everything looks ok, I'm not sure why it's segfaulting
is the file
model/reordering-table.*
empty? If it is, then you should look in the log file
steps/*/TRAINING_build-reordering.*.STDERR
or is
evaluation/*.filtered.*/reordering-table.1.*
empty? is your test set empty?
On 8 December 2013 09:47, amir haghighi <amir.haghighi...@gmail.com> wrote:
> yes, the parallel data is UTF8.(one is UTF8 and one is ascii).
> all of the pre-processioning steps are done with moses scripts.
>
> here is the EMS config file content:
>
> ################################################
> ### CONFIGURATION FILE FOR AN SMT EXPERIMENT ###
> ################################################
>
> [GENERAL]
>
> ### directory in which experiment is run
> #
> working-dir = /opt/tools/workingEms
>
> # specification of the language pair
> input-extension = En
> output-extension = Fa
> pair-extension = En-Fa
>
> ### directories that contain tools and data
> #
> # moses
> moses-src-dir =
> /opt/tools/mosesdecoder-RELEASE-1.0/mosesdecoder-RELEASE-1.0
> #
> # moses binaries
> moses-bin-dir = $moses-src-dir/bin
> #
> # moses scripts
> moses-script-dir = $moses-src-dir/scripts
> #
> # directory where GIZA++/MGIZA programs resides
> external-bin-dir = $moses-src-dir/tools
> #
> # srilm
> #srilm-dir = $moses-src-dir/srilm/bin/i686
> #
> # irstlm
> irstlm-dir = /opt/tools/irstlm/bin
> #
> # randlm
> #randlm-dir = $moses-src-dir/randlm/bin
> #
> # data
> toy-data = /opt/tools/dataset/mizan
>
> ### basic tools
> #
> # moses decoder
> decoder = $moses-bin-dir/moses
>
> # conversion of phrase table into binary on-disk format
> ttable-binarizer = $moses-bin-dir/processPhraseTable
>
> # conversion of rule table into binary on-disk format
> #ttable-binarizer = "$moses-bin-dir/CreateOnDiskPt 1 1 5 100 2"
>
> # tokenizers - comment out if all your data is already tokenized
> input-tokenizer = "$moses-script-dir/tokenizer/tokenizer.perl -a -l
> $input-extension"
> output-tokenizer = "$moses-script-dir/tokenizer/tokenizer.perl -a -l
> $output-extension"
>
> # truecasers - comment out if you do not use the truecaser
> input-truecaser = $moses-script-dir/recaser/truecase.perl
> output-truecaser = $moses-script-dir/recaser/truecase.perl
> detruecaser = $moses-script-dir/recaser/detruecase.perl
>
> ### generic parallelizer for cluster and multi-core machines
> # you may specify a script that allows the parallel execution
> # parallizable steps (see meta file). you also need specify
> # the number of jobs (cluster) or cores (multicore)
> #
> #generic-parallelizer =
> $moses-script-dir/ems/support/generic-parallelizer.perl
> #generic-parallelizer =
> $moses-script-dir/ems/support/generic-multicore-parallelizer.perl
>
> ### cluster settings (if run on a cluster machine)
> # number of jobs to be submitted in parallel
> #
> #jobs = 10
>
> # arguments to qsub when scheduling a job
> #qsub-settings = ""
>
> # project for priviledges and usage accounting
> #qsub-project = iccs_smt
>
> # memory and time
> #qsub-memory = 4
> #qsub-hours = 48
>
> ### multi-core settings
> # when the generic parallelizer is used, the number of cores
> # specified here
> cores = 8
>
> #################################################################
> # PARALLEL CORPUS PREPARATION:
> # create a tokenized, sentence-aligned corpus, ready for training
>
> [CORPUS]
>
> ### long sentences are filtered out, since they slow down GIZA++
> # and are a less reliable source of data. set here the maximum
> # length of a sentence
> #
> max-sentence-length = 80
>
> [CORPUS:toy]
>
> ### command to run to get raw corpus files
> #
> # get-corpus-script =
>
> ### raw corpus files (untokenized, but sentence aligned)
> #
> raw-stem = $toy-data/M_Tr
>
> ### tokenized corpus files (may contain long sentences)
> #
> #tokenized-stem =
>
> ### if sentence filtering should be skipped,
> # point to the clean training data
> #
> #clean-stem =
>
> ### if corpus preparation should be skipped,
> # point to the prepared training data
> #
