try:
    import os, sys, json
    import numpy as np
    import torch
    import torch.nn as nn
    import math
    from . import util
    HAS_TORCH = True

except Exception as e:
    HAS_TORCH = False

import arcgis
from arcgis.learn import PSPNetClassifier

try:
    import arcpy
except:
    pass

def calculate_rectangle_size_from_batch_size(batch_size):
    '''
    calculate number of rows and cols to composite a rectangle given a batch size
    :param batch_size:
    :return: number of cols and number of rows
    '''
    rectangle_height = int(math.sqrt(batch_size) + 0.5)
    rectangle_width = int(batch_size / rectangle_height)

    if rectangle_height * rectangle_width > batch_size:
        if rectangle_height >= rectangle_width:
            rectangle_height = rectangle_height - 1
        else:
            rectangle_width = rectangle_width - 1

    if (rectangle_height + 1) * rectangle_width <= batch_size:
        rectangle_height = rectangle_height + 1
    if (rectangle_width + 1) * rectangle_height <= batch_size:
        rectangle_width = rectangle_width + 1

    # swap col and row to make a horizontal rect
    if rectangle_height > rectangle_width:
        rectangle_height, rectangle_width = rectangle_width, rectangle_height

    if rectangle_height * rectangle_width != batch_size:
        return batch_size, 1

    return rectangle_height, rectangle_width


def get_tile_size(model_height, model_width, padding, batch_height, batch_width):
    '''
    Calculate request tile size given model and batch dimensions
    :param model_height:
    :param model_width:
    :param padding:
    :param batch_width:
    :param batch_height:
    :return: tile height and tile width
    '''
    tile_height = (model_height - 2 * padding) * batch_height
    tile_width = (model_width - 2 * padding) * batch_width

    return tile_height, tile_width


def tile_to_batch(pixel_block, model_height, model_width, padding, fixed_tile_size=True, **kwargs):
    inner_width = model_width - 2 * padding
    inner_height = model_height - 2 * padding

    band_count, pb_height, pb_width = pixel_block.shape
    pixel_type = pixel_block.dtype

    if fixed_tile_size is True:
        batch_height = kwargs['batch_height']
        batch_width = kwargs['batch_width']
    else:
        batch_height = math.ceil((pb_height - 2 * padding) / inner_height)
        batch_width = math.ceil((pb_width - 2 * padding) / inner_width)

    batch = np.zeros(shape=(batch_width * batch_height, band_count, model_height, model_width), dtype=pixel_type)
    for b in range(batch_width * batch_height):
        y = int(b / batch_width)
        x = int(b % batch_width)

        # pixel block might not be the shape (band_count, model_height, model_width)
        sub_pixel_block = pixel_block[:, y * inner_height: y * inner_height + model_height,
                    x * inner_width: x * inner_width + model_width]
        sub_pixel_block_shape = sub_pixel_block.shape
        batch[b, :, :sub_pixel_block_shape[1], :sub_pixel_block_shape[2]] = sub_pixel_block

    return batch, batch_height, batch_width


def batch_to_tile(batch, batch_height, batch_width):
    batch_size, bands, inner_height, inner_width = batch.shape
    tile = np.zeros(shape=(bands, inner_height * batch_height, inner_width * batch_width), dtype=batch.dtype)

    for b in range(batch_width * batch_height):
        y = int(b / batch_width)
        x = int(b % batch_width)

        tile[:, y * inner_height: (y+1) * inner_height, x * inner_width:(x+1) * inner_width] = batch[b]

    return tile

class ChildImageClassifier:

    def initialize(self, model, model_as_file):

        if not HAS_TORCH:
            raise Exception('PyTorch is not installed. Install it using conda install -c pytorch pytorch torchvision')

        if arcpy.env.processorType == "GPU" and torch.cuda.is_available():
            self.device = torch.device('cuda')
            arcgis.env._processorType = "GPU"
        else:
            self.device = torch.device('cpu')
            arcgis.env._processorType = "CPU"

        if model_as_file:
            with open(model, 'r') as f:
                self.json_info = json.load(f)
        else:
            self.json_info = json.load(model)

        model_path = self.json_info['ModelFile']
        if model_as_file and not os.path.isabs(model_path):
            model_path = os.path.abspath(os.path.join(os.path.dirname(model), model_path))

        self.psp = PSPNetClassifier.from_model(data=None, emd_path=model)
        self.model = self.psp.learn.model.to(self.device)
        self.model.eval()

        
    def getParameterInfo(self, required_parameters):
        required_parameters.extend(
            [
                {
                    'name': 'padding',
                    'dataType': 'numeric',
                    'value': int(self.json_info['ImageHeight'])//4,
                    'required': False,
                    'displayName': 'Padding',
                    'description': 'Padding'
                },
                {
                    'name': 'batch_size',
                    'dataType': 'numeric',
                    'required': False,
                    'value': 4,
                    'displayName': 'Batch Size',
                    'description': 'Batch Size'
                },
                {
                    'name': 'predict_background',
                    'dataType': 'string',
                    'required': False,
                    'value': 'True',
                    'displayName': 'Predict Background',
                    'description': 'If False, will never predict the background/NoData Class.'
                }
            ]
        )
        return required_parameters

    def getConfiguration(self, **scalars):
        self.padding = int(scalars.get('padding', self.json_info['ImageHeight'] // 4)) ## Default padding Imageheight//4.
        self.batch_size = int(math.sqrt(int(scalars.get('batch_size', 4)))) ** 2  ## Default 4 batch_size 
        self.predict_background = scalars.get('predict_background', 'true').lower() in ['true', '1', 't', 'y', 'yes']  ## Default value True

        self.rectangle_height, self.rectangle_width = calculate_rectangle_size_from_batch_size(self.batch_size)
        ty, tx = get_tile_size(self.json_info['ImageHeight'], self.json_info['ImageWidth'],
                                         self.padding, self.rectangle_height, self.rectangle_width)

        return {
            'extractBands': tuple(self.json_info['ExtractBands']),
            'padding': self.padding,
            'tx': tx,
            'ty': ty,
            'fixedTileSize': 1
        }

    def updatePixels(self, tlc, shape, props, **pixelBlocks): # 8 x 224 x 224 x 3
        input_image = pixelBlocks['raster_pixels'].astype(np.float32)
        batch, batch_height, batch_width = \
            tile_to_batch(input_image,
                                    self.json_info['ImageHeight'],
                                    self.json_info['ImageWidth'],
                                    self.padding,
                                    fixed_tile_size=True,
                                    batch_height=self.rectangle_height,
                                    batch_width=self.rectangle_width)

        semantic_predictions = util.pixel_classify_image(self.model, batch, self.device, classes=[clas['Name'] for clas in self.json_info['Classes']], predict_bg=self.predict_background, model_info=self.json_info)
        semantic_predictions = batch_to_tile(semantic_predictions.unsqueeze(dim=1).cpu().numpy(), batch_height, batch_width)
        return semantic_predictions

