import time
import NeuroFlex
from . import ddpm
import numpy as np
from neuroflex import NeuroFlex, train_model
from neuroflex.jax_module import JAXModel
from neuroflex.tensorflow_module import TensorFlowModel
from neuroflex.pytorch_module import PyTorchModel
from neuroflex.quantum_module import QuantumModel
from neuroflex.bioinformatics_integration import BioinformaticsIntegration
from neuroflex.scikit_bio_integration import ScikitBioIntegration
from neuroflex.ete_integration import ETEIntegration
from neuroflex.alphafold_integration import AlphaFoldIntegration
from neuroflex.xarray_integration import XarrayIntegration
from neuroflex.quantum_nn_module import QuantumNeuralNetwork
from neuroflex.tokenisation import tokenize_text
from neuroflex.correctgrammer import correct_grammar

# Define your model
class SelfCuringAlgorithm:
    def __init__(self, model):
        self.model = model

    def diagnose(self):
        issues = []
        if not hasattr(self.model, 'is_trained') or not self.model.is_trained:
            issues.append("Model is not trained")
        if not hasattr(self.model, 'performance') or self.model.performance < 0.8:
            issues.append("Model performance is below threshold")
        if not hasattr(self.model, 'last_update') or (time.time() - self.model.last_update > 86400):
            issues.append("Model hasn't been updated in 24 hours")
        return issues

    def heal(self, issues):
        for issue in issues:
            if issue == "Model is not trained":
                self.train_model()
            elif issue == "Model performance is below threshold":
                self.improve_model()
            elif issue == "Model hasn't been updated in 24 hours":
                self.update_model()

    def train_model(self):
        print("Training model...")
        # Actual training logic would go here
        self.model.is_trained = True
        self.model.last_update = time.time()

    def improve_model(self):
        print("Improving model performance...")
        # Logic to improve model performance would go here
        self.model.performance = 0.9  # Placeholder improvement

    def update_model(self):
        print("Updating model...")
        # Logic to update the model with new data would go here
        self.model.last_update = time.time()

class NeuroFlex:
    def __init__(self, features, use_cnn=False, use_rnn=False, use_gan=False, fairness_constraint=None,
                 use_quantum=False, use_alphafold=False, backend='jax', jax_model=None, tensorflow_model=None,
                 pytorch_model=None, quantum_model=None, bioinformatics_integration=None, scikit_bio_integration=None,
                 ete_integration=None, alphafold_integration=None, alphafold_params=None):
        self.features = features
        self.use_cnn = use_cnn
        self.use_rnn = use_rnn
        self.use_gan = use_gan
        self.fairness_constraint = fairness_constraint
        self.use_quantum = use_quantum
        self.use_alphafold = use_alphafold
        self.backend = backend
        self.jax_model = jax_model
        self.tensorflow_model = tensorflow_model
        self.pytorch_model = pytorch_model
        self.quantum_model = quantum_model
        self.bioinformatics_integration = bioinformatics_integration
        self.scikit_bio_integration = scikit_bio_integration
        self.ete_integration = ete_integration
        self.alphafold_integration = alphafold_integration
        self.alphafold_params = alphafold_params or {}

    def process_text(self, text):
        """
        Process the input text by correcting grammar and tokenizing.

        Args:
            text (str): The input text to be processed.

        Returns:
            List[str]: A list of tokens from the processed text.
        """
        corrected_text = correct_grammar(text)
        tokens = tokenize_text(corrected_text)
        return tokens

model = NeuroFlex(
    features=[64, 32, 10],
    use_cnn=True,
    use_rnn=True,
    use_gan=True,
    fairness_constraint=0.1,
    use_quantum=True,
    use_alphafold=True,
    backend='jax',
    jax_model=JAXModel,
    tensorflow_model=TensorFlowModel,
    pytorch_model=PyTorchModel,
    quantum_model=QuantumModel,
    bioinformatics_integration=BioinformaticsIntegration(),
    scikit_bio_integration=ScikitBioIntegration(),
    ete_integration=ETEIntegration(),
    alphafold_integration=AlphaFoldIntegration(),
    alphafold_params={'max_recycling': 3}
)

