import { pipeline } from '@huggingface/transformers';

const classifier = await pipeline('sentiment-analysis');
const output = await classifier('I love transformers!');
// [{'label': 'POSITIVE', 'score': 0.999817686}]

const classifier = await pipeline('sentiment-analysis', 'Xenova/distilbert-base-uncased-finetuned-sst-2-english');
const output = await classifier('I love transformers!');
// [{ label: 'POSITIVE', score: 0.999788761138916 }]

const classifier = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment');
const output = await classifier('Le meilleur film de tous les temps.', { top_k: 5 });
// [
//   { label: '5 stars', score: 0.9610759615898132 },
//   { label: '4 stars', score: 0.03323351591825485 },
//   { label: '3 stars', score: 0.0036155181005597115 },
//   { label: '1 star', score: 0.0011325967498123646 },
//   { label: '2 stars', score: 0.0009423971059732139 }
// ]

const classifier = await pipeline('text-classification', 'Xenova/toxic-bert');
const output = await classifier('I hate you!', { top_k: null });
// [
//   { label: 'toxic', score: 0.9593140482902527 },
//   { label: 'insult', score: 0.16187334060668945 },
//   { label: 'obscene', score: 0.03452680632472038 },
//   { label: 'identity_hate', score: 0.0223250575363636 },
//   { label: 'threat', score: 0.019197041168808937 },
//   { label: 'severe_toxic', score: 0.005651099607348442 }
// ]

const classifier = await pipeline('token-classification', 'Xenova/bert-base-NER');
const output = await classifier('My name is Sarah and I live in London');
// [
//   { entity: 'B-PER', score: 0.9980202913284302, index: 4, word: 'Sarah' },
//   { entity: 'B-LOC', score: 0.9994474053382874, index: 9, word: 'London' }
// ]

const classifier = await pipeline('token-classification', 'Xenova/bert-base-NER');
const output = await classifier('Sarah lives in the United States of America', { ignore_labels: [] });
// [
//   { entity: 'B-PER', score: 0.9966587424278259, index: 1, word: 'Sarah' },
//   { entity: 'O', score: 0.9987385869026184, index: 2, word: 'lives' },
//   { entity: 'O', score: 0.9990072846412659, index: 3, word: 'in' },
//   { entity: 'O', score: 0.9988298416137695, index: 4, word: 'the' },
//   { entity: 'B-LOC', score: 0.9995510578155518, index: 5, word: 'United' },
//   { entity: 'I-LOC', score: 0.9990395307540894, index: 6, word: 'States' },
//   { entity: 'I-LOC', score: 0.9986724853515625, index: 7, word: 'of' },
//   { entity: 'I-LOC', score: 0.9975294470787048, index: 8, word: 'America' }
// ]

const answerer = await pipeline('question-answering', 'Xenova/distilbert-base-uncased-distilled-squad');
const question = 'Who was Jim Henson?';
const context = 'Jim Henson was a nice puppet.';
const output = await answerer(question, context);
// {
//   answer: "a nice puppet",
//   score: 0.5768911502526741
// }

const unmasker = await pipeline('fill-mask', 'Xenova/bert-base-cased');
const output = await unmasker('The goal of life is [MASK].');
// [
//   { token_str: 'survival', score: 0.06137419492006302, token: 8115, sequence: 'The goal of life is survival.' },
//   { token_str: 'love', score: 0.03902450203895569, token: 1567, sequence: 'The goal of life is love.' },
//   { token_str: 'happiness', score: 0.03253183513879776, token: 9266, sequence: 'The goal of life is happiness.' },
//   { token_str: 'freedom', score: 0.018736306577920914, token: 4438, sequence: 'The goal of life is freedom.' },
//   { token_str: 'life', score: 0.01859794743359089, token: 1297, sequence: 'The goal of life is life.' }
// ]

const unmasker = await pipeline('fill-mask', 'Xenova/bert-base-cased');
const output = await unmasker('The Milky Way is a [MASK] galaxy.', { top_k: 1 });
// [{ token_str: 'spiral', score: 0.6299987435340881, token: 14061, sequence: 'The Milky Way is a spiral galaxy.' }]