> #lowercased-stem =
>
> #################################################################
> # LANGUAGE MODEL TRAINING
>
> [LM]
>
> ### tool to be used for language model training
> # srilm
> #lm-training = $srilm-dir/ngram-count
> #settings = "-interpolate -kndiscount -unk"
>
> # irstlm training
> # msb = modified kneser ney; p=0 no singleton pruning
> #lm-training = "$moses-script-dir/generic/trainlm-irst2.perl -cores $cores
> -irst-dir $irstlm-dir -temp-dir $working-dir/tmp"
> #settings = "-s msb -p 0"
>
> # order of the language model
> order = 5
>
> ### tool to be used for training randomized language model from scratch
> # (more commonly, a SRILM is trained)
> #
> #rlm-training = "$randlm-dir/buildlm -falsepos 8 -values 8"
>
> ### script to use for binary table format for irstlm or kenlm
> # (default: no binarization)
>
> # irstlm
> #lm-binarizer = $irstlm-dir/compile-lm
>
> # kenlm, also set type to 8
> #lm-binarizer = $moses-bin-dir/build_binary
> #type = 8
>
> ### script to create quantized language model format (irstlm)
> # (default: no quantization)
> #
> #lm-quantizer = $irstlm-dir/quantize-lm
>
> ### script to use for converting into randomized table format
> # (default: no randomization)
> #
> #lm-randomizer = "$randlm-dir/buildlm -falsepos 8 -values 8"
>
> ### each language model to be used has its own section here
>
> [LM:toy]
>
> ### command to run to get raw corpus files
> #
> #get-corpus-script = ""
>
> ### raw corpus (untokenized)
> #
> raw-corpus = $toy-data/M_Tr.$output-extension
>
> ### tokenized corpus files (may contain long sentences)
> #
> #tokenized-corpus =
>
> ### if corpus preparation should be skipped,
> # point to the prepared language model
> #
> lm = /opt/tools/lm2/M_FaforLm.blm.Fa
>
> #################################################################
> # INTERPOLATING LANGUAGE MODELS
>
> [INTERPOLATED-LM]
>
> # if multiple language models are used, these may be combined
> # by optimizing perplexity on a tuning set
> # see, for instance [Koehn and Schwenk, IJCNLP 2008]
>
> ### script to interpolate language models
> # if commented out, no interpolation is performed
> #
> # script = $moses-script-dir/ems/support/interpolate-lm.perl
>
> ### tuning set
> # you may use the same set that is used for mert tuning (reference set)
> #
> #tuning-sgm =
> #raw-tuning =
> #tokenized-tuning =
> #factored-tuning =
> #lowercased-tuning =
> #split-tuning =
>
> ### group language models for hierarchical interpolation
> # (flat interpolation is limited to 10 language models)
> #group = "first,second fourth,fifth"
>
> ### script to use for binary table format for irstlm or kenlm
> # (default: no binarization)
>
> # irstlm
> #lm-binarizer = $irstlm-dir/compile-lm
>
> # kenlm, also set type to 8
> #lm-binarizer = $moses-bin-dir/build_binary
> type = 8
>
> ### script to create quantized language model format (irstlm)
> # (default: no quantization)
> #
> #lm-quantizer = $irstlm-dir/quantize-lm
>
> ### script to use for converting into randomized table format
> # (default: no randomization)
> #
> #lm-randomizer = "$randlm-dir/buildlm -falsepos 8 -values 8"
>
> #################################################################
> # MODIFIED MOORE LEWIS FILTERING
>
> [MML] IGNORE
>
> ### specifications for language models to be trained
> #
> #lm-training = $srilm-dir/ngram-count
> #lm-settings = "-interpolate -kndiscount -unk"
> #lm-binarizer = $moses-src-dir/bin/build_binary
> #lm-query = $moses-src-dir/bin/query
> #order = 5
>
> ### in-/out-of-domain source/target corpora to train the 4 language model
> #
> # in-domain: point either to a parallel corpus
> #outdomain-stem = [CORPUS:toy:clean-split-stem]
>
> # ... or to two separate monolingual corpora
> #indomain-target = [LM:toy:lowercased-corpus]
> #raw-indomain-source = $toy-data/M_Tr.$input-extension
>
> # point to out-of-domain parallel corpus
> #outdomain-stem = [CORPUS:giga:clean-split-stem]
>
> # settings: number of lines sampled from the corpora to train each
> language model on