# Initialize quantum model and self-curing algorithm
quantum_model = QuantumModel()
self_curing_algorithm = SelfCuringAlgorithm(model)

# Prepare bioinformatics data
bio_integration = BioinformaticsIntegration()
scikit_bio_integration = ScikitBioIntegration()
ete_integration = ETEIntegration()
alphafold_integration = AlphaFoldIntegration()
xarray_integration = XarrayIntegration()

sequences = bio_integration.read_sequence_file("path/to/sequence/file.fasta")
processed_sequences = bio_integration.process_sequences(sequences)
sequence_summaries = bio_integration.sequence_summary(processed_sequences)

# Prepare ScikitBio data
dna_sequences = [seq.seq for seq in processed_sequences]
alignments = scikit_bio_integration.align_dna_sequences(dna_sequences)
msa = scikit_bio_integration.msa_maker(dna_sequences)
gc_contents = [scikit_bio_integration.dna_gc_content(seq) for seq in dna_sequences]

# Prepare ETE data
newick_string = "(A:0.1,B:0.2,(C:0.3,D:0.4):0.5);"
tree = ete_integration.create_tree(newick_string)
ete_integration.visualize_tree(tree, "output_tree.png")
tree_stats = ete_integration.get_tree_statistics(tree)

# Prepare AlphaFold data
alphafold_integration.setup_model({'max_recycling': 3})  # Add appropriate model parameters
protein_sequences = [seq for seq in processed_sequences if not bio_integration._is_dna(seq.seq)]
alphafold_structures = []
alphafold_plddt_scores = []
alphafold_pae_scores = []
for seq in protein_sequences:
    alphafold_integration.prepare_features(str(seq.seq))
    structure = alphafold_integration.predict_structure()
    alphafold_structures.append(structure)
    plddt_scores = alphafold_integration.get_plddt_scores()
    pae_scores = alphafold_integration.get_predicted_aligned_error()
    alphafold_plddt_scores.append(plddt_scores)
    alphafold_pae_scores.append(pae_scores)

# Print average scores
print(f"Average pLDDT score: {np.mean([np.mean(scores) for scores in alphafold_plddt_scores])}")
print(f"Average predicted aligned error: {np.mean([np.mean(scores) for scores in alphafold_pae_scores])}")

# Combine bioinformatics data
bioinformatics_data = {
    'sequence_summaries': sequence_summaries,
    'alignments': alignments,
    'msa': msa,
    'gc_contents': gc_contents,
    'phylogenetic_tree': tree,
    'tree_statistics': tree_stats,
    'alphafold_structures': alphafold_structures
}

# Create Xarray datasets
xarray_integration.create_dataset('gc_content',
                                  {'gc': np.array(gc_contents)},
                                  {'sequence': np.arange(len(gc_contents))})

xarray_integration.create_dataset('tree_stats',
                                  tree_stats,
                                  {'stat': list(tree_stats.keys())})

# Perform operations on datasets
gc_mean = xarray_integration.apply_operation('gc_content', 'mean')
tree_stats_max = xarray_integration.apply_operation('tree_stats', 'max')

# Merge datasets
merged_dataset = xarray_integration.merge_datasets(['gc_content', 'tree_stats'])

# Save merged dataset
xarray_integration.save_dataset('merged_bio_data', 'path/to/save/merged_bio_data.nc')

# Prepare training data (placeholder)
train_data = None  # Replace with actual training data
val_data = None    # Replace with actual validation data

# Train your model
trained_state, trained_model = train_model(
    model, train_data, val_data,
    num_epochs=10, batch_size=32, learning_rate=1e-3,
    bioinformatics_data=bioinformatics_data,
    use_alphafold=True,
    use_quantum=True,
    alphafold_structures=alphafold_structures,
    quantum_params=quantum_model.get_params()
)