const generator = await pipeline('text2text-generation', 'Xenova/LaMini-Flan-T5-783M');
const output = await generator('how can I become more healthy?', {
  max_new_tokens: 100,
});
// [{ generated_text: "To become more healthy, you can: 1. Eat a balanced diet with plenty of fruits, vegetables, whole grains, lean proteins, and healthy fats. 2. Stay hydrated by drinking plenty of water. 3. Get enough sleep and manage stress levels. 4. Avoid smoking and excessive alcohol consumption. 5. Regularly exercise and maintain a healthy weight. 6. Practice good hygiene and sanitation. 7. Seek medical attention if you experience any health issues." }]

const generator = await pipeline('summarization', 'Xenova/distilbart-cnn-6-6');
const text = 'The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, ' +
  'and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. ' +
  'During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest ' +
  'man-made structure in the world, a title it held for 41 years until the Chrysler Building in New ' +
  'York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to ' +
  'the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the ' +
  'Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second ' +
  'tallest free-standing structure in France after the Millau Viaduct.';
const output = await generator(text, {
  max_new_tokens: 100,
});
// [{ summary_text: ' The Eiffel Tower is about the same height as an 81-storey building and the tallest structure in Paris. It is the second tallest free-standing structure in France after the Millau Viaduct.' }]

const translator = await pipeline('translation', 'Xenova/nllb-200-distilled-600M');
const output = await translator('जीवन एक चॉकलेट बॉक्स की तरह है।', {
  src_lang: 'hin_Deva', // Hindi
  tgt_lang: 'fra_Latn', // French
});
// [{ translation_text: 'La vie est comme une boîte à chocolat.' }]

const translator = await pipeline('translation', 'Xenova/m2m100_418M');
const output = await translator('生活就像一盒巧克力。', {
  src_lang: 'zh', // Chinese
  tgt_lang: 'en', // English
});
// [{ translation_text: 'Life is like a box of chocolate.' }]

const translator = await pipeline('translation', 'Xenova/mbart-large-50-many-to-many-mmt');
const output = await translator('संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है', {
  src_lang: 'hi_IN', // Hindi
  tgt_lang: 'fr_XX', // French
});
// [{ translation_text: 'Le chef des Nations affirme qu 'il n 'y a military solution in Syria.' }]

const generator = await pipeline('text-generation', 'Xenova/distilgpt2');
const text = 'I enjoy walking with my cute dog,';
const output = await generator(text);
// [{ generated_text: "I enjoy walking with my cute dog, and I love to play with the other dogs." }]

const generator = await pipeline('text-generation', 'Xenova/distilgpt2');
const text = 'Once upon a time, there was';
const output = await generator(text, {
  temperature: 2,
  max_new_tokens: 10,
  repetition_penalty: 1.5,
  no_repeat_ngram_size: 2,
  num_beams: 2,
  num_return_sequences: 2,
});
// [{
//   "generated_text": "Once upon a time, there was an abundance of information about the history and activities that"
// }, {
//   "generated_text": "Once upon a time, there was an abundance of information about the most important and influential"
// }]

const generator = await pipeline('text-generation', 'Xenova/codegen-350M-mono');
const text = 'def fib(n):';
const output = await generator(text, {
  max_new_tokens: 44,
});
// [{
//   generated_text: 'def fib(n):\n' +
//     '    if n == 0:\n' +
//     '        return 0\n' +
//     '    elif n == 1:\n' +
//     '        return 1\n' +
//     '    else:\n' +
//     '        return fib(n-1) + fib(n-2)\n'
// }]

const classifier = await pipeline('zero-shot-classification', 'Xenova/mobilebert-uncased-mnli');
const text = 'Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.';
const labels = [ 'mobile', 'billing', 'website', 'account access' ];
const output = await classifier(text, labels);
// {
//   sequence: 'Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.',
//   labels: [ 'mobile', 'website', 'billing', 'account access' ],
//   scores: [ 0.5562091040482018, 0.1843621307860853, 0.13942646639336376, 0.12000229877234923 ]
// }