> # (if used at all, should be small as a percentage of corpus)
> #settings = "--line-count 100000"
>
> #################################################################
> # TRANSLATION MODEL TRAINING
>
> [TRAINING]
>
> ### training script to be used: either a legacy script or
> # current moses training script (default)
> #
> script = $moses-script-dir/training/train-model.perl
>
> ### general options
> # these are options that are passed on to train-model.perl, for instance
> # * "-mgiza -mgiza-cpus 8" to use mgiza instead of giza
> # * "-sort-buffer-size 8G -sort-compress gzip" to reduce on-disk sorting
> # * "-sort-parallel 8 -cores 8" to speed up phrase table building
> #
> #training-options = ""
>
> ### factored training: specify here which factors used
> # if none specified, single factor training is assumed
> # (one translation step, surface to surface)
> #
> #input-factors = word lemma pos morph
> #output-factors = word lemma pos
> #alignment-factors = "word -> word"
> #translation-factors = "word -> word"
> #reordering-factors = "word -> word"
> #generation-factors = "word -> pos"
> #decoding-steps = "t0, g0"
>
> ### parallelization of data preparation step
> # the two directions of the data preparation can be run in parallel
> # comment out if not needed
> #
> parallel = yes
>
> ### pre-computation for giza++
> # giza++ has a more efficient data structure that needs to be
> # initialized with snt2cooc. if run in parallel, this may reduces
> # memory requirements. set here the number of parts
> #
> #run-giza-in-parts = 5
>
> ### symmetrization method to obtain word alignments from giza output
> # (commonly used: grow-diag-final-and)
> #
> alignment-symmetrization-method = grow-diag-final-and
>
> ### use of berkeley aligner for word alignment
> #
> #use-berkeley = true
> #alignment-symmetrization-method = berkeley
> #berkeley-train = $moses-script-dir/ems/support/berkeley-train.sh
> #berkeley-process = $moses-script-dir/ems/support/berkeley-process.sh
> #berkeley-jar = /your/path/to/berkeleyaligner-1.1/berkeleyaligner.jar
> #berkeley-java-options = "-server -mx30000m -ea"
> #berkeley-training-options = "-Main.iters 5 5 -EMWordAligner.numThreads 8"
> #berkeley-process-options = "-EMWordAligner.numThreads 8"
> #berkeley-posterior = 0.5
>
> ### use of baseline alignment model (incremental training)
> #
> #baseline = 68
> #baseline-alignment-model =
> "$working-dir/training/prepared.$baseline/$input-extension.vcb \
> # $working-dir/training/prepared.$baseline/$output-extension.vcb \
> #
> $working-dir/training/giza.$baseline/${output-extension}-$input-extension.cooc
> \
> #
> $working-dir/training/giza-inverse.$baseline/${input-extension}-$output-extension.cooc
> \
> #
> $working-dir/training/giza.$baseline/${output-extension}-$input-extension.thmm.5
> \
> #
> $working-dir/training/giza.$baseline/${output-extension}-$input-extension.hhmm.5
> \
> #
> $working-dir/training/giza-inverse.$baseline/${input-extension}-$output-extension.thmm.5
> \
> #
> $working-dir/training/giza-inverse.$baseline/${input-extension}-$output-extension.hhmm.5"
>
> ### if word alignment should be skipped,
> # point to word alignment files
> #
> #word-alignment = $working-dir/model/aligned.1
>
> ### filtering some corpora with modified Moore-Lewis
> # specify corpora to be filtered and ratio to be kept, either before or
> after word alignment
> #mml-filter-corpora = toy
> #mml-before-wa = "-proportion 0.9"
> #mml-after-wa = "-proportion 0.9"
>
> ### create a bilingual concordancer for the model
> #
> #biconcor = $moses-script-dir/ems/biconcor/biconcor
>
> ### lexicalized reordering: specify orientation type
> # (default: only distance-based reordering model)
> #
> lexicalized-reordering = msd-bidirectional-fe
>
> ### hierarchical rule set
> #
> #hierarchical-rule-set = true
>
> ### settings for rule extraction
> #
> #extract-settings = ""
> max-phrase-length = 5
>
> ### add extracted phrases from baseline model
> #
> #baseline-extract = $working-dir/model/extract.$baseline
