o h^@sZdZddlZddlZddlmZddlTddlmZmZej j Z dZ dZ Gdd d ZdS) z Basic testing of French pipeline The benefit of this test is to verify that the bulk processing works for languages with MWT in them NDocument)*) check_on_gpu check_on_cpuuAlors encore inconnu du grand public, Emmanuel Macron devient en 2014 ministre de l'Économie, de l'Industrie et du Numérique.u) [ [ { "id": 1, "text": "Alors", "lemma": "alors", "upos": "ADV", "head": 3, "deprel": "advmod", "start_char": 0, "end_char": 5 }, { "id": 2, "text": "encore", "lemma": "encore", "upos": "ADV", "head": 3, "deprel": "advmod", "start_char": 6, "end_char": 12 }, { "id": 3, "text": "inconnu", "lemma": "inconnu", "upos": "ADJ", "feats": "Gender=Masc|Number=Sing", "head": 11, "deprel": "advcl", "start_char": 13, "end_char": 20 }, { "id": [ 4, 5 ], "text": "du", "start_char": 21, "end_char": 23 }, { "id": 4, "text": "de", "lemma": "de", "upos": "ADP", "head": 7, "deprel": "case" }, { "id": 5, "text": "le", "lemma": "le", "upos": "DET", "feats": "Definite=Def|Gender=Masc|Number=Sing|PronType=Art", "head": 7, "deprel": "det" }, { "id": 6, "text": "grand", "lemma": "grand", "upos": "ADJ", "feats": "Gender=Masc|Number=Sing", "head": 7, "deprel": "amod", "start_char": 24, "end_char": 29 }, { "id": 7, "text": "public", "lemma": "public", "upos": "NOUN", "feats": "Gender=Masc|Number=Sing", "head": 3, "deprel": "obl:arg", "start_char": 30, "end_char": 36, "misc": "SpaceAfter=No" }, { "id": 8, "text": ",", "lemma": ",", "upos": "PUNCT", "head": 3, "deprel": "punct", "start_char": 36, "end_char": 37 }, { "id": 9, "text": "Emmanuel", "lemma": "Emmanuel", "upos": "PROPN", "head": 11, "deprel": "nsubj", "start_char": 38, "end_char": 46 }, { "id": 10, "text": "Macron", "lemma": "Macron", "upos": "PROPN", "head": 9, "deprel": "flat:name", "start_char": 47, "end_char": 53 }, { "id": 11, "text": "devient", "lemma": "devenir", "upos": "VERB", "feats": "Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin", "head": 0, "deprel": "root", "start_char": 54, "end_char": 61 }, { "id": 12, "text": "en", "lemma": "en", "upos": "ADP", "head": 13, "deprel": "case", "start_char": 62, "end_char": 64 }, { "id": 13, "text": "2014", "lemma": "2014", "upos": "NUM", "feats": "Number=Plur", "head": 11, "deprel": "obl:mod", "start_char": 65, "end_char": 69 }, { "id": 14, "text": "ministre", "lemma": "ministre", "upos": "NOUN", "feats": "Gender=Masc|Number=Sing", "head": 11, "deprel": "xcomp", "start_char": 70, "end_char": 78 }, { "id": 15, "text": "de", "lemma": "de", "upos": "ADP", "head": 17, "deprel": "case", "start_char": 79, "end_char": 81 }, { "id": 16, "text": "l'", "lemma": "le", "upos": "DET", "feats": "Definite=Def|Number=Sing|PronType=Art", "head": 17, "deprel": "det", "start_char": 82, "end_char": 84, "misc": "SpaceAfter=No" }, { "id": 17, "text": "Économie", "lemma": "économie", "upos": "NOUN", "feats": "Gender=Fem|Number=Sing", "head": 14, "deprel": "nmod", "start_char": 84, "end_char": 92, "misc": "SpaceAfter=No" }, { "id": 18, "text": ",", "lemma": ",", "upos": "PUNCT", "head": 21, "deprel": "punct", "start_char": 92, "end_char": 93 }, { "id": 19, "text": "de", "lemma": "de", "upos": "ADP", "head": 21, "deprel": "case", "start_char": 94, "end_char": 96 }, { "id": 20, "text": "l'", "lemma": "le", "upos": "DET", "feats": "Definite=Def|Number=Sing|PronType=Art", "head": 21, "deprel": "det", "start_char": 97, "end_char": 99, "misc": "SpaceAfter=No" }, { "id": 21, "text": "Industrie", "lemma": "industrie", "upos": "NOUN", "feats": "Gender=Fem|Number=Sing", "head": 17, "deprel": "conj", "start_char": 99, "end_char": 108 }, { "id": 22, "text": "et", "lemma": "et", "upos": "CCONJ", "head": 25, "deprel": "cc", "start_char": 109, "end_char": 111 }, { "id": [ 23, 24 ], "text": "du", "start_char": 112, "end_char": 114 }, { "id": 23, "text": "de", "lemma": "de", "upos": "ADP", "head": 25, "deprel": "case" }, { "id": 24, "text": "le", "lemma": "le", "upos": "DET", "feats": "Definite=Def|Gender=Masc|Number=Sing|PronType=Art", "head": 25, "deprel": "det" }, { "id": 25, "text": "Numérique", "lemma": "numérique", "upos": "NOUN", "feats": "Gender=Masc|Number=Sing", "head": 17, "deprel": "conj", "start_char": 115, "end_char": 124, "misc": "SpaceAfter=No" }, { "id": 26, "text": ".", "lemma": ".", "upos": "PUNCT", "head": 11, "deprel": "punct", "start_char": 124, "end_char": 125, "misc": "SpaceAfter=No" } ] ] c@s@eZdZejddddZddZddZd d Zd d Z d S)TestFrenchPipelineclass)scopecCstjdtdd}|S)z& Create a pipeline with French models ztokenize,mwt,pos,lemma,depparsefr) processorsdirlang)stanzaPipelineTEST_MODELS_DIRselfpipelinere/var/www/html/env_mimamsha/lib/python3.10/site-packages/stanza/tests/pipeline/test_french_pipeline.pyr>szTestFrenchPipeline.pipelinecCs|t}tt|tdS)N)FR_MWT_SENTENCEcompare_ignoring_whitespacestrEXPECTED_RESULT)rrdocrrr test_singleDszTestFrenchPipeline.test_singlecCs~d}tg|}dd|D}||}t||ksJ|D] }tt|tt|jdks.J|jdks5J|jdksKsz0TestFrenchPipeline.test_bulk..)rlenrrr sentences num_words num_tokens)rrNUM_DOCSraw_textraw_docresultrrrr test_bulkHs zTestFrenchPipeline.test_bulkcCs t|dS)zL The default pipeline should have all the models on the GPU N)rrrrr test_on_gpuVs zTestFrenchPipeline.test_on_gpucCstjdtdd}t|dS)zP Create a pipeline on the CPU, check that all the models on CPU r F)r use_gpuN)rrrrrrrr test_on_cpu\s zTestFrenchPipeline.test_on_cpuN) __name__ __module__ __qualname__pytestfixturerrr,r-r/rrrrr=s   r)__doc__r3rstanza.models.common.docr stanza.tests+stanza.tests.pipeline.pipeline_device_testsrrmarkr pytestmarkrrrrrrrs *