import os, json, random, io, logging

from pulp import LpProblem, LpVariable, LpMinimize, LpStatus, lpSum

from helpers import aws_helper, tar_helper, csv_helper, service_helper, solver_helper
from lib.errors.item_generation_error import ItemGenerationError

from models.solver_run import SolverRun
from models.solution import Solution
from models.form import Form
from models.item import Item

from services.base import Base

class LoftService(Base):
  def process(self):
    try:
      self.solver_run = self.create_solver_run_from_attributes()
      self.solver_run.generate_bundles()
      self.solution = self.generate_solution()
      self.result = self.stream_to_s3_bucket()
    except ItemGenerationError as error:
      self.result = self.stream_to_s3_bucket(error)
    except TypeError as error:
      logging.error(error)
      self.result = self.stream_to_s3_bucket(ItemGenerationError("Provided params causing error in calculation results"))

  def create_solver_run_from_attributes(self):
    logging.info('Retrieving attributes from message...')
    # get s3 object
    self.key = aws_helper.get_key_from_message(self.source)
    s3_object = aws_helper.get_object(self.key, aws_helper.get_bucket_from_message(self.source))

    # convert to tar
    self.tar = tar_helper.raw_to_tar(s3_object)

    # get attributes file and convert to dict
    attributes = json.loads(tar_helper.extract_file_from_tar(self.tar , 'solver_run_attributes.json').read())

    # create solver run
    solver_run = SolverRun.parse_obj(attributes)

    # get items file and convert to dict
    items_csv = tar_helper.extract_file_from_tar(self.tar , 'items.csv')
    items_csv_reader = csv_helper.file_stream_reader(items_csv)

    # add items to solver run
    for item in service_helper.items_csv_to_dict(items_csv_reader, solver_run):
      solver_run.items.append(Item.parse_obj(item))

    logging.info('Processed Attributes...')

    return solver_run

  def generate_solution(self):
    # unsolved solution
    solution = Solution(
      response_id=random.randint(100, 5000),
      forms=[]
    )

    # counter for number of forms
    f = 0

    # iterate for number of forms that require creation
    # currently creates distinc forms with no item overlap
    while f < self.solver_run.total_forms:
      # setup vars
      items = LpVariable.dicts(
        "Item", [item.id for item in self.solver_run.items], lowBound=1, upBound=1, cat='Binary')
      bundles = LpVariable.dicts(
        "Bundle", [bundle.id for bundle in self.solver_run.bundles], lowBound=1, upBound=1, cat='Binary')

      problem_objection_functions = []

      # create problem
      problem = LpProblem("ata-form-generate", LpMinimize)

      # dummy objective function, because it just makes things easier™
      problem += lpSum([items[item.id]
                    for item in self.solver_run.items])

      # constraints
      problem += lpSum([items[item.id]
                    for item in self.solver_run.items]) == self.solver_run.total_form_items, 'Total form items'

      # dynamic constraints
      problem = solver_helper.build_constraints(self.solver_run, problem, items, bundles)

      # multi-objective constraints
      for target in self.solver_run.objective_function.tif_targets:
        problem += lpSum([item.iif(self.solver_run, target.theta)*items[item.id]
                         for item in self.solver_run.items]) >= target.value - 8, f'max tif theta ({target.theta}) target value {target.value}'
        problem += lpSum([item.iif(self.solver_run, target.theta)*items[item.id]
                         for item in self.solver_run.items]) <= target.value + 8, f'min tif theta ({target.theta}) target value {target.value}'

      for target in self.solver_run.objective_function.tcc_targets:
        problem += lpSum([item.irf(self.solver_run, target.theta)*items[item.id]
                         for item in self.solver_run.items]) >= target.value - 20, f'max tcc theta ({target.theta}) target value {target.value}'
        problem += lpSum([item.irf(self.solver_run, target.theta)*items[item.id]
                         for item in self.solver_run.items]) <= target.value + 20, f'min tcc theta ({target.theta}) target value {target.value}'

      # solve problem
      problem.solve()

      # add return items and create as a form
      form_items = service_helper.solution_items(problem.variables(), self.solver_run)

      # add form to solution
      solution.forms.append(Form.create(form_items, self.solver_run, LpStatus[problem.status]))

      # successfull form, increment
      f += 1

    return solution

  def stream_to_s3_bucket(self, error = None):
    self.file_name = f'{service_helper.key_to_uuid(self.key)}.csv'
    solution_file = None
    # setup writer buffer and write processed forms to file
    buffer = io.StringIO()

    if error:
      logging.info('Streaming %s error response to s3 bucket - %s', self.file_name, os.environ['S3_PROCESSED_BUCKET'])
      solution_file = service_helper.error_to_file(buffer, error)
    else:
      logging.info('Streaming %s to s3 bucket - %s', self.file_name, os.environ['S3_PROCESSED_BUCKET'])
      solution_file = service_helper.solution_to_file(buffer, self.solver_run.total_form_items, self.solution.forms)

    # upload generated file to s3 and return result
    return aws_helper.file_stream_upload(solution_file, self.file_name, os.environ['S3_PROCESSED_BUCKET'])