/**
 * @license
 * Copyright 2023 Google LLC.
 * 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.
 * =============================================================================
 */
/**
 *  Base class for Backbone models.
 */
/* Original source: keras_nlp/models/gpt2/gpt2_backbone.py */
import { serialization } from '@tensorflow/tfjs-core';
import { RandomNormal } from '../../../../initializers';
import { input } from '../../../../exports';
import { Embedding } from '../../../embeddings';
import { PositionEmbedding } from '../../modeling/position_embedding';
import { add } from '../../../../exports_layers';
import { Dropout } from '../../../core';
import { TransformerDecoder } from '../../modeling/transformer_decoder';
import { getActivation } from '../../../../activations';
import { LayerNormalization } from '../../../normalization';
import { Backbone } from '../backbone';
function gpt2KernelInitializer(stddev = 0.02) {
    return new RandomNormal({ stddev });
}
/**
 * GPT-2 core network with hyperparameters.
 *
 * This network implements a Transformer-based decoder network,
 * Generative Pretrained Transformer-2 (GPT-2), as described in
 * ["Language Models are Unsupervised Multitask Learners"](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf).
 * It includes the embedding lookups and transformer layers.
 *
 * The default constructor gives a fully customizable, randomly initialized
 * GPT-2 model with any number of layers, heads, and embedding
 * dimensions. To load preset architectures and weights, use the `fromPreset`
 * constructor.
 *
 * Disclaimer: Pre-trained models are provided on an "as is" basis, without
 * warranties or conditions of any kind. The underlying model is provided by a
 * third party and subject to a separate license, available
 * [here](https://github.com/openai/gpt-2).
 *
 *
 * Example usage:
 * ```js
 * const tokenIds = tf.ones([1, 12]), dtype="int32");
 * const paddingMask = tf.tensor(
 *  [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]], 'int32');
 *
 * # Pretrained GPT-2 decoder.
 * model = GPT2Backbone.fromPreset("gpt2_base_en");
 * model.apply(inputData, {paddingMask});
 *
 * # Randomly initialized GPT-2 decoder with custom config.
 * model = kerasNlp.models.GPT2Backbone({
 *     vocabularySize: 50257,
 *     numLayers: 12,
 *     numHeads: 12,
 *     hiddenDim: 768,
 *     intermediateDim: 3072,
 *     maxSequenceLength: 1024,
 * });
 * model.apply(inputData, {paddingMask});
 * ```
 */
class GPT2Backbone extends Backbone {
    constructor(args) {
        var _a, _b, _c, _d;
        args.dropout = (_a = args.dropout) !== null && _a !== void 0 ? _a : 0.1;
        args.maxSequenceLength = (_b = args.maxSequenceLength) !== null && _b !== void 0 ? _b : 1024;
        // Inputs
        const tokenIds = input({ shape: [null], dtype: 'int32', name: 'token_ids' });
        const paddingMask = input({ shape: [null], dtype: 'int32', name: 'padding_mask' });
        // Embed tokens, positions.
        const tokenEmbedding = new Embedding({
            inputDim: args.vocabularySize,
            outputDim: args.hiddenDim,
            embeddingsInitializer: gpt2KernelInitializer(0.01),
            name: 'token_embedding',
        }).apply(tokenIds);
        const positionEmbedding = new PositionEmbedding({
            initializer: gpt2KernelInitializer(0.02),
            sequenceLength: args.maxSequenceLength,
            name: 'position_embedding',
        }).apply(tokenEmbedding);
        // Sum and apply dropout to embeddings.
        let x = add({ name: 'embeddings_add' })
            .apply([tokenEmbedding, positionEmbedding]);
        x = new Dropout({ rate: args.dropout, name: 'embeddings_dropout' })
            .apply(x);
        // Apply successive transformer decoder blocks.
        for (let i = 0; i < args.numLayers; i++) {
            x = new TransformerDecoder({
                intermediateDim: args.intermediateDim,
                numHeads: args.numHeads,
                dropout: args.dropout,
                layerNormEpsilon: 1e-05,
                // TODO(pforderique): Implement gelu.
                activation: getActivation('relu'),
                kernelInitializer: gpt2KernelInitializer(0.02),
                normalizeFirst: true,
                name: `transformer_layer_${i}`,
            }).apply(x, { decoderPaddingMask: paddingMask });
        }
        const sequenceOutput = new LayerNormalization({
            name: 'layer_norm',
            axis: -1,
            epsilon: 1e-05,
            dtype: 'float32',
        }).apply(x);
        // Instantiate using Functional API Model constructor.
