mirror of
https://github.com/guillaume-be/rust-bert.git
synced 2024-10-26 14:07:25 +03:00
430 lines
14 KiB
Rust
430 lines
14 KiB
Rust
use rust_bert::gpt2::{
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GPT2LMHeadModel, Gpt2Config, Gpt2ConfigResources, Gpt2MergesResources, Gpt2ModelResources,
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Gpt2VocabResources,
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};
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use rust_bert::pipelines::conversation::{
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ConversationConfig, ConversationManager, ConversationModel,
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};
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use rust_bert::pipelines::generation::{
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Cache, GPT2Generator, GenerateConfig, LMHeadModel, LanguageGenerator,
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};
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use rust_bert::resources::{download_resource, RemoteResource, Resource};
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use rust_bert::Config;
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use rust_tokenizers::{Gpt2Tokenizer, Tokenizer, TruncationStrategy};
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use tch::{nn, Device, Tensor};
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#[test]
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fn gpt2_lm_model() -> failure::Fallible<()> {
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// Resources paths
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let config_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2ConfigResources::GPT2));
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let vocab_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2VocabResources::GPT2));
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let merges_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2MergesResources::GPT2));
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let weights_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2ModelResources::GPT2));
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let config_path = download_resource(&config_resource)?;
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let vocab_path = download_resource(&vocab_resource)?;
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let merges_path = download_resource(&merges_resource)?;
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let weights_path = download_resource(&weights_resource)?;
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// Set-up masked LM model
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let device = Device::Cpu;
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let mut vs = nn::VarStore::new(device);
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let tokenizer: Gpt2Tokenizer = Gpt2Tokenizer::from_file(
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vocab_path.to_str().unwrap(),
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merges_path.to_str().unwrap(),
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false,
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);
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let config = Gpt2Config::from_file(config_path);
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let gpt2_model = GPT2LMHeadModel::new(&vs.root(), &config);
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vs.load(weights_path)?;
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// Define input
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let input = ["One two three four"];
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let tokenized_input =
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tokenizer.encode_list(input.to_vec(), 128, &TruncationStrategy::LongestFirst, 0);
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let max_len = tokenized_input
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.iter()
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.map(|input| input.token_ids.len())
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.max()
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.unwrap();
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let tokenized_input = tokenized_input
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.iter()
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.map(|input| input.token_ids.clone())
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.map(|mut input| {
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input.extend(vec![0; max_len - input.len()]);
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input
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})
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.map(|input| Tensor::of_slice(&(input)))
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.collect::<Vec<_>>();
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let input_tensor = Tensor::stack(tokenized_input.as_slice(), 0).to(device);
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// Forward pass
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let (output, _, past, _, _) = gpt2_model
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.forward_t(
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&Some(input_tensor),
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Cache::None,
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&None,
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&None,
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&None,
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&None,
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None,
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&None,
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false,
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)
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.unwrap();
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let next_word_id = output.get(0).get(-1).argmax(-1, true).int64_value(&[0]);
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let next_word = tokenizer.decode(vec![next_word_id], true, true);
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assert_eq!(output.size(), vec!(1, 4, 50257));
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match past {
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Cache::GPT2Cache(past) => {
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assert!(past.is_some());
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assert_eq!(past.as_ref().unwrap().len(), config.n_layer as usize);
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assert_eq!(
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past.as_ref().unwrap()[0].size(),
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vec!(2, 1, config.n_head, 4, 64)
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);
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}
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_ => panic!("Wrong cache returned for GPT2"),
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}
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assert!(
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(output.double_value(&[0, output.size()[1] - 1, next_word_id]) - (-69.4948)).abs() < 1e-4
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);
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assert_eq!(next_word_id, 1936i64);
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assert_eq!(next_word, String::from(" five"));
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Ok(())
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}
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#[test]
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fn gpt2_generation_greedy() -> failure::Fallible<()> {
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// Resources definition
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let config_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2ConfigResources::GPT2));
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let vocab_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2VocabResources::GPT2));
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let merges_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2MergesResources::GPT2));
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let model_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2ModelResources::GPT2));
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// Set-up masked LM model
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let generate_config = GenerateConfig {
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model_resource,
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config_resource,
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vocab_resource,
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merges_resource,
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max_length: 40,
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do_sample: false,
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num_beams: 1,
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temperature: 1.1,
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repetition_penalty: 1.1,
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..Default::default()
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};
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let model = GPT2Generator::new(generate_config)?;
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let input_context = "The cat";
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let output = model.generate(Some(vec![input_context]), None);
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assert_eq!(output.len(), 1);
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assert_eq!(output[0], "The cat was found in a field near the town of Keflavik, about 30 miles (48 kilometers) south-east of Moscow.\n\n\n");
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Ok(())
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}
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#[test]
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fn gpt2_generation_beam_search() -> failure::Fallible<()> {
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// Resources definition
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let config_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2ConfigResources::GPT2));
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let vocab_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2VocabResources::GPT2));
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let merges_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2MergesResources::GPT2));
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let model_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2ModelResources::GPT2));
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// Set-up masked LM model
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let generate_config = GenerateConfig {
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model_resource,
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config_resource,
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vocab_resource,
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merges_resource,
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max_length: 20,
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do_sample: false,
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num_beams: 5,
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temperature: 1.2,
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num_return_sequences: 3,
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..Default::default()
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};
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let model = GPT2Generator::new(generate_config)?;
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let input_context = "The dog";
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let output = model.generate(Some(vec![input_context]), None);
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assert_eq!(output.len(), 3);
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assert_eq!(
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output[0],
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"The dog was found in the backyard of a home in the 6200 block of South Main Street."
