import evaluate
import datasets
# import moses
# from moses import metrics
import pandas as pd
# from tdc import Evaluator
# from tdc import Oracle
# from metrics import novelty, fraction_valid, fraction_unique, SAscore, internal_diversity,fcd_metric, SYBAscore, oracles

import os
from collections import Counter
from functools import partial
import numpy as np
import pandas as pd
import scipy.sparse
import torch
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import MACCSkeys
from rdkit.Chem.AllChem import GetMorganFingerprintAsBitVect as Morgan
from rdkit.Chem.QED import qed
from rdkit.Chem.Scaffolds import MurckoScaffold
from rdkit.Chem import Descriptors

import random
from multiprocessing import Pool
from collections import UserList, defaultdict
import numpy as np
import pandas as pd
from rdkit import rdBase
import sys

from rdkit.Chem import RDConfig
import os
sys.path.append(os.path.join(RDConfig.RDContribDir, 'SA_Score'))
import sascorer
import pandas as pd
from fcd_torch import FCD
from syba.syba import SybaClassifier

from tdc import Evaluator
from tdc import Oracle


def get_mol(smiles_or_mol):
    '''
    Loads SMILES/molecule into RDKit's object
    '''
    if isinstance(smiles_or_mol, str):
        if len(smiles_or_mol) == 0:
            return None
        mol = Chem.MolFromSmiles(smiles_or_mol)
        if mol is None:
            return None
        try:
            Chem.SanitizeMol(mol)
        except ValueError:
            return None
        return mol
    return smiles_or_mol

def mapper(n_jobs):
    '''
    Returns function for map call.
    If n_jobs == 1, will use standard map
    If n_jobs > 1, will use multiprocessing pool
    If n_jobs is a pool object, will return its map function
    '''
    if n_jobs == 1:
        def _mapper(*args, **kwargs):
            return list(map(*args, **kwargs))

        return _mapper
    if isinstance(n_jobs, int):
        pool = Pool(n_jobs)

        def _mapper(*args, **kwargs):
            try:
                result = pool.map(*args, **kwargs)
            finally:
                pool.terminate()
            return result

        return _mapper
    return n_jobs.map

def fraction_valid(gen, n_jobs=1):
    """
    Computes a number of valid molecules
    Parameters:
        gen: list of SMILES
        n_jobs: number of threads for calculation
    """
    gen = mapper(n_jobs)(get_mol, gen)
    return 1 - gen.count(None) / len(gen)


def canonic_smiles(smiles_or_mol):
    mol = get_mol(smiles_or_mol)
    if mol is None:
        return None
    return Chem.MolToSmiles(mol)

def fraction_unique(gen, k=None, n_jobs=1, check_validity=True):
    """
    Computes a number of unique molecules
    Parameters:
        gen: list of SMILES
        k: compute unique@k
        n_jobs: number of threads for calculation
        check_validity: raises ValueError if invalid molecules are present
    """
    if k is not None:
        if len(gen) < k:
            warnings.warn(
                "Can't compute unique@{}.".format(k) +
                "gen contains only {} molecules".format(len(gen))
            )
        gen = gen[:k]
    canonic = set(mapper(n_jobs)(canonic_smiles, gen))

    if None in canonic and check_validity:
        raise ValueError("Invalid molecule passed to unique@k")
    return len(canonic) / len(gen)

def novelty(gen, train, n_jobs=1):
    gen_smiles = mapper(n_jobs)(canonic_smiles, gen)
    gen_smiles_set = set(gen_smiles) - {None}
    train_set = set(train)
    return len(gen_smiles_set - train_set) / len(gen_smiles_set)


def SAscore(gen):
    """
    Calculate the average Synthetic Accessibility Score (SAscore) for a list of molecules represented by their SMILES strings.

    Parameters:
    - smiles_list (list of str): A list containing the SMILES representations of the molecules.

