import copy
import os
import json

from hpbandster.core.base_iteration import  Datum
class Run(object):
	"""
		Not a proper class, more a 'struct' to bundle important
		information about a particular run
	"""
	def __init__(self, config_id, budget, loss, info, time_stamps, error_logs):
		self.config_id   = config_id
		self.budget      = budget
		self.error_logs  = error_logs
		self.loss        = loss
		self.info        = info
		self.time_stamps = time_stamps

	def __repr__(self):
		return(\
			"config_id: %s\t"%(self.config_id,) + \
			"budget: %f\t"%self.budget + \
			"loss: %s\n"%self.loss + \
			"time_stamps: {submitted} (submitted), {started} (started), {finished} (finished)\n".format(**self.time_stamps) + \
			"info: %s\n"%self.info
		)
	def __getitem__ (self, k):
		"""
			 in case somebody wants to use it like a dictionary
		"""
		return(getattr(self, k))


def extract_HB_learning_curves(runs):
	"""
		function to get the hyperband learning curves

		This is an example function showing the interface to use the
		HB_result.get_learning_curves method.

		Parameters:
		-----------

		runs: list of HB_result.run objects
			the performed runs for an unspecified config

		Returns:
		--------

		list of learning curves: list of lists of tuples
			An individual learning curve is a list of (t, x_t) tuples.
			This function must return a list of these. One could think
			of cases where one could extract multiple learning curves
			from these runs, e.g. if each run is an independent training
			run of a neural network on the data.
		
	"""
	sr = sorted(runs, key=lambda r: r.budget)
	return([[(r.budget, r.loss) for r in sr],])
		

class json_result_logger(object):
	"""
		convenience logger for 'semi-live-results'

		Logger that writes job results into two files (configs.json and results.json).
		Both files contain propper json objects in each line.

		This version (v1) opens and closes the files for each result.
		This might be very slow if individual runs are fast and the
		filesystem is rather slow (e.g. a NFS).

	"""
	def __init__(self, directory, overwrite=False):
		"""
			Parameters:
			-----------

			directory: string
				the directory where the two files 'configs.json' and
				'results.json' are stored
			overwrite: bool
				In case the files already exist, this flag controls the
				behavior:
					> True:   The existing files will be overwritten.
					          Potential risk of deleting previous results
					> False:  A FileEvistsError is raised and the files are
							  not modified.
		"""

		os.makedirs(directory, exist_ok=True)

		
		self.config_fn  = os.path.join(directory, 'configs.json')
		self.results_fn = os.path.join(directory, 'results.json')


		try:
			with open(self.config_fn, 'x') as fh: pass
		except FileExistsError:
			if overwrite:
				with open(self.config_fn, 'w') as fh: pass
			else:
				raise FileExistsError('The file %s already exists.'%self.config_fn)
		except:
			raise

		try:
			with open(self.results_fn, 'x') as fh: pass
		except FileExistsError:
			if overwrite:
				with open(self.results_fn, 'w') as fh: pass
			else:
				raise FileExistsError('The file %s already exists.'%self.config_fn)

		except:
			raise

		self.config_ids = set()

	def new_config(self, config_id, config, config_info):
		if not config_id in self.config_ids:
			self.config_ids.add(config_id)
			with open(self.config_fn, 'a') as fh:
				fh.write(json.dumps([config_id, config, config_info]))
				fh.write('\n')

	def __call__(self, job):
		if not job.id in self.config_ids:
			#should never happen! TODO: log warning here!
			self.config_ids.add(job.id)
			with open(self.config_fn, 'a') as fh:
				fh.write(json.dumps([job.id, job.kwargs['config'], {}]))
				fh.write('\n')
		with open(self.results_fn, 'a') as fh:
			fh.write(json.dumps([job.id, job.kwargs['budget'], job.timestamps, job.result, job.exception]))
			fh.write("\n")


def logged_results_to_HB_result(directory):
	"""
		function to import logged 'live-results' and return a HB_result object