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);
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assert_eq!(
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output[1],
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"The dog was found in the backyard of a home in the 6500 block of South Main Street."
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);
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assert_eq!(
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output[2],
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"The dog was found in the backyard of a home in the 6200 block of South Main Street,"
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);
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Ok(())
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}
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#[test]
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fn gpt2_generation_beam_search_multiple_prompts_without_padding() -> failure::Fallible<()> {
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// Resources definition
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let config_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2ConfigResources::GPT2));
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let vocab_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2VocabResources::GPT2));
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let merges_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2MergesResources::GPT2));
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let model_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2ModelResources::GPT2));
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// Set-up masked LM model
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let generate_config = GenerateConfig {
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model_resource,
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config_resource,
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vocab_resource,
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merges_resource,
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max_length: 20,
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do_sample: false,
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num_beams: 5,
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temperature: 1.2,
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num_return_sequences: 3,
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..Default::default()
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};
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let model = GPT2Generator::new(generate_config)?;
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let input_context_1 = "The dog";
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let input_context_2 = "The cat";
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let output = model.generate(Some(vec![input_context_1, input_context_2]), None);
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assert_eq!(output.len(), 6);
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assert_eq!(
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output[0],
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"The dog was found in the backyard of a home in the 6200 block of South Main Street."
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);
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assert_eq!(
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output[1],
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"The dog was found in the backyard of a home in the 6500 block of South Main Street."
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);
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assert_eq!(
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output[2],
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"The dog was found in the backyard of a home in the 6200 block of South Main Street,"
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);
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assert_eq!(
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output[3],
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"The cat-and-mouse game.\n\n\"I think it\'s going to be interesting to"
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);
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assert_eq!(
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output[4],
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"The cat-and-mouse game.\n\n\"I think it\'s going to be a very"
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);
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assert_eq!(
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output[5],
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"The cat-and-mouse game.\n\n\"I think it\'s going to be very interesting"
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);
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Ok(())
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}
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#[test]
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fn gpt2_generation_beam_search_multiple_prompts_with_padding() -> failure::Fallible<()> {
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// Resources definition
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let config_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2ConfigResources::GPT2));
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let vocab_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2VocabResources::GPT2));
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let merges_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2MergesResources::GPT2));
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let model_resource =
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Resource::Remote(RemoteResource::from_pretrained(Gpt2ModelResources::GPT2));
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// Set-up masked LM model
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let generate_config = GenerateConfig {
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model_resource,
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config_resource,
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vocab_resource,
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merges_resource,
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max_length: 20,
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do_sample: false,
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num_beams: 5,
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temperature: 1.2,
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num_return_sequences: 3,
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..Default::default()
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};
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let model = GPT2Generator::new(generate_config)?;
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let input_context_1 = "The dog";
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let input_context_2 = "The cat was";
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let output = model.generate(Some(vec![input_context_1, input_context_2]), None);
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assert_eq!(output.len(), 6);
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assert_eq!(
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output[0],
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"The dog was found dead on the side of the road in the middle of the night.\n"
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);
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assert_eq!(
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output[1],
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"The dog was found dead on the side of the road in the middle of the night on Sunday"
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);
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assert_eq!(
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output[2],
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"The dog was found dead on the side of the road in the middle of the night on Saturday"
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);
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assert_eq!(
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output[3],
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"The cat was taken to a local hospital, where it was treated and released.\n\nPolice said"
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);
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assert_eq!(
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output[4],
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"The cat was taken to a local hospital, where it was treated and released.\n\n\"It"
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);
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assert_eq!(
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output[5],
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"The cat was taken to a local hospital, where it was treated and released.\n\n\"We"
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);
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Ok(())
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}
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#[test]
