import collections
import heapq
import math
import pickle
import sys
import gzip
import time
import pandas as pd
from numpy import inf
import gradio as gr
from typing import List

from datetime import datetime

today_rev = datetime.now().strftime("%Y%m%d")

from search_funcs.clean_funcs import initial_clean # get_lemma_tokens, stem_sentence
from search_funcs.helper_functions import get_file_path_end_with_ext, get_file_path_end, create_highlighted_excel_wb, ensure_output_folder_exists, output_folder, load_spacy_model

PARAM_K1 = 1.5
PARAM_B = 0.75
IDF_CUTOFF = -inf
bm25 = "" # Placeholder just so initial load doesn't fail

# Class built off https://github.com/Inspirateur/Fast-BM25

class BM25:
	"""Fast Implementation of Best Matching 25 ranking function.

	Attributes
	----------
	t2d : <token: <doc, freq>>
		Dictionary with terms frequencies for each document in `corpus`.
	idf: <token, idf score>
		Pre computed IDF score for every term.
	doc_len : list of int
		List of document lengths.
	avgdl : float
		Average length of document in `corpus`.
	"""
	def __init__(self, corpus, k1=PARAM_K1, b=PARAM_B, alpha=IDF_CUTOFF):
		"""
		Parameters
		----------
		corpus : list of list of str
			Given corpus.
		k1 : float
			Constant used for influencing the term frequency saturation. After saturation is reached, additional
			presence for the term adds a significantly less additional score. According to [1]_, experiments suggest
			that 1.2 < k1 < 2 yields reasonably good results, although the optimal value depends on factors such as
			the type of documents or queries.
		b : float
			Constant used for influencing the effects of different document lengths relative to average document length.
			When b is bigger, lengthier documents (compared to average) have more impact on its effect. According to
			[1]_, experiments suggest that 0.5 < b < 0.8 yields reasonably good results, although the optimal value
			depends on factors such as the type of documents or queries.
		alpha: float
			IDF cutoff, terms with a lower idf score than alpha will be dropped. A higher alpha will lower the accuracy
			of BM25 but increase performance
		"""
		self.k1 = k1
		self.b = b
		self.alpha = alpha
		self.corpus = corpus

		self.avgdl = 0
		self.t2d = {}
		self.idf = {}
		self.doc_len = []
		if corpus:
			self._initialize(corpus)

	@property
	def corpus_size(self):
		return len(self.doc_len)

	def _initialize(self, corpus, progress=gr.Progress()):
		"""Calculates frequencies of terms in documents and in corpus. Also computes inverse document frequencies."""
		i = 0
		for document in progress.tqdm(corpus, desc = "Preparing search index", unit = "rows"):
			self.doc_len.append(len(document))

			for word in document:
				if word not in self.t2d:
					self.t2d[word] = {}
				if i not in self.t2d[word]:
					self.t2d[word][i] = 0
				self.t2d[word][i] += 1
			i += 1

		self.avgdl = sum(self.doc_len)/len(self.doc_len)
		to_delete = []
		for word, docs in self.t2d.items():
			idf = math.log(self.corpus_size - len(docs) + 0.5) - math.log(len(docs) + 0.5)
			# only store the idf score if it's above the threshold
			if idf > self.alpha:
				self.idf[word] = idf
			else:
				to_delete.append(word)
		print(f"Dropping {len(to_delete)} terms")
		for word in to_delete:
			del self.t2d[word]

		if len(self.idf) == 0:
			print("Alpha value too high - all words removed from dataset.")
			self.average_idf = 0

		else:
			self.average_idf = sum(self.idf.values())/len(self.idf)

		if self.average_idf < 0:
			print(
				f'Average inverse document frequency is less than zero. Your corpus of {self.corpus_size} documents'
				' is either too small or it does not originate from natural text. BM25 may produce'
				' unintuitive results.',
				file=sys.stderr
			)

	def get_top_n(self, query, documents, n=5):
		"""
		Retrieve the top n documents for the query.

