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How to use sizes method in Playwright Python

Best Python code snippet using playwright-python

picture.py

Source:picture.py

1# -*- coding: utf-8 -*-2import os3ccdir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))4template = """<!DOCTYPE html>5<meta charset=utf-8>6"""7errors = {8    # missing src on img9    "img-no-src": "<img alt>",10    "img-no-src-with-srcset": "<img srcset=x alt>",11    "img-no-src-with-picture": "<picture><img alt></picture>",12    "img-no-src-with-srcset-and-picture": "<picture><img srcset=x alt></picture>",13    "img-no-src-with-source": "<picture><source srcset=x><img alt></picture>",14    # junk content in picture15    "junk-text-before-img": "<picture>x<img src=x alt></picture>",16    "junk-text-after-img": "<picture><img src=x alt>x</picture>",17    "junk-text-before-source": "<picture>x<source srcset=x><img src=x alt></picture>",18    "junk-text-after-source": "<picture><source srcset=x>x<img src=x alt></picture>",19    "junk-br-before-img": "<picture><br><img src=x alt></picture>",20    "junk-br-after-img": "<picture><img src=x alt><br></picture>",21    "junk-br-before-source": "<picture><br><source srcset=x><img src=x alt></picture>",22    "junk-br-after-source": "<picture><source srcset=x><br><img src=x alt></picture>",23    "junk-video-before": "<picture><video></video><source srcset=x><img src=x alt></picture>",24    "junk-video-no-img": "<picture><video></video></picture>",25    "junk-p-before": "<picture><p></p><source srcset=x><img src=x alt></picture>",26    "junk-p-after": "<picture><source srcset=x><img src=x alt><p></p></picture>",27    "junk-p-wrapping": "<picture><p><source srcset=x><img src=x alt></p></picture>",28    "junk-span-before": "<picture><span></span><source srcset=x><img src=x alt></picture>",29    "junk-span-after": "<picture><source srcset=x><img src=x alt><span></span></picture>",30    "junk-span-wrapping": "<picture><span><source srcset=x><img src=x alt></span></picture>",31    "junk-picture-before": "<picture><picture><img src=x alt></picture><img src=x alt></picture>",32    "junk-picture-wrapping": "<picture><picture><img src=x alt></picture></picture>",33    "junk-figure-wrapping": "<picture><figure><img src=x alt></figure></picture>",34    "junk-input-type-hidden": "<picture><input type=hidden name=x value=x><img src=x alt></picture>",35    "junk-style-scroped": "<picture><style scroped></style><img src=x alt></picture>",36    "junk-noscript": "<picture><img src=x alt><noscript></noscript></picture>",37    "junk-noscript-after-source-no-img": "<picture><source srcset=x><noscript><img src=x alt></noscript></picture>",38    "junk-svg": "<picture><img src=x alt><svg></svg></picture>",39    "junk-svg-no-img": "<picture><svg></svg></picture>",40    "junk-math-nog-img": "<picture><math></math></picture>",41    # parents42    "parent-ul": "<ul><picture><img src=x alt></picture></ul>",43    "parent-dl": "<dl><picture><img src=x alt></picture></dl>",44    "parent-hgroup": "<hgroup><h1>x</h1><picture><img src=x alt></picture></hgroup>",45    "parent-noscript-in-head": "<noscript><picture><img src=x alt></picture></noscript>",46    # invalid html syntax47    "html-syntax-source-end-tag": "<picture><source srcset=x></source><img src=x alt></picture>",48    "html-syntax-img-end-tag": "<picture><img src=x alt></img></picture>",49    "html-syntax-picture-no-end-tag": "<picture><img src=x alt>",50    "html-syntax-picture-slash": "<picture/><img src=x alt></picture>",51    "html-syntax-picture-slash-no-end-tag": "<picture/><img src=x alt>",52    # missing img in picture53    "missing-img-empty-picture": "<picture></picture>",54    "missing-img-only-source": "<picture><source srcset=x></picture>",55    "missing-img-only-script": "<picture><script></script></picture>",56    "missing-img-script-and-source": "<picture><script></script><source srcset=x></picture>",57    "missing-img-source-and-script": "<picture><source srcset=x><script></script></picture>",58    # multiple img in picture59    "multiple-img": "<picture><img src=x alt><img src=x alt></picture>",60    "multiple-img-with-script": "<picture><img src=x alt><script></script><img src=x alt></picture>",61    "multiple-img-with-source": "<picture><source srcset=x><img src=x alt><img src=x alt></picture>",62    "multiple-img-with-source-and-script": "<picture><source srcset=x><img src=x alt><script></script><img src=x alt></picture>",63    # source after img64    "source-after-img": "<picture><img src=x alt><source srcset=x></picture>",65    "source-before-and-after-img": "<picture><source srcset=x><img src=x alt><source srcset=x></picture>",66    # source with following sibling source element or img element with a srcset attribute67    "always-matching-source-with-following-img-srcset": "<picture><source srcset=x><img src=x srcset=x alt></picture>",68    "always-matching-source-with-following-source-srcset": "<picture><source srcset=x><source srcset=x><img src=x alt></picture>",69    "always-matching-source-with-following-source-media": "<picture><source srcset=x><source srcset=x media=screen><img src=x alt></picture>",70    "always-matching-source-with-following-source-type": "<picture><source srcset=x><source srcset=x type=image/gif><img src=x alt></picture>",71    "always-matching-source-media-empty-with-following-source-srcset": "<picture><source srcset=x media><source srcset=x><img src=x alt></picture>",72    "always-matching-source-media-spaces-with-following-source-srcset": "<picture><source srcset=x media=' \n\t'><source srcset=x><img src=x alt></picture>",73    "always-matching-source-media-all-with-following-source-srcset": "<picture><source srcset=x media=all><source srcset=x><img src=x alt></picture>",74    "always-matching-source-media-uppercase-with-following-source-srcset": "<picture><source srcset=x media=ALL><source srcset=x><img src=x alt></picture>",75    "always-matching-source-media-all-spaces-with-following-source-srcset": "<picture><source srcset=x media=' all '><source srcset=x><img src=x alt></picture>",76    "always-matching-source-sizes-with-following-source-srcset": "<picture><source srcset='x 100w' sizes=50vw><source srcset=x><img src=x alt></picture>",77    # sizes present78    "img-srcset-no-descriptor-with-sizes": "<img src=x srcset='x' sizes=50vw alt>",79    "img-srcset-w-and-x-width-sizes": "<img src=x srcset='x 100w, y 2x' sizes=50vw alt>",80    "source-srcset-x-with-sizes": "<picture><source srcset='x 1x, y 2x' sizes=50vw><img src=x alt></picture>",81    "source-srcset-h-with-sizes": "<picture><source srcset='x 100h, y 200h' sizes=50vw><img src=x alt></picture>",82    "source-srcset-w-and-x-with-sizes": "<picture><source srcset='x 100w, y 2x' sizes=50vw><img src=x alt></picture>",83    "img-with-sizes-no-srcset": "<img sizes=50vw src=foo alt>",84    # width descriptor without sizes85    "img-srcset-w-no-sizes": "<img srcset='x 100w, y 200w' src=x alt>",86    "source-srcset-w-no-sizes": "<picture><source srcset='x 100w, y 200w'><img src=x alt></picture>",87    "source-type-srcset-w": "<picture><source srcset='x 100w, y 200w' type=image/gif><img src=x alt></picture>",88    # invalid attributes on source89    "source-src": "<picture><source src=x><img src=x alt></picture>",90    "source-src-srcset": "<picture><source src=x srcset=x><img src=x alt></picture>",91    "source-alt": "<picture><source srcset=x alt><img src=x alt></picture>",92    "source-width": "<picture><source