fixed double tests
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@ -1,133 +0,0 @@
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import os
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import torch
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from aiia import AIIABase, AIIABaseShared, AIIAExpert, AIIAmoe, AIIAchunked, AIIAConfig
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def test_aiiabase_creation():
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config = AIIAConfig()
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model = AIIABase(config)
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assert isinstance(model, AIIABase)
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def test_aiiabase_save_load():
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config = AIIAConfig()
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model = AIIABase(config)
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save_path = "test_aiiabase_save_load"
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# Save the model
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model.save(save_path)
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assert os.path.exists(os.path.join(save_path, "model.pth"))
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assert os.path.exists(os.path.join(save_path, "config.json"))
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# Load the model
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loaded_model = AIIABase.load(save_path)
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# Check if the loaded model is an instance of AIIABase
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assert isinstance(loaded_model, AIIABase)
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# Clean up
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os.remove(os.path.join(save_path, "model.pth"))
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os.remove(os.path.join(save_path, "config.json"))
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os.rmdir(save_path)
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def test_aiiabase_shared_creation():
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config = AIIAConfig()
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model = AIIABaseShared(config)
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assert isinstance(model, AIIABaseShared)
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def test_aiiabase_shared_save_load():
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config = AIIAConfig()
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model = AIIABaseShared(config)
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save_path = "test_aiiabase_shared_save_load"
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# Save the model
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model.save(save_path)
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assert os.path.exists(os.path.join(save_path, "model.pth"))
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assert os.path.exists(os.path.join(save_path, "config.json"))
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# Load the model
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loaded_model = AIIABaseShared.load(save_path)
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# Check if the loaded model is an instance of AIIABaseShared
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assert isinstance(loaded_model, AIIABaseShared)
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# Clean up
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os.remove(os.path.join(save_path, "model.pth"))
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os.remove(os.path.join(save_path, "config.json"))
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os.rmdir(save_path)
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def test_aiiaexpert_creation():
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config = AIIAConfig()
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model = AIIAExpert(config)
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assert isinstance(model, AIIAExpert)
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def test_aiiaexpert_save_load():
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config = AIIAConfig()
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model = AIIAExpert(config)
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save_path = "test_aiiaexpert_save_load"
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# Save the model
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model.save(save_path)
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assert os.path.exists(os.path.join(save_path, "model.pth"))
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assert os.path.exists(os.path.join(save_path, "config.json"))
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# Load the model
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loaded_model = AIIAExpert.load(save_path)
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# Check if the loaded model is an instance of AIIAExpert
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assert isinstance(loaded_model, AIIAExpert)
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# Clean up
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os.remove(os.path.join(save_path, "model.pth"))
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os.remove(os.path.join(save_path, "config.json"))
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os.rmdir(save_path)
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def test_aiiamoe_creation():
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config = AIIAConfig()
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model = AIIAmoe(config, num_experts=5)
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assert isinstance(model, AIIAmoe)
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def test_aiiamoe_save_load():
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config = AIIAConfig()
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model = AIIAmoe(config, num_experts=5)
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save_path = "test_aiiamoe_save_load"
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# Save the model
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model.save(save_path)
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assert os.path.exists(os.path.join(save_path, "model.pth"))
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assert os.path.exists(os.path.join(save_path, "config.json"))
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# Load the model
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loaded_model = AIIAmoe.load(save_path)
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# Check if the loaded model is an instance of AIIAmoe
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assert isinstance(loaded_model, AIIAmoe)
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# Clean up
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os.remove(os.path.join(save_path, "model.pth"))
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os.remove(os.path.join(save_path, "config.json"))
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os.rmdir(save_path)
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def test_aiiachunked_creation():
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config = AIIAConfig()
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model = AIIAchunked(config)
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assert isinstance(model, AIIAchunked)
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def test_aiiachunked_save_load():
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config = AIIAConfig()
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model = AIIAchunked(config)
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save_path = "test_aiiachunked_save_load"
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# Save the model
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model.save(save_path)
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assert os.path.exists(os.path.join(save_path, "model.pth"))
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assert os.path.exists(os.path.join(save_path, "config.json"))
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# Load the model
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loaded_model = AIIAchunked.load(save_path)
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# Check if the loaded model is an instance of AIIAchunked
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assert isinstance(loaded_model, AIIAchunked)
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# Clean up
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os.remove(os.path.join(save_path, "model.pth"))
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os.remove(os.path.join(save_path, "config.json"))
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os.rmdir(save_path)
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@ -1,75 +0,0 @@
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import os
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import tempfile
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import pytest
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import torch.nn as nn
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from aiia import AIIAConfig
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def test_aiia_config_initialization():
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config = AIIAConfig()
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assert config.model_name == "AIIA"
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assert config.kernel_size == 3
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assert config.activation_function == "GELU"
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assert config.hidden_size == 512
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assert config.num_hidden_layers == 12
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assert config.num_channels == 3
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assert config.learning_rate == 5e-5
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def test_aiia_config_custom_initialization():
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config = AIIAConfig(
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model_name="CustomModel",
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kernel_size=5,
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activation_function="ReLU",
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hidden_size=1024,
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num_hidden_layers=8,
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num_channels=1,
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learning_rate=1e-4
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)
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assert config.model_name == "CustomModel"
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assert config.kernel_size == 5
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assert config.activation_function == "ReLU"
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assert config.hidden_size == 1024
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assert config.num_hidden_layers == 8
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assert config.num_channels == 1
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assert config.learning_rate == 1e-4
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def test_aiia_config_invalid_activation_function():
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with pytest.raises(ValueError):
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AIIAConfig(activation_function="InvalidFunction")
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def test_aiia_config_to_dict():
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config = AIIAConfig()
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config_dict = config.to_dict()
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assert isinstance(config_dict, dict)
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assert config_dict["model_name"] == "AIIA"
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assert config_dict["kernel_size"] == 3
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def test_aiia_config_save_and_load():
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with tempfile.TemporaryDirectory() as tmpdir:
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config = AIIAConfig(model_name="TempModel")
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save_path = os.path.join(tmpdir, "config")
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config.save(save_path)
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loaded_config = AIIAConfig.load(save_path)
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assert loaded_config.model_name == "TempModel"
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assert loaded_config.kernel_size == 3
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assert loaded_config.activation_function == "GELU"
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def test_aiia_config_save_and_load_with_custom_attributes():
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with tempfile.TemporaryDirectory() as tmpdir:
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config = AIIAConfig(model_name="TempModel", custom_attr="value")
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save_path = os.path.join(tmpdir, "config")
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config.save(save_path)
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loaded_config = AIIAConfig.load(save_path)
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assert loaded_config.model_name == "TempModel"
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assert loaded_config.custom_attr == "value"
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def test_aiia_config_save_and_load_with_nested_attributes():
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with tempfile.TemporaryDirectory() as tmpdir:
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config = AIIAConfig(model_name="TempModel", nested={"key": "value"})
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save_path = os.path.join(tmpdir, "config")
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config.save(save_path)
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loaded_config = AIIAConfig.load(save_path)
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assert loaded_config.model_name == "TempModel"
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assert loaded_config.nested == {"key": "value"}
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