		Parameters
		----------
		query: list of str
			The tokenized query
		documents: list
			The documents to return from
		n: int
			The number of documents to return

		Returns
		-------
		list
			The top n documents
		"""
		assert self.corpus_size == len(documents), "The documents given don't match the index corpus!"
		scores = collections.defaultdict(float)
		for token in query:
			if token in self.t2d:
				for index, freq in self.t2d[token].items():
					denom_cst = self.k1 * (1 - self.b + self.b * self.doc_len[index] / self.avgdl)
					scores[index] += self.idf[token]*freq*(self.k1 + 1)/(freq + denom_cst)

		return [documents[i] for i in heapq.nlargest(n, scores.keys(), key=scores.__getitem__)]
	

	def get_top_n_with_score(self, query:str, documents:List[str], n=5):
		"""
		Retrieve the top n documents for the query along with their scores.

		Parameters
		----------
		query: list of str
			The tokenized query
		documents: list
			The documents to return from
		n: int
			The number of documents to return

		Returns
		-------
		list
			The top n documents along with their scores and row indices in the format (index, document, score)
		"""
		assert self.corpus_size == len(documents), "The documents given don't match the index corpus!"
		scores = collections.defaultdict(float)
		for token in query:
			if token in self.t2d:
				for index, freq in self.t2d[token].items():
					denom_cst = self.k1 * (1 - self.b + self.b * self.doc_len[index] / self.avgdl)
					scores[index] += self.idf[token] * freq * (self.k1 + 1) / (freq + denom_cst)

		top_n_indices = heapq.nlargest(n, scores.keys(), key=scores.__getitem__)
		return [(i, documents[i], scores[i]) for i in top_n_indices]
	
	def extract_documents_and_scores(self, query, documents, n=5):
		"""
		Extract top n documents and their scores into separate lists.

		Parameters
		----------
		query: list of str
			The tokenized query
		documents: list
			The documents to return from
		n: int
			The number of documents to return

		Returns
		-------
		tuple: (list, list)
			The first list contains the top n documents and the second list contains their scores.
		"""
		results = self.get_top_n_with_score(query, documents, n)
		try:
			indices, docs, scores = zip(*results)
		except:
			print("No search results returned")
			return [], [], []
		return list(indices), docs, list(scores)

	def save(self, filename):
		with open(f"{output_folder}{filename}.pkl", "wb") as fsave:
			pickle.dump(self, fsave, protocol=pickle.HIGHEST_PROTOCOL)

	@staticmethod
	def load(filename):
		with open(f"{output_folder}{filename}.pkl", "rb") as fsave:
			return pickle.load(fsave)


def tokenise_text_spacy(prepared_text_as_list:List[str], progress = gr.Progress(track_tqdm=True)):
	'''
	Tokenise a list of texts using the spaCy package and the en_core_web_sm model.
	'''

	# Load spaCy model
	nlp = load_spacy_model()

	prepared_search_text_list = []
	batch_size = 256
	for doc in nlp.tokenizer.pipe(progress.tqdm(prepared_text_as_list, desc = "Tokenising text", unit = "rows"), batch_size=batch_size):
		prepared_search_text_list.append([token.text for token in doc])

	return prepared_search_text_list

def tokenise_text_nltk(prepared_text_as_list: List[str], progress= gr.Progress(track_tqdm=True)):
	"""
	Tokenise a list of texts using the NLTK package.
	"""
	import nltk
	nltk.download('punkt', quiet=True)  # Download the necessary resource if not already present

	prepared_search_text_list = []
	for text in progress.tqdm(prepared_text_as_list, desc="Tokenising text", unit="rows"):
		prepared_search_text_list.append(nltk.word_tokenize(text.lower()))  # Lowercase for consistency

	return prepared_search_text_list

def prepare_bm25_input_data(
    in_file: list, 
    text_column: str, 
    data_state: pd.DataFrame, 
    tokenised_state: list, 
    clean: str = "No",  
    return_intermediate_files: str = "No", 
    progress: gr.Progress = gr.Progress(track_tqdm=True)
) -> tuple:
	"""
	Prepare BM25 input data by loading, cleaning, and tokenizing the text data.