srcset=x width=100><img src=x alt></picture>",93    "source-height": "<picture><source srcset=x height=100><img src=x alt></picture>",94    "source-usemap": "<picture><source srcset=x usemap><img src=x alt></picture>",95    "source-ismap": "<picture><source srcset=x ismap><img src=x alt></picture>",96    "source-crossorigin": "<picture><source srcset=x crossorigin><img src=x alt></picture>",97    "source-name": "<picture><source srcset=x crossorigin><img src=x alt></picture>",98    "source-align": "<picture><source srcset=x align=left><img src=x alt></picture>",99    "source-hspace": "<picture><source srcset=x hspace=1><img src=x alt></picture>",100    "source-vspace": "<picture><source srcset=x vspace=1><img src=x alt></picture>",101    "source-longdesc": "<picture><source srcset=x longdesc=x><img src=x alt></picture>",102    "source-border": "<picture><source srcset=x border=1><img src=x alt></picture>",103    # missing srcset on source104    "source-no-srcset": "<picture><source><img src=x alt></picture>",105    "source-no-srcset-with-sizes": "<picture><source sizes=50vw><img src=x alt></picture>",106    "source-no-srcset-with-media": "<picture><source media=screen><img src=x alt></picture>",107    "source-no-srcset-with-type": "<picture><source type='image/webp'><img src=x alt></picture>",108    # invalid attributes on picture109    "picture-src": "<picture src=x><img src=x alt></picture>",110    "picture-srcset": "<picture srcset=x><img src=x alt></picture>",111    "picture-media": "<picture media=screen><img src=x alt></picture>",112    "picture-sizes": "<picture sizes=50vw><img src=x alt></picture>",113    "picture-alt": "<picture alt><img src=x alt></picture>",114    "picture-width": "<picture width=100><img src=x alt></picture>",115    "picture-height": "<picture height=100><img src=x alt></picture>",116    "picture-usemap": "<picture usemap><img src=x alt></picture>",117    "picture-ismap": "<picture ismap><img src=x alt></picture>",118    "picture-crossorigin": "<picture crossorigin><img src=x alt></picture>",119    "picture-name": "<picture name=x><img src=x alt></picture>",120    "picture-lowsrc": "<picture lowsrc=x><img src=x alt></picture>",121    "picture-align": "<picture align=left><img src=x alt></picture>",122    "picture-hspace": "<picture hspace=1><img src=x alt></picture>",123    "picture-vspace": "<picture vspace=1><img src=x alt></picture>",124    "picture-longdesc": "<picture longdesc=x><img src=x alt></picture>",125    "picture-border": "<picture border=1><img src=x alt></picture>",126    # invalid attributes on source in video127    "video-source-srcset": "<video><source srcset=x></video>",128    "video-source-srcset-src": "<video><source srcset=x src=x></video>",129    "video-source-sizes-srcset": "<video><source sizes=50vw srcset='x 100w'></video>",130    "video-source-media-src": "<video><source media=screen src=x></video>",131    # srcset on other elements132    "link-rel-icon-srcset": "<link rel=icon srcset=x href=x>",133    "input-type-image-srcset": "<input type=image src=x srcset=x alt=x>",134    "object-srcset": "<object data=x srcset=x></object>",135    "video-srcset": "<video src=x srcset=x></video>",136    "audio-srcset": "<audio src=x srcset=x></audio>",137    "track-srcset": "<video src=x><track src=x srcset=x></video>",138    "svg-image-srcset": "<svg><image xlink:href=x srcset=x width=1 height=1 /></svg>",139    # invalid attributes on img140    "img-type": "<img src=x type=image/gif alt>",141    "img-type-with-picture": "<picture><img src=x type=image/gif alt></picture>",142    # sizes microsyntax143    "sizes-microsyntax-media-all": "<img sizes='all 500px, 100vw' srcset='x 100w, y 200w' src=x alt>",144    "sizes-microsyntax-media-all-and-min-width": "<img sizes='all and (min-width:500px) 500px, 100vw' srcset='x 100w, y 200w' src=x alt>",145    "sizes-microsyntax-media-min-width-no-parenthesis": "<img sizes='min-width:500px 500px, 100vw' srcset='x 100w, y 200w' src=x alt>",146    "sizes-microsyntax-media-general-enclosed-junk": "<img sizes='(123) 500px, 100vw' srcset='x 100w, y 200w' src=x alt>",147    "sizes-microsyntax-media-bad-junk": "<img sizes='(}) 500px, 100vw' srcset='x 100w, y 200w' src=x alt>",148    "sizes-microsyntax-two-defaults": "<img sizes='500px, 100vw' srcset='x 100w, y 200w' src=x alt>",149    "sizes-microsyntax-default-first": "<img sizes='100vw, (min-width:500px) 500px' srcset='x 100w, y 200w' src=x alt>",150    "sizes-microsyntax-trailing-comma": "<img sizes='(min-width:500px) 500px, 100vw,' srcset='x 100w, y 200w' src=x alt>",151    "sizes-microsyntax-trailing-junk": "<img sizes='(min-width:500px) 500px, 100vw, foo bar' srcset='x 100w, y 200w' src=x alt>",152    "sizes-microsyntax-junk-in-default": "<img sizes='(min-width:500px) 500px, 100vw foo bar' srcset='x 100w, y 200w' src=x alt>",153    "sizes-microsyntax-junk-in-source-size": "<img sizes='(min-width:500px) 500px foo bar, 100vw' srcset='x 100w, y 200w' src=x alt>",154    "sizes-microsyntax-percent-in-source-size-value": "<img sizes='(min-width:500px) 50%, 100vw' srcset='x 100w, y 200w' src=x alt>",155    "sizes-microsyntax-no-unit-in-source-size-value": "<img sizes='(min-width:500px) 50, 100vw' srcset='x 100w, y 200w' src=x alt>",156    "sizes-microsyntax-deg-source-size-value": "<img sizes='1deg' srcset='x 100w, y 200w' src=x alt>",157    "sizes-microsyntax-grad-source-size-value": "<img sizes='1grad' srcset='x 100w, y 200w' src=x alt>",158    "sizes-microsyntax-rad-source-size-value": "<img sizes='1rad' srcset='x 100w, y 200w' src=x alt>",159    "sizes-microsyntax-turn-source-size-value": "<img sizes='1turn' srcset='x 100w, y 200w' src=x alt>",160    "sizes-microsyntax-s-source-size-value": "<img sizes='1s' srcset='x 100w, y 200w' src=x alt>",161    "sizes-microsyntax-ms-source-size-value": "<img sizes='1ms' srcset='x 100w, y 200w' src=x alt>",162    "sizes-microsyntax-hz-source-size-value": "<img sizes='1Hz' srcset='x 100w, y 200w' src=x alt>",163    "sizes-microsyntax-khz-source-size-value": "<img sizes='1kHz' srcset='x 100w, y 200w' src=x alt>",164    "sizes-microsyntax-dpi-source-size-value": "<img sizes='1dpi' srcset='x 100w, y 200w' src=x alt>",165    "sizes-microsyntax-dpcm-source-size-value": "<img sizes='1dpcm' srcset='x 100w, y 200w' src=x alt>",166    "sizes-microsyntax-dppx-source-size-value": "<img sizes='1dppx' srcset='x 100w, y 200w' src=x alt>",167    "sizes-microsyntax-auto-source-size-value": "<img sizes='auto' srcset='x 100w, y 200w' src=x alt>",168    "sizes-microsyntax-inherit-source-size-value": "<img sizes='inherit' srcset='x 100w, y 200w' src=x alt>",169    "sizes-microsyntax-initial-source-size-value": "<img sizes='initial' srcset='x 100w, y 200w' src=x alt>",170    "sizes-microsyntax-default-source-size-value": "<img sizes='default' srcset='x 100w, y 200w' src=x alt>",171    "sizes-microsyntax-foo-bar-source-size-value": "<img sizes='foo-bar' srcset='x 100w, y 200w' src=x alt>",172    "sizes-microsyntax-negative-source-size-value": "<img sizes='-1px' srcset='x 100w, y 200w' src=x alt>",173    "sizes-microsyntax-empty": "<img sizes='' srcset='x 100w, y 200w' src=x alt>",174    "sizes-microsyntax-comma": "<img sizes=',' srcset='x 100w, y 200w' src=x alt>",175    "sizes-microsyntax-css-comment-after-plus": "<img sizes='+/**/50vw' srcset='x 100w, y 200w' src=x alt>",176    "sizes-microsyntax-css-comment-before-unit": "<img sizes='50/**/vw' srcset='x 100w, y 200w' src=x alt>",177    "sizes-microsyntax-scientific-notation-negative": "<img sizes='-1e+0px' srcset='x 100w, y 200w' src=x alt>",178    "sizes-microsyntax-scientific-notation-non-integer-in-exponent": "<img sizes='1e+1.5px' srcset='x 100w, y 200w' src=x alt>",179    # srcset microsyntax180    "srcset-microsyntax-leading-comma": "<img srcset=',x' src=x alt>",181    "srcset-microsyntax-leading-comma-multiple": "<img srcset=',,,x' src=x alt>",182    "srcset-microsyntax-trailing-comma": "<img srcset='x,' src=x alt>",183    "srcset-microsyntax-trailing-comma-multiple": "<img srcset='x,,,' src=x alt>",184    "srcset-microsyntax-broken-url": "<img srcset='http: 1x' src=x alt>",185    "srcset-microsyntax-non-integer-w": "<img srcset='x 1.5w' sizes=100vw src=x alt>",186    "srcset-microsyntax-uppercase-w": "<img srcset='x 1W' sizes=100vw src=x alt>",187    "srcset-microsyntax-plus-w": "<img srcset='x +1w' sizes=100vw src=x alt>",188    "srcset-microsyntax-scientific-notation-w": "<img srcset='x 1e0w' sizes=100vw src=x alt>",189    "srcset-microsyntax-zero-w": "<img srcset='x 0w' sizes=100vw src=x alt>",190    "srcset-microsyntax-negative-zero-w": "<img srcset='x -0w' sizes=100vw src=x alt>",191    "srcset-microsyntax-negative-w": "<img srcset='x -1w' sizes=100vw src=x alt>",192    "srcset-microsyntax-plus-x": "<img srcset='x +1x' src=x alt>",193    "srcset-microsyntax-negative-x": "<img srcset='x -1x' src=x alt>",194    "srcset-microsyntax-zero-x": "<img srcset='x 0x' src=x alt>",195    "srcset-microsyntax-negative-zero-x": "<img srcset='x -0x' src=x alt>",196    "srcset-microsyntax-leading-dot-x": "<img srcset='x .5x' src=x alt>",197    "srcset-microsyntax-nan-x": "<img srcset='x NaNx' src=x alt>",198    "srcset-microsyntax-infinity-x": "<img srcset='x Infinityx' src=x alt>",199    "srcset-microsyntax-x-and-w": "<img srcset='x 1x 1w' sizes=100vw src=x alt>",200    "srcset-microsyntax-x-and-h": "<img srcset='x 1x 1h' sizes=100vw src=x alt>",201    "srcset-microsyntax-w-and-h": "<img srcset='x 1w 1h' sizes=100vw src=x alt>",202    "srcset-microsyntax-h": "<img srcset='x 1h' sizes=100vw src=x alt>",203    "srcset-microsyntax-function": "<img srcset='x foobar(baz quux, lol), y 1x' src=x alt>",204    "srcset-microsyntax-parenthesis-junk": "<img srcset='x ><(((((o)>, y 1x' src=x alt>",205    "srcset-microsyntax-square-bracket-junk": "<img srcset='x [, y 1x' src=x alt>",206    "srcset-microsyntax-curly-bracket-junk": "<img srcset='x {, y 1x' src=x alt>",207    "srcset-microsyntax-pipe-junk": "<img srcset='x ||, y 1x' src=x alt>",208    "srcset-microsyntax-w-and-no-descriptor": "<img srcset='x 1w, y' sizes=100vw src=x alt>",209    "srcset-microsyntax-unique-descriptors-1x-and-omitted": "<img srcset='x 1x, y' src=x alt>",210    "srcset-microsyntax-unique-descriptors-2x": "<img srcset='x 2x, y 2x' src=x alt>",211    "srcset-microsyntax-unique-descriptors-integer-and-decimals-x": "<img srcset='x 1x, y 1.0x' src=x alt>",212    "srcset-microsyntax-unique-descriptors-w": "<img srcset='x 1w, y 1w' sizes=100vw src=x alt>",213    "srcset-microsyntax-empty": "<img srcset='' src=x alt>",214    "srcset-microsyntax-comma": "<img srcset=',' src=x alt>",215    "srcset-microsyntax-css-comment-after-descriptor": "<img srcset='x 2x/**/' src=x alt>",216    # aria217    "picture-aria-role-img": "<picture role=img><img src=x alt></picture>",218    "picture-aria-role-button": "<picture role=button><img src=x alt></picture>",219    "picture-aria-role-region": "<picture role=region><img src=x alt></picture>",220    "picture-aria-role-application": "<picture role=application><img src=x alt></picture>",221    "source-aria-role-img": "<picture><source role=img srcset=x><img src=x alt></picture>",222    "picture-aria-role-presentation": "<picture role=presentation><img src=x alt></picture>",223    "source-aria-role-presentation": "<picture><source role=presentation srcset=x><img src=x alt></picture>",224}225non_errors_in_head = {226    "parent-template-in-head": "<template><picture><img src=x alt></picture></template>",227}228non_errors = {229    # basic230    "basic-img-src": "<img src=x alt>",231    "basic-picture-img-src": "<picture><img src=x alt></picture>",232    "basic-picture-source": "<picture><source srcset=x><img src=x alt></picture>",233    # inter-element whitespace234    "inter-element-whitespace": "<picture> <!--x--> <source srcset=x> <!--x--> <img src=x alt> <!--x--> </picture>",235    # parents236    "parent-p": "<p><picture><img src=x alt></picture></p>",237    "parent-h1": "<h1><picture><img src=x alt=x></picture></h1>",238    "parent-noscript-in-body": "<noscript><picture><img src=x alt></picture></noscript>",239    "parent-object": "<object data=x><picture><img src=x alt></picture></object>",240    "parent-video": "<video src=x><picture><img src=x alt></picture></video>",241    "parent-section": "<section><h2>x</h2><picture><img src=x alt></picture></section>",242    "parent-main": "<main><picture><img src=x alt></picture></main>",243    "parent-canvas": "<canvas><picture><img src=x alt></picture></canvas>",244    "parent-template-in-body": "<template><picture><img src=x alt></picture></template>",245    "parent-ruby": "<ruby><picture><img src=x alt></picture><rt>x</rt></ruby>",246    "parent-rt": "<ruby>x<rt><picture><img src=x alt></picture></rt></ruby>",247    "parent-rp": "<ruby>x<rp><picture><img src=x alt></picture></rp><rt>x</rt><rp>x</rp></ruby>",248    "parent-a": "<a href=x><picture><img src=x alt></picture></a>",249    "parent-button": "<button><picture><img src=x alt></picture></button>",250    "parent-td": "<table><tr><td><picture><img src=x alt></picture></table>",251    # script-supporting elements252    "script-first": "<picture><script></script><source srcset=x><img src=x alt></picture>",253    "template-first": "<picture><template></template><source srcset=x><img src=x alt></picture>",254    "script-between": "<picture><source srcset=x><script></script><img src=x alt></picture>",255    "script-after": "<picture><source srcset=x><img src=x alt><script></script></picture>",256    "script-before-after": "<picture><script></script><source srcset=x><img src=x alt><script></script></picture>",257    "script-before-between-after": "<picture><script></script><source srcset=x><script></script><img src=x alt><script></script></picture>",258    "script-and-template": "<picture><template></template><source srcset=x><script></script><img src=x alt><template></template></picture>",259    # source with following sibling source element or img element with a srcset attribute260    "source-with-media-img-with-srcset": "<picture><source srcset=x media=screen><img src=x srcset=x alt></picture>",261    "source-with-media-uppercase-img-with-srcset": "<picture><source srcset=x media=SCREEN><img src=x srcset=x alt></picture>",262    "source-with-media-spaces-img-with-srcset": "<picture><source srcset=x media=' \n\tscreen \n\t'><img src=x srcset=x alt></picture>",263    "source-with-media-source-with-srcset": "<picture><source srcset=x media=screen><source srcset=x><img src=x alt></picture>",264    "source-with-type-img-with-srcset": "<picture><source srcset=x type=image/gif><img src=x srcset=x alt></picture>",265    "source-with-type-source-with-srcset": "<picture><source srcset=x type=image/gif><source srcset=x><img src=x alt></picture>",266    # sizes present267    "img-with-sizes": "<img srcset='x 100w, y 200w' sizes=50vw src=x alt>",268    "source-with-sizes": "<picture><source srcset='x 100w, y 