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#[cfg_attr(not(feature = "all-tests"), ignore)]
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fn dialogpt_single_multi_turn_conversation() -> failure::Fallible<()> {
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// Set-up conversation model
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let conversation_config = ConversationConfig {
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do_sample: false,
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device: Device::Cpu,
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..Default::default()
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};
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let conversation_model = ConversationModel::new(conversation_config)?;
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// Set-up conversation manager and add a conversation
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let mut conversation_manager = ConversationManager::new();
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let conversation_id =
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conversation_manager.create("Going to the movies tonight - any suggestions?");
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// Turn 1
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let output = conversation_model.generate_responses(&mut conversation_manager);
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assert_eq!(output.len(), 1);
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assert_eq!(output.get(&conversation_id).unwrap(), &"The Big Lebowski");
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// Turn 2
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let _ = conversation_manager
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.get(&conversation_id)
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.unwrap()
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.add_user_input("Is it an action movie?");
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let output = conversation_model.generate_responses(&mut conversation_manager);
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assert_eq!(output.len(), 1);
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assert_eq!(output.get(&conversation_id).unwrap(), &"It\'s a comedy.");
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// Turn 3 (no new user input)
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let output = conversation_model.generate_responses(&mut conversation_manager);
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assert_eq!(output.len(), 0);
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Ok(())
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}
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#[test]
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#[cfg_attr(not(feature = "all-tests"), ignore)]
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fn dialogpt_multiple_multi_turn_conversation() -> failure::Fallible<()> {
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// Set-up conversation model
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let conversation_config = ConversationConfig {
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do_sample: false,
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device: Device::Cpu,
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..Default::default()
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};
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let conversation_model = ConversationModel::new(conversation_config)?;
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// Set-up conversation manager and add a conversation
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let mut conversation_manager = ConversationManager::new();
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let conversation_1_id =
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conversation_manager.create("Going to the movies tonight - any suggestions?");
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let conversation_2_id = conversation_manager.create("What's the last book you have read?");
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// Turn 1
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let output = conversation_model.generate_responses(&mut conversation_manager);
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assert_eq!(output.len(), 2);
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assert_eq!(output.get(&conversation_1_id).unwrap(), &"The Big Lebowski");
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assert_eq!(
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output.get(&conversation_2_id).unwrap(),
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&"The Last Question"
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);
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// Turn 2
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let _ = conversation_manager
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.get(&conversation_1_id)
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.unwrap()
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.add_user_input("Is it an action movie?");
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let output = conversation_model.generate_responses(&mut conversation_manager);
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assert_eq!(output.len(), 1);
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assert_eq!(output.get(&conversation_1_id).unwrap(), &"It\'s a comedy.");
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// Turn 3 (no new user input)
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let output = conversation_model.generate_responses(&mut conversation_manager);
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assert_eq!(output.len(), 0);
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Ok(())
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}
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#[test]
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#[cfg_attr(not(feature = "all-tests"), ignore)]
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fn dialogpt_multiple_multi_turn_conversation_with_conversation_deletion() -> failure::Fallible<()> {
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// Set-up conversation model
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let conversation_config = ConversationConfig {
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do_sample: false,
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device: Device::Cpu,
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..Default::default()
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};
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let conversation_model = ConversationModel::new(conversation_config)?;
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// Set-up conversation manager and add a conversation
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let mut conversation_manager = ConversationManager::new();
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let conversation_1_id =
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conversation_manager.create("Going to the movies tonight - any suggestions?");
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let conversation_2_id = conversation_manager.create("What's the last book you have read?");
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// Turn 1
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let output = conversation_model.generate_responses(&mut conversation_manager);
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assert_eq!(output.len(), 2);
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assert_eq!(output.get(&conversation_1_id).unwrap(), &"The Big Lebowski");
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assert_eq!(
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output.get(&conversation_2_id).unwrap(),
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&"The Last Question"
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);
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// Turn 2
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let _ = conversation_manager.remove(&conversation_1_id);
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let _ = conversation_manager
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.get(&conversation_2_id)
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.unwrap()
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.add_user_input("Why do you recommend it?");
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let output = conversation_model.generate_responses(&mut conversation_manager);
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assert_eq!(output.len(), 1);
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assert_eq!(
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output.get(&conversation_2_id).unwrap(),
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&"It's a good book."
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);
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// Turn 3 (no new user input)
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let output = conversation_model.generate_responses(&mut conversation_manager);
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assert_eq!(output.len(), 0);
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Ok(())
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}
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