	Parameters
	----------
	in_file: list
		List of input files to be processed.
	text_column: str
		The name of the text column in the data file to search.
	data_state: pd.DataFrame
		The current state of the data.
	tokenised_state: list
		The current state of the tokenized data.
	clean: str, optional
		Whether to clean the text data (default is "No").
	return_intermediate_files: str, optional
		Whether to return intermediate processing files (default is "No").
	progress: gr.Progress, optional
		Progress tracker for the function (default is gr.Progress(track_tqdm=True)).

	Returns
	-------
	tuple
		A tuple containing the prepared search text list, a message, the updated data state, 
		the tokenized data, the search index, and a dropdown component for the text column.
	"""

	ensure_output_folder_exists(output_folder)

	if not in_file:
		print("No input file found. Please load in at least one file.")
		return None, "No input file found. Please load in at least one file.", data_state, None, None, gr.Dropdown(allow_custom_value=True, value=text_column, choices=data_state.columns.to_list())

	progress(0, desc = "Loading in data")
	file_list = [string.name for string in in_file]

	#print(file_list)

	data_file_names = [string for string in file_list if "tokenised" not in string.lower() and "npz" not in string.lower() and "gz" not in string.lower()]

	if not data_file_names:
		return None, "Please load in at least one csv/Excel/parquet data file.", data_state, None, None, gr.Dropdown(allow_custom_value=True, value=text_column, choices=data_state.columns.to_list())

	if not text_column:
		return None, "Please enter a column name to search.", data_state, None, None, gr.Dropdown(allow_custom_value=True, value=text_column, choices=data_state.columns.to_list())

	data_file_name = data_file_names[0]

	df = data_state #read_file(data_file_name)
	#data_file_out_name = get_file_path_end_with_ext(data_file_name)
	data_file_out_name_no_ext = get_file_path_end(data_file_name)

	## Load in pre-tokenised prepared_search_text_list if exists
	#tokenised_df = pd.DataFrame()

	#tokenised_file_names = [string for string in file_list if "tokenised" in string.lower()]
	search_index_file_names = [string for string in file_list if "gz" in string.lower()]

	# Set all search text to lower case
	df[text_column] = df[text_column].astype(str).str.lower()

	if "copy_of_case_note_id" in df.columns:
		print("copy column found")
		df.loc[~df["copy_of_case_note_id"].isna(), text_column] = ""

	if search_index_file_names:
		prepared_search_text_list = list(df[text_column])
		message = "Tokenisation skipped - loading search index from file."
		print(message)
		return prepared_search_text_list, message, df, None, None, gr.Dropdown(allow_custom_value=True, value=text_column, choices=data_state.columns.to_list())

	
	if clean == "Yes":
		progress(0.1, desc = "Cleaning data")
		clean_tic = time.perf_counter()
		print("Starting data clean.")

		#df = df.drop_duplicates(text_column)
		prepared_text_as_list = list(df[text_column])
		prepared_text_as_list = initial_clean(prepared_text_as_list)

		# Save to file if you have cleaned the data
		out_file_name, text_column, df  = save_prepared_bm25_data(data_file_name, prepared_text_as_list, df, text_column)
	
		clean_toc = time.perf_counter()
		clean_time_out = f"Cleaning the text took {clean_toc - clean_tic:0.1f} seconds."
		print(clean_time_out)

	else:
		# Don't clean or save file to disk
		prepared_text_as_list = list(df[text_column])
		print("No data cleaning performed")
		out_file_name = None
		
	# Tokenise data. If tokenised df already exists, no need to do anything
	
	progress(0.4, desc = "Tokenising text")

	print("Tokenised state:", tokenised_state)

	if tokenised_state:
		prepared_search_text_list = tokenised_state.iloc[:,0].tolist()
		print("Tokenised data loaded from file")
		
		#print("prepared_search_text_list is: ", prepared_search_text_list[0:5])

	else:
		tokeniser_tic = time.perf_counter()
		prepared_search_text_list = []

		prepared_search_text_list = tokenise_text_spacy(prepared_text_as_list)

		tokeniser_toc = time.perf_counter()
		tokenizer_time_out = f"Tokenising the text took {tokeniser_toc - tokeniser_tic:0.1f} seconds."
		print(tokenizer_time_out)
		#print("prepared_search_text_list is: ", prepared_search_text_list[0:5])

	if len(prepared_text_as_list) >= 20:
		message = "Data loaded"
	else:
		message = "Data loaded. Warning: dataset may be too short to get consistent search results."