200w' sizes=50vw><img src=x alt></picture>",269    # embed allows any attributes270    "embed-srcset-empty": "<embed srcset>",271    "embed-srcset-junk": "<embed srcset='foo bar'>",272    "embed-sizes-empty": "<embed sizes>",273    "embed-sizes-junk": "<embed sizes='foo bar'>",274    # img src also in srcset275    "img-src-also-in-srcset-1x": "<img src=x srcset='x 1x, y 2x' alt>",276    "img-src-also-in-srcset-2x": "<img src=x srcset='y 1x, x 2x' alt>",277    "img-src-also-in-srcset-w": "<img src=x srcset='x 100w, y 200w' sizes=100vw alt>",278    # img src not in srcset279    "img-src-not-in-srcset-x": "<img src=x srcset='y 1x, z 2x' alt>",280    "img-src-not-in-srcset-w": "<img src=x srcset='y 100w, z 200w' sizes=100vw alt>",281    # source type282    "source-type": "<picture><source srcset=x type=image/gif><img src=x alt></picture>",283    "source-type-srcset-x": "<picture><source srcset='x 1x, y 2x' type=image/gif><img src=x alt></picture>",284    "source-type-srcset-w-sizes": "<picture><source srcset='x 100w, y 200w' type=image/gif sizes=50vw><img src=x alt></picture>",285    # sizes microsyntax286    "sizes-microsyntax-media-min-width": "<img sizes='(min-width:500px) 500px, 100vw' srcset='x 100w, y 200w' src=x alt>",287    "sizes-microsyntax-multiple-source-sizes": "<img sizes='(min-width:1500px) 500px, (min-width:1000px) 33vw, (min-width:500px) 50vw, 100vw' srcset='x 100w, y 200w' src=x alt>",288    "sizes-microsyntax-no-default": "<img sizes='(min-width:500px) 500px' srcset='x 100w, y 200w' src=x alt>",289    "sizes-microsyntax-media-not-and": "<img sizes='not (width:500px) and (width:500px) 500px' srcset='x 100w, y 200w' src=x alt>",290    "sizes-microsyntax-only-default": "<img sizes='500px' srcset='x 100w, y 200w' src=x alt>",291    "sizes-microsyntax-calc-in-default": "<img sizes='calc(500px)' srcset='x 100w, y 200w' src=x alt>",292    "sizes-microsyntax-calc-in-source-size-value": "<img sizes='(min-width:500px) calc(500px)' srcset='x 100w, y 200w' src=x alt>",293    "sizes-microsyntax-calc-in-media": "<img sizes='(min-width:calc(500px)) 500px' srcset='x 100w, y 200w' src=x alt>",294    "sizes-microsyntax-zero": "<img sizes='0' srcset='x 100w, y 200w' src=x alt>",295    "sizes-microsyntax-minus-zero": "<img sizes='-0' srcset='x 100w, y 200w' src=x alt>",296    "sizes-microsyntax-em-in-source-size-value": "<img sizes='1em' srcset='x 100w, y 200w' src=x alt>",297    "sizes-microsyntax-ex-in-source-size-value": "<img sizes='1ex' srcset='x 100w, y 200w' src=x alt>",298    "sizes-microsyntax-ch-in-source-size-value": "<img sizes='1ch' srcset='x 100w, y 200w' src=x alt>",299    "sizes-microsyntax-rem-in-source-size-value": "<img sizes='1rem' srcset='x 100w, y 200w' src=x alt>",300    "sizes-microsyntax-vw-in-source-size-value": "<img sizes='1vw' srcset='x 100w, y 200w' src=x alt>",301    "sizes-microsyntax-vh-in-source-size-value": "<img sizes='1vh' srcset='x 100w, y 200w' src=x alt>",302    "sizes-microsyntax-vmin-in-source-size-value": "<img sizes='1vmin' srcset='x 100w, y 200w' src=x alt>",303    "sizes-microsyntax-vmax-in-source-size-value": "<img sizes='1vmax' srcset='x 100w, y 200w' src=x alt>",304    "sizes-microsyntax-cm-in-source-size-value": "<img sizes='1cm' srcset='x 100w, y 200w' src=x alt>",305    "sizes-microsyntax-mm-in-source-size-value": "<img sizes='1mm' srcset='x 100w, y 200w' src=x alt>",306    "sizes-microsyntax-q-in-source-size-value": "<img sizes='1q' srcset='x 100w, y 200w' src=x alt>",307    "sizes-microsyntax-in-in-source-size-value": "<img sizes='1in' srcset='x 100w, y 200w' src=x alt>",308    "sizes-microsyntax-pc-in-source-size-value": "<img sizes='1pc' srcset='x 100w, y 200w' src=x alt>",309    "sizes-microsyntax-pt-in-source-size-value": "<img sizes='1pt' srcset='x 100w, y 200w' src=x alt>",310    "sizes-microsyntax-px-in-source-size-value": "<img sizes='1px' srcset='x 100w, y 200w' src=x alt>",311    "sizes-microsyntax-non-integer-px-in-source-size-value": "<img sizes='0.2px' srcset='x 100w, y 200w' src=x alt>",312    "sizes-microsyntax-leading-css-comment": "<img sizes='/**/50vw' srcset='x 100w, y 200w' src=x alt>",313    "sizes-microsyntax-trailing-css-comment": "<img sizes='50vw/**/' srcset='x 100w, y 200w' src=x alt>",314    "sizes-microsyntax-plus": "<img sizes='+50vw' srcset='x 100w, y 200w' src=x alt>",315    "sizes-microsyntax-non-integer-omitted-zero": "<img sizes='.2px' srcset='x 100w, y 200w' src=x alt>",316    "sizes-microsyntax-scientifi-notation-0": "<img sizes='-0e-0px' srcset='x 100w, y 200w' src=x alt>",317    "sizes-microsyntax-scientifi-notation-1": "<img sizes='+11.11e+11px' srcset='x 100w, y 200w' src=x alt>",318    "sizes-microsyntax-scientifi-notation-2": "<img sizes='2.2e2px' srcset='x 100w, y 200w' src=x alt>",319    "sizes-microsyntax-scientifi-notation-3": "<img sizes='33E33px' srcset='x 100w, y 200w' src=x alt>",320    "sizes-microsyntax-scientifi-notation-4": "<img sizes='.4E4px' srcset='x 100w, y 200w' src=x alt>",321    # srcset microsyntax322    "srcset-microsyntax-comma-in-url": "<img srcset='x,x' src=x alt>",323    "srcset-microsyntax-percent-escaped-leading-comma-in-url": "<img srcset='%2Cx' src=x alt>",324    "srcset-microsyntax-percent-escaped-trailing-comma-in-url": "<img srcset='x%2C' src=x alt>",325    "srcset-microsyntax-percent-escaped-space-in-url": "<img srcset='%20' src=x alt>",326    "srcset-microsyntax-w": "<img srcset='x 1w' sizes=100vw src=x alt>",327    "srcset-microsyntax-x": "<img srcset='x 1x' src=x alt>",328    "srcset-microsyntax-non-integer-x": "<img srcset='x 1.5x' src=x alt>",329    "srcset-microsyntax-scientific-notation-x": "<img srcset='x 1e0x' src=x alt>",330    "srcset-microsyntax-scientific-notation-decimals-x": "<img srcset='x 1.5e0x' src=x alt>",331    "srcset-microsyntax-scientific-notation-e-plus-x": "<img srcset='x 1e+0x' src=x alt>",332    "srcset-microsyntax-scientific-notation-e-minus-x": "<img srcset='x 1e-0x' src=x alt>",333    "srcset-microsyntax-scientific-notation-e-uppercase-x": "<img srcset='x 1E0x' src=x alt>",334    "srcset-microsyntax-no-space-between-candidates": "<img srcset='x 1x,y 2x' src=x alt>",335    # valid attributes on img in picture336    "img-crossorigin-with-picture": "<picture><img crossorigin src=x alt></picture>",337    "img-usemap-with-picture": "<picture><img usemap=#x src=x alt></picture><map name=x></map>",338    "img-ismap-with-picture": "<a href=x><picture><img ismap src=x alt></picture></a>",339    "img-width-height-with-picture": "<picture><img src=x alt width=1 height=1></picture>",340    "img-width-height-zero-with-picture": "<picture><img src=x alt width=0 height=0></picture>",341    # global attributes on picture342    "picture-global-attributes": "<picture title=x class=x dir=ltr hidden id=asdf tabindex=0><img src=x alt></picture>",343}344for key in errors.keys():345    template_error = template346    template_error += '<title>invalid %s</title>\n' % key347    template_error += errors[key]348    file = open(os.path.join(ccdir, "html/elements/picture/%s-novalid.html" % key), 'wb')349    file.write(template_error)350    file.close()351file = open(os.path.join(ccdir, "html/elements/picture/picture-isvalid.html"), 'wb')352file.write(template + '<title>valid picture</title>\n')353for key in non_errors_in_head.keys():354    file.write('%s <!-- %s -->\n' % (non_errors_in_head[key], key))355file.write('<body>\n')356for key in non_errors.keys():357    file.write('%s <!-- %s -->\n' % (non_errors[key], key))358file.close()...