    Returns:
    - float: The average Synthetic Accessibility Score for the valid molecules in the list. Returns None if no valid molecules are found.
    """
    scores = []
    for smiles in gen:
        mol = Chem.MolFromSmiles(smiles)
        if mol:  # Ensures the molecule could be parsed from the SMILES string
            score = sascorer.calculateScore(mol)
            scores.append(score)
    
    if scores:  # Checks if there are any scores calculated
        return np.mean(scores)
    else:
        return None



def average_agg_tanimoto(stock_vecs, gen_vecs,
                         batch_size=5000, agg='max',
                         device='cpu', p=1):
    """
    For each molecule in gen_vecs finds closest molecule in stock_vecs.
    Returns average tanimoto score for between these molecules

    Parameters:
        stock_vecs: numpy array <n_vectors x dim>
        gen_vecs: numpy array <n_vectors' x dim>
        agg: max or mean
        p: power for averaging: (mean x^p)^(1/p)
    """
    assert agg in ['max', 'mean'], "Can aggregate only max or mean"
    agg_tanimoto = np.zeros(len(gen_vecs))
    total = np.zeros(len(gen_vecs))
    for j in range(0, stock_vecs.shape[0], batch_size):
        x_stock = torch.tensor(stock_vecs[j:j + batch_size]).to(device).float()
        for i in range(0, gen_vecs.shape[0], batch_size):
            y_gen = torch.tensor(gen_vecs[i:i + batch_size]).to(device).float()
            y_gen = y_gen.transpose(0, 1)
            tp = torch.mm(x_stock, y_gen)
            jac = (tp / (x_stock.sum(1, keepdim=True) +
                         y_gen.sum(0, keepdim=True) - tp)).cpu().numpy()
            jac[np.isnan(jac)] = 1
            if p != 1:
                jac = jac**p
            if agg == 'max':
                agg_tanimoto[i:i + y_gen.shape[1]] = np.maximum(
                    agg_tanimoto[i:i + y_gen.shape[1]], jac.max(0))
            elif agg == 'mean':
                agg_tanimoto[i:i + y_gen.shape[1]] += jac.sum(0)
                total[i:i + y_gen.shape[1]] += jac.shape[0]
    if agg == 'mean':
        agg_tanimoto /= total
    if p != 1:
        agg_tanimoto = (agg_tanimoto)**(1/p)
    return np.mean(agg_tanimoto)

def fingerprint(smiles_or_mol, fp_type='maccs', dtype=None, morgan__r=2,
                morgan__n=1024, *args, **kwargs):
    """
    Generates fingerprint for SMILES
    If smiles is invalid, returns None
    Returns numpy array of fingerprint bits

    Parameters:
        smiles: SMILES string
        type: type of fingerprint: [MACCS|morgan]
        dtype: if not None, specifies the dtype of returned array
    """
    fp_type = fp_type.lower()
    molecule = get_mol(smiles_or_mol, *args, **kwargs)
    if molecule is None:
        return None
    if fp_type == 'maccs':
        keys = MACCSkeys.GenMACCSKeys(molecule)
        keys = np.array(keys.GetOnBits())
        fingerprint = np.zeros(166, dtype='uint8')
        if len(keys) != 0:
            fingerprint[keys - 1] = 1  # We drop 0-th key that is always zero
    elif fp_type == 'morgan':
        fingerprint = np.asarray(Morgan(molecule, morgan__r, nBits=morgan__n),
                                 dtype='uint8')
    else:
        raise ValueError("Unknown fingerprint type {}".format(fp_type))
    if dtype is not None:
        fingerprint = fingerprint.astype(dtype)
    return fingerprint


