# Copyright 2020 The Magenta Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pdb
# Lint as: python3
"""Defines the Material Design Icons Problem."""
import io
import numpy as np
import re

from PIL import Image
from itertools import zip_longest
from skimage import draw
import sys

SVG_PREFIX_BIG = ('<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="'
                  'http://www.w3.org/1999/xlink" width="256px" height="256px"'
                  ' style="-ms-transform: rotate(360deg); -webkit-transform:'
                  ' rotate(360deg); transform: rotate(360deg);" '
                  'preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24">')
PATH_PREFIX_1 = '<path d="'
PATH_POSFIX_1 = '" fill="currentColor"/>'
SVG_POSFIX = '</svg>'

NUM_ARGS = {'v': 1, 'V': 1, 'h': 1, 'H': 1, 'a': 7, 'A': 7, 'l': 2, 'L': 2,
            't': 2, 'T': 2, 'c': 6, 'C': 6, 'm': 2, 'M': 2, 's': 4, 'S': 4,
            'q': 4, 'Q': 4, 'z': 0}
# in order of arg complexity, with absolutes clustered
# recall we don't handle all commands (see docstring)

#note  args:
# v, h: vertical horizental lines
# a: elliptical Arc 椭圆
# l:  lineto
# t: smooth quadratic Bézier curveto 2次贝塞尔曲线
# c: curveto
# m: moveto
# s: smooth curveto
# Q: quadratic Bézier curve 2次贝塞尔曲线
# z: closepath
#CMDS_LIST = 'zhvmltsqcaHVMLTSQCA'
CMDS_LIST = 'zHVMLTSQCAhvmltsqca'
CMD_MAPPING = {cmd: i for i, cmd in enumerate(CMDS_LIST)}

FEATURE_DIM = 10


############################### GENERAL UTILS #################################
def grouper(iterable, batch_size, fill_value=None):
    """Helper method for returning batches of size batch_size of a dataset."""
    # grouper('ABCDEF', 3) -> 'ABC', 'DEF'
    args = [iter(iterable)] * batch_size
    return zip_longest(*args, fillvalue=fill_value)


def _map_uni_to_alphanum(uni):
    """Maps [0-9 A-Z a-z] to numbers 0-62."""
    if 48 <= uni <= 57:
        return uni - 48
    elif 65 <= uni <= 90:
        return uni - 65 + 10
    return uni - 97 + 36


def _map_uni_to_alpha(uni):
    """Maps [A-Z a-z] to numbers 0-52."""
    if 65 <= uni <= 90:
        return uni - 65
    return uni - 97 + 26


############# UTILS FOR CONVERTING SFD/SPLINESETS TO SVG PATHS ################
def _get_spline(sfd):
    if 'SplineSet' not in sfd:
        return ''
    pro = sfd[sfd.index('SplineSet') + 10:]  # 10 is the 'SplineSet'
    pro = pro[:pro.index('EndSplineSet')]
    return pro


def _spline_to_path_list(spline, height, replace_with_prev=False):
    """Converts SplineSet to a list of tokenized commands in svg path."""
    path = []
    prev_xy = []
    for line in spline.splitlines():
        if not line:
            continue
        tokens = line.split(' ')
        cmd = tokens[-2]
        if cmd not in 'cml':
            # COMMAND NOT RECOGNIZED.
            return []
            # assert cmd in 'cml', 'Command not recognized: {}'.format(cmd)
        args = tokens[:-2]
        args = [float(x) for x in args if x]

        if replace_with_prev and cmd in 'c':
            args[:2] = prev_xy
        prev_xy = args[-2:]

        new_y_args = []
        for i, a in enumerate(args):
            if i % 2 == 1:
                new_y_args.append((height - a))
            else:
                new_y_args.append((a))

        path.append([cmd.upper()] + new_y_args)
    return path


def _sfd_to_path_list(single, replace_with_prev=False):
    """Converts the given SFD glyph into a path."""
    return _spline_to_path_list(_get_spline(single['sfd']), single['vwidth'], replace_with_prev)


#################### UTILS FOR PROCESSING TOKENIZED PATHS #####################
def _add_missing_cmds(path, remove_zs=False):
    """Adds missing cmd tags to the commands in the svg."""
    # For instance, the command 'a' takes 7 arguments, but some SVGs declare:
    #   a 1 2 3 4 5 6 7 8 9 10 11 12 13 14
    # Which is 14 arguments. This function converts the above to the equivalent:
    #   a 1 2 3 4 5 6 7  a 8 9 10 11 12 13 14
    #
    # Note: if remove_zs is True, this also removes any occurences of z commands.
    new_path = []
    for cmd in path:
        if not remove_zs or cmd[0] not in 'Zz':
            for new_cmd in add_missing_cmd(cmd):
                new_path.append(new_cmd)
    return new_path