ragged_tensor_shape_test.py

Source:ragged_tensor_shape_test.py

...75          expected_dim_sizes=[2, [2, 1], [2, 1, 2]]),76  ])77  def testFromTensor(self, value, expected_dim_sizes):78    shape = RaggedTensorDynamicShape.from_tensor(value)79    expected = RaggedTensorDynamicShape.from_dim_sizes(expected_dim_sizes)80    self.assertShapeEq(shape, expected)81  @parameterized.parameters([82      dict(dim_sizes=[], rank=0, expected_dim_sizes=[]),83      dict(dim_sizes=[], rank=3, expected_dim_sizes=[1, 1, 1]),84      dict(dim_sizes=[3], rank=1, expected_dim_sizes=[3]),85      dict(dim_sizes=[3], rank=3, expected_dim_sizes=[1, 1, 3]),86      dict(dim_sizes=[2, 3], rank=3, expected_dim_sizes=[1, 2, 3]),87      dict(dim_sizes=[3, [3, 2, 4]], rank=2, expected_dim_sizes=[3, [3, 2, 4]]),88      dict(89          dim_sizes=[3, [3, 2, 4]],90          rank=4,91          expected_dim_sizes=[1, 1, 3, [3, 2, 4]]),92      dict(93          dim_sizes=[3, [3, 2, 4], 2, 3],94          rank=5,95          expected_dim_sizes=[1, 3, [3, 2, 4], 2, 3]),96  ])97  def testBroadcastToRank(self, dim_sizes, rank, expected_dim_sizes):98    shape = RaggedTensorDynamicShape.from_dim_sizes(dim_sizes)99    expected = RaggedTensorDynamicShape.from_dim_sizes(expected_dim_sizes)100    broadcasted_shape = shape.broadcast_to_rank(rank)101    self.assertShapeEq(broadcasted_shape, expected)102    self.assertEqual(broadcasted_shape.rank, rank)103  @parameterized.parameters([104      #=========================================================================105      # dimension[axis] is uniform inner; and row_lengths is a scalar106      #=========================================================================107      # shape: [BROADCAST(UNIFORM), UNIFORM, UNIFORM]108      dict(axis=0,109           row_length=3,110           original_dim_sizes=[1, 4, 5],111           broadcast_dim_sizes=[3, 4, 5]),112      # shape: [UNIFORM, UNIFORM, BROADCAST(UNIFORM)]113      dict(axis=2,114           row_length=5,115           original_dim_sizes=[3, 4, 1],116           broadcast_dim_sizes=[3, 4, 5]),117      # shape: [UNIFORM, RAGGED, BROADCAST(UNIFORM)]118      dict(axis=2,119           row_length=5,120           original_dim_sizes=[3, [3, 2, 8], 1],121           broadcast_dim_sizes=[3, [3, 2, 8], 5]),122      # shape: [UNIFORM, RAGGED, RAGGED, UNIFORM, UNIFORM, BROADCAST(UNIFORM)]123      dict(axis=5,124           row_length=5,125           original_dim_sizes=[2, [2, 1], [3, 2, 8], 3, 4, 1],126           broadcast_dim_sizes=[2, [2, 1], [3, 2, 8], 3, 4, 5]),127      #=========================================================================128      # dimension[axis] is uniform inner; and row_lengths is a vector129      #=========================================================================130      # shape: [UNIFORM, BROADCAST(UNIFORM)]131      dict(axis=1,132           row_length=[2, 0, 1],133           original_dim_sizes=[3, 1],134           broadcast_dim_sizes=[3, [2, 0, 1]]),135      # shape: [UNIFORM, BROADCAST(UNIFORM), UNIFORM]136      dict(axis=1,137           row_length=[2, 0, 1],138           original_dim_sizes=[3, 1, 5],139           broadcast_dim_sizes=[3, [2, 0, 1], 5]),140      # shape: [UNIFORM, UNIFORM, BROADCAST(UNIFORM)]141      dict(axis=2,142           row_length=[2, 0, 1, 3, 8, 2, 3, 4, 1, 8, 7, 0],143           original_dim_sizes=[4, 3, 1],144           broadcast_dim_sizes=[4, 3, [2, 0, 1, 3, 8, 2, 3, 4, 1, 8, 7, 0]]),145      # shape: [UNIFORM, RAGGED, BROADCAST(UNIFORM)]146      dict(axis=2,147           row_length=[2, 5, 3],148           original_dim_sizes=[2, [2, 1], 1],149           broadcast_dim_sizes=[2, [2, 1], [2, 5, 3]]),150      # shape: [UNIFORM, RAGGED, UNIFORM, UNIFORM, BROADCAST(UNIFORM), UNIFORM]151      dict(axis=4,152           row_length=list(range(18)),153           original_dim_sizes=[2, [2, 1], 3, 2, 1, 8],154           broadcast_dim_sizes=[2, [2, 1], 3, 2, list(range(18)), 8]),155      #=========================================================================156      # dimension[axis] is uniform partitioned; and row_lengths is a scalar157      #=========================================================================158      # shape: [BROADCAST(UNIFORM), RAGGED]159      dict(axis=0,160           row_length=3,161           original_dim_sizes=[1, [5]],162           broadcast_dim_sizes=[3, [5, 5, 5]]),163      # shape: [BROADCAST(UNIFORM), UNIFORM, RAGGED]164      dict(axis=0,165           row_length=2,166           original_dim_sizes=[1, 3, [3, 0, 2]],167           broadcast_dim_sizes=[2, 3, [3, 0, 2, 3, 0, 2]]),168      # shape: [BROADCAST(UNIFORM), RAGGED, RAGGED, UNIFORM, UNIFORM]169      dict(axis=0,170           row_length=3,171           original_dim_sizes=[1, [3], [3, 5, 2], 9, 4, 5],172           broadcast_dim_sizes=[3, [3, 3, 3], [3, 5, 2, 3, 5, 2, 3, 5, 2],173                                9, 4, 5]),174      # shape: [BROADCAST(UNIFORM), UNIFORM, RAGGED, UNIFORM]175      dict(axis=0,176           row_length=2,177           original_dim_sizes=[1, 2, [2, 1], [3, 5, 2], 2],178           broadcast_dim_sizes=[2, 2, [2, 1, 2, 1], [3, 5, 2, 3, 5, 2], 2]),179      # shape: [UNIFORM, BROADCAST(UNIFORM), RAGGED, UNIFORM]180      dict(axis=1,181           row_length=2,182           original_dim_sizes=[3, 1, [4, 0, 2], 5],183           broadcast_dim_sizes=[3, 2, [4, 0, 2, 4, 0, 2], 5]),184      # shape: [UNIFORM, BROADCAST(UNIFORM), RAGGED]185      dict(axis=1,186           row_length=1,187           original_dim_sizes=[2, 3, (1, 2, 3, 4, 5, 6)],188           broadcast_dim_sizes=[2, 3, (1, 2, 3, 4, 5, 6)]),189      #=========================================================================190      # dimension[axis] is uniform partitioned; and row_lengths is a vector191      #=========================================================================192      # shape: [UNIFORM, BROADCAST(UNIFORM), RAGGED, UNIFORM]193      dict(axis=1,194           row_length=[4, 1, 2],195           original_dim_sizes=[196               3,                          # axis=0197               1,                          # axis=1 (broadcast)198               [3, 1, 2],                  # axis=2199               5],                         # axis=3200           broadcast_dim_sizes=[201               3,                          # axis=0202               [4, 1, 2],                  # axis=1 (broadcast)203               [3, 3, 3, 3, 1, 2, 2],      # axis=2204               5]),                        # axis=3205      # shape: [UNIFORM, BROADCAST(UNIFORM), RAGGED, RAGGED]206      dict(axis=1,207           row_length=[2, 0, 3],208           original_dim_sizes=[209               3,                                         # axis=0210               1,                                         # axis=1 (broadcast)211               [3, 1, 2],                                 # axis=2212               [3, 1, 4, 1, 5, 9]],                       # axis=3213           broadcast_dim_sizes=[214               3,                                         # axis=0215               [2, 0, 3],                                 # axis=1 (broadcast)216               [3, 3, 2, 2, 2],                           # axis=2217               [3, 1, 4, 3, 1, 4, 5, 9, 5, 9, 5, 9]]),    # axis=3218      # shape: [UNIFORM, RAGGED, BROADCAST(UNIFORM), RAGGED, RAGGED, UNIFORM]219      dict(axis=2,220           row_length=[4, 1, 2],221           original_dim_sizes=[222               3,                                         # axis=0223               [2, 0, 1],                                 # axis=1224               1,                                         # axis=2 (broadcast)225               [3, 2, 1],                                 # axis=3226               [1, 0, 1, 0, 2, 3],                        # axis=4227               5],                                        # axis=5228           broadcast_dim_sizes=[229               3,                                         # axis=0230               [2, 0, 1],                                 # axis=2231               [4, 1, 2],                                 # axis=2 (broadcast)232               [3, 3, 3, 3, 2, 1, 1],                     # axis=3233               [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0,    # axis=4234                2, 3, 3],235               5]),                                       # axis=5236      dict(axis=0,237           row_length=2,238           original_dim_sizes=[1, 1, 2, (2, 1)],239           broadcast_dim_sizes=[2, 1, 2, (2, 1, 2, 1)]),240      dict(axis=1,241           row_length=(2, 1),242           original_dim_sizes=[2, 1, 2, (2, 1, 2, 