import json

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from torch import Tensor
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
from sentence_transformers import SentenceTransformer, util

from utils.constants import *
from utils.api_utils import embedding_generate
from table2tree.feature_tree import *


def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor:
    """Average masked pooling: average token vectors into one vector per sample."""
    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]

def get_detailed_instruct(task_description: str, query: str) -> str:
    return f"Instruct: {task_description}\nQuery: {query}"

def find_topk_indices(lst, k):
    import heapq

    topk_with_indices = heapq.nlargest(k, enumerate(lst), key=lambda x: x[1])
    indices = [index for index, value in topk_with_indices]
    return indices

class EmbeddingModel:
    """Embedding cannot be preprocessed because a task must be specified."""

    _instance = None  # Class variable used to store the singleton instance

    def __init__(self):
        if EMBEDDING_TYPE == 'local':
            self.model_path = EMBEDDING_MODE_PATH
            self.model = SentenceTransformer(EMBEDDING_MODE_PATH)
        else:
            self.similarity = util.cos_sim

    def __new__(cls, *args, **kwargs):
        if not cls._instance:
            cls._instance = super().__new__(cls)
        return cls._instance

    # TODO
    def get_entity_embedding(self, entity_list):
        entity_list = ["#" if str(x).strip() == '' else x for x in entity_list]
        if EMBEDDING_TYPE == 'local':
            embeddings = self.model.encode(entity_list)
        else:
            embeddings = embedding_generate(input_texts=entity_list)
        return embeddings

    def get_embedding_dict(self, entity_list):
        embeddings = self.get_entity_embedding(entity_list)
        embedding_dict = {
            str(entity): embedding.tolist()
            for entity, embedding in zip(entity_list, embeddings)
        }
        return embedding_dict

    def save_embedding_dict(self, embedding_dict, output_file):  
        with open(output_file, "w") as f:
            json.dump(embedding_dict, f, ensure_ascii=False)

    def load_embedding_dict(self, input_file):
        # Load from a JSON file
        with open(input_file, "r") as f:
            loaded_embedding_dict = json.load(f)

        # Convert lists back to NumPy arrays
        loaded_embedding_dict = {
            k: np.array(v) for k, v in loaded_embedding_dict.items()
        }

        return loaded_embedding_dict

    def split_embedding_dict(self, embedding_dict):
        values = []
        embeddings = []
        for (
            k,
            v,
        ) in embedding_dict.items():
            values.append(k)
            embeddings.append(v.tolist())
        return values, embeddings
    
    def one_to_many_semilarity(self, value, value_list=None, embedding_cache_file=None):
        """One of value_list or embedding_cache_file must be specified"""
        if embedding_cache_file is None:  # without cache
            input_texts = [value] + value_list
            input_texts = [str(s) for s in input_texts]

            embeddings = self.get_entity_embedding(input_texts)

            scores = (embeddings[:1] @ embeddings[1:].T) * 100
            scores = scores.tolist()
        else:
            embedding_dict = self.load_embedding_dict(embedding_cache_file)
            value_list, embedding_list = self.split_embedding_dict(embedding_dict)
            value_embedding = self.get_entity_embedding([value]).astype(np.float64)

            if EMBEDDING_TYPE == 'local':
                scores = self.model.similarity(value_embedding, embedding_list)
            else:
                scores = self.similarity(value_embedding, embedding_list)
            scores = scores.tolist()

        return scores

    def topk_match(
        self,
        entities: list,
        table: list = None,
        k=10,
        embedding_cache_file=None,
    ):
        """One of table or embedding_cache_file must be specified"""
        if embedding_cache_file is None:  # without cache
            if table is None or len(table) == 0:
                return [[x] for x in entities]
            input_texts = entities + table
            input_texts = [str(s) for s in input_texts]

            embeddings = self.get_entity_embedding(input_texts)

            scores = (embeddings[: len(entities)] @ embeddings[len(entities) :].T) * 100
            scores = scores.tolist()
            if not isinstance(scores, list): scores = [[scores]]

            # Find Max Top-k Values
            values = []
            for index, score_lst in enumerate(scores):
                indices = find_topk_indices(score_lst, k)
                values.append([table[i] for i in indices])
        else:
            embedding_dict = self.load_embedding_dict(embedding_cache_file)
            value_list, embedding_list = self.split_embedding_dict(embedding_dict)
            value_embedding = self.get_entity_embedding(entities)