		You can load live run results with this function and the returned
		HB_result object gives you access to the results the same way
		a finished run would.
	"""
	data = {}
	time_ref = float('inf')
	budget_set = set()
	
	with open(os.path.join(directory, 'configs.json')) as fh:
		for line in fh:
			
			line = json.loads(line)
			
			if len(line) == 3:
				config_id, config, config_info = line
			if len(line) == 2:
				config_id, config, = line
				config_info = 'N/A'

			data[tuple(config_id)] = Datum(config=config, config_info=config_info)

	with open(os.path.join(directory, 'results.json')) as fh:
		for line in fh:
			config_id, budget,time_stamps, result, exception = json.loads(line)

			id = tuple(config_id)
			
			data[id].time_stamps[budget] = time_stamps
			data[id].results[budget] = result
			data[id].exceptions[budget] = exception

			budget_set.add(budget)
			time_ref = min(time_ref, time_stamps['submitted'])


	# infer the hyperband configuration from the data
	budget_list = sorted(list(budget_set))
	
	HB_config = {
						'eta'        : None if len(budget_list) < 2 else budget_list[1]/budget_list[0],
						'min_budget' : min(budget_set),
						'max_budget' : max(budget_set),
						'budgets'    : budget_list,
						'max_SH_iter': len(budget_set),
						'time_ref'   : time_ref
				}
	return(Result([data], HB_config))


class Result(object):
	"""
		Object returned by the HB_master.run function

		This class offers a simple API to access the information from
		a Hyperband run.
	"""
	def __init__ (self, HB_iteration_data, HB_config):
		self.data = HB_iteration_data
		self.HB_config = HB_config
		self._merge_results()

	def __getitem__(self, k):
		return(self.data[k])


	def get_incumbent_id(self):
		"""
			Find the config_id of the incumbent.

			The incumbent here is the configuration with the smallest loss
			among all runs on the maximum budget! If no run finishes on the
			maximum budget, None is returned!
		"""
		tmp_list = []
		for k,v in self.data.items():
			try:
				# only things run for the max budget are considered
				res = v.results[self.HB_config['max_budget']]
				if not res is None:
					tmp_list.append((res['loss'], k))
			except KeyError as e:
				pass
			except:
				raise

		if len(tmp_list) > 0:
			return(min(tmp_list)[1])
		return(None)



	def get_incumbent_trajectory(self, all_budgets=True, bigger_is_better=True, non_decreasing_budget=True):
		"""
			Returns the best configurations over time
			
			
			Parameters:
			-----------
				all_budgets: bool
					If set to true all runs (even those not with the largest budget) can be the incumbent.
					Otherwise, only full budget runs are considered
				bigger_is_better:bool
					flag whether an evaluation on a larger budget is always considered better.
					If True, the incumbent might increase for the first evaluations on a bigger budget
				non_decreasing_budget: bool
					flag whether the budget of a new incumbent should be at least as big as the one for
					the current incumbent.
			Returns:
			--------
				dict:
					dictionary with all the config IDs, the times the runs
					finished, their respective budgets, and corresponding losses
		"""
		all_runs = self.get_all_runs(only_largest_budget = not all_budgets)
		
		if not all_budgets:
			all_runs = list(filter(lambda r: r.budget==res.HB_config['max_budget'], all_runs))
		
		all_runs.sort(key=lambda r: r.time_stamps['finished'])
		
		return_dict = { 'config_ids' : [],
						'times_finished': [],
						'budgets'    : [],
						'losses'     : [],
		}
	
		current_incumbent = float('inf')
		incumbent_budget = self.HB_config['min_budget']
		
		for r in all_runs:
			if r.loss is None: continue
			
			new_incumbent = False
			
			if bigger_is_better and r.budget > incumbent_budget:
				new_incumbent = True
			
			
			if r.loss < current_incumbent:
				new_incumbent = True
			
			if non_decreasing_budget and r.budget < incumbent_budget:
				new_incumbent = False
			
			if new_incumbent:
				current_incumbent = r.loss
				incumbent_budget  = r.budget
				
				return_dict['config_ids'].append(r.config_id)
				return_dict['times_finished'].append(r.time_stamps['finished'])
				return_dict['budgets'].append(r.budget)
				return_dict['losses'].append(r.loss)

		if current_incumbent != r.loss:
			r = all_runs[-1]
		
			return_dict['config_ids'].append(return_dict['config_ids'][-1])
			return_dict['times_finished'].append(r.time_stamps['finished'])
			return_dict['budgets'].append(return_dict['budgets'][-1])
			return_dict['losses'].append(return_dict['losses'][-1])

			
		return (return_dict)


	def get_runs_by_id(self, config_id):
		"""
			returns a list of runs for a given config id