	if return_intermediate_files == "Yes":

		if clean == "Yes":
			tokenised_data_file_name = output_folder + data_file_out_name_no_ext + "_cleaned_tokenised.parquet"
		else:
			tokenised_data_file_name = output_folder + data_file_out_name_no_ext + "_tokenised.parquet"

		pd.DataFrame(data={"prepared_search_text_list":prepared_search_text_list}).to_parquet(tokenised_data_file_name)

		return prepared_search_text_list, message, df, out_file_name, tokenised_data_file_name, gr.Dropdown(allow_custom_value=True, value=text_column, choices=data_state.columns.to_list()) # prepared_text_as_list, 

	return prepared_search_text_list, message, df, out_file_name, None, gr.Dropdown(allow_custom_value=True, value=text_column, choices=data_state.columns.to_list()) # prepared_text_as_list, 

def save_prepared_bm25_data(in_file_name: str, prepared_text_list: list, in_df: pd.DataFrame, in_bm25_column: str, progress: gr.Progress = gr.Progress(track_tqdm=True)) -> tuple:
	"""
	Save the prepared BM25 data to a file.

	This function ensures the output folder exists, checks if the length of the prepared text list matches the input dataframe,
	and saves the prepared data to a file in the specified format. The original column in the input dataframe is dropped to reduce file size.

	Parameters:
	- in_file_name (str): The name of the input file.
	- prepared_text_list (list): The list of prepared text.
	- in_df (pd.DataFrame): The input dataframe.
	- in_bm25_column (str): The name of the column to be processed.
	- progress (gr.Progress, optional): The progress tracker for the operation.

	Returns:
	- tuple: A tuple containing the file name, new text column name, and the prepared dataframe.
	"""

	ensure_output_folder_exists(output_folder)

	# Check if the list and the dataframe have the same length
	if len(prepared_text_list) != len(in_df):
		raise ValueError("The length of 'prepared_text_list' and 'in_df' must match.")

	file_end = ".parquet"

	file_name = output_folder + get_file_path_end(in_file_name) + "_cleaned" + file_end

	new_text_column = in_bm25_column + "_cleaned"
	prepared_text_df = pd.DataFrame(data={new_text_column:prepared_text_list})

	# Drop original column from input file to reduce file size
	in_df = in_df.drop(in_bm25_column, axis = 1)

	prepared_df = pd.concat([in_df, prepared_text_df], axis = 1)

	if file_end == ".csv":
		prepared_df.to_csv(file_name)
	elif file_end == ".parquet":
		prepared_df.to_parquet(file_name)
	else: file_name = None

	return file_name, new_text_column, prepared_df

def prepare_bm25(
    prepared_search_text_list: List[str], 
    in_file: List[gr.File], 
    text_column: str, 
    search_index: BM25, 
    clean: str, 
    return_intermediate_files: str, 
    k1: float = 1.5, 
    b: float = 0.75, 
    alpha: float = -5, 
    progress: gr.Progress = gr.Progress(track_tqdm=True)
) -> tuple:
	"""
	Prepare the BM25 search index.

	This function prepares the BM25 search index from the provided text list and input file. It ensures the necessary
	files and columns are present, processes the data, and optionally saves intermediate files.

	Parameters:
	- prepared_search_text_list (List[str]): The list of prepared search text.
	- in_file (List[gr.File]): The list of input files.
	- text_column (str): The name of the column to search.
	- search_index (BM25): The BM25 search index.
	- clean (str): Indicates whether to clean the data.
	- return_intermediate_files (str): Indicates whether to return intermediate files.
	- k1 (float, optional): The k1 parameter for BM25. Default is 1.5.
	- b (float, optional): The b parameter for BM25. Default is 0.75.
	- alpha (float, optional): The alpha parameter for BM25. Default is -5.
	- progress (gr.Progress, optional): The progress tracker for the operation.