def fingerprints(smiles_mols_array, n_jobs=1, already_unique=False, *args,
                 **kwargs):
    '''
    Computes fingerprints of smiles np.array/list/pd.Series with n_jobs workers
    e.g.fingerprints(smiles_mols_array, type='morgan', n_jobs=10)
    Inserts np.NaN to rows corresponding to incorrect smiles.
    IMPORTANT: if there is at least one np.NaN, the dtype would be float
    Parameters:
        smiles_mols_array: list/array/pd.Series of smiles or already computed
            RDKit molecules
        n_jobs: number of parralel workers to execute
        already_unique: flag for performance reasons, if smiles array is big
            and already unique. Its value is set to True if smiles_mols_array
            contain RDKit molecules already.
    '''
    if isinstance(smiles_mols_array, pd.Series):
        smiles_mols_array = smiles_mols_array.values
    else:
        smiles_mols_array = np.asarray(smiles_mols_array)
    if not isinstance(smiles_mols_array[0], str):
        already_unique = True

    if not already_unique:
        smiles_mols_array, inv_index = np.unique(smiles_mols_array,
                                                 return_inverse=True)

    fps = mapper(n_jobs)(
        partial(fingerprint, *args, **kwargs), smiles_mols_array
    )

    length = 1
    for fp in fps:
        if fp is not None:
            length = fp.shape[-1]
            first_fp = fp
            break
    fps = [fp if fp is not None else np.array([np.NaN]).repeat(length)[None, :]
           for fp in fps]
    if scipy.sparse.issparse(first_fp):
        fps = scipy.sparse.vstack(fps).tocsr()
    else:
        fps = np.vstack(fps)
    if not already_unique:
        return fps[inv_index]
    return fps

def internal_diversity(gen, n_jobs=1, device='cpu', fp_type='morgan',
                       gen_fps=None, p=1):
    """
    Computes internal diversity as:
    1/|A|^2 sum_{x, y in AxA} (1-tanimoto(x, y))
    """
    if gen_fps is None:
        gen_fps = fingerprints(gen, fp_type=fp_type, n_jobs=n_jobs)
    return 1 - (average_agg_tanimoto(gen_fps, gen_fps,
                                     agg='mean', device=device, p=p)).mean()


def fcd_metric(gen, train, n_jobs = 8, device = 'cuda:0'):
    fcd = FCD(device=device, n_jobs= n_jobs)
    return fcd(gen, train)

def SYBAscore(gen):
    """
    Compute the average SYBA score for a list of SMILES strings.

    Parameters:
    - smiles_list (list of str): A list of SMILES strings representing molecules.

    Returns:
    - float: The average SYBA score for the list of molecules.
    """
    syba = SybaClassifier()
    syba.fitDefaultScore()
    scores = []

    for smiles in gen:
        try:
            score = syba.predict(smi=smiles)
            scores.append(score)
        except Exception as e:
            print(f"Error processing SMILES '{smiles}': {e}")
            continue

    if scores:
        return sum(scores) / len(scores)
    else:
        return None  # Or handle empty list or all failed predictions as needed

def oracles(gen, train):
        Result = {}
        # evaluator = Evaluator(name = 'KL_Divergence')
        # KL_Divergence = evaluator(gen, train)
                
        # Result["KL_Divergence"]: KL_Divergence


        oracle_list = [
        'QED', 'SA', 'MPO', 'GSK3B', 'JNK3',
        'DRD2', 'LogP', 'Rediscovery', 'Similarity',
        'Median', 'Isomers', 'Valsartan_SMARTS', 'Hop'
        ]
    
        for oracle_name in oracle_list:
            oracle = Oracle(name=oracle_name)
            if oracle_name in ['Rediscovery', 'MPO', 'Similarity', 'Median', 'Isomers', 'Hop']:
                score = oracle(gen)
                if isinstance(score, dict):
                    score = {key: sum(values)/len(values) for key, values in score.items()}
            else:
                score = oracle(gen)
                if isinstance(score, list):
                    score = sum(score) / len(score)
            
            Result[f"{oracle_name}"] = score
        
        return Result



_DESCRIPTION = """

Comprehensive suite of metrics designed to assess the performance of molecular generation models, for understanding how well a model can produce novel, chemically valid molecules that are relevant to specific research objectives.

