdS(søConstruct a `IdTableWithHashBuckets` object. Args: table: Table that maps string to ids. num_oov_buckets: Number of buckets to use for out-of-vocabulary keys. hasher_spec: A `HasherSpec` to specify the hash function to use for assignation of out-of-vocabulary buckets (optional). name: A name for the operation (optional). Raises: ValueError: when `table` in None and `num_oov_buckets` is not positive. TypeError: when `hasher_spec` is invalid. R&is0oov_buckets must be > 0 if no table is supplied.t hash_buckets.hasher_spec must be of type HasherSpec, got %siÿÿÿÿN(trstripRRRWRVt_tableRRUR$t_num_oov_bucketsR9RlRt _hasher_specR(RwRR*(RRFtnum_oov_bucketst hasher_specR((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR–s"       cC`s:|jr|jjStjddƒtjƒSWdQXdS(sThe table initialization op.RN(RzRRR6R$Rtno_op(R((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR¿s  cC`sgtj|d|jƒH}|jr:|jj|ƒ}ntjddtjƒ}||jSWdQXdS(s-Compute the number of elements in this table.s%s_SizeiR'N( RR6RRzRR,RRVR{(RRR8ttsize((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRÇs  cC`st|tƒs"td|ƒ‚n|jdkr8tjS|jdkrNtjS|jdkrvtjtj d|j ƒSt d|jƒ‚dS(sCReturns the string_to_hash_bucket op to use based on `hasher_spec`.s)hasher_spec must be of type HasherSpec %sRptlegacyRrRnsUnknown hasher %sN( R9RlRRmR tstring_to_hash_bucket_fasttstring_to_hash_buckett functoolstpartialtstring_to_hash_bucket_strongRnRU(RR~((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyt_get_string_to_hash_bucket_fnÐsc C`so|j|jkr1td|j|jfƒ‚n|}t|tjƒrU|j}n|jdkr|jj |d|ƒ}nÁt j |d|j ƒ¦}|j |jƒ}||d|jddƒ}|jr4|jj |ƒ}tj||jjƒƒ}tj||jjƒ}tj|||d|ƒ}n|}WdQXt|tjƒrktj|j||jƒS|S(s³Looks up `keys` in the table, outputs the corresponding values. It assigns out-of-vocabulary keys to buckets based in their hashes. Args: keys: Keys to look up. May be either a `SparseTensor` or dense `Tensor`. name: Optional name for the op. Returns: A `SparseTensor` if keys are sparse, otherwise a dense `Tensor`. Raises: TypeError: when `keys` doesn't match the table key data type. s2Signature mismatch. Keys must be dtype %s, got %s.iRs %s_Lookupt num_bucketsRxN(R'RRR9RR:R;R{RzRRR6RR‡R|R taddRt not_equalR4RtwhereR>R?( RRRt string_valuestidsR8tstr_to_hash_buckettbucketstis_id_non_default((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRÝs0      N( R R!R"t FastHashSpecR$RR#RRR‡R(((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRwps$ % iÿÿÿÿc C`sA|stdƒ‚n|dkr4td|ƒ‚n|dk r_|dkr_td|ƒ‚ntj|dƒÊ}d}d}tjddƒx} |rºd ||tjtjf}nd |tjtjf}t|d |d d ƒ} t| |d|d | ƒ}WdQX|r3t |d|d|d |ƒ}n|SWdQXdS(s=Returns a lookup table that converts a string tensor into int64 IDs. This operation constructs a lookup table to convert tensor of strings into int64 IDs. The mapping can be initialized from a vocabulary file specified in `vocabulary_file`, where the whole line is the key and the zero-based line number is the ID. Any lookup of an out-of-vocabulary token will return a bucket ID based on its hash if `num_oov_buckets` is greater than zero. Otherwise it is assigned the `default_value`. The bucket ID range is `[vocabulary size, vocabulary size + num_oov_buckets]`. The underlying table must be initialized by calling `tf.tables_initializer.run()` or `table.init.run()` once. Sample Usages: If we have a vocabulary file "test.txt" with the following content: ``` emerson lake palmer ``` ```python features = tf.constant(["emerson", "lake", "and", "palmer"]) table = tf.contrib.lookup.string_to_index_table_from_file( vocabulary_file="test.txt", num_oov_buckets=1) ids = table.lookup(features) ... tf.tables_initializer().run() ids.eval() ==> [0, 1, 3, 2] # where 3 is the out-of-vocabulary bucket ``` Args: vocabulary_file: The vocabulary filename. num_oov_buckets: The number of out-of-vocabulary buckets. vocab_size: Number of the elements in the vocabulary, if known. default_value: The value to use for out-of-vocabulary feature values. Defaults to -1. hasher_spec: A `HasherSpec` to specify the hash function to use for assignation of out-of-vocabulary buckets. name: A name for this op (optional). Returns: The lookup table to map a string `Tensor` to index `int64` `Tensor`. Raises: ValueError: If `vocabulary_file` is not set. ValueError: If `num_oov_buckets` is negative or `vocab_size` is not greater than zero. s"vocabulary_file must be specified.is8num_oov_buckets must be greater or equal than 0, got %d.is*vocab_size must be greater than 0, got %d.tstring_to_indextRBshash_table_%s_%d_%s_%sshash_table_%s_%s_%sR`Rt table_initRCNR}R~( RUR$RR6RPRQRRRjRARw( tvocabulary_fileR}R`R4R~Rtfeat_to_id_scopeRFRCthash_table_scopeR((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pytstring_to_index_table_from_file s:<     c C`s#|d krtdƒ‚n|dkr:td|ƒ‚ntj|dƒÑ}tj|tjƒ}tj|ƒ}t j t j |ƒtj ƒ}d} tjd dƒC} t ||tjtj ddƒ} t| |d | d| ƒ} Wd QX|rt| d |d |d|ƒ} n| SWd QXd S( s"Returns a lookup table that converts a string tensor into int64 IDs. This operation constructs a lookup table to convert tensor of strings into int64 IDs. The mapping can be initialized from a string `mapping` 1-D tensor where each element is a key and corresponding index within the tensor is the value. Any lookup of an out-of-vocabulary token will return a bucket ID based on its hash if `num_oov_buckets` is greater than zero. Otherwise it is assigned the `default_value`. The bucket ID range is `[mapping size, mapping size + num_oov_buckets]`. The underlying table must be initialized by calling `tf.tables_initializer.run()` or `table.init.run()` once. Elements in `mapping` cannot have duplicates, otherwise when executing the table initializer op, it will throw a `FailedPreconditionError`. Sample Usages: ```python mapping_strings = t.constant(["emerson", "lake", "palmer") table = tf.contrib.lookup.string_to_index_table_from_tensor( mapping=mapping_strings, num_oov_buckets=1, default_value=-1) features = tf.constant(["emerson", "lake", "and", "palmer"]) ids = table.lookup(features) ... tf.tables_initializer().run() ids.eval() ==> [0, 1, 4, 2] ``` Args: mapping: A 1-D string `Tensor` that specifies the mapping of strings to indices. num_oov_buckets: The number of out-of-vocabulary buckets. default_value: The value to use for out-of-vocabulary feature values. Defaults to -1. hasher_spec: A `HasherSpec` to specify the hash function to use for assignation of out-of-vocabulary buckets. name: A name for this op (optional). Returns: The lookup table to map a string `Tensor` to index `int64` `Tensor`. Raises: ValueError: `mapping` is invalid. ValueError: If `num_oov_buckets` is negative. smapping must be specified.is8num_oov_buckets must be greater or equal than 0, got %d.R’R“RBRR”RCNR}R~(R$RURR6R,RRWRRR tcasttrangeRVRGRARw( tmappingR}R4R~RR–Rt num_elementsR;RCR—RRF((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyt!string_to_index_table_from_tensorns,6    s 2017-01-07s˜This op will be removed after the deprecation date. Please switch to string_to_index_table_from_tensor and call the lookup method of the returned table.cC`s(td|d|d|ƒ}|j|ƒS(sbMaps `tensor` of strings into `int64` indices based on `mapping`. This operation converts `tensor` of strings into `int64` indices. The mapping is initialized from a string `mapping` tensor where each element is a key and corresponding index within the tensor is the value. Any entry in the input which does not have a corresponding entry in 'mapping' (an out-of-vocabulary entry) is assigned the `default_value` Elements in `mapping` cannot be duplicated, otherwise the initialization will throw a FailedPreconditionError. The underlying table must be initialized by calling `tf.tables_initializer.run()` once. For example: ```python mapping_strings = t.constant(["emerson", "lake", "palmer") feats = tf.constant(["emerson", "lake", "and", "palmer"]) ids = tf.contrib.lookup.string_to_index( feats, mapping=mapping_strings, default_value=-1) ... tf.tables_initializer().run() ids.eval() ==> [0, 1, -1, 2] ``` Args: tensor: A 1-D input `Tensor` with the strings to map to indices. mapping: A 1-D string `Tensor` that specifies the mapping of strings to indices. default_value: The `int64` value to use for out-of-vocabulary strings. Defaults to -1. name: A name for this op (optional). Returns: The mapped indices. It has the same shape and tensor type (dense or sparse) as `tensor`. R›R4R(RR(ttensorR›R4RRF((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR’Às-tUNKc C`sÔ|stdƒ‚n|d k r@|dkr@td|ƒ‚ntj|dƒ|}d}|r€d||tjtjf}nd|tjtjf}t|d|d d ƒ}t||d |d |ƒSWd QXd S( sœReturns a lookup table that maps a `Tensor` of indices into strings. This operation constructs a lookup table to map int64 indices into string values. The table is initialized from a vocabulary file specified in `vocabulary_file`, where the whole line is the value and the zero-based line number is the index. Any input which does not have a corresponding index in the vocabulary file (an out-of-vocabulary entry) is assigned the `default_value` The underlying table must be initialized by calling `tf.tables_initializer.run()` or `table.init.run()` once. Sample Usages: If we have a vocabulary file "test.txt" with the following content: ``` emerson lake palmer ``` ```python indices = tf.constant([1, 5], tf.int64) table = tf.contrib.lookup.index_to_string_from_file( vocabulary_file="test.txt", default_value="UNKNOWN") values = table.lookup(indices) ... tf.tables_initializer().run() values.eval() ==> ["lake", "UNKNOWN"] ``` Args: vocabulary_file: The vocabulary filename. vocab_size: Number of the elements in the vocabulary, if known. default_value: The value to use for out-of-vocabulary indices. name: A name for this op (optional). Returns: The lookup table to map a string values associated to a given index `int64` `Tensors`. Raises: ValueError: when `vocabulary_file` is empty. ValueError: when `vocab_size` is invalid. s"vocabulary_file must be specified.is*vocab_size must be greater than 0, got %d.tindex_to_stringR“shash_table_%s_%d_%s_%sshash_table_%s_%s_%sR`RR”RCN( RUR$RR6RPRRRQRfRA(R•R`R4RR8RCR((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pytindex_to_string_table_from_fileòs 4  c C`s¼|dkrtdƒ‚ntj|dƒ‰}tj|tjƒ}tj|ƒ}t j t j |ƒtj ƒ}d}t ||tj tjddƒ}t||d|d|ƒSWdQXdS(s“Returns a lookup table that maps a `Tensor` of indices into strings. This operation constructs a lookup table to map int64 indices into string values. The mapping is initialized from a string `mapping` 1-D `Tensor` where each element is a value and the corresponding index within the tensor is the key. Any input which does not have a corresponding index in 'mapping' (an out-of-vocabulary entry) is assigned the `default_value` The underlying table must be initialized by calling `tf.tables_initializer.run()` or `table.init.run()` once. Elements in `mapping` cannot have duplicates, otherwise when executing the table initializer op, it will throw a `FailedPreconditionError`. Sample Usages: ```python mapping_string = t.constant(["emerson", "lake", "palmer") indices = tf.constant([1, 5], tf.int64) table = tf.contrib.lookup.index_to_string_from_tensor( mapping_string, default_value="UNKNOWN") values = table.lookup(indices) ... tf.tables_initializer().run() values.eval() ==> ["lake", "UNKNOWN"] ``` Args: mapping: A 1-D string `Tensor` that specifies the strings to map from indices. default_value: The value to use for out-of-vocabulary indices. name: A name for this op (optional). Returns: The lookup table to map a string values associated to a given index `int64` `Tensors`. Raises: ValueError: when `mapping` is not set. smapping must be specified.R R“RR”RCN(R$RURR6R,RRWRRR R™RšRVRGRA( R›R4RR8R;RœRRCR((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyt!index_to_string_table_from_tensor?s- s˜This op will be removed after the deprecation date. Please switch to index_to_string_table_from_tensor and call the lookup method of the returned table.cC`s(td|d|d|ƒ}|j|ƒS(sóMaps `tensor` of indices into string values based on `mapping`. This operation converts `int64` indices into string values. The mapping is initialized from a string `mapping` tensor where each element is a value and the corresponding index within the tensor is the key. Any input which does not have a corresponding index in 'mapping' (an out-of-vocabulary entry) is assigned the `default_value` The underlying table must be initialized by calling `tf.tables_initializer.run()` once. For example: ```python mapping_string = t.constant(["emerson", "lake", "palmer") indices = tf.constant([1, 5], tf.int64) values = tf.contrib.lookup.index_to_string( indices, mapping=mapping_string, default_value="UNKNOWN") ... tf.tables_initializer().run() values.eval() ==> ["lake", "UNKNOWN"] ``` Args: tensor: A `int64` `Tensor` with the indices to map to strings. mapping: A 1-D string `Tensor` that specifies the strings to map from indices. default_value: The string value to use for out-of-vocabulary indices. name: A name for this op (optional). Returns: The strings values associated to the indices. The resultant dense feature value tensor has the same shape as the corresponding `indices`. R›R4R(R¢R(RžR›R4RRF((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR {s)tMutableHashTablecB`sieZdZd ded„Zd d„Zd d„Zd d„Zd d„Z de j fd„ƒYZ RS( sÙA generic mutable hash table implementation. Data can be inserted by calling the insert method. It does not support initialization via the init method. Example usage: ```python table = tf.contrib.lookup.MutableHashTable(key_dtype=tf.string, value_dtype=tf.int64, default_value=-1) table.insert(keys, values) out = table.lookup(query_keys) print out.eval() ``` R£c C`s'tj|d|ƒ|_|jjƒ|_|o9|d k}|jjƒjdkr„tjd|d|d|d|d|ƒ|_ n<tj d|d|d|d|d|jjƒd|ƒ|_ t t |ƒj |||j jjjd ƒd ƒ|r#t j||ƒ}tjtjj|ƒnd S( sHCreates an empty `MutableHashTable` object. Creates a table, the type of its keys and values are specified by key_dtype and value_dtype, respectively. Args: key_dtype: the type of the key tensors. value_dtype: the type of the value tensors. default_value: The value to use if a key is missing in the table. shared_name: If non-empty, this table will be shared under the given name across multiple sessions. name: A name for the operation (optional). checkpoint: if True, the contents of the table are saved to and restored from checkpoints. If `shared_name` is empty for a checkpointed table, it is shared using the table node name. Returns: A `MutableHashTable` object. Raises: ValueError: If checkpoint is True and no name was specified. R'iRCtuse_node_name_sharingRRRt value_shapeR&iÿÿÿÿN(RR,R-R.t _value_shapeR$tndimsR t_mutable_hash_tableR+t_mutable_hash_table_of_tensorsR(R£RR)RR*t _SaveableRLRMtSAVEABLE_OBJECTS( RRRR4RCRt checkpointR¤tsaveable((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR»s0     cC`sEtj|d|j|jgƒ}tj|jd|ƒSWdQXdS(sÃCompute the number of elements in this table. Args: name: A name for the operation (optional). Returns: A scalar tensor containing the number of elements in this table. s%s_SizeRN(RR6RR+R R7(RR((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRùs c C`s£|j|jkr1td|j|jfƒ‚ntj|d|j|j|gƒ(}tj|j||j d|ƒ}WdQX|j |j ƒj |j ƒƒ|S(séLooks up `keys` in a table, outputs the corresponding values. The `default_value` is used for keys not present in the table. Args: keys: Keys to look up. Can be a tensor of any shape. Must match the table's key_dtype. name: A name for the operation (optional). Returns: A tensor containing the values in the same shape as `keys` using the table's value type. Raises: TypeError: when `keys` do not match the table data types. s2Signature mismatch. Keys must be dtype %s, got %s.s%s_lookup_table_findRN(R'RRRR6RR+R R<R-R=R.t concatenateR¦(RRRR;((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRs  c C`sm|j|j|jƒtj|d|j|j||gƒ)}tj|j||d|ƒ}|SWdQXdS(sÙAssociates `keys` with `values`. Args: keys: Keys to insert. Can be a tensor of any shape. Must match the table's key type. values: Values to be associated with keys. Must be a tensor of the same shape as `keys` and match the table's value type. name: A name for the operation (optional). Returns: The created Operation. Raises: TypeError: when `keys` or `values` doesn't match the table data types. s%s_lookup_table_insertRN(RR'RR6RR+R t_lookup_table_insert(RRR;RR)((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pytinsert's c C`s~tj|d|j|jgƒ1}tj|j|j|jd|ƒ\}}WdQX|j|j ƒj |j ƒƒ||fS(sReturns tensors of all keys and values in the table. Args: name: A name for the operation (optional). Returns: A pair of tensors with the first tensor containing all keys and the second tensors containing all values in the table. s%s_lookup_table_export_valuesRN( RR6RR+R t_lookup_table_exportRRR=R.R®R¦(RRt exported_keystexported_values((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pytexport@s   RªcB`s eZdZd„Zd„ZRS(s3SaveableObject implementation for MutableHashTable.cC`si|jƒ}tj|dd|dƒtj|dd|dƒg}ttj|ƒj|||ƒdS(NiR“s-keysis-values(R´R tSaveSpecR(R£RªR(RRFRttensorstspecs((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRZs  cC`s!tj|jj|d|dƒS(Nii(R t_lookup_table_importR)R+(Rtrestored_tensorstunused_restored_shapes((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pytrestorecs(R R!R"RR»(((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRªWs N( R R!R"R$tTrueRRRR°R´R tSaveableObjectRª(((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR£©s 8   tMutableDenseHashTablecB`sleZdZd d ded„Zd d„Zd d„Zd d„Zd d„Z de j fd„ƒYZ RS( s¼A generic mutable hash table implementation using tensors as backing store. Data can be inserted by calling the insert method. It does not support initialization via the init method. It uses "open addressing" with quadratic reprobing to resolve collisions. Compared to `MutableHashTable` the insert and lookup operations in a `MutableDenseHashTable` are typically faster, but memory usage can be higher. However, `MutableDenseHashTable` does not require additional memory for temporary tensors created during checkpointing and restore operations. Example usage: ```python table = tf.contrib.lookup.MutableDenseHashTable(key_dtype=tf.int64, value_dtype=tf.int64, default_value=-1, empty_key=0) table.insert(keys, values) out = table.lookup(query_keys) print out.eval() ``` R¾c C`sôtj|d|ƒ|_|jjƒ|_|o9|d k} tj|d|ƒ}tjd|d|d| d|d|jd|d|ƒ|_t t |ƒj |||jj j jd ƒd ƒ|rðt j||ƒ} tjtjj| ƒnd S( sCreates an empty `MutableDenseHashTable` object. Creates a table, the type of its keys and values are specified by key_dtype and value_dtype, respectively. Args: key_dtype: the type of the key tensors. value_dtype: the type of the value