1)],243           broadcast_dim_sizes=[2, (2, 1), 2, (2, 1, 2, 1, 2, 1)]),244      dict(axis=2,245           row_length=2,246           original_dim_sizes=[2, (2, 1), 2, (2, 1, 2, 1, 2, 1)],247           broadcast_dim_sizes=[2, (2, 1), 2, (2, 1, 2, 1, 2, 1)]),248      dict(axis=3,249           row_length=(2, 1, 2, 1, 2, 1),250           original_dim_sizes=[2, (2, 1), 2, 1],251           broadcast_dim_sizes=[2, (2, 1), 2, (2, 1, 2, 1, 2, 1)]),252  ])  # pyformat: disable253  def testBroadcastDimension(self, axis, row_length, original_dim_sizes,254                             broadcast_dim_sizes):255    """Tests for the broadcast_dimension method.256    Verifies that:257    * `original.broadcast_dimension(axis, row_length) == broadcast`258    * `broadcast.broadcast_dimension(axis, row_length) == broadcast`259    * `broadcast.broadcast_dimension(axis, 1) == broadcast`260    Args:261      axis: The axis to broadcast262      row_length: The slice lengths to broadcast to.263      original_dim_sizes: The dimension sizes before broadcasting.264        original_dim_sizes[axis] should be equal to `1` or `row_length`.265      broadcast_dim_sizes: THe dimension sizes after broadcasting.266    """267    original_shape = RaggedTensorDynamicShape.from_dim_sizes(original_dim_sizes)268    bcast_shape = RaggedTensorDynamicShape.from_dim_sizes(broadcast_dim_sizes)269    self.assertEqual(original_shape.rank, bcast_shape.rank)270    # shape[axis].value == 1 and row_length > 1:271    bcast1 = original_shape.broadcast_dimension(axis, row_length)272    # shape[axis].value > 1 and row_length == shape[axis].value:273    bcast2 = bcast_shape.broadcast_dimension(axis, row_length)274    # shape[axis].value > 1 and row_length == 1:275    bcast3 = bcast_shape.broadcast_dimension(axis, 1)276    self.assertShapeEq(bcast1, bcast_shape)277    self.assertShapeEq(bcast2, bcast_shape)278    self.assertShapeEq(bcast3, bcast_shape)279  @parameterized.parameters(280      [281          # Broadcast scalar282          dict(x_dims=[], y_dims=[], expected_dims=[]),283          dict(x_dims=[], y_dims=[2], expected_dims=[2]),284          dict(x_dims=[], y_dims=[2, 3], expected_dims=[2, 3]),285          dict(286              x_dims=[],287              y_dims=[2, (2, 3), (5, 7, 2, 0, 9)],288              expected_dims=[2, (2, 3), (5, 7, 2, 0, 9)]),289          # Broadcast vector290          dict(x_dims=[3], y_dims=[4, 2, 3], expected_dims=[4, 2, 3]),291          dict(x_dims=[1], y_dims=[4, 2, 3], expected_dims=[4, 2, 3]),292          dict(x_dims=[3], y_dims=[4, 2, 1], expected_dims=[4, 2, 3]),293          dict(294              x_dims=[3],295              y_dims=[3, (2, 3, 1), 1],296              expected_dims=[3, (2, 3, 1), 3]),297          dict(x_dims=[1], y_dims=[3, (2, 1, 3)], expected_dims=[3, (2, 1, 3)]),298          dict(299              x_dims=[1],300              y_dims=[3, (2, 1, 3), 8],301              expected_dims=[3, (2, 1, 3), 8]),302          dict(303              x_dims=[1],304              y_dims=[2, (2, 3), (5, 7, 2, 0, 9)],305              expected_dims=[2, (2, 3), (5, 7, 2, 0, 9)]),306          # Mixed broadcasting307          dict(308              x_dims=[309                  1,  # axis=0310                  3,  # axis=1311                  (3, 0, 2),  # axis=2312                  1,  # axis=3313                  2,  # axis=4314              ],315              y_dims=[316                  2,  # axis=0317                  1,  # axis=1318                  1,  # axis=2319                  (7, 2),  # axis=3320                  1,  # axis=4321              ],322              expected_dims=[323                  2,  # axis=0324                  3,  # axis=1325                  (3, 0, 2, 3, 0, 2),  # axis=2326                  (7, 7, 7, 7, 7, 2, 2, 2, 2, 2),  # axis=3327                  2,  # axis=4328              ]),329          dict(330              x_dims=[2, (2, 1), 2, 1],331              y_dims=[1, 1, 2, (2, 1)],332              expected_dims=[2, (2, 1), 2, (2, 1, 2, 1, 2, 1)]),333      ])334  def testBroadcastDynamicShape(self, x_dims, y_dims, expected_dims):335    x_shape = RaggedTensorDynamicShape.from_dim_sizes(x_dims)336    y_shape = RaggedTensorDynamicShape.from_dim_sizes(y_dims)337    expected = RaggedTensorDynamicShape.from_dim_sizes(expected_dims)338    result1 = ragged_tensor_shape.broadcast_dynamic_shape(x_shape, y_shape)339    result2 = ragged_tensor_shape.broadcast_dynamic_shape(y_shape, x_shape)340    self.assertShapeEq(expected, result1)341    self.assertShapeEq(expected, result2)342  def testRepr(self):343    shape = RaggedTensorDynamicShape.from_dim_sizes([2, (2, 1), 2, 1])344    self.assertRegex(345        repr(shape), r'RaggedTensorDynamicShape\('346        r'partitioned_dim_sizes=\(<[^>]+>, <[^>]+>\), '347        r'inner_dim_sizes=<[^>]+>\)')348  @parameterized.parameters([349      dict(350          x=[[10], [20], [30]],  # shape=[3, 1]351          dim_sizes=[3, 2],352          expected=[[10, 10], [20, 20], [30, 30]]),353      dict(354          x=[[10], [20], [30]],  # shape=[3, 1]355          dim_sizes=[3, [3, 0, 2]],356          expected=ragged_factory_ops.constant_value(357              [[10, 10, 10], [], [30, 30]], dtype=np.int32)),358      dict(359          x=[[[1, 2, 3]], [[4, 5, 6]]],  # shape = [2, 1, 3]360          dim_sizes=[2, [2, 3], 3],361          expected=ragged_factory_ops.constant_value(362              [[[1, 2, 3], [1, 2, 3]], [[4, 5, 6], [4, 5, 6], [4, 5, 6]]],363              dtype=np.int32,364              ragged_rank=1)),365      dict(366          x=[[[1]], [[2]]],  # shape = [2, 1, 1]367          dim_sizes=[2, [2, 3], [0, 2, 1, 2, 0]],368          expected=ragged_factory_ops.constant_value(369              [[[], [1, 1]], [[2], [2, 2], []]], dtype=np.int32,370              ragged_rank=2)),371      dict(372          x=10,373          dim_sizes=[3, [3, 0, 2]],374          expected=ragged_factory_ops.constant_value([[10, 10, 10], [],375                                                      [10, 10]])),376      dict(377          x=ragged_factory_ops.constant_value([[[1], [2]], [[3]]],378                                              ragged_rank=1),379          dim_sizes=[2, [2, 1], 2],380          expected=ragged_factory_ops.constant_value(381              [[[1, 1], [2, 2]], [[3, 3]]], ragged_rank=1)),382  ])383  def testRaggedBroadcastTo(self, x, dim_sizes, expected):384    shape = RaggedTensorDynamicShape.from_dim_sizes(dim_sizes)385    result = ragged_tensor_shape.broadcast_to(x, shape)386    self.assertEqual(387        getattr(result, 'ragged_rank', 0), getattr(expected, 'ragged_rank', 0))388    self.assertAllEqual(result, expected)389  @parameterized.parameters(390      [391          dict(392              doc='x.shape=[3, (D1)]; y.shape=[3, 1]; bcast.shape=[3, (D1)]',393              x=ragged_factory_ops.constant_value([[1, 2, 3], [], [4, 5]],394                                                  dtype=np.int32),395              y=[[10], [20], [30]],396              expected=ragged_factory_ops.constant_value([[11, 12, 13], [],397                                                          [34, 35]])),398          dict(...

sampled_multi_dataset.py

Source:sampled_multi_dataset.py

...282                    [b["tgt_lang_id"] for b in batches]283                )284        return batch285    @property286    def sizes(self):287        if self._sizes is not None:288            return self._sizes289        start_time = time.time()290        in_sub_dataset_indices = [291            self._cur_indices[292                0 if i == 0 else self.cumulated_sizes[i - 1] : self.cumulated_sizes[i]293            ]294            for i in range(len(self.datasets))295        ]296        sub_dataset_sizes = [297            d.sizes[indices]298            for d, indices in zip(self.datasets, in_sub_dataset_indices)299        ]300        self._sizes = np.vstack(sub_dataset_sizes)301        logger.info(f"sizes() calling time: {get_time_gap(start_time, time.time())}")302        return self._sizes303    def ordered_indices(self):304        if self.shuffle:305            indices = np.random.permutation(len(self))306        else:307            indices = np.arange(len(self))308        sizes = self.sizes309        tgt_sizes = sizes[:, 1] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else None310        src_sizes = (311            sizes[:, 0] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else sizes312        )313        # sort by target length, then source length314        if tgt_sizes is not None:315            indices = indices[np.argsort(tgt_sizes[indices], kind="mergesort")]...