			The runs are sorted by ascending budget, so '-1' will give
			the longest run for this config.
		"""
		d = self.data[config_id]

		runs = []
		for b in d.results.keys():
			try:
				err_logs = d.exceptions.get(b, None)

				if d.results[b] is None:
					r = Run(config_id, b, None, None , d.time_stamps[b], err_logs)
				else:
					r = Run(config_id, b, d.results[b]['loss'], d.results[b]['info'] , d.time_stamps[b], err_logs)
				runs.append(r)
			except:
				raise
		runs.sort(key=lambda r: r.budget)
		return(runs)


	def get_learning_curves(self, lc_extractor=extract_HB_learning_curves, config_ids=None):
		"""
			extracts all learning curves from all run configurations

			Parameters:
			-----------
				lc_extractor: callable
					a function to return a list of learning_curves.
					defaults to hpbanster.HB_result.extract_HP_learning_curves
				config_ids: list of valid config ids
					if only a subset of the config ids is wanted

			Returns:
			--------
				dict
					a dictionary with the config_ids as keys and the
					learning curves as values
		"""

		config_ids = self.data.keys() if config_ids is None else config_ids
		
		lc_dict = {}
		
		for id in config_ids:
			runs = self.get_runs_by_id(id)
			lc_dict[id] = lc_extractor(runs)
			
		return(lc_dict)


	def get_all_runs(self, only_largest_budget=False):
		"""
			returns all runs performed

			Parameters:
			-----------
				only_largest_budget: boolean
					if True, only the largest budget for each configuration
					is returned. This makes sense if the runs are continued
					across budgets and the info field contains the information
					you care about. If False, all runs of a configuration
					are returned
		"""
		all_runs = []

		for k in self.data.keys():
			runs = self.get_runs_by_id(k)

			if len(runs) > 0:
				if only_largest_budget:
					all_runs.append(runs[-1])
				else:
					all_runs.extend(runs)

		return(all_runs)

	def get_id2config_mapping(self):
		"""
			returns a dict where the keys are the config_ids and the values
			are the actual configurations
		"""
		new_dict = {}
		for k, v in self.data.items():
			new_dict[k] = {}
			new_dict[k]['config'] = copy.deepcopy(v.config)
			try:
				new_dict[k]['config_info'] = copy.deepcopy(v.config_info)
			except:
				pass
		return(new_dict)

	def _merge_results(self):
		"""
			hidden function to merge the list of results into one
			dictionary and 'normalize' the time stamps
		"""
		new_dict = {}
		for it in self.data:
			new_dict.update(it)

		for k,v in new_dict.items():
			for kk, vv in v.time_stamps.items():
				for kkk,vvv in vv.items():
					new_dict[k].time_stamps[kk][kkk] = vvv - self.HB_config['time_ref']

		self.data = new_dict

	def num_iterations(self):
		return(max([k[0] for k in self.data.keys()]) + 1)
		

	def get_fANOVA_data(self, config_space, budgets=None):

		import numpy as np
		import ConfigSpace as CS

		id2conf = self.get_id2config_mapping()

		if budgets is None:
			budgets = self.HB_config['budgets']

		if len(budgets)>1:
			config_space.add_hyperparameter(CS.UniformFloatHyperparameter('budget', min(budgets), max(budgets), log=True))
		
		hp_names = list(map( lambda hp: hp.name, config_space.get_hyperparameters()))

		all_runs = self.get_all_runs(only_largest_budget=False)


		all_runs=list(filter( lambda r: r.budget in budgets, all_runs))

		X = []
		y = []

		for r in all_runs:
			if r.loss is None: continue
			config = id2conf[r.config_id]['config']
			if len(budgets)>1:
				config['budget'] = r.budget

			config = CS.Configuration(config_space, config)

			X.append([config[n] for n in hp_names])
			y.append(r.loss)

		return(np.array(X), np.array(y), config_space)




