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<h1>Source code for apache_beam.ml.inference.utils</h1><div class="highlight"><pre>
<span></span><span class="c1">#</span>
<span class="c1"># Licensed to the Apache Software Foundation (ASF) under one or more</span>
<span class="c1"># contributor license agreements. See the NOTICE file distributed with</span>
<span class="c1"># this work for additional information regarding copyright ownership.</span>
<span class="c1"># The ASF licenses this file to You under the Apache License, Version 2.0</span>
<span class="c1"># (the &quot;License&quot;); you may not use this file except in compliance with</span>
<span class="c1"># the License. You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>
<span class="c1">#</span>
<span class="c1"># pytype: skip-file</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Util/helper functions used in apache_beam.ml.inference.</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Dict</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Iterable</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span>
<span class="kn">import</span> <span class="nn">apache_beam</span> <span class="k">as</span> <span class="nn">beam</span>
<span class="kn">from</span> <span class="nn">apache_beam.io.fileio</span> <span class="kn">import</span> <span class="n">EmptyMatchTreatment</span>
<span class="kn">from</span> <span class="nn">apache_beam.io.fileio</span> <span class="kn">import</span> <span class="n">MatchContinuously</span>
<span class="kn">from</span> <span class="nn">apache_beam.ml.inference.base</span> <span class="kn">import</span> <span class="n">ModelMetadata</span>
<span class="kn">from</span> <span class="nn">apache_beam.ml.inference.base</span> <span class="kn">import</span> <span class="n">PredictionResult</span>
<span class="kn">from</span> <span class="nn">apache_beam.transforms</span> <span class="kn">import</span> <span class="n">trigger</span>
<span class="kn">from</span> <span class="nn">apache_beam.transforms</span> <span class="kn">import</span> <span class="n">window</span>
<span class="kn">from</span> <span class="nn">apache_beam.transforms.userstate</span> <span class="kn">import</span> <span class="n">CombiningValueStateSpec</span>
<span class="kn">from</span> <span class="nn">apache_beam.utils.timestamp</span> <span class="kn">import</span> <span class="n">MAX_TIMESTAMP</span>
<span class="kn">from</span> <span class="nn">apache_beam.utils.timestamp</span> <span class="kn">import</span> <span class="n">Timestamp</span>
<span class="n">_START_TIME_STAMP</span> <span class="o">=</span> <span class="n">Timestamp</span><span class="o">.</span><span class="n">now</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">_convert_to_result</span><span class="p">(</span>
<span class="n">batch</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">,</span>
<span class="n">predictions</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Iterable</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">]],</span>
<span class="n">model_id</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">PredictionResult</span><span class="p">]:</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
<span class="c1"># Go from one dictionary of type: {key_type1: Iterable&lt;val_type1&gt;,</span>
<span class="c1"># key_type2: Iterable&lt;val_type2&gt;, ...} where each Iterable is of</span>
<span class="c1"># length batch_size, to a list of dictionaries:</span>
<span class="c1"># [{key_type1: value_type1, key_type2: value_type2}]</span>
<span class="n">predictions_per_tensor</span> <span class="o">=</span> <span class="p">[</span>
<span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">predictions</span><span class="o">.</span><span class="n">keys</span><span class="p">(),</span> <span class="n">v</span><span class="p">))</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">predictions</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
<span class="p">]</span>
<span class="k">return</span> <span class="p">[</span>
<span class="n">PredictionResult</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">model_id</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span>
<span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">predictions_per_tensor</span><span class="p">)</span>
<span class="p">]</span>
<span class="k">return</span> <span class="p">[</span><span class="n">PredictionResult</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">model_id</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)]</span>
<span class="k">class</span> <span class="nc">_ConvertIterToSingleton</span><span class="p">(</span><span class="n">beam</span><span class="o">.</span><span class="n">DoFn</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Internal only; No backwards compatibility.</span>
<span class="sd"> The MatchContinuously transform examines all files present in a given</span>
<span class="sd"> directory and returns those that have timestamps older than the</span>
<span class="sd"> pipeline&#39;s start time. This can produce an Iterable rather than a</span>
