[CLEANUP]

tests/models/test_fire_function_caller.py

@@ -1,42 +0,0 @@
-from unittest.mock import MagicMock
-
-from swarms.models.fire_function import FireFunctionCaller
-
-
-def test_fire_function_caller_run(mocker):
-    # Create mock model and tokenizer
-    model = MagicMock()
-    tokenizer = MagicMock()
-    mocker.patch.object(FireFunctionCaller, "model", model)
-    mocker.patch.object(FireFunctionCaller, "tokenizer", tokenizer)
-
-    # Create mock task and arguments
-    task = "Add 2 and 3"
-    args = (2, 3)
-    kwargs = {}
-
-    # Create mock generated_ids and decoded output
-    generated_ids = [1, 2, 3]
-    decoded_output = "5"
-    model.generate.return_value = generated_ids
-    tokenizer.batch_decode.return_value = [decoded_output]
-
-    # Create FireFunctionCaller instance
-    fire_function_caller = FireFunctionCaller()
-
-    # Run the function
-    fire_function_caller.run(task, *args, **kwargs)
-
-    # Assert model.generate was called with the correct inputs
-    model.generate.assert_called_once_with(
-        tokenizer.apply_chat_template.return_value,
-        max_new_tokens=fire_function_caller.max_tokens,
-        *args,
-        **kwargs,
-    )
-
-    # Assert tokenizer.batch_decode was called with the correct inputs
-    tokenizer.batch_decode.assert_called_once_with(generated_ids)
-
-    # Assert the decoded output is printed
-    assert decoded_output in mocker.patch.object(print, "call_args_list")

tests/models/test_speech_t5.py

@@ -1,162 +0,0 @@
-import os
-
-import pytest
-import torch
-
-from swarms.models.speecht5 import SpeechT5
-
-
-# Create fixtures if needed
-@pytest.fixture
-def speecht5_model():
-    return SpeechT5()
-
-
-# Test cases for the SpeechT5 class
-
-
-def test_speecht5_init(speecht5_model):
-    assert isinstance(
-        speecht5_model.processor, SpeechT5.processor.__class__
-    )
-    assert isinstance(speecht5_model.model, SpeechT5.model.__class__)
-    assert isinstance(speecht5_model.vocoder, SpeechT5.vocoder.__class__)
-    assert isinstance(
-        speecht5_model.embeddings_dataset, torch.utils.data.Dataset
-    )
-
-
-def test_speecht5_call(speecht5_model):
-    text = "Hello, how are you?"
-    speech = speecht5_model(text)
-    assert isinstance(speech, torch.Tensor)
-
-
-def test_speecht5_save_speech(speecht5_model):
-    text = "Hello, how are you?"
-    speech = speecht5_model(text)
-    filename = "test_speech.wav"
-    speecht5_model.save_speech(speech, filename)
-    assert os.path.isfile(filename)
-    os.remove(filename)
-
-
-def test_speecht5_set_model(speecht5_model):
-    old_model_name = speecht5_model.model_name
-    new_model_name = "facebook/speecht5-tts"
-    speecht5_model.set_model(new_model_name)
-    assert speecht5_model.model_name == new_model_name
-    assert speecht5_model.processor.model_name == new_model_name
-    assert speecht5_model.model.config.model_name_or_path == new_model_name
-    speecht5_model.set_model(old_model_name)  # Restore original model
-
-
-def test_speecht5_set_vocoder(speecht5_model):
-    old_vocoder_name = speecht5_model.vocoder_name
-    new_vocoder_name = "facebook/speecht5-hifigan"
-    speecht5_model.set_vocoder(new_vocoder_name)
-    assert speecht5_model.vocoder_name == new_vocoder_name
-    assert (
-        speecht5_model.vocoder.config.model_name_or_path
-        == new_vocoder_name
-    )
-    speecht5_model.set_vocoder(
-        old_vocoder_name
-    )  # Restore original vocoder
-
-
-def test_speecht5_set_embeddings_dataset(speecht5_model):
-    old_dataset_name = speecht5_model.dataset_name
-    new_dataset_name = "Matthijs/cmu-arctic-xvectors-test"
-    speecht5_model.set_embeddings_dataset(new_dataset_name)
-    assert speecht5_model.dataset_name == new_dataset_name
-    assert isinstance(
-        speecht5_model.embeddings_dataset, torch.utils.data.Dataset
-    )
-    speecht5_model.set_embeddings_dataset(
-        old_dataset_name
-    )  # Restore original dataset
-
-
-def test_speecht5_get_sampling_rate(speecht5_model):
-    sampling_rate = speecht5_model.get_sampling_rate()
-    assert sampling_rate == 16000
-
-
-def test_speecht5_print_model_details(speecht5_model, capsys):
-    speecht5_model.print_model_details()
-    captured = capsys.readouterr()
-    assert "Model Name: " in captured.out
-    assert "Vocoder Name: " in captured.out
-
-
-def test_speecht5_quick_synthesize(speecht5_model):
-    text = "Hello, how are you?"
-    speech = speecht5_model.quick_synthesize(text)
-    assert isinstance(speech, list)
-    assert isinstance(speech[0], dict)
-    assert "audio" in speech[0]
-
-
-def test_speecht5_change_dataset_split(speecht5_model):
-    split = "test"
-    speecht5_model.change_dataset_split(split)
-    assert speecht5_model.embeddings_dataset.split == split
-
-
-def test_speecht5_load_custom_embedding(speecht5_model):
-    xvector = [0.1, 0.2, 0.3, 0.4, 0.5]
-    embedding = speecht5_model.load_custom_embedding(xvector)
-    assert torch.all(
-        torch.eq(embedding, torch.tensor(xvector).unsqueeze(0))
-    )
-
-
-def test_speecht5_with_different_speakers(speecht5_model):
-    text = "Hello, how are you?"
-    speakers = [7306, 5324, 1234]
-    for speaker_id in speakers:
-        speech = speecht5_model(text, speaker_id=speaker_id)
-        assert isinstance(speech, torch.Tensor)
-
-
-def test_speecht5_save_speech_with_different_extensions(
-    speecht5_model,
-):
-    text = "Hello, how are you?"
-    speech = speecht5_model(text)
-    extensions = [".wav", ".flac"]
-    for extension in extensions:
-        filename = f"test_speech{extension}"
-        speecht5_model.save_speech(speech, filename)
-        assert os.path.isfile(filename)
-        os.remove(filename)
-
-
-def test_speecht5_invalid_speaker_id(speecht5_model):
-    text = "Hello, how are you?"
-    invalid_speaker_id = (
-        9999  # Speaker ID that does not exist in the dataset
-    )
-    with pytest.raises(IndexError):
-        speecht5_model(text, speaker_id=invalid_speaker_id)
-
-
-def test_speecht5_invalid_save_path(speecht5_model):
-    text = "Hello, how are you?"
-    speech = speecht5_model(text)
-    invalid_path = "/invalid_directory/test_speech.wav"
-    with pytest.raises(FileNotFoundError):
-        speecht5_model.save_speech(speech, invalid_path)
-
-
-def test_speecht5_change_vocoder_model(speecht5_model):
-    text = "Hello, how are you?"
-    old_vocoder_name = speecht5_model.vocoder_name
-    new_vocoder_name = "facebook/speecht5-hifigan-ljspeech"
-    speecht5_model.set_vocoder(new_vocoder_name)
-    speech = speecht5_model(text)
-    assert isinstance(speech, torch.Tensor)
-    speecht5_model.set_vocoder(
-        old_vocoder_name
-    )  # Restore original vocoder