conv2d_test.py

Source:conv2d_test.py

1# Copyright 2017 The TensorFlow Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15"""Tests for Conv2D via the XLA JIT.16The canned results in these tests are created by running each test using the17Tensorflow CPU device and saving the output.18"""19from __future__ import absolute_import20from __future__ import division21from __future__ import print_function22import numpy as np23from tensorflow.compiler.tests.xla_test import XLATestCase24from tensorflow.python.framework import dtypes25from tensorflow.python.ops import array_ops26from tensorflow.python.ops import gen_nn_ops27from tensorflow.python.ops import nn_ops28from tensorflow.python.platform import googletest29class Conv2DTest(XLATestCase):30  def _VerifyValues(self, input_sizes, filter_sizes, stride, padding, expected):31    """Tests that tf.nn.conv2d produces the expected value.32    Args:33      input_sizes: Input tensor dimensions in34        [batch, input_rows, input_cols, input_depth].35      filter_sizes: Filter tensor dimensions in36        [kernel_rows, kernel_cols, input_depth, output_depth].37      stride: Stride.38      padding: Padding type.39      expected: Expected output.40    """41    total_size_1 = np.prod(input_sizes)42    total_size_2 = np.prod(filter_sizes)43    x1 = np.arange(1, total_size_1 + 1, dtype=np.float32).reshape(input_sizes)44    x2 = np.arange(1, total_size_2 + 1, dtype=np.float32).reshape(filter_sizes)45    strides = [1, stride, stride, 1]46    with self.test_session() as sess:47      with self.test_scope():48        t1 = array_ops.placeholder(dtypes.float32, shape=input_sizes)49        t2 = array_ops.placeholder(dtypes.float32, shape=filter_sizes)50        out = nn_ops.conv2d(51            t1, t2, strides=strides, padding=padding, data_format="NHWC")52      value = sess.run(out, {t1: x1, t2: x2})53      self.assertArrayNear(expected, np.ravel(value), 1e-3)54  def testConv2D1x1Filter(self):55    expected_output = [56        30.0, 36.0, 42.0, 66.0, 81.0, 96.0, 102.0, 126.0, 150.0, 138.0, 171.0,57        204.0, 174.0, 216.0, 258.0, 210.0, 261.0, 312.058    ]59    self._VerifyValues(60        input_sizes=[1, 2, 3, 3],61        filter_sizes=[1, 1, 3, 3],62        stride=1,63        padding="VALID",64        expected=expected_output)65  def testConv2D2x2Filter(self):66    expected_output = [2271.0, 2367.0, 2463.0, 2901.0, 3033.0, 3165.0]67    self._VerifyValues(68        input_sizes=[1, 2, 3, 3],69        filter_sizes=[2, 2, 3, 3],70        stride=1,71        padding="VALID",72        expected=expected_output)73  def testConv2D1x2Filter(self):74    expected_output = [75        231.0, 252.0, 273.0, 384.0, 423.0, 462.0, 690.0, 765.0, 840.0, 843.0,76        936.0, 1029.077    ]78    self._VerifyValues(79        input_sizes=[1, 2, 3, 3],80        filter_sizes=[1, 2, 3, 3],81        stride=1,82        padding="VALID",83        expected=expected_output)84  def testConv2D2x2FilterStride2(self):85    expected_output = [2271.0, 2367.0, 2463.0]86    self._VerifyValues(87        input_sizes=[1, 2, 3, 3],88        filter_sizes=[2, 2, 3, 3],89        stride=2,90        padding="VALID",91        expected=expected_output)92  def testConv2D2x2FilterStride2Same(self):93    expected_output = [2271.0, 2367.0, 2463.0, 1230.0, 1305.0, 1380.0]94    self._VerifyValues(95        input_sizes=[1, 2, 3, 3],96        filter_sizes=[2, 2, 3, 3],97        stride=2,98        padding="SAME",99        expected=expected_output)100class Conv2DBackpropInputTest(XLATestCase):101  def _VerifyValues(self, input_sizes, filter_sizes, out_backprop_sizes, stride,102                    padding, expected):103    """Tests that gen_nn_ops.conv2d_backprop_input produces the expected output.104    Args:105      input_sizes: Input tensor dimensions in106        [batch, input_rows, input_cols, input_depth].107      filter_sizes: Filter tensor dimensions in108        [kernel_rows, kernel_cols, input_depth, output_depth].109      out_backprop_sizes: Output gradients tensor dimensions.110      stride: Stride.111      padding: Padding type.112      expected: Expected output.113    """114    total_size_1 = np.prod(filter_sizes)115    total_size_2 = np.prod(out_backprop_sizes)116    x1 = np.arange(1, total_size_1 + 1, dtype=np.float32).reshape(filter_sizes)117    x2 = np.arange(118        1, total_size_2 + 1, dtype=np.float32).reshape(out_backprop_sizes)119    strides = [1, stride, stride, 1]120    with self.test_session() as sess:121      with self.test_scope():122        t1 = array_ops.placeholder(dtypes.float32, shape=filter_sizes)123        t2 = array_ops.placeholder(dtypes.float32, shape=out_backprop_sizes)124        out = gen_nn_ops.conv2d_backprop_input(125            input_sizes=input_sizes,126            filter=t1,127            out_backprop=t2,128            strides=strides,129            padding=padding,130            data_format="NHWC")131      value = sess.run(out, {t1: x1, t2: x2})132      self.assertArrayNear(expected, np.ravel(value), 1e-3)133  def testConv2D1x1Filter(self):134    expected_output = [135        5, 11, 17, 11, 25, 39, 17, 39, 61, 23, 53, 83, 29, 67, 105, 35, 81, 127,136        41, 95, 149, 47, 109, 171, 53, 123, 193, 59, 137, 215, 65, 151, 237, 71,137        165, 259, 77, 179, 281, 83, 193, 303, 89, 207, 325, 95, 221, 347.138    ]139    self._VerifyValues(140        input_sizes=[1, 4, 4, 3],141        filter_sizes=[1, 1, 3, 2],142        out_backprop_sizes=[1, 4, 4, 2],143        stride=1,144        padding="VALID",145        expected=expected_output)146  def testConv2D1x2FilterStride3Width5(self):147    expected_output = [1, 2, 0, 2, 4]148    self._VerifyValues(149        input_sizes=[1, 1, 5, 1],150        filter_sizes=[1, 2, 1, 1],151        out_backprop_sizes=[1, 1, 2, 1],152        stride=3,153        padding="VALID",154        expected=expected_output)155  def testConv2D1x2FilterStride3Width6(self):156    expected_output = [1, 2, 0, 2, 4, 0]157    self._VerifyValues(158        input_sizes=[1, 1, 6, 1],159        filter_sizes=[1, 2, 1, 1],160        out_backprop_sizes=[1, 1, 2, 1],161        stride=3,162        padding="VALID",163        expected=expected_output)164  def testConv2D1x2FilterStride3Width7(self):165    expected_output = [1, 2, 0, 2, 4, 0, 0]166    self._VerifyValues(167        input_sizes=[1, 1, 7, 1],168        filter_sizes=[1, 2, 1, 1],169        out_backprop_sizes=[1, 1, 2, 1],170        stride=3,171        padding="VALID",172        expected=expected_output)173  def testConv2D2x2FilterC1Same(self):174    expected_output = [1, 4, 7, 7, 23, 33]175    self._VerifyValues(176        input_sizes=[1, 2, 3, 1],177        filter_sizes=[2, 2, 1, 1],178        out_backprop_sizes=[1, 2, 3, 1],179        stride=1,180        padding="SAME",181        expected=expected_output)182  def testConv2D2x2Filter(self):183    expected_output = [184        14, 32, 50, 100, 163, 226, 167, 212, 257, 122, 140, 158, 478, 541, 604,185        437, 482, 527186    ]187    self._VerifyValues(188        input_sizes=[1, 2, 3, 3],189        filter_sizes=[2, 2, 3, 3],190        out_backprop_sizes=[1, 1, 2, 3],191        stride=1,192        padding="VALID",193        expected=expected_output)194  def testConv2D2x2FilterSame(self):195    expected_output = [196        14, 32, 50, 100, 163, 226, 217, 334, 451, 190, 307, 424, 929, 1217,197        1505, 1487, 1883, 2279198    ]199    self._VerifyValues(200        input_sizes=[1, 2, 3, 3],201        filter_sizes=[2, 2, 3, 3],202        out_backprop_sizes=[1, 2, 3, 3],203        stride=1,204        padding="SAME",205        expected=expected_output)206  def testConv2D1x2Filter(self):207    expected_output = [1, 4, 4, 3, 10, 8, 5, 16, 12]208    self._VerifyValues(209        input_sizes=[1, 3, 3, 1],210        filter_sizes=[1, 2, 1, 1],211        out_backprop_sizes=[1, 3, 2, 1],212        stride=1,213        padding="VALID",214        expected=expected_output)215  