"""


_KWARGS_DESCRIPTION = """
Args:
    generated_smiles (`list` of `string`): A collection of SMILES (Simplified Molecular Input Line Entry System) strings generated by the model, ideally encompassing more than 30,000 samples.
    train_smiles (`list` of `string`): The dataset of SMILES strings used to train the model, serving as a reference to evaluate the novelty and diversity of the generated molecules.

Returns:
    Dectionary item containing various metrics to evaluate model performance
"""


_CITATION = """
@article{DBLP:journals/corr/abs-1811-12823,
  author       = {Daniil Polykovskiy and
                  Alexander Zhebrak and
                  Benjam{\'{\i}}n S{\'{a}}nchez{-}Lengeling and
                  Sergey Golovanov and
                  Oktai Tatanov and
                  Stanislav Belyaev and
                  Rauf Kurbanov and
                  Aleksey Artamonov and
                  Vladimir Aladinskiy and
                  Mark Veselov and
                  Artur Kadurin and
                  Sergey I. Nikolenko and
                  Al{\'{a}}n Aspuru{-}Guzik and
                  Alex Zhavoronkov},
  title        = {Molecular Sets {(MOSES):} {A} Benchmarking Platform for Molecular
                  Generation Models},
  journal      = {CoRR},
  volume       = {abs/1811.12823},
  year         = {2018},
  url          = {http://arxiv.org/abs/1811.12823},
  eprinttype    = {arXiv},
  eprint       = {1811.12823},
  timestamp    = {Fri, 26 Nov 2021 15:34:30 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1811-12823.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
"""


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class molgenevalmetric(evaluate.Metric):
    def _info(self):
        return evaluate.MetricInfo(
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            features=datasets.Features(
                {
                    "generated_smiles": datasets.Sequence(datasets.Value("string")),
                    "train_smiles": datasets.Sequence(datasets.Value("string")),
                }
                if self.config_name == "multilabel"
                else {
                    "generated_smiles": datasets.Value("string"),
                    "train_smiles": datasets.Value("string"),
                }
            ),
                
            reference_urls=["https://github.com/molecularsets/moses", "https://tdcommons.ai/functions/oracles/"],
        )

    def _compute(self, gensmi, trainsmi):

        metrics = {}
        metrics['novelty'] = novelty(gen = gensmi, train = trainsmi)
        metrics['valid'] = fraction_valid(gen=gensmi)
        metrics['unique'] = fraction_unique(gen=gensmi)
        metrics['IntDiv'] = internal_diversity(gen=gensmi)
        metrics['FCD'] = fcd_metric(gen = gensmi, train = trainsmi)
        metrics['SA'] = SAscore(gen=gensmi)
        metrics['SCS'] = SAscore(gen=trainsmi)

        return metrics

        
        # generated_smiles = [s for s in generated_smiles if s != '']

        # evaluator = Evaluator(name = 'KL_Divergence')
        # KL_Divergence = evaluator(generated_smiles, train_smiles)
                
        # Results.update({
        #     "KL_Divergence": KL_Divergence,
        # })


        # oracle_list = [
        # 'QED', 'SA', 'MPO', 'GSK3B', 'JNK3',
        # 'DRD2', 'LogP', 'Rediscovery', 'Similarity',
        # 'Median', 'Isomers', 'Valsartan_SMARTS', 'Hop'
        # ]
    
        # for oracle_name in oracle_list:
        #     oracle = Oracle(name=oracle_name)
        #     if oracle_name in ['Rediscovery', 'MPO', 'Similarity', 'Median', 'Isomers', 'Hop']:
        #         score = oracle(generated_smiles)
        #         if isinstance(score, dict):
        #             score = {key: sum(values)/len(values) for key, values in score.items()}
        #     else:
        #         score = oracle(generated_smiles)
        #         if isinstance(score, list):
        #             score = sum(score) / len(score)
            
        #     Results.update({f"{oracle_name}": score})

        # # keys_to_remove = ["FCD/TestSF", "SNN/TestSF", "Frag/TestSF", "Scaf/TestSF"]
        # # for key in keys_to_remove:
        # #     Results.pop(key, None)    

        # return {"results": Results}