# Importing essential packages
import torch
from codecot import LargeLanguageModel, ProgrammerAGIAgent, CodeEvaluation, TestSuite
from torch import optim

# Instantiate the Large Language Model
large_language_model = LargeLanguageModel()

# Initialize the PragmaticProgrammerAGIAgent with the Large Language Model
programmer_agent = ProgrammerAGIAgent(large_language_model)

# Initialization of CodeCoT metrics evaluator
metrics_evaluator = CodeCoT()

# Let's consider a code generation scenario
prompt = "Create a function that sorts a list of numbers in ascending order."

# The agent generates the code
generated_code = programmer_agent.generate_code(prompt)

# We evaluate the code using CodeCoT's metrics
evaluation_result = metrics_evaluator.evaluate(generated_code)

# Printing the evaluation result
print(evaluation_result)

# We generate tests for the code
test_suite = programmer_agent.generate_tests(generated_code)

# We evaluate the tests using CodeCoT's metrics
test_evaluation_result = metrics_evaluator.evaluate(test_suite)

# Printing the test evaluation results
print(test_evaluation_result)

# We can also provide these evaluations as feedback to the large language model for it to learn and adapt
optimizer = optim.Adam(large_language_model.parameters())
optimizer.zero_grad()
evaluation_loss = torch.tensor(evaluation_result["loss"])
evaluation_loss.backward()
optimizer.step()