<span class="sd"> Singleton. This class only returns the file path when it is first</span>
<span class="sd"> encountered, and it is cached as part of the side input caching mechanism.</span>
<span class="sd"> If the path is seen again, it will not return anything.</span>
<span class="sd"> By doing this, we can ensure that the output of this transform can be wrapped</span>
<span class="sd"> with beam.pvalue.AsSingleton().</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">COUNT_STATE</span> <span class="o">=</span> <span class="n">CombiningValueStateSpec</span><span class="p">(</span><span class="s1">&#39;count&#39;</span><span class="p">,</span> <span class="n">combine_fn</span><span class="o">=</span><span class="nb">sum</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">element</span><span class="p">,</span> <span class="n">count_state</span><span class="o">=</span><span class="n">beam</span><span class="o">.</span><span class="n">DoFn</span><span class="o">.</span><span class="n">StateParam</span><span class="p">(</span><span class="n">COUNT_STATE</span><span class="p">)):</span>
<span class="n">counter</span> <span class="o">=</span> <span class="n">count_state</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="k">if</span> <span class="n">counter</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">count_state</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="k">yield</span> <span class="n">element</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="k">class</span> <span class="nc">_GetLatestFileByTimeStamp</span><span class="p">(</span><span class="n">beam</span><span class="o">.</span><span class="n">DoFn</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Internal only; No backwards compatibility.</span>
<span class="sd"> This DoFn checks the timestamps of files against the time that the pipeline</span>
<span class="sd"> began running. It returns the files that were modified after the pipeline</span>
<span class="sd"> started. If no such files are found, it returns a default file as fallback.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">TIME_STATE</span> <span class="o">=</span> <span class="n">CombiningValueStateSpec</span><span class="p">(</span>
<span class="s1">&#39;max&#39;</span><span class="p">,</span> <span class="n">combine_fn</span><span class="o">=</span><span class="n">partial</span><span class="p">(</span><span class="nb">max</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="n">_START_TIME_STAMP</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">process</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">element</span><span class="p">,</span> <span class="n">time_state</span><span class="o">=</span><span class="n">beam</span><span class="o">.</span><span class="n">DoFn</span><span class="o">.</span><span class="n">StateParam</span><span class="p">(</span><span class="n">TIME_STATE</span><span class="p">)):</span>
<span class="n">_</span><span class="p">,</span> <span class="n">file_metadata</span> <span class="o">=</span> <span class="n">element</span>
<span class="n">new_ts</span> <span class="o">=</span> <span class="n">file_metadata</span><span class="o">.</span><span class="n">last_updated_in_seconds</span>
<span class="n">old_ts</span> <span class="o">=</span> <span class="n">time_state</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="k">if</span> <span class="n">new_ts</span> <span class="o">&gt;</span> <span class="n">old_ts</span><span class="p">:</span>
<span class="n">time_state</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>
<span class="n">time_state</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">new_ts</span><span class="p">)</span>
<span class="n">model_path</span> <span class="o">=</span> <span class="n">file_metadata</span><span class="o">.</span><span class="n">path</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">model_path</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
<span class="n">model_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">model_path</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">return</span> <span class="p">[</span>
<span class="p">(</span><span class="n">model_path</span><span class="p">,</span> <span class="n">ModelMetadata</span><span class="p">(</span><span class="n">model_id</span><span class="o">=</span><span class="n">model_path</span><span class="p">,</span> <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">))</span>
<span class="p">]</span>
<div class="viewcode-block" id="WatchFilePattern"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.utils.html#apache_beam.ml.inference.utils.WatchFilePattern">[docs]</a><span class="k">class</span> <span class="nc">WatchFilePattern</span><span class="p">(</span><span class="n">beam</span><span class="o">.</span><span class="n">PTransform</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">file_pattern</span><span class="p">,</span>
<span class="n">interval</span><span class="o">=</span><span class="mi">360</span><span class="p">,</span>