tensors. default_value: The value to use if a key is missing in the table. empty_key: the key to use to represent empty buckets internally. Must not be used in insert or lookup operations. initial_num_buckets: the initial number of buckets. shared_name: If non-empty, this table will be shared under the given name across multiple sessions. name: A name for the operation (optional). checkpoint: if True, the contents of the table are saved to and restored from checkpoints. If `shared_name` is empty for a checkpointed table, it is shared using the table node name. Returns: A `MutableHashTable` object. Raises: ValueError: If checkpoint is True and no name was specified. R't empty_keyRCR¤RR¥tinitial_num_bucketsRR&iÿÿÿÿN(RR,R-R.R¦R$R t_mutable_dense_hash_tableR+R(R¾RR)RR*RªRLRMR«( RRRR4R¿RÀRCRR¬R¤R­((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR„s$"   #cC`sEtj|d|j|jgƒ}tj|jd|ƒSWdQXdS(sÃCompute the number of elements in this table. Args: name: A name for the operation (optional). Returns: A scalar tensor containing the number of elements in this table. s%s_SizeRN(RR6RR+R R7(RR((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRÀs c C`sã|j|jkr1td|j|jfƒ‚ntj|d|j|j|gƒ(}tj|j||j d|ƒ}WdQX|j ƒj dk rß|j ƒj dkrß|j tj|j ƒjdgƒj|jƒƒn|S(séLooks up `keys` in a table, outputs the corresponding values. The `default_value` is used for keys not present in the table. Args: keys: Keys to look up. Can be a tensor of any shape. Must match the table's key_dtype. name: A name for the operation (optional). Returns: A tensor containing the values in the same shape as `keys` using the table's value type. Raises: TypeError: when `keys` do not match the table data types. s2Signature mismatch. Keys must be dtype %s, got %s.s%s_lookup_table_findRNi(R'RRRR6RR+R R<R-R.R§R$R=Rt TensorShapetdimsR®R¦(RRRR;((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRÎs!*c C`sm|j|j|jƒtj|d|j|j||gƒ)}tj|j||d|ƒ}|SWdQXdS(sÙAssociates `keys` with `values`. Args: keys: Keys to insert. Can be a tensor of any shape. Must match the table's key type. values: Values to be associated with keys. Must be a tensor of the same shape as `keys` and match the table's value type. name: A name for the operation (optional). Returns: The created Operation. Raises: TypeError: when `keys` or `values` doesn't match the table data types. s%s_lookup_table_insertRN(RR'RR6RR+R R¯(RRR;RR)((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR°ïs c C`s~tj|d|j|jgƒ1}tj|j|j|jd|ƒ\}}WdQX|j|j ƒj |j ƒƒ||fS(sReturns tensors of all keys and values in the table. Args: name: A name for the operation (optional). Returns: A pair of tensors with the first tensor containing all keys and the second tensors containing all values in the table. s%s_lookup_table_export_valuesRN( RR6RR+R R±RRR=R.R®R¦(RRR²R³((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR´s * RªcB`s eZdZd„Zd„ZRS(s8SaveableObject implementation for MutableDenseHashTable.cC`si|jƒ}tj|dd|dƒtj|dd|dƒg}ttj|ƒj|||ƒdS(NiR“s-keysis-values(R´R RµR(R¾RªR(RRFRR¶R·((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRs  cC`s!tj|jj|d|dƒS(Nii(R R¸R)R+(RR¹Rº((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR»(s(R R!R"RR»(((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyRªs N( R R!R"R$R¼RRRR°R´R R½Rª(((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pyR¾is  4  !  (0R"t __future__RRRt collectionsR„ttensorflow.python.frameworkRRRRttensorflow.python.opsRRR R R t tensorflow.python.training.saverR ttensorflow.python.utilR t"tensorflow.python.util.deprecationRtobjectRR%RARERGRPRSRfRjt namedtupleRlR$R‘RqRwR˜RR’R¡R¢R R£R¾(((sf/tmp/pip-build-h1VYrz/tensorflow/tensorflow-1.0.1.data/purelib/tensorflow/contrib/lookup/lookup_ops.pytsh  >_,0£22(› _ N / J< *À