            # Find Max Top-k Values
            if EMBEDDING_TYPE == 'local':
                scores = self.model.similarity(value_embedding.tolist(), embedding_list)
            else:
                scores = self.similarity(value_embedding.tolist(), embedding_list)
            scores = scores.tolist()
            if not isinstance(scores, list): scores = [[scores]]
            
            values = []
            for index, score_lst in enumerate(scores):
                indices = find_topk_indices(score_lst, k)
                values.append([value_list[i] for i in indices])

        return values

    def top1_match(self, entities: list, table: list = None, embedding_cache_file=None):
        """One of table or file must be specified"""
        return flatten_nested_list(
            self.topk_match(entities=entities, table=table, k=1, embedding_cache_file=embedding_cache_file)
        )

    # def one_to_many_semilarity(
    #     self,
    #     value,
    #     value_list,
    #     task="Given an sentence, retrieve relevant sentences that relevant to the sentence.",
    # ):
    #     value = get_detailed_instruct(task, value)
    #     input_texts = [value] + value_list
    #     input_texts = [str(s) for s in input_texts]

    #     # Tokenize the input texts
    #     batch_dict = self.tokenizer(
    #         input_texts,
    #         max_length=512,
    #         padding=True,
    #         truncation=True,
    #         return_tensors="pt",
    #     )

    #     outputs = self.model(**batch_dict)
    #     embeddings = average_pool(
    #         outputs.last_hidden_state, batch_dict["attention_mask"]
    #     )

    #     # normalize embeddings
    #     embeddings = F.normalize(embeddings, p=2, dim=1)
    #     scores = (embeddings[:1] @ embeddings[1:].T) * 100
    #     scores = scores.tolist()

    #     return scores

    # def topk_match(
    #     self,
    #     entities: list,
    #     table: list,
    #     k=10,
    #     task="Given an entity, retrieve relevant values that relevant to the entity.",
    #     log_file=None,
    # ):
    #     entities = [get_detailed_instruct(task, query) for query in entities]
    #     input_texts = entities + table
    #     input_texts = [str(s) for s in input_texts]

    #     # Tokenize the input texts
    #     batch_dict = self.tokenizer(
    #         input_texts,
    #         max_length=512,
    #         padding=True,
    #         truncation=True,
    #         return_tensors="pt",
    #     )

    #     outputs = self.model(**batch_dict)
    #     embeddings = average_pool(
    #         outputs.last_hidden_state, batch_dict["attention_mask"]
    #     )

    #     # normalize embeddings
    #     embeddings = F.normalize(embeddings, p=2, dim=1)
    #     scores = (embeddings[: len(entities)] @ embeddings[len(entities) :].T) * 100
    #     scores = scores.tolist()

    #     # Find Max Top-k Values
    #     values = []
    #     for index, score_lst in enumerate(scores):
    #         indices = find_topk_indices(score_lst, k)
    #         values.append([table[i] for i in indices])

    #     if log_file is not None:  # Log
    #         with open(log_file, "a") as file:
    #             file.write(f"{DELIMITER} Top-{k} Match Result {DELIMITER}\n")
    #             for i, (entity, values) in enumerate(zip(entities, values)):
    #                 file.write(f"Entity: {entity}\n")
    #                 file.write(f"Values: {values}\n")

    #     return values

    # def top1_match(
    #     self,
    #     entities: list,
    #     table: list,
    #     task="Given an string, retrieve most relevant value that relevant to the entity.",
    # ):
    #     return flatten_nested_list(
    #         self.topk_match(entities=entities, table=table, k=1, task=task)
    #     )


def calculate_topk_similarity(query_vectors, target_vectors, topk=6):
    """
    Compute similarities between two embedding vector lists and return the Top-K most relevant results.

    Args:
        query_vectors (np.ndarray): Query vectors to match, shaped (n, embedding_dim).
        target_vectors (np.ndarray): Target vectors to be matched, shaped (m, embedding_dim).
        topk (int): Number of Top-K relevant results to return.

    Returns:
        topk_indices (list): Indices of the Top-K most relevant items, shaped (n, topk).
        topk_scores (list): Similarity scores of the Top-K most relevant items, shaped (n, topk).
    """
    # Compute the cosine similarity matrix
    similarity_matrix = cosine_similarity(query_vectors, target_vectors)  # shape (n, m)

    # Get the Top-K indices and scores
    topk_indices = np.argsort(similarity_matrix, axis=1)[:, -topk:][
        :, ::-1
    ]  # shape (n, topk)
    topk_scores = np.take_along_axis(
        similarity_matrix, topk_indices, axis=1
    )  # shape (n, topk)

    return topk_indices.tolist(), topk_scores.tolist()


