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2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c
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/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
class PassthroughLoader {
    constructor(modelArtifacts) {
        this.modelArtifacts = modelArtifacts;
    }
    load() {
        return this.modelArtifacts;
    }
}
class PassthroughSaver {
    constructor(saveHandler) {
        this.saveHandler = saveHandler;
    }
    save(modelArtifacts) {
        return this.saveHandler(modelArtifacts);
    }
}
class PassthroughAsync {
    constructor(handler) {
        if (handler.load) {
            this.load = () => Promise.resolve(handler.load());
        }
        if (handler.save) {
            this.save = (modelArtifacts) => Promise.resolve(handler.save(modelArtifacts));
        }
    }
}
/**
 * Creates an IOHandler that loads model artifacts from memory.
 *
 * When used in conjunction with `tf.loadLayersModel`, an instance of
 * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
 *
 * ```js
 * const model = await tf.loadLayersModel(tf.io.fromMemory(
 *     modelTopology, weightSpecs, weightData));
 * ```
 *
 * @param modelArtifacts a object containing model topology (i.e., parsed from
 *   the JSON format).
 * @param weightSpecs An array of `WeightsManifestEntry` objects describing the
 *   names, shapes, types, and quantization of the weight data. Optional.
 * @param weightData A single `ArrayBuffer` containing the weight data,
 *   concatenated in the order described by the weightSpecs. Optional.
 * @param trainingConfig Model training configuration. Optional.
 *
 * @returns A passthrough `IOHandler` that simply loads the provided data.
 */
export function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {
    const args = arguments;
    return new PassthroughAsync(fromMemorySync(...args));
}
/**
 * Creates an IOHandler that loads model artifacts from memory.
 *
 * When used in conjunction with `tf.loadLayersModel`, an instance of
 * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
 *
 * ```js
 * const model = await tf.loadLayersModel(tf.io.fromMemory(
 *     modelTopology, weightSpecs, weightData));
 * ```
 *
 * @param modelArtifacts a object containing model topology (i.e., parsed from
 *   the JSON format).
 * @param weightSpecs An array of `WeightsManifestEntry` objects describing the
 *   names, shapes, types, and quantization of the weight data. Optional.
 * @param weightData A single `ArrayBuffer` containing the weight data,
 *   concatenated in the order described by the weightSpecs. Optional.
 * @param trainingConfig Model training configuration. Optional.
 *
 * @returns A passthrough `IOHandlerSync` that simply loads the provided data.
 */
export function fromMemorySync(modelArtifacts, weightSpecs, weightData, trainingConfig) {
    if (arguments.length === 1) {
        const isModelArtifacts = modelArtifacts.modelTopology != null ||
            modelArtifacts.weightSpecs != null;
        if (isModelArtifacts) {
            return new PassthroughLoader(modelArtifacts);
        }
        else {
            // Legacy support: with only modelTopology.
            // TODO(cais): Remove this deprecated API.
            console.warn('Please call tf.io.fromMemory() with only one argument. ' +
                'The argument should be of type ModelArtifacts. ' +
                'The multi-argument signature of tf.io.fromMemory() has been ' +
                'deprecated and will be removed in a future release.');
            return new PassthroughLoader({ modelTopology: modelArtifacts });
        }
    }
    else {
        // Legacy support.
        // TODO(cais): Remove this deprecated API.
        console.warn('Please call tf.io.fromMemory() with only one argument. ' +
            'The argument should be of type ModelArtifacts. ' +
            'The multi-argument signature of tf.io.fromMemory() has been ' +
            'deprecated and will be removed in a future release.');
        return new PassthroughLoader({
            modelTopology: modelArtifacts,
            weightSpecs,
            weightData,
            trainingConfig
        });
    }
}
/**
 * Creates an IOHandler that passes saved model artifacts to a callback.
 *
 * ```js
 * function handleSave(artifacts) {
 *   // ... do something with the artifacts ...
 *   return {modelArtifactsInfo: {...}, ...};
 * }
 *
 * const saveResult = model.save(tf.io.withSaveHandler(handleSave));
 * ```
 *
 * @param saveHandler A function that accepts a `ModelArtifacts` and returns a
 *     promise that resolves to a `SaveResult`.
 */
export function withSaveHandler(saveHandler) {
    return new PassthroughSaver(saveHandler);
}
/**
 * Creates an IOHandlerSync that passes saved model artifacts to a callback.
 *
 * ```js
 * function handleSave(artifacts) {
 *   // ... do something with the artifacts ...
 *   return {modelArtifactsInfo: {...}, ...};
 * }
 *
 * const saveResult = model.save(tf.io.withSaveHandler(handleSave));
 * ```
 *
 * @param saveHandler A function that accepts a `ModelArtifacts` and returns a
 *     `SaveResult`.