        super({
            inputs: [tokenIds, paddingMask],
            outputs: sequenceOutput,
            name: 'gpt2_backbone'
        });
        this.vocabularySize = args.vocabularySize;
        this.numLayers = args.numLayers;
        this.numHeads = args.numHeads;
        this.hiddenDim = args.hiddenDim;
        this.intermediateDim = args.intermediateDim;
        this.dropout = (_c = args.dropout) !== null && _c !== void 0 ? _c : 0.1;
        this.maxSequenceLength = (_d = args.maxSequenceLength) !== null && _d !== void 0 ? _d : 1024;
    }
    getConfig() {
        const config = {
            vocabularySize: this.vocabularySize,
            numLayers: this.numLayers,
            numHeads: this.numHeads,
            hiddenDim: this.hiddenDim,
            intermediateDim: this.intermediateDim,
            dropout: this.dropout,
            maxSequenceLength: this.maxSequenceLength,
        };
        const baseConfig = super.getConfig();
        Object.assign(config, baseConfig);
        return config;
    }
    get tokenEmbedding() {
        return this.getLayer('token_embedding');
    }
}
/** @nocollapse */
GPT2Backbone.className = 'GPT2Backbone';
export { GPT2Backbone };
serialization.registerClass(GPT2Backbone);
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* @license\n * Copyright 2023 Google LLC.\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 *  Base class for Backbone models.\n */\n\n/* Original source: keras_nlp/models/gpt2/gpt2_backbone.py */\nimport { serialization } from '@tensorflow/tfjs-core';\n\nimport { RandomNormal } from '../../../../initializers';\nimport { input } from '../../../../exports';\nimport { Embedding } from '../../../embeddings';\nimport { SymbolicTensor } from '../../../../engine/topology';\nimport { PositionEmbedding } from '../../modeling/position_embedding';\nimport { add } from '../../../../exports_layers';\nimport { Dropout } from '../../../core';\nimport { TransformerDecoder } from '../../modeling/transformer_decoder';\nimport { getActivation } from '../../../../activations';\nimport { LayerNormalization } from '../../../normalization';\nimport { Backbone } from '../backbone';\n\nfunction gpt2KernelInitializer(stddev = 0.02) {\n  return new RandomNormal({stddev});\n}\n\nexport interface GPT2BackboneArgs  {\n  /**\n   * Integer. The size of the token vocabulary.\n   */\n  vocabularySize: number;\n\n  /**\n   * Integer. The number of transformer layers.\n   */\n  numLayers: number;\n\n  /**\n   * Integer. The number of attention heads for each transformer.\n   * The hidden size must be divisible by the number of attention heads.\n   */\n  numHeads: number;\n\n  /**\n   * Integer. The size of the transformer encoding and pooler layers.\n   */\n  hiddenDim: number;\n\n  /**\n   * Integer. The output dimension of the first Dense layer in a two-layer\n   * feedforward network for each transformer.\n   */\n  intermediateDim: number;\n\n  /**\n   * Float. Dropout probability for the Transformer encoder.\n   * Defaults to 0.2.\n   */\n  dropout?: number;\n\n  /**\n   * Integer. The maximum sequence length that this encoder can consume.\n   * If `null`, `maxSequenceLength` uses the value from sequence length.\n   * This determines the variable shape for positional embeddings.\n   * Defaults to 1024.\n   */\n  maxSequenceLength?: number;\n}\n\n/**\n * GPT-2 core network with hyperparameters.\n *\n * This network implements a Transformer-based decoder network,\n * Generative Pretrained Transformer-2 (GPT-2), as described in\n * [\"Language Models are Unsupervised Multitask Learners\"](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf).\n * It includes the embedding lookups and transformer layers.\n *\n * The default constructor gives a fully customizable, randomly initialized\n * GPT-2 model with any number of layers, heads, and embedding\n * dimensions. To load preset architectures and weights, use the `fromPreset`\n * constructor.\n *\n * Disclaimer: Pre-trained models are provided on an \"as is\" basis, without\n * warranties or conditions of any kind. The underlying model is provided by a\n * third party and subject to a separate license, available\n * [here](https://github.com/openai/gpt-2).