# class EmbeddingModelAllMiniLML6V2:

#     _instance = None  # Class variable used to store the singleton instance

#     def __init__(self, model_path=ALLMINILM_MODEL_PATH):
#         self.model_path = model_path
#         self.model = SentenceTransformer(model_path)

#     def __new__(cls, *args, **kwargs):
#         if not cls._instance:
#             cls._instance = super().__new__(cls)
#         return cls._instance

#     def get_entity_embedding(self, entity_list):
#         embeddings = self.model.encode(entity_list)
#         return embeddings

#     def get_embedding_dict(self, entity_list):
#         embeddings = self.get_entity_embedding(entity_list)
#         embedding_dict = {
#             str(entity): embedding.tolist()
#             for entity, embedding in zip(entity_list, embeddings)
#         }
#         return embedding_dict

#     def save_embedding_dict(self, embedding_dict, output_file):
#         with open(output_file, "w") as f:
#             json.dump(embedding_dict, f, ensure_ascii=False)

#     def load_embedding_dict(self, input_file):
#         # Load from a JSON file
#         with open(input_file, "r") as f:
#             loaded_embedding_dict = json.load(f)

#         # Convert lists back to NumPy arrays
#         loaded_embedding_dict = {
#             k: np.array(v) for k, v in loaded_embedding_dict.items()
#         }

#         return loaded_embedding_dict

#     def split_embedding_dict(self, embedding_dict):
#         values = []
#         embeddings = []
#         for (
#             k,
#             v,
#         ) in embedding_dict.items():
#             values.append(k)
#             embeddings.append(v.tolist())
#         return values, embeddings

#     def one_to_many_semilarity(self, value, value_list=None, embedding_cache_file=None):
#         """One of value_list or embedding_cache_file must be specified"""
#         if embedding_cache_file is None:  # without cache
#             input_texts = [value] + value_list
#             input_texts = [str(s) for s in input_texts]

#             embeddings = self.get_entity_embedding(input_texts)

#             scores = (embeddings[:1] @ embeddings[1:].T) * 100
#             scores = scores.tolist()
#         else:
#             embedding_dict = self.load_embedding_dict(embedding_cache_file)
#             value_list, embedding_list = self.split_embedding_dict(embedding_dict)
#             value_embedding = self.get_entity_embedding([value]).astype(np.float64)

#             scores = self.model.similarity(
#                 value_embedding, embedding_dict
#             )
#             scores = scores.tolist()

#         return scores

#     def topk_match(
#         self,
#         entities: list,
#         table: list = None,
#         k=10,
#         threshold=None,
#         embedding_cache_file=None,
#         log_file=None,
#     ):
#         """One of table or embedding_cache_file must be specified"""
#         if embedding_cache_file is None:  # without cache
#             input_texts = entities + table
#             input_texts = [str(s) for s in input_texts]

#             embeddings = self.get_entity_embedding(input_texts)

#             scores = (embeddings[: len(entities)] @ embeddings[len(entities) :].T) * 100
#             scores = scores.tolist()
#             if not isinstance(scores, list): scores = [[scores]]

#             # Find Max Top-k Values
#             values = []
#             for index, score_lst in enumerate(scores):
#                 indices = find_topk_indices(score_lst, k)
#                 values.append([table[i] for i in indices])
#         else:
#             embedding_dict = self.load_embedding_dict(embedding_cache_file)
#             value_list, embedding_list = self.split_embedding_dict(embedding_dict)
#             value_embedding = self.get_entity_embedding(entities)

#             # Find Max Top-k Values
#             scores = self.model.similarity(
#                 value_embedding.tolist(), embedding_list
#             )
#             scores = scores.tolist()
#             if not isinstance(scores, list): scores = [[scores]]
            
#             values = []
#             for index, score_lst in enumerate(scores):
#                 indices = find_topk_indices(score_lst, k)
#                 values.append([value_list[i] for i in indices])

#         if log_file is not None:  # Log
#             with open(log_file, "a") as file:
#                 file.write(f"{DELIMITER} Top-{k} Match Result {DELIMITER}\n")
#                 for i, (entity, values) in enumerate(zip(entities, values)):
#                     file.write(f"Entity: {entity}\n")
#                     file.write(f"Values: {values}\n")