	Returns:
	- tuple: A tuple containing the output message, BM25 search index, and other relevant information.
	"""

	if not in_file:
		out_message ="No input file found. Please load in at least one file."
		print(out_message)
		return out_message, None, None, None

	if not prepared_search_text_list:
		out_message = "No data file found. Please load in at least one csv/Excel/Parquet file."
		print(out_message)
		return out_message, None, None, None

	if not text_column:
		out_message = "Please enter a column name to search."
		print(out_message)
		return  out_message, None, None, None

	file_list = [string.name for string in in_file]

	#print(file_list)

	# Get data file name
	data_file_names = [string for string in file_list if "tokenised" not in string.lower() and "npz" not in string.lower() and "gz" not in string.lower()]

	if not data_file_names:
		return "Please load in at least one csv/Excel/parquet data file.", None, None, None

	data_file_name = data_file_names[0]
	data_file_out_name = get_file_path_end_with_ext(data_file_name)
	data_file_name_no_ext = get_file_path_end(data_file_name)

	progress(0.6, desc = "Preparing search index")

	if search_index:
		bm25 = search_index
	else:
		print("Preparing BM25 search corpus")

		bm25 = BM25(prepared_search_text_list, k1=k1, b=b, alpha=alpha)

	#global bm25
	#bm25 = bm25_load

	if return_intermediate_files == "Yes":
		print("Saving search index file")
		progress(0.8, desc = "Saving search index to file")

		if clean == "Yes":
			bm25_search_file_name = output_folder + data_file_name_no_ext + '_cleaned_search_index.pkl.gz'
		else:
			bm25_search_file_name = output_folder + data_file_name_no_ext + '_search_index.pkl.gz'
			#np.savez_compressed(bm25_search_file_name, bm25)

		with gzip.open(bm25_search_file_name, 'wb') as file:
				pickle.dump(bm25, file)

		print("Search index saved to file")

		message = "Search parameters loaded."

		return message, bm25_search_file_name, bm25, prepared_search_text_list 

	message = "Search parameters loaded."

	print(message)

	return message, None, bm25, prepared_search_text_list

def convert_bm25_query_to_tokens(free_text_query:str):
	"""
	Split open text query into tokens.
	"""
	split_query = tokenise_text_spacy([free_text_query.lower()])

	# Flatten the list of lists into a single list
	flattened_query = [token for sublist in split_query for token in sublist]

	print("Search query out is:", flattened_query)

	return flattened_query	

def bm25_search(
    free_text_query: str, 
    in_no_search_results: int, 
    original_data: pd.DataFrame, 
    searched_data: pd.DataFrame, 
    text_column: str, 
    in_join_file: str, 
    clean: str, 
    bm25: BM25, 
    prepared_search_text_list_state: list, 
    in_join_column: str = "", 
    search_df_join_column: str = "", 
    k1: float = 1.5, 
    b: float = 0.75, 
    alpha: float = -5, 
    progress: gr.Progress = gr.Progress(track_tqdm=True)
) -> tuple:
	"""
	Perform a BM25 search on the provided text data.

	Parameters
	----------
	free_text_query : str
		The query text to search for.
	in_no_search_results : int
		The number of search results to return.
	original_data : pd.DataFrame
		The original data containing the text to be searched.
	searched_data : pd.DataFrame
		The data that has been prepared for searching.
	text_column : str
		The name of the column in the data to search.
	in_join_file : str
		The file to join the search results with.
	clean : str
		Whether to clean the text data.
	bm25 : BM25
		The BM25 object used for searching.
	prepared_search_text_list_state : list
		The state of the prepared search text list.
	in_join_column : str, optional
		The column to join on in the input file (default is "").
	search_df_join_column : str, optional
		The column to join on in the search dataframe (default is "").
	k1 : float, optional
		The k1 parameter for BM25 (default is 1.5).
	b : float, optional
		The b parameter for BM25 (default is 0.75).
	alpha : float, optional
		The alpha parameter for BM25 (default is -5).
	progress : gr.Progress, optional
		Progress tracker for the function (default is gr.Progress(track_tqdm=True)).

