import tensorflow as tf
from keras import regularizers
from keras.layers import Input, Dense, Dropout, Activation, Conv1D
from keras.layers import GlobalAveragePooling1D, AveragePooling1D
from keras.layers import Bidirectional, LSTM
from keras import Model
from keras.metrics import MeanSquaredError

import pandas as pd
import numpy as np

import requests
from functools import reduce
from operator import add
import tabulate
from difflib import SequenceMatcher

import cyvcf2
import parasail

import re

ntmap = {'A': (1, 0, 0, 0),
         'C': (0, 1, 0, 0),
         'G': (0, 0, 1, 0),
         'T': (0, 0, 0, 1)
         }

def get_seqcode(seq):
    return np.array(reduce(add, map(lambda c: ntmap[c], seq.upper()))).reshape((1, len(seq), -1))

def BiLSTM_model(input_shape):
    input = Input(shape=input_shape)

    conv1 = Conv1D(128, 5, activation="relu")(input)
    pool1 = AveragePooling1D(2)(conv1)
    drop1 = Dropout(0.1)(pool1)

    conv2 = Conv1D(128, 5, activation="relu")(drop1)
    pool2 = AveragePooling1D(2)(conv2)
    drop2 = Dropout(0.1)(pool2)

    lstm1 = Bidirectional(LSTM(128,
                               dropout=0.1,
                               activation='tanh',
                               return_sequences=True,
                               kernel_regularizer=regularizers.l2(1e-4)))(drop2)
    avgpool = GlobalAveragePooling1D()(lstm1)

    dense1 = Dense(128,
                   kernel_regularizer=regularizers.l2(1e-4),
                   bias_regularizer=regularizers.l2(1e-4),
                   activation="relu")(avgpool)
    drop3 = Dropout(0.1)(dense1)

    dense2 = Dense(32,
                   kernel_regularizer=regularizers.l2(1e-4),
                   bias_regularizer=regularizers.l2(1e-4),
                   activation="relu")(drop3)
    drop4 = Dropout(0.1)(dense2)

    dense3 = Dense(32,
                   kernel_regularizer=regularizers.l2(1e-4),
                   bias_regularizer=regularizers.l2(1e-4),
                   activation="relu")(drop4)
    drop5 = Dropout(0.1)(dense3)

    output = Dense(1, activation="linear")(drop5)

    model = Model(inputs=[input], outputs=[output])
    return model

def fetch_ensembl_transcripts(gene_symbol):
    url = f"https://rest.ensembl.org/lookup/symbol/homo_sapiens/{gene_symbol}?expand=1;content-type=application/json"
    response = requests.get(url)
    if response.status_code == 200:
        gene_data = response.json()
        if 'Transcript' in gene_data:
            return gene_data['Transcript']
        else:
            print("No transcripts found for gene:", gene_symbol)
            return None
    else:
        print(f"Error fetching gene data from Ensembl: {response.text}")
        return None

def fetch_ensembl_sequence(transcript_id):
    url = f"https://rest.ensembl.org/sequence/id/{transcript_id}?content-type=application/json"
    response = requests.get(url)
    if response.status_code == 200:
        sequence_data = response.json()
        if 'seq' in sequence_data:
            return sequence_data['seq']
        else:
            print("No sequence found for transcript:", transcript_id)
            return None
    else:
        print(f"Error fetching sequence data from Ensembl: {response.text}")
        return None

def apply_mutation(ref_sequence, offset, ref, alt):
    """
    Apply a single mutation to the sequence.
    """
    if len(ref) == len(alt) and alt != "*":  # SNP
        mutated_seq = ref_sequence[:offset] + alt + ref_sequence[offset+len(alt):]

    elif len(ref) < len(alt):  # Insertion
        mutated_seq = ref_sequence[:offset] + alt + ref_sequence[offset+1:]

    elif len(ref) == len(alt) and alt == "*":  # Deletion
        mutated_seq = ref_sequence[:offset] + ref_sequence[offset+1:]

    elif len(ref) > len(alt) and alt != "*":  # Deletion
        mutated_seq = ref_sequence[:offset] + alt + ref_sequence[offset+len(ref):]

    elif len(ref) > len(alt) and alt == "*":  # Deletion
        mutated_seq = ref_sequence[:offset] + ref_sequence[offset+len(ref):]

    return mutated_seq


def construct_combinations(sequence, mutations):
    """
    Construct all combinations of mutations.
    mutations is a list of tuples (position, ref, [alts])
    """
    if not mutations:
        return [sequence]

    # Take the first mutation and recursively construct combinations for the rest
    first_mutation = mutations[0]
    rest_mutations = mutations[1:]
    offset, ref, alts = first_mutation

    sequences = []
    for alt in alts:
        mutated_sequence = apply_mutation(sequence, offset, ref, alt)
        sequences.extend(construct_combinations(mutated_sequence, rest_mutations))

    return sequences

def needleman_wunsch_alignment(query_seq, ref_seq):
    """
    Use Needleman-Wunsch alignment to find the maximum alignment position in ref_seq
    Use this position to represent the position of target sequence with mutations
    """
    # Needleman-Wunsch alignment
    alignment = parasail.nw_trace(query_seq, ref_seq, 10, 1, parasail.blosum62)