#         return values

#     def top1_match(self, entities: list, table: list = None, embedding_cache_file=None, threshold=None):
#         """One of table or file must be specified"""
#         return flatten_nested_list(
#             self.topk_match(entities=entities, table=table, k=1, embedding_cache_file=embedding_cache_file, threshold=threshold)
#         )


def match_sub_table(entities: list, f_tree):  #: FeatureTree):
    """Use embedding vectors to extract a subtree from FeatureTree and return JSON."""
    model = EmbeddingModel()

    values = model.topk_match(entities, f_tree.body_value_list())

    return values


def flatten_nested_list(value):
    res = []
    for x in value:
        if isinstance(x, list):
            res.extend(flatten_nested_list(x))
        else:
            res.append(x)
    return res


def get_sub_json(values: list, json_dict: dict):
    values = list(set(flatten_nested_list(values)))

    def dfs(values: list, j_dict: dict):
        return_dict = {}
        for key, value in j_dict.items():
            if isinstance(value, list):
                tmp_list = []
                for x in value:
                    if isinstance(x, dict):
                        x = dfs(values, x)
                        if len(x) > 0:
                            tmp_list.append(x)
                    else:
                        if x in values:
                            tmp_list.append(x)
                if len(tmp_list) > 0:
                    return_dict[key] = tmp_list
            elif isinstance(value, dict):
                value = dfs(values, value)
                if len(value) > 0:
                    return_dict[key] = value
            else:
                if value in values or key in values:
                    return_dict[key] = value
        return return_dict

    return dfs(values, json_dict)


def demo():
    # Each query must come with a one-sentence instruction that describes the task
    task = "Given a web search query, retrieve relevant passages that answer the query"
    queries = [
        get_detailed_instruct(task, "how much protein should a female eat"),
        get_detailed_instruct(task, "Homestyle pumpkin recipes"),
    ]
    # No need to add instruction for retrieval documents
    documents = [
        "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
        "1. Stir-fried shredded pumpkin. Ingredients: half a tender pumpkin. Seasonings: scallion, salt, sugar, chicken bouillon. Steps: 1. Peel the pumpkin thinly and scoop out the pulp. 2. Shred it finely. 3. Heat oil in a wok and stir-fry scallions until fragrant. 4. Add the shredded pumpkin and stir-fry briefly, then season and serve. 2. Pumpkin with scallions. Ingredients: 1 pumpkin. Seasonings: scallions, minced garlic, olive oil, salt. Steps: 1. Peel and slice the pumpkin. 2. Heat oil and saute the garlic. 3. Add the pumpkin slices and stir-fry. 4. Add a little water as needed. 5. Add salt and stir evenly. 6. Once the pumpkin is soft, turn off the heat. 7. Sprinkle scallions and serve.",
    ]
    input_texts = queries + documents

    tokenizer = AutoTokenizer.from_pretrained("intfloat/multilingual-e5-large-instruct")
    model = AutoModel.from_pretrained("intfloat/multilingual-e5-large-instruct")

    # Tokenize the input texts
    batch_dict = tokenizer(
        input_texts, max_length=512, padding=True, truncation=True, return_tensors="pt"
    )

    outputs = model(**batch_dict)
    embeddings = average_pool(outputs.last_hidden_state, batch_dict["attention_mask"])

    # normalize embeddings
    embeddings = F.normalize(embeddings, p=2, dim=1)
    scores = (embeddings[:2] @ embeddings[2:].T) * 100
    print(scores.tolist())
    # => [[91.92852783203125, 67.580322265625], [70.3814468383789, 92.1330795288086]]


def main():
    model = EmbeddingModel()

    res = model.one_to_many_semilarity(
        "How many people are funded by public finance?",
        [
            "How many second-tier subordinate units does the Zhanjiang Human Resources and Social Security Bureau have?",
            "How many second-tier subordinate units does this department have?",
            "How many subordinate institutions does the Zhanjiang Human Resources and Social Security Bureau have?",
            "What is the total number of second-tier units under this department?",
        ],
    )

    print(res)


def main2():
    model = EmbeddingModel()

    res = model.topk_match(
        entities=["Overall Budget Situation", "Urban and Rural Resident Pension"],
        embedding_cache_file="/Users/tangzirui/Desktop/SJTU-DB/TaQA/dataset_json/sstqa/table/1.embedding.json",
        k=3,
    )
    print(res)
    with open(
        "/Users/tangzirui/Desktop/SJTU-DB/TaQA/dataset_json/sstqa/table/1_embedding.json",
        "r",
    ) as f:
        data: dict = json.load(f)
    res = model.topk_match(
        entities=["Overall Budget Situation", "Urban and Rural Resident Pension"], table=list(data.keys()), k=3
    )
    print(res)

def main3():
    model = EmbeddingModel()
    res = model.top1_match(["1", "2"], ["1", "2", "3", "4"])
    print(res)

if __name__ == "__main__":
    # main()
    # main2()
    main3()