const classifier = await pipeline('zero-shot-classification', 'Xenova/nli-deberta-v3-xsmall');
const text = 'I have a problem with my iphone that needs to be resolved asap!';
const labels = [ 'urgent', 'not urgent', 'phone', 'tablet', 'computer' ];
const output = await classifier(text, labels, { multi_label: true });
// {
//   sequence: 'I have a problem with my iphone that needs to be resolved asap!',
//   labels: [ 'urgent', 'phone', 'computer', 'tablet', 'not urgent' ],
//   scores: [ 0.9958870956360275, 0.9923963400697035, 0.002333537946160235, 0.0015134138567598765, 0.0010699384208377163 ]
// }

const extractor = await pipeline('feature-extraction', 'Xenova/bert-base-uncased', { revision: 'default' });
const output = await extractor('This is a simple test.');
// Tensor {
//   type: 'float32',
//   data: Float32Array [0.05939924716949463, 0.021655935794115067, ...],
//   dims: [1, 8, 768]
// }

const extractor = await pipeline('feature-extraction', 'Xenova/bert-base-uncased', { revision: 'default' });
const output = await extractor('This is a simple test.', { pooling: 'mean', normalize: true });
// Tensor {
//   type: 'float32',
//   data: Float32Array [0.03373778983950615, -0.010106077417731285, ...],
//   dims: [1, 768]
// }

const extractor = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
const output = await extractor('This is a simple test.', { pooling: 'mean', normalize: true });
// Tensor {
//   type: 'float32',
//   data: Float32Array [0.09094982594251633, -0.014774246141314507, ...],
//   dims: [1, 384]
// }

const extractor = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
const output = await extractor('This is a simple test.', { pooling: 'mean', quantize: true, precision: 'binary' });
// Tensor {
//   type: 'int8',
//   data: Int8Array [49, 108, 24, ...],
//   dims: [1, 48]
// }

const image_feature_extractor = await pipeline('image-feature-extraction', 'Xenova/vit-base-patch16-224-in21k');
const url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cats.png';
const features = await image_feature_extractor(url);
// Tensor {
//   dims: [ 1, 197, 768 ],
//   type: 'float32',
//   data: Float32Array(151296) [ ... ],
//   size: 151296
// }

const image_feature_extractor = await pipeline('image-feature-extraction', 'Xenova/clip-vit-base-patch32');
const url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cats.png';
const features = await image_feature_extractor(url);
// Tensor {
//   dims: [ 1, 512 ],
//   type: 'float32',
//   data: Float32Array(512) [ ... ],
//   size: 512
// }

const classifier = await pipeline('audio-classification', 'Xenova/wav2vec2-large-xlsr-53-gender-recognition-librispeech');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
const output = await classifier(url);
// [
//   { label: 'male', score: 0.9981542229652405 },
//   { label: 'female', score: 0.001845747814513743 }
// ]

const classifier = await pipeline('audio-classification', 'Xenova/ast-finetuned-audioset-10-10-0.4593');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cat_meow.wav';
const output = await classifier(url, { top_k: 4 });
// [
//   { label: 'Meow', score: 0.5617874264717102 },
//   { label: 'Cat', score: 0.22365376353263855 },
//   { label: 'Domestic animals, pets', score: 0.1141069084405899 },
//   { label: 'Animal', score: 0.08985692262649536 },
// ]

const classifier = await pipeline('zero-shot-audio-classification', 'Xenova/clap-htsat-unfused');
const audio = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/dog_barking.wav';
const candidate_labels = ['dog', 'vaccum cleaner'];
const scores = await classifier(audio, candidate_labels);
// [
//   { score: 0.9993992447853088, label: 'dog' },
//   { score: 0.0006007603369653225, label: 'vaccum cleaner' }
// ]

const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
const output = await transcriber(url);
// { text: " And so my fellow Americans ask not what your country can do for you, ask what you can do for your country." }

const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
const output = await transcriber(url, { return_timestamps: true });
// {
//   text: " And so my fellow Americans ask not what your country can do for you, ask what you can do for your country."
//   chunks: [
//     { timestamp: [0, 8],  text: " And so my fellow Americans ask not what your country can do for you" }
//     { timestamp: [8, 11], text: " ask what you can do for your country." }
//   ]
// }