    # extract CIGAR object
    cigar = alignment.cigar
    cigar_string = cigar.decode.decode("utf-8")

    # record ref_pos
    ref_pos = 0

    matches = re.findall(r'(\d+)([MIDNSHP=X])', cigar_string)
    max_num_before_equal = 0
    max_equal_index = -1
    total_before_max_equal = 0

    for i, (num_str, op) in enumerate(matches):
        num = int(num_str)
        if op == '=':
            if num > max_num_before_equal:
                max_num_before_equal = num
                max_equal_index = i
    total_before_max_equal = sum(int(matches[j][0]) for j in range(max_equal_index))

    ref_pos = total_before_max_equal

    return ref_pos

def find_gRNA_with_mutation(ref_sequence, exon_chr, start, end, strand, transcript_id,
                            exon_id, gene_symbol, vcf_reader, pam="TTTN", target_length=34):
    # initialization
    mutated_sequences = [ref_sequence]

    # find mutations within interested region
    mutations = vcf_reader(f"{exon_chr}:{start}-{end}")
    if mutations:
        # find mutations
        mutation_list = []
        for mutation in mutations:
            offset = mutation.POS - start
            ref = mutation.REF
            alts = mutation.ALT[:-1]
            mutation_list.append((offset, ref, alts))

        # replace reference sequence of mutation
        mutated_sequences = construct_combinations(ref_sequence, mutation_list)

    # find gRNA in ref_sequence or all mutated_sequences
    targets = []
    for seq in mutated_sequences:
        len_sequence = len(seq)
        dnatorna = {'A': 'A', 'T': 'U', 'C': 'C', 'G': 'G'}
        for i in range(len_sequence - target_length + 1):
            target_seq = seq[i:i + target_length]
            if target_seq[4:7] == 'TTT':
                pos = ref_sequence.find(target_seq)
                if pos != -1:
                    is_mut = False
                    if strand == -1:
                        tar_start = end - pos - target_length + 1
                    else:
                        tar_start = start + pos
                else:
                    is_mut = True
                    nw_pos = needleman_wunsch_alignment(target_seq, ref_sequence)
                    if strand == -1:
                        tar_start = str(end - nw_pos - target_length + 1) + '*'
                    else:
                        tar_start = str(start + nw_pos) + '*'
                gRNA = ''.join([dnatorna[base] for base in target_seq[8:28]])
                targets.append([target_seq, gRNA, exon_chr, str(strand), str(tar_start), transcript_id, exon_id, gene_symbol, is_mut])

    # filter duplicated targets
    unique_targets_set = set(tuple(element) for element in targets)
    unique_targets = [list(element) for element in unique_targets_set]

    return unique_targets

def format_prediction_output_with_mutation(targets, model_path):
    Crispr_BiLSTM = BiLSTM_model(input_shape=(34, 4))
    Crispr_BiLSTM.load_weights(model_path)

    formatted_data = []
    for target in targets:
        # Predict
        encoded_seq = get_seqcode(target[0])
        prediction = float(list(Crispr_BiLSTM.predict(encoded_seq, verbose=0)[0])[0])
        if prediction > 100:
            prediction = 100

        # Format output
        gRNA = target[1]
        exon_chr = target[2]
        strand = target[3]
        tar_start = target[4]
        transcript_id = target[5]
        exon_id = target[6]
        gene_symbol = target[7]
        is_mut = target[8]
        formatted_data.append([gene_symbol, exon_chr, strand, tar_start, transcript_id, exon_id, target[0], gRNA, prediction, is_mut])

    return formatted_data

def process_gene(gene_symbol, vcf_reader, model_path):
    transcripts = fetch_ensembl_transcripts(gene_symbol)
    results = []
    all_exons = []  # To accumulate all exons
    all_gene_sequences = []  # To accumulate all gene sequences

    if transcripts:
        for transcript in transcripts:
            Exons = transcript['Exon']
            all_exons.extend(Exons)  # Add all exons from this transcript to the list
            transcript_id = transcript['id']

            for Exon in Exons:
                exon_id = Exon['id']
                gene_sequence = fetch_ensembl_sequence(exon_id)  # Reference exon sequence
                if gene_sequence:
                    all_gene_sequences.append(gene_sequence)  # Add this gene sequence to the list
                    exon_chr = Exon['seq_region_name']
                    start = Exon['start']
                    end = Exon['end']
                    strand = Exon['strand']

                    targets = find_gRNA_with_mutation(gene_sequence, exon_chr, start, end, strand, transcript_id, exon_id, gene_symbol, vcf_reader)
                    if targets:
                        # Predict on-target efficiency for each gRNA site
                        formatted_data = format_prediction_output_with_mutation(targets, model_path)
                        results.extend(formatted_data)  # Flatten the results
                else:
                    print(f"Failed to retrieve gene sequence for exon {exon_id}.")
    else:
        print("Failed to retrieve transcripts.")

    # Sort results based on prediction score (assuming score is at the 8th index)
    sorted_results = sorted(results, key=lambda x: x[8], reverse=True)

    # Return the sorted output, combined gene sequences, and all exons
    return sorted_results, all_gene_sequences, all_exons