> #
> # requires aligned parallel corpus for re-estimating lexical translation
> probabilities
> #baseline-corpus = $working-dir/training/corpus.$baseline
> #baseline-alignment =
> $working-dir/model/aligned.$baseline.$alignment-symmetrization-method
>
> ### unknown word labels (target syntax only)
> # enables use of unknown word labels during decoding
> # label file is generated during rule extraction
> #
> #use-unknown-word-labels = true
>
> ### if phrase extraction should be skipped,
> # point to stem for extract files
> #
> # extracted-phrases =
>
> ### settings for rule scoring
> #
> score-settings = "--GoodTuring"
>
> ### include word alignment in phrase table
> #
> #include-word-alignment-in-rules = yes
>
> ### sparse lexical features
> #
> #sparse-lexical-features = "target-word-insertion top 50,
> source-word-deletion top 50, word-translation top 50 50, phrase-length"
>
> ### domain adaptation settings
> # options: sparse, any of: indicator, subset, ratio
> #domain-features = "subset"
>
> ### if phrase table training should be skipped,
> # point to phrase translation table
> #
> # phrase-translation-table =
>
> ### if reordering table training should be skipped,
> # point to reordering table
> #
> # reordering-table =
>
> ### filtering the phrase table based on significance tests
> # Johnson, Martin, Foster and Kuhn. (2007): "Improving Translation Quality
> by Discarding Most of the Phrasetable"
> # options: -n number of translations; -l 'a+e', 'a-e', or a positive real
> value -log prob threshold
> #salm-index = /path/to/project/salm/Bin/Linux/Index/IndexSA.O64
> #sigtest-filter = "-l a+e -n 50"
>
> ### if training should be skipped,
> # point to a configuration file that contains
> # pointers to all relevant model files
> #
> #config-with-reused-weights =
>
> #####################################################
> ### TUNING: finding good weights for model components
>
> [TUNING]
>
> ### instead of tuning with this setting, old weights may be recycled
> # specify here an old configuration file with matching weights
> #
> weight-config = $toy-data/weight.ini
>
> ### tuning script to be used
> #
> tuning-script = $moses-script-dir/training/mert-moses.pl
> tuning-settings = "-mertdir $moses-bin-dir"
>
> ### specify the corpus used for tuning
> # it should contain 1000s of sentences
> #
> #input-sgm =
> #raw-input =
> #tokenized-input =
> #factorized-input =
> #input =
> #
> #reference-sgm =
> #raw-reference =
> #tokenized-reference =
> #factorized-reference =
> #reference =
>
> ### size of n-best list used (typically 100)
> #
> nbest = 100
>
> ### ranges for weights for random initialization
> # if not specified, the tuning script will use generic ranges
> # it is not clear, if this matters
> #
> # lambda =
>
> ### additional flags for the filter script
> #
> filter-settings = ""
>
> ### additional flags for the decoder
> #
> decoder-settings = ""
>
> ### if tuning should be skipped, specify this here
> # and also point to a configuration file that contains
> # pointers to all relevant model files
> #
> #config =
>
> #########################################################
> ## RECASER: restore case, this part only trains the model
>
> [RECASING]
>
> #decoder = $moses-bin-dir/moses
>
> ### training data
> # raw input needs to be still tokenized,
> # also also tokenized input may be specified
> #
> #tokenized = [LM:europarl:tokenized-corpus]
>
> # recase-config =
>
> #lm-training = $srilm-dir/ngram-count
>
> #######################################################
> ## TRUECASER: train model to truecase corpora and input
>
> [TRUECASER]
>
> ### script to train truecaser models
> #
> trainer = $moses-script-dir/recaser/train-truecaser.perl
>
> ### training data
> # data on which truecaser is trained
> # if no training data is specified, parallel corpus is used
> #
> # raw-stem =
> # tokenized-stem =
>
> ### trained model
> #
> # truecase-model =
>
> ######################################################################