 */
export function withSaveHandlerSync(saveHandler) {
    return new PassthroughSaver(saveHandler);
}
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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandlers that pass through the in-memory ModelArtifacts format.\n */\n\nimport {IOHandler, IOHandlerSync, LoadHandler, ModelArtifacts, SaveHandler, SaveResult, TrainingConfig, WeightData, WeightsManifestEntry} from './types';\n\nclass PassthroughLoader implements IOHandlerSync {\n  constructor(private readonly modelArtifacts?: ModelArtifacts) {}\n\n  load(): ModelArtifacts {\n    return this.modelArtifacts;\n  }\n}\n\nclass PassthroughSaver<R extends SaveResult | Promise<SaveResult>> {\n  constructor(\n    private readonly saveHandler: (artifacts: ModelArtifacts) => R) {}\n\n  save(modelArtifacts: ModelArtifacts): R {\n    return this.saveHandler(modelArtifacts);\n  }\n}\n\nclass PassthroughAsync implements IOHandler {\n  load?: LoadHandler;\n  save?: SaveHandler;\n\n  constructor(handler: IOHandlerSync) {\n    if (handler.load) {\n      this.load = () => Promise.resolve(handler.load());\n    }\n    if (handler.save) {\n      this.save = (modelArtifacts: ModelArtifacts) =>\n        Promise.resolve(handler.save(modelArtifacts));\n    }\n  }\n}\n\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadLayersModel(tf.io.fromMemory(\n *     modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelArtifacts a object containing model topology (i.e., parsed from\n *   the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n *   names, shapes, types, and quantization of the weight data. Optional.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n *   concatenated in the order described by the weightSpecs. Optional.\n * @param trainingConfig Model training configuration. Optional.\n *\n * @returns A passthrough `IOHandler` that simply loads the provided data.\n */\nexport function fromMemory(\n    modelArtifacts: {}|ModelArtifacts, weightSpecs?: WeightsManifestEntry[],\n    weightData?: WeightData, trainingConfig?: TrainingConfig): IOHandler {\n\n  const args = arguments as unknown as Parameters<typeof fromMemory>;\n  return new PassthroughAsync(fromMemorySync(...args));\n}\n\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadLayersModel(tf.io.fromMemory(\n *     modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelArtifacts a object containing model topology (i.e., parsed from\n *   the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n *   names, shapes, types, and quantization of the weight data. Optional.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n *   concatenated in the order described by the weightSpecs. Optional.\n * @param trainingConfig Model training configuration. Optional.\n *\n * @returns A passthrough `IOHandlerSync` that simply loads the provided data.\n */\nexport function fromMemorySync(\n    modelArtifacts: {}|ModelArtifacts, weightSpecs?: WeightsManifestEntry[],\n    weightData?: WeightData, trainingConfig?: TrainingConfig): IOHandlerSync {\n  if (arguments.length === 1) {\n    const isModelArtifacts =\n        (modelArtifacts as ModelArtifacts).modelTopology != null ||\n        (modelArtifacts as ModelArtifacts).weightSpecs != null;\n    if (isModelArtifacts) {\n      return new PassthroughLoader(modelArtifacts as ModelArtifacts);\n    } else {\n      // Legacy support: with only modelTopology.\n      // TODO(cais): Remove this deprecated API.\n      console.warn(\n          'Please call tf.io.fromMemory() with only one argument. ' +\n          'The argument should be of type ModelArtifacts. ' +\n          'The multi-argument signature of tf.io.fromMemory() has been ' +\n          'deprecated and will be removed in a future release.');\n      return new PassthroughLoader({modelTopology: modelArtifacts as {}});\n    }\n  } else {\n    // Legacy support.\n    // TODO(cais): Remove this deprecated API.\n    console.warn(\n        'Please call tf.io.fromMemory() with only one argument. ' +\n        'The argument should be of type ModelArtifacts. ' +\n        'The multi-argument signature of tf.io.fromMemory() has been ' +\n        'deprecated and will be removed in a future release.');\n    return new PassthroughLoader({\n      modelTopology: modelArtifacts as {},\n      weightSpecs,\n      weightData,\n      trainingConfig\n    });\n  }\n}\n\n/**\n * Creates an IOHandler that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n *   // ... do something with the artifacts ...\n *   return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n *     promise that resolves to a `SaveResult`.\n */\nexport function withSaveHandler(\n    saveHandler: (artifacts: ModelArtifacts) =>\n        Promise<SaveResult>): IOHandler {\n  return new PassthroughSaver(saveHandler);\n}\n\n/**\n * Creates an IOHandlerSync that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n *   // ... do something with the artifacts ...\n *   return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n *     `SaveResult`.\n */\nexport function withSaveHandlerSync(\n    saveHandler: (artifacts: ModelArtifacts) => SaveResult): IOHandlerSync {\n  return new PassthroughSaver<SaveResult>(saveHandler);\n}\n"]}