const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
const output = await transcriber(url, { return_timestamps: 'word' });
// {
//   "text": " And so my fellow Americans ask not what your country can do for you ask what you can do for your country.",
//   "chunks": [
//     { "text": " And", "timestamp": [0, 0.78] },
//     { "text": " so", "timestamp": [0.78, 1.06] },
//     { "text": " my", "timestamp": [1.06, 1.46] },
//     ...
//     { "text": " for", "timestamp": [9.72, 9.92] },
//     { "text": " your", "timestamp": [9.92, 10.22] },
//     { "text": " country.", "timestamp": [10.22, 13.5] }
//   ]
// }

const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-small');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/french-audio.mp3';
const output = await transcriber(url, { language: 'french', task: 'transcribe' });
// { text: " J'adore, j'aime, je n'aime pas, je déteste." }

const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-small');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/french-audio.mp3';
const output = await transcriber(url, { language: 'french', task: 'translate' });
// { text: " I love, I like, I don't like, I hate." }

const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/ted_60.wav';
const output = await transcriber(url, { chunk_length_s: 30, stride_length_s: 5 });
// { text: " So in college, I was a government major, which means [...] So I'd start off light and I'd bump it up" }

const captioner = await pipeline('image-to-text', 'Xenova/vit-gpt2-image-captioning');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
const output = await captioner(url);
// [{ generated_text: 'a cat laying on a couch with another cat' }]

const captioner = await pipeline('image-to-text', 'Xenova/trocr-small-handwritten');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/handwriting.jpg';
const output = await captioner(url);
// [{ generated_text: 'Mr. Brown commented icily.' }]

const classifier = await pipeline('image-classification', 'Xenova/vit-base-patch16-224');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
const output = await classifier(url);
// [
//   { label: 'tiger, Panthera tigris', score: 0.632695734500885 },
// ]

const classifier = await pipeline('image-classification', 'Xenova/vit-base-patch16-224');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
const output = await classifier(url, { top_k: 3 });
// [
//   { label: 'tiger, Panthera tigris', score: 0.632695734500885 },
//   { label: 'tiger cat', score: 0.3634825646877289 },
//   { label: 'lion, king of beasts, Panthera leo', score: 0.00045060308184474707 },
// ]

const classifier = await pipeline('image-classification', 'Xenova/vit-base-patch16-224');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
const output = await classifier(url, { top_k: 0 });
// [
//   { label: 'tiger, Panthera tigris', score: 0.632695734500885 },
//   { label: 'tiger cat', score: 0.3634825646877289 },
//   { label: 'lion, king of beasts, Panthera leo', score: 0.00045060308184474707 },
//   { label: 'jaguar, panther, Panthera onca, Felis onca', score: 0.00035465499968267977 },
//   ...
// ]

const segmenter = await pipeline('image-segmentation', 'Xenova/detr-resnet-50-panoptic');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
const output = await segmenter(url);
// [
//   { label: 'remote', score: 0.9984649419784546, mask: RawImage { ... } },
//   { label: 'cat', score: 0.9994316101074219, mask: RawImage { ... } }
// ]

const classifier = await pipeline('zero-shot-image-classification', 'Xenova/clip-vit-base-patch32');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
const output = await classifier(url, ['tiger', 'horse', 'dog']);
// [
//   { score: 0.9993917942047119, label: 'tiger' },
//   { score: 0.0003519294841680676, label: 'horse' },
//   { score: 0.0002562698791734874, label: 'dog' }
// ]

const detector = await pipeline('object-detection', 'Xenova/detr-resnet-50');
const img = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
const output = await detector(img, { threshold: 0.9 });
// [{
//   score: 0.9976370930671692,
//   label: "remote",
//   box: { xmin: 31, ymin: 68, xmax: 190, ymax: 118 }
// },
// ...
// {
//   score: 0.9984092116355896,
//   label: "cat",
//   box: { xmin: 331, ymin: 19, xmax: 649, ymax: 371 }
// }]