> ## EVALUATION: translating a test set using the tuned system and score it
>
> [EVALUATION]
>
> ### additional flags for the filter script
> #
> #filter-settings = ""
>
> ### additional decoder settings
> # switches for the Moses decoder
> # common choices:
> # "-threads N" for multi-threading
> # "-mbr" for MBR decoding
> # "-drop-unknown" for dropping unknown source words
> # "-search-algorithm 1 -cube-pruning-pop-limit 5000 -s 5000" for cube
> pruning
> #
> decoder-settings = "-search-algorithm 1 -cube-pruning-pop-limit 5000 -s
> 5000"
>
> ### specify size of n-best list, if produced
> #
> #nbest = 100
>
> ### multiple reference translations
> #
> #multiref = yes
>
> ### prepare system output for scoring
> # this may include detokenization and wrapping output in sgm
> # (needed for nist-bleu, ter, meteor)
> #
> detokenizer = "$moses-script-dir/tokenizer/detokenizer.perl -l
> $output-extension"
> #recaser = $moses-script-dir/recaser/recase.perl
> wrapping-script = "$moses-script-dir/ems/support/wrap-xml.perl
> $output-extension"
> #output-sgm =
>
> ### BLEU
> #
> nist-bleu = $moses-script-dir/generic/mteval-v13a.pl
> nist-bleu-c = "$moses-script-dir/generic/mteval-v13a.pl -c"
> #multi-bleu = $moses-script-dir/generic/multi-bleu.perl
> #ibm-bleu =
>
> ### TER: translation error rate (BBN metric) based on edit distance
> # not yet integrated
> #
> # ter =
>
> ### METEOR: gives credit to stem / worknet synonym matches
> # not yet integrated
> #
> # meteor =
>
> ### Analysis: carry out various forms of analysis on the output
> #
> analysis = $moses-script-dir/ems/support/analysis.perl
> #
> # also report on input coverage
> analyze-coverage = yes
> #
> # also report on phrase mappings used
> report-segmentation = yes
> #
> # report precision of translations for each input word, broken down by
> # count of input word in corpus and model
> #report-precision-by-coverage = yes
> #
> # further precision breakdown by factor
> #precision-by-coverage-factor = pos
> #
> # visualization of the search graph in tree-based models
> #analyze-search-graph = yes
>
> [EVALUATION:test]
>
> ### input data
> #
> input-sgm = $toy-data/M_Ts.$input-extension
> # raw-input =
> # tokenized-input =
> # factorized-input =
> # input =
>
> ### reference data
> #
> reference-sgm = $toy-data/M_Ts.$output-extension
> # raw-reference =
> # tokenized-reference =
> # reference =
>
> ### analysis settings
> # may contain any of the general evaluation analysis settings
> # specific setting: base coverage statistics on earlier run
> #
> #precision-by-coverage-base = $working-dir/evaluation/test.analysis.5
>
> ### wrapping frame
> # for nist-bleu and other scoring scripts, the output needs to be wrapped
> # in sgm markup (typically like the input sgm)
> #
> wrapping-frame = $input-sgm
>
> ##########################################
> ### REPORTING: summarize evaluation scores
>
> [REPORTING]
>
> ### currently no parameters for reporting section
>
>>
>>
>
> On Sat, Dec 7, 2013 at 7:21 PM, Hieu Hoang <hieu.ho...@ed.ac.uk> wrote:
>
>> are you sure the parallel data is encoded in UTF8? Was it tokenized,
>> cleaned and escaped by the Moses scripts or by another external script?
>>
>> Can you please send me you EMS config file too
>>
>>
>> On 7 December 2013 14:03, amir haghighi <amir.haghighi...@gmail.com>wrote:
>>
>>> Hi,
>>>
>>> I have also the same problem in evaluation step with EMS and I would be
>>> thankful if you could help me.
>>> the lexical reordering file is emtpy and the log of the output in
>>> evaluation_test_filter.2.stderr is:
>>>
>>> Using SCRIPTS_ROOTDIR:
>>> /opt/tools/mosesdecoder-RELEASE-1.0/mosesdecoder-RELEASE-1.0/scripts
>>> (9) create moses.ini @ Sat Dec 7 04:50:15 PST 2013
>>> Executing: mkdir -p /opt/tools/workingEms/evaluation/test.filtered.2
>>> Considering factor 0
>>> Considering factor 0
>>> filtering /opt/tools/workingEms/model/phrase-table.2 ->
>>> /opt/tools/workingEms/evaluation/test.filtered.2/phrase-table.0-0.1.1...