<span class="n">stop_timestamp</span><span class="o">=</span><span class="n">MAX_TIMESTAMP</span><span class="p">,</span>
<span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Watches a directory for updates to files matching a given file pattern.</span>
<span class="sd"> Args:</span>
<span class="sd"> file_pattern: The file path to read from as a local file path or a</span>
<span class="sd"> GCS ``gs://`` path. The path can contain glob characters</span>
<span class="sd"> (``*``, ``?``, and ``[...]`` sets).</span>
<span class="sd"> interval: Interval at which to check for files matching file_pattern</span>
<span class="sd"> in seconds.</span>
<span class="sd"> stop_timestamp: Timestamp after which no more files will be checked.</span>
<span class="sd"> **Note**:</span>
<span class="sd"> 1. Any previously used filenames cannot be reused. If a file is added</span>
<span class="sd"> or updated to a previously used filename, this transform will ignore</span>
<span class="sd"> that update. To trigger a model update, always upload a file with</span>
<span class="sd"> unique name.</span>
<span class="sd"> 2. Initially, before the pipeline startup time, WatchFilePattern expects</span>
<span class="sd"> at least one file present that matches the file_pattern.</span>
<span class="sd"> 3. This transform is supported in streaming mode since</span>
<span class="sd"> MatchContinuously produces an unbounded source. Running in batch</span>
<span class="sd"> mode can lead to undesired results or result in pipeline being stuck.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">file_pattern</span> <span class="o">=</span> <span class="n">file_pattern</span>
<span class="bp">self</span><span class="o">.</span><span class="n">interval</span> <span class="o">=</span> <span class="n">interval</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stop_timestamp</span> <span class="o">=</span> <span class="n">stop_timestamp</span>
<div class="viewcode-block" id="WatchFilePattern.expand"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.utils.html#apache_beam.ml.inference.utils.WatchFilePattern.expand">[docs]</a> <span class="k">def</span> <span class="nf">expand</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pcoll</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">beam</span><span class="o">.</span><span class="n">PCollection</span><span class="p">[</span><span class="n">ModelMetadata</span><span class="p">]:</span>
<span class="k">return</span> <span class="p">(</span>
<span class="n">pcoll</span>
<span class="o">|</span> <span class="s1">&#39;MatchContinuously&#39;</span> <span class="o">&gt;&gt;</span> <span class="n">MatchContinuously</span><span class="p">(</span>
<span class="n">file_pattern</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">file_pattern</span><span class="p">,</span>
<span class="n">interval</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">interval</span><span class="p">,</span>
<span class="n">stop_timestamp</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">stop_timestamp</span><span class="p">,</span>
<span class="n">empty_match_treatment</span><span class="o">=</span><span class="n">EmptyMatchTreatment</span><span class="o">.</span><span class="n">DISALLOW</span><span class="p">)</span>
<span class="o">|</span> <span class="s2">&quot;AttachKey&quot;</span> <span class="o">&gt;&gt;</span> <span class="n">beam</span><span class="o">.</span><span class="n">Map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">path</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>
<span class="o">|</span> <span class="s2">&quot;GetLatestFileMetaData&quot;</span> <span class="o">&gt;&gt;</span> <span class="n">beam</span><span class="o">.</span><span class="n">ParDo</span><span class="p">(</span><span class="n">_GetLatestFileByTimeStamp</span><span class="p">())</span>
<span class="o">|</span> <span class="s2">&quot;AcceptNewSideInputOnly&quot;</span> <span class="o">&gt;&gt;</span> <span class="n">beam</span><span class="o">.</span><span class="n">ParDo</span><span class="p">(</span><span class="n">_ConvertIterToSingleton</span><span class="p">())</span>
<span class="o">|</span> <span class="s1">&#39;ApplyGlobalWindow&#39;</span> <span class="o">&gt;&gt;</span> <span class="n">beam</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">WindowInto</span><span class="p">(</span>
<span class="n">window</span><span class="o">.</span><span class="n">GlobalWindows</span><span class="p">(),</span>
<span class="n">trigger</span><span class="o">=</span><span class="n">trigger</span><span class="o">.</span><span class="n">Repeatedly</span><span class="p">(</span><span class="n">trigger</span><span class="o">.</span><span class="n">AfterProcessingTime</span><span class="p">(</span><span class="mi">1</span><span class="p">)),</span>
<span class="n">accumulation_mode</span><span class="o">=</span><span class="n">trigger</span><span class="o">.</span><span class="n">AccumulationMode</span><span class="o">.</span><span class="n">DISCARDING</span><span class="p">))</span></div></div>
</pre></div>
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