\n *\n *\n * Example usage:\n * ```js\n * const tokenIds = tf.ones([1, 12]), dtype=\"int32\");\n * const paddingMask = tf.tensor(\n *  [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]], 'int32');\n *\n * # Pretrained GPT-2 decoder.\n * model = GPT2Backbone.fromPreset(\"gpt2_base_en\");\n * model.apply(inputData, {paddingMask});\n *\n * # Randomly initialized GPT-2 decoder with custom config.\n * model = kerasNlp.models.GPT2Backbone({\n *     vocabularySize: 50257,\n *     numLayers: 12,\n *     numHeads: 12,\n *     hiddenDim: 768,\n *     intermediateDim: 3072,\n *     maxSequenceLength: 1024,\n * });\n * model.apply(inputData, {paddingMask});\n * ```\n */\nexport class GPT2Backbone extends Backbone {\n  /** @nocollapse */\n  static override className = 'GPT2Backbone';\n\n  private vocabularySize: number;\n  private numLayers: number;\n  private numHeads: number;\n  private hiddenDim: number;\n  private intermediateDim: number;\n  private dropout: number;\n  private maxSequenceLength: number;\n\n  constructor(args: GPT2BackboneArgs) {\n    args.dropout = args.dropout ?? 0.1;\n    args.maxSequenceLength = args.maxSequenceLength ?? 1024;\n\n    // Inputs\n    const tokenIds = input({shape: [null], dtype: 'int32', name: 'token_ids'});\n    const paddingMask =\n      input({shape: [null], dtype: 'int32', name: 'padding_mask'});\n\n    // Embed tokens, positions.\n    const tokenEmbedding = new Embedding({\n      inputDim: args.vocabularySize,\n      outputDim: args.hiddenDim,\n      embeddingsInitializer: gpt2KernelInitializer(0.01),\n      name: 'token_embedding',\n    }).apply(tokenIds) as SymbolicTensor;\n\n    const positionEmbedding = new PositionEmbedding({\n      initializer: gpt2KernelInitializer(0.02),\n      sequenceLength: args.maxSequenceLength,\n      name: 'position_embedding',\n    }).apply(tokenEmbedding) as SymbolicTensor;\n\n    // Sum and apply dropout to embeddings.\n    let x = add({name: 'embeddings_add'})\n      .apply([tokenEmbedding, positionEmbedding]) as SymbolicTensor;\n    x = new Dropout({rate: args.dropout, name: 'embeddings_dropout'})\n      .apply(x) as SymbolicTensor;\n\n    // Apply successive transformer decoder blocks.\n    for(let i = 0; i < args.numLayers; i++) {\n      x = new TransformerDecoder({\n        intermediateDim: args.intermediateDim,\n        numHeads: args.numHeads,\n        dropout: args.dropout,\n        layerNormEpsilon: 1e-05,\n        // TODO(pforderique): Implement gelu.\n        activation: getActivation('relu'),\n        kernelInitializer: gpt2KernelInitializer(0.02),\n        normalizeFirst: true,\n        name: `transformer_layer_${i}`,\n      }).apply(x, {decoderPaddingMask: paddingMask}) as SymbolicTensor;\n    }\n\n    const sequenceOutput = new LayerNormalization({\n      name: 'layer_norm',\n      axis: -1,\n      epsilon: 1e-05,\n      dtype: 'float32',\n    }).apply(x) as SymbolicTensor;\n\n    // Instantiate using Functional API Model constructor.\n    super({\n      inputs: [tokenIds, paddingMask],\n      outputs: sequenceOutput,\n      name: 'gpt2_backbone'\n    });\n    this.vocabularySize = args.vocabularySize;\n    this.numLayers = args.numLayers;\n    this.numHeads = args.numHeads;\n    this.hiddenDim = args.hiddenDim;\n    this.intermediateDim = args.intermediateDim;\n    this.dropout = args.dropout ?? 0.1;\n    this.maxSequenceLength = args.maxSequenceLength ?? 1024;\n  }\n\n  override getConfig(): serialization.ConfigDict {\n    const config: serialization.ConfigDict = {\n      vocabularySize: this.vocabularySize,\n      numLayers: this.numLayers,\n      numHeads: this.numHeads,\n      hiddenDim: this.hiddenDim,\n      intermediateDim: this.intermediateDim,\n      dropout: this.dropout,\n      maxSequenceLength: this.maxSequenceLength,\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n\n  override get tokenEmbedding(): Embedding {\n    return this.getLayer('token_embedding') as Embedding;\n  }\n}\nserialization.registerClass(GPT2Backbone);\n"]}