	Returns
	-------
	tuple
		A tuple containing a message and the output search results files (if any).
	"""

	output_files = []

	progress(0, desc = "Conducting keyword search")

	print("in_join_file at start of bm25_search:", in_join_file)

	if not bm25:
		print("Preparing BM25 search corpus")

		bm25 = BM25(prepared_search_text_list_state, k1=k1, b=b, alpha=alpha)

	# print("bm25:", bm25)
	
	# Prepare query
	token_query = convert_bm25_query_to_tokens(free_text_query)

	# Perform search
	print("Searching")

	results_index, results_text, results_scores = bm25.extract_documents_and_scores(token_query, bm25.corpus, n=in_no_search_results) #bm25.corpus #original_data[text_column]
	if not results_index:
		return "No search results found", None

	print("Search complete")

	# Prepare results and export
	joined_texts = [' '.join(inner_list) for inner_list in results_text]
	results_df = pd.DataFrame(data={"index": results_index,
									"search_text": joined_texts,
									"search_score_abs": results_scores})
	results_df['search_score_abs'] = abs(round(results_df['search_score_abs'], 2))

	# Join scores onto searched data
	results_df_out = results_df[['index', 'search_text', 'search_score_abs']].merge(searched_data,left_on="index", right_index=True, how="left", suffixes = ("", "_y")).drop("index_y", axis=1, errors="ignore")
	

	# Join on data from duplicate case notes
	if ("copy_of_case_note_id" in original_data.columns) and ("note_id" in results_df_out.columns):
		if clean == "No":
			print("Clean is no")
			orig_text_column = text_column
		else:
			print("Clean is yes")
			orig_text_column = text_column.replace("_cleaned", "")

		original_data["original_note_id"] = original_data["copy_of_case_note_id"]
		original_data["original_note_id"] = original_data["original_note_id"].combine_first(original_data["note_id"])

		results_df_out = results_df_out.merge(original_data[["original_note_id", "note_id", "copy_of_case_note_id", "person_id"]],left_on="note_id", right_on="original_note_id", how="left", suffixes=("_primary", "")) # .drop(orig_text_column, axis = 1)
		results_df_out.loc[~results_df_out["copy_of_case_note_id"].isnull(), "search_text"] = ""
		results_df_out.loc[~results_df_out["copy_of_case_note_id"].isnull(), text_column] = ""
	
	print("in_join_file:", in_join_file)

	# Join on additional files
	if not in_join_file.empty:
		progress(0.5, desc = "Joining on additional data file")
		#join_df = in_join_file
		# Prepare join columns as string and remove .0 at end of stringified numbers
		in_join_file[in_join_column] = in_join_file[in_join_column].astype(str).str.replace("\.0$","", regex=True)
		results_df_out[search_df_join_column] = results_df_out[search_df_join_column].astype(str).str.replace("\.0$","", regex=True)

		# Duplicates dropped so as not to expand out dataframe
		in_join_file = in_join_file.drop_duplicates(in_join_column)

		results_df_out = results_df_out.merge(in_join_file,left_on=search_df_join_column, right_on=in_join_column, how="left", suffixes=('','_y'))#.drop(in_join_column, axis=1)

	# Reorder results by score, and whether there is text
	results_df_out = results_df_out.sort_values(['search_score_abs', "search_text"], ascending=False)	

	# Out file
	ensure_output_folder_exists(output_folder)

	query_str_file = ("_").join(token_query)
	results_df_name = output_folder + "keyword_search_result_" + today_rev + "_" +  query_str_file + ".xlsx"

	print("Saving search file output")
	progress(0.7, desc = "Saving search output to file")

	# Highlight found text and save to file
	results_df_out_wb = create_highlighted_excel_wb(results_df_out, free_text_query, "search_text")
	results_df_out_wb.save(results_df_name)

	results_first_text = results_df_out[text_column].iloc[0]

	output_files.append(results_df_name)

	#csv_output_file = output_folder + "keyword_search_result_" + today_rev + "_" +  query_str_file + ".csv" 
	#results_df_out.to_csv(csv_output_file, index=None)
	#output_files.append(csv_output_file)

	print("Returning results")

	return results_first_text, output_files