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

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

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

let classifier = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment');
let output = await classifier('Le meilleur film de tous les temps.', { topk: 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 }
// ]

let classifier = await pipeline('text-classification', 'Xenova/toxic-bert');
let output = await classifier('I hate you!', { topk: 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 }
// ]

let classifier = await pipeline('token-classification', 'Xenova/bert-base-NER');
let 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' }
// ]

let classifier = await pipeline('token-classification', 'Xenova/bert-base-NER');
let 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' }
// ]

let question = 'Who was Jim Henson?';
let context = 'Jim Henson was a nice puppet.';

let answerer = await pipeline('question-answering', 'Xenova/distilbert-base-uncased-distilled-squad');
let output = await answerer(question, context);
// {
//   "answer": "a nice puppet",
//   "score": 0.5768911502526741
// }

let unmasker = await pipeline('fill-mask', 'Xenova/bert-base-cased');
let 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.' }
// ]

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

let generator = await pipeline('text2text-generation', 'Xenova/LaMini-Flan-T5-783M');
let output = await generator('how can I become more healthy?', {
  max_new_tokens: 100,
});
// [ '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.' ]

let 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.';

let generator = await pipeline('summarization', 'Xenova/distilbart-cnn-6-6');
let 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.' }]

let translator = await pipeline('translation', 'Xenova/nllb-200-distilled-600M');
let 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.' }]

let translator = await pipeline('translation', 'Xenova/m2m100_418M');
let 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.' }]

let translator = await pipeline('translation', 'Xenova/mbart-large-50-many-to-many-mmt');
let 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.' }]

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

let text = 'Once upon a time, there was';
let classifier = await pipeline('text-generation', 'Xenova/distilgpt2');
let output = await classifier(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"
// }]

let text = 'def fib(n):';
let classifier = await pipeline('text-generation', 'Xenova/codegen-350M-mono');
let output = await classifier(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'
// }]

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

let text = 'I have a problem with my iphone that needs to be resolved asap!';
let labels = [ 'urgent', 'not urgent', 'phone', 'tablet', 'computer' ];
let classifier = await pipeline('zero-shot-classification', 'Xenova/nli-deberta-v3-xsmall');
let 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 ]
// }

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

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

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

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

let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
let transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
let 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." }

let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
let transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
let 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." }
//   ]
// }

let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
let transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en', {
    revision: 'output_attentions',
});
let 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] }
//   ]
// }

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

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

let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/ted_60.wav';
let transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
let 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" }

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

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

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

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

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

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

let img = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';

let detector = await pipeline('object-detection', 'Xenova/detr-resnet-50');
let 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 }
// }]