const detector = await pipeline('zero-shot-object-detection', 'Xenova/owlvit-base-patch32');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/astronaut.png';
const candidate_labels = ['human face', 'rocket', 'helmet', 'american flag'];
const output = await detector(url, candidate_labels);
// [
//   {
//     score: 0.24392342567443848,
//     label: 'human face',
//     box: { xmin: 180, ymin: 67, xmax: 274, ymax: 175 }
//   },
//   {
//     score: 0.15129457414150238,
//     label: 'american flag',
//     box: { xmin: 0, ymin: 4, xmax: 106, ymax: 513 }
//   },
//   {
//     score: 0.13649864494800568,
//     label: 'helmet',
//     box: { xmin: 277, ymin: 337, xmax: 511, ymax: 511 }
//   },
//   {
//     score: 0.10262022167444229,
//     label: 'rocket',
//     box: { xmin: 352, ymin: -1, xmax: 463, ymax: 287 }
//   }
// ]

const detector = await pipeline('zero-shot-object-detection', 'Xenova/owlvit-base-patch32');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/beach.png';
const candidate_labels = ['hat', 'book', 'sunglasses', 'camera'];
const output = await detector(url, candidate_labels, { top_k: 4, threshold: 0.05 });
// [
//   {
//     score: 0.1606510728597641,
//     label: 'sunglasses',
//     box: { xmin: 347, ymin: 229, xmax: 429, ymax: 264 }
//   },
//   {
//     score: 0.08935828506946564,
//     label: 'hat',
//     box: { xmin: 38, ymin: 174, xmax: 258, ymax: 364 }
//   },
//   {
//     score: 0.08530698716640472,
//     label: 'camera',
//     box: { xmin: 187, ymin: 350, xmax: 260, ymax: 411 }
//   },
//   {
//     score: 0.08349756896495819,
//     label: 'book',
//     box: { xmin: 261, ymin: 280, xmax: 494, ymax: 425 }
//   }
// ]

const qa_pipeline = await pipeline('document-question-answering', 'Xenova/donut-base-finetuned-docvqa');
const image = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/invoice.png';
const question = 'What is the invoice number?';
const output = await qa_pipeline(image, question);
// [{ answer: 'us-001' }]

const synthesizer = await pipeline('text-to-speech', 'Xenova/speecht5_tts', { quantized: false });
const speaker_embeddings = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/speaker_embeddings.bin';
const out = await synthesizer('Hello, my dog is cute', { speaker_embeddings });
// {
//   audio: Float32Array(26112) [-0.00005657337896991521, 0.00020583874720614403, ...],
//   sampling_rate: 16000
// }

import wavefile from 'wavefile';
import fs from 'fs';

const wav = new wavefile.WaveFile();
wav.fromScratch(1, out.sampling_rate, '32f', out.audio);
fs.writeFileSync('out.wav', wav.toBuffer());

const synthesizer = await pipeline('text-to-speech', 'Xenova/mms-tts-fra');
const out = await synthesizer('Bonjour');
// {
//   audio: Float32Array(23808) [-0.00037693005288019776, 0.0003325853613205254, ...],
//   sampling_rate: 16000
// }

const upscaler = await pipeline('image-to-image', 'Xenova/swin2SR-classical-sr-x2-64');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/butterfly.jpg';
const output = await upscaler(url);
// RawImage {
//   data: Uint8Array(786432) [ 41, 31, 24,  43, ... ],
//   width: 512,
//   height: 512,
//   channels: 3
// }

const depth_estimator = await pipeline('depth-estimation', 'Xenova/dpt-hybrid-midas');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
const out = await depth_estimator(url);
// {
//   predicted_depth: Tensor {
//     dims: [ 384, 384 ],
//     type: 'float32',
//     data: Float32Array(147456) [ 542.859130859375, 545.2833862304688, 546.1649169921875, ... ],
//     size: 147456
//   },
//   depth: RawImage {
//     data: Uint8Array(307200) [ 86, 86, 86, ... ],
//     width: 640,
//     height: 480,
//     channels: 1
//   }
// }