def testConv2D1x2FilterSame(self):216    expected_output = [1, 4, 7, 4, 13, 16, 7, 22, 25]217    self._VerifyValues(218        input_sizes=[1, 3, 3, 1],219        filter_sizes=[1, 2, 1, 1],220        out_backprop_sizes=[1, 3, 3, 1],221        stride=1,222        padding="SAME",223        expected=expected_output)224  def testConv2D2x2FilterStride2(self):225    expected_output = [1, 2, 5, 4, 6, 0, 0, 0, 0, 0, 3, 6, 13, 8, 12]226    self._VerifyValues(227        input_sizes=[1, 3, 5, 1],228        filter_sizes=[1, 3, 1, 1],229        out_backprop_sizes=[1, 2, 2, 1],230        stride=2,231        padding="VALID",232        expected=expected_output)233  def testConv2D2x2FilterStride2Same(self):234    expected_output = [1, 2, 2, 3, 4, 6]235    self._VerifyValues(236        input_sizes=[1, 2, 3, 1],237        filter_sizes=[2, 2, 1, 1],238        out_backprop_sizes=[1, 1, 2, 1],239        stride=2,240        padding="SAME",241        expected=expected_output)242class Conv2DBackpropFilterTest(XLATestCase):243  def _VerifyValues(self, input_sizes, filter_sizes, out_backprop_sizes, stride,244                    padding, expected):245    """Tests that gen_nn_ops.conv2d_backprop_filter produces the right output.246    Args:247      input_sizes: Input tensor dimensions in248        [batch, input_rows, input_cols, input_depth].249      filter_sizes: Filter tensor dimensions in250        [kernel_rows, kernel_cols, input_depth, output_depth].251      out_backprop_sizes: Output gradients tensor dimensions.252      stride: Stride.253      padding: Padding type.254      expected: Expected output.255    """256    total_size_1 = np.prod(input_sizes)257    total_size_2 = np.prod(out_backprop_sizes)258    x1 = np.arange(1, total_size_1 + 1, dtype=np.float32).reshape(input_sizes)259    x2 = np.arange(260        1, total_size_2 + 1, dtype=np.float32).reshape(out_backprop_sizes)261    strides = [1, stride, stride, 1]262    with self.test_session() as sess:263      with self.test_scope():264        t1 = array_ops.placeholder(dtypes.float32, shape=input_sizes)265        t2 = array_ops.placeholder(dtypes.float32, shape=out_backprop_sizes)266        tensor = gen_nn_ops.conv2d_backprop_filter(267            input=t1,268            filter_sizes=filter_sizes,269            out_backprop=t2,270            strides=strides,271            padding=padding,272            data_format="NHWC")273      value = sess.run(tensor, {t1: x1, t2: x2})274      self.assertArrayNear(expected, np.ravel(value), 1e-3)275  def testConv2D1x1Filter(self):276    expected_output = [8056, 8432, 8312, 8704, 8568, 8976]277    self._VerifyValues(278        input_sizes=[1, 4, 4, 3],279        filter_sizes=[1, 1, 3, 2],280        out_backprop_sizes=[1, 4, 4, 2],281        stride=1,282        padding="VALID",283        expected=expected_output)284  def testConv2D1x2Filter(self):285    expected_output = [120, 141]286    self._VerifyValues(287        input_sizes=[1, 3, 3, 1],288        filter_sizes=[1, 2, 1, 1],289        out_backprop_sizes=[1, 3, 2, 1],290        stride=1,291        padding="VALID",292        expected=expected_output)293  def testConv2D2x2FilterDepth1(self):294    expected_output = [5, 8, 14, 17]295    self._VerifyValues(296        input_sizes=[1, 2, 3, 1],297        filter_sizes=[2, 2, 1, 1],298        out_backprop_sizes=[1, 1, 2, 1],299        stride=1,300        padding="VALID",301        expected=expected_output)302  def testConv2D2x2Filter(self):303    expected_output = [304        17, 22, 27, 22, 29, 36, 27, 36, 45, 32, 43, 54, 37, 50, 63, 42, 57, 72,305        62, 85, 108, 67, 92, 117, 72, 99, 126, 77, 106, 135, 82, 113, 144, 87,306        120, 153307    ]308    self._VerifyValues(309        input_sizes=[1, 2, 3, 3],310        filter_sizes=[2, 2, 3, 3],311        out_backprop_sizes=[1, 1, 2, 3],312        stride=1,313        padding="VALID",314        expected=expected_output)315  def testConv2D1x2FilterStride3Width5(self):316    expected_output = [9, 12]317    self._VerifyValues(318        input_sizes=[1, 1, 5, 1],319        filter_sizes=[1, 2, 1, 1],320        out_backprop_sizes=[1, 1, 2, 1],321        stride=3,322        padding="VALID",323        expected=expected_output)324  def testConv2D1x2FilterStride3Width6(self):325    expected_output = [9, 12]326    self._VerifyValues(327        input_sizes=[1, 1, 6, 1],328        filter_sizes=[1, 2, 1, 1],329        out_backprop_sizes=[1, 1, 2, 1],330        stride=3,331        padding="VALID",332        expected=expected_output)333  def testConv2D1x2FilterStride3Width7(self):334    expected_output = [9, 12]335    self._VerifyValues(336        input_sizes=[1, 1, 7, 1],337        filter_sizes=[1, 2, 1, 1],338        out_backprop_sizes=[1, 1, 2, 1],339        stride=3,340        padding="VALID",341        expected=expected_output)342  def testConv2D1x3Filter(self):343    expected_output = [5, 8, 11]344    self._VerifyValues(345        input_sizes=[1, 1, 4, 1],346        filter_sizes=[1, 3, 1, 1],347        out_backprop_sizes=[1, 1, 2, 1],348        stride=1,349        padding="VALID",350        expected=expected_output)351  def testConv2D1x3FilterSame(self):352    expected_output = [20, 30, 20]353    self._VerifyValues(354        input_sizes=[1, 1, 4, 1],355        filter_sizes=[1, 3, 1, 1],356        out_backprop_sizes=[1, 1, 4, 1],357        stride=1,358        padding="SAME",359        expected=expected_output)360  def testConv2D1x3FilterSameOutbackprop2(self):361    expected_output = [7, 10, 3]362    self._VerifyValues(363        input_sizes=[1, 1, 4, 1],364        filter_sizes=[1, 3, 1, 1],365        out_backprop_sizes=[1, 1, 2, 1],366        stride=2,367        padding="SAME",368        expected=expected_output)369  def testConv2D2x2FilterC1Same(self):370    expected_output = [91, 58, 32, 17]371    self._VerifyValues(372        input_sizes=[1, 2, 3, 1],373        filter_sizes=[2, 2, 1, 1],374        out_backprop_sizes=[1, 2, 3, 1],375        stride=1,376        padding="SAME",377        expected=expected_output)378  def testConv2D2x2FilterStride2(self):379    expected_output = [92, 102, 112]380    self._VerifyValues(381        input_sizes=[1, 3, 5, 1],382        filter_sizes=[1, 3, 1, 1],383        out_backprop_sizes=[1, 2, 2, 1],384        stride=2,385        padding="VALID",386        expected=expected_output)387  def testConv2D2x2FilterStride2Same(self):388    expected_output = [7, 2, 16, 5]389    self._VerifyValues(390        input_sizes=[1, 2, 3, 1],391        filter_sizes=[2, 2, 1, 1],392        out_backprop_sizes=[1, 1, 2, 1],393        stride=2,394        padding="SAME",395        expected=expected_output)396if __name__ == "__main__":...

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Playwright vs Puppeteer: Get to know about the difference between two testing frameworks and how they are different than one another, which browsers they support, and what features they provide.
Run Playwright Tests on LambdaTest: Playwright testing with LambdaTest leverages test performance to the utmost. You can run multiple Playwright tests in Parallel with the LammbdaTest test cloud. Get a step-by-step guide to run your Playwright test on the LambdaTest platform.
Playwright Python Tutorial: Playwright automation framework support all major languages such as Python, JavaScript, TypeScript, .NET and etc. However, there are various advantages to Python end-to-end testing with Playwright because of its versatile utility. Get the hang of Playwright python testing with this chapter.
Playwright End To End Testing Tutorial: Get your hands on with Playwright end-to-end testing and learn to use some exciting features such as TraceViewer, Debugging, Networking, Component testing, Visual testing, and many more.
Playwright Video Tutorial: Watch the video tutorials on Playwright testing from experts and get a consecutive in-depth explanation of Playwright automation testing.