>>> 0 of 2197240 phrases pairs used (0.00%) - note: max length 10
>>> binarizing...cat
>>> /opt/tools/workingEms/evaluation/test.filtered.2/phrase-table.0-0.1.1 |
>>> LC_ALL=C sort -T /opt/tools/workingEms/evaluation/test.filtered.2 |
>>>
>>> /opt/tools/mosesdecoder-RELEASE-1.0/mosesdecoder-RELEASE-1.0/bin/processPhraseTable
>>> -ttable 0 0 - -nscores 5 -out
>>> /opt/tools/workingEms/evaluation/test.filtered.2/phrase-table.0-0.1.1
>>> processing ptree for stdin
>>> Segmentation fault (core dumped)
>>> filtering
>>>
>>> /opt/tools/workingEms/model/reordering-table.2.wbe-msd-bidirectional-fe.gz
>>> ->
>>>
>>> /opt/tools/workingEms/evaluation/test.filtered.2/reordering-table.2.wbe-msd-bidirectional-fe...
>>> 0 of 2197240 phrases pairs used (0.00%) - note: max length 10
>>>
>>> binarizing.../opt/tools/mosesdecoder-RELEASE-1.0/mosesdecoder-RELEASE-1.0/bin/processLexicalTable
>>> -in
>>>
>>> /opt/tools/workingEms/evaluation/test.filtered.2/reordering-table.2.wbe-msd-bidirectional-fe
>>> -out
>>>
>>> /opt/tools/workingEms/evaluation/test.filtered.2/reordering-table.2.wbe-msd-bidirectional-fe
>>> processLexicalTable v0.1 by Konrad Rawlik
>>> processing
>>>
>>> /opt/tools/workingEms/evaluation/test.filtered.2/reordering-table.2.wbe-msd-bidirectional-fe
>>> to
>>>
>>> /opt/tools/workingEms/evaluation/test.filtered.2/reordering-table.2.wbe-msd-bidirectional-fe.*
>>> ERROR: empty lexicalised reordering file
>>>
>>>
>>>
>>> Barry Haddow <bhaddow@...> writes:
>>>
>>> >
>>> > Hi Irene
>>> >
>>> > > But the output is empty. And the errors are 1. segmentation fault
>>> > 2. error: empty lexicalized
>>> > > reordering file
>>> >
>>> > Is this lexicalised reordering file empty then?
>>> >
>>> > It would be helpful if you could post the full log of the output when
>>> > your run the filter command,
>>> >
>>> > cheers - Barry
>>> >
>>> > On 26/10/12 17:59, Irene Huang wrote:
>>> > > Hi, I have trained and tuned the model, now I am using
>>> > >
>>> > > ~/mosesdecoder/scripts/training/filter-model-given-input.pl
>>> > > <http://filter-model-given-input.pl> filtered-newstest2011
>>> > > mert-work/moses.ini ~/corpus/newstest2011.true.fr
>>> > > <http://newstest2011.true.fr> \
>>> > > -Binarizer ~/mosesdecoder/bin/processPhraseTable
>>> > >
>>> > > to filter the phrase table.
>>> > >
>>> > > But the output is empty. And the errors are 1. segmentation fault
>>> > > 2. error: empty lexicalized reordering file
>>> > >
>>> > > So does this mean it's out of memory error?
>>> > >
>>> > > Thanks
>>> > >
>>> > >
>>> > > _______________________________________________
>>> > > Moses-support mailing list
>>> > > Moses-support@...
>>> > > http://mailman.mit.edu/mailman/listinfo/moses-support
>>> >
>>>
>>>
>>>
>>>
>>> _______________________________________________
>>> Moses-support mailing list
>>> Moses-support@mit.edu
>>> http://mailman.mit.edu/mailman/listinfo/moses-support
>>>
>>>
>>
>>
>> --
>> Hieu Hoang
>> Research Associate
>> University of Edinburgh
>> http://www.hoang.co.uk/hieu
>>
>>
>
> _______________________________________________
> Moses-support mailing list
> Moses-support@mit.edu
> http://mailman.mit.edu/mailman/listinfo/moses-support
>
>
--
Hieu Hoang
Research Associate
University of Edinburgh
http://www.hoang.co.uk/hieu
_______________________________________________
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