def add_missing_cmd(command_list):
    """Adds missing cmd tags to the given command list."""
    # E.g.: given:
    #   ['a', '0', '0', '0', '0', '0', '0', '0',
    #         '0', '0', '0', '0', '0', '0', '0']
    # Converts to:
    #   [['a', '0', '0', '0', '0', '0', '0', '0'],
    #    ['a', '0', '0', '0', '0', '0', '0', '0']]
    # And returns a string that joins these elements with spaces.
    cmd_tag = command_list[0]
    args = command_list[1:]

    final_cmds = []
    for arg_batch in grouper(args, NUM_ARGS[cmd_tag]):
        final_cmds.append([cmd_tag] + list(arg_batch))

    if not final_cmds:
        # command has no args (e.g.: 'z')
        final_cmds = [[cmd_tag]]

    return final_cmds


def _normalize_args(arglist, norm, add=None, flip=False):
    """Normalize the given args with the given norm value."""
    new_arglist = []
    for i, arg in enumerate(arglist):
        new_arg = float(arg)

        if add is not None:
            add_to_x, add_to_y = add

            # This argument is an x-coordinate if even, y-coordinate if odd
            # except when flip == True
            if i % 2 == 0:
                new_arg += add_to_y if flip else add_to_x
            else:
                new_arg += add_to_x if flip else add_to_y

        new_arglist.append(str(24 * new_arg / norm))
    return new_arglist


def _normalize_based_on_viewbox(path, viewbox):
    """Normalizes all args in a path to a standard 24x24 viewbox."""
    # Each SVG lives in a 2D plane. The viewbox determines the region of that
    # plane that gets rendered. For instance, some designers may work with a
    # viewbox that's 24x24, others with one that's 100x100, etc.

    # Suppose I design the the letter "h" in the Arial style using a 100x100
    # viewbox (let's call it icon A). Let's suppose the icon has height 75. Then,
    # I design the same character using a 20x20 viewbox (call this icon B), with
    # height 15 (=75% of 20). This means that, when rendered, both icons with look
    # exactly the same, but the scale of the commands each icon is using is
    # different. For instance, if icon A has a command like "lineTo 100 100", the
    # equivalent command in icon B will be "lineTo 20 20".

    # In order to avoid this problem and bring all real values to the same scale,
    # I scale all icons' commands to use a 24x24 viewbox. This function does this:
    # it converts a path that exists in the given viewbox into a standard 24x24
    # viewbox.
    viewbox = viewbox.split(' ')
    norm = max(int(viewbox[-1]), int(viewbox[-2]))

    if int(viewbox[-1]) > int(viewbox[-2]):
        add_to_y = 0
        add_to_x = abs(int(viewbox[-1]) - int(viewbox[-2])) / 2
    else:
        add_to_y = abs(int(viewbox[-1]) - int(viewbox[-2])) / 2
        add_to_x = 0

    new_path = []
    for command in path:
        if command[0] == 'a':
            new_path.append([command[0]] + _normalize_args(command[1:3], norm)
                            + command[3:6] + _normalize_args(command[6:], norm))
        elif command[0] == 'A':
            new_path.append([command[0]] + _normalize_args(command[1:3], norm)
                            + command[3:6] + _normalize_args(command[6:], norm, add=(add_to_x, add_to_y)))
        elif command[0] == 'V':
            new_path.append([command[0]] + _normalize_args(command[1:], norm, add=(add_to_x, add_to_y), flip=True))
        elif command[0] == command[0].upper():
            new_path.append([command[0]] + _normalize_args(command[1:], norm, add=(add_to_x, add_to_y)))
        elif command[0] in 'zZ':
            new_path.append([command[0]])
        else:
            new_path.append([command[0]] + _normalize_args(command[1:], norm))

    return new_path


def _convert_args(args, curr_pos, cmd):
    """Converts given args to relative values."""
    # NOTE: glyphs only use a very small subset of commands (L, C, M, and Z -- I
    # believe). So I'm not handling A and H for now.
    if cmd in 'AH':
        raise NotImplementedError('These commands have >6 args (not supported).')

    new_args = []
    for i, arg in enumerate(args):
        x_or_y = i % 2
        if cmd == 'H':
            x_or_y = (i + 1) % 2
        new_args.append(str(float(arg) - curr_pos[x_or_y]))

    return new_args


def _update_curr_pos(curr_pos, cmd, start_of_path):
    """Calculate the position of the pen after cmd is applied."""
    if cmd[0] in 'ml':
        curr_pos = [curr_pos[0] + float(cmd[1]), curr_pos[1] + float(cmd[2])]
        if cmd[0] == 'm':
            start_of_path = curr_pos
    elif cmd[0] in 'z':
        curr_pos = start_of_path
    elif cmd[0] in 'h':
        curr_pos = [curr_pos[0] + float(cmd[1]), curr_pos[1]]
    elif cmd[0] in 'v':
        curr_pos = [curr_pos[0], curr_pos[1] + float(cmd[1])]
    elif cmd[0] in 'ctsqa':
        curr_pos = [curr_pos[0] + float(cmd[-2]), curr_pos[1] + float(cmd[-1])]

    return curr_pos, start_of_path


def _make_relative(cmds):
    """Convert commands in a path to relative positioning."""
    curr_pos = (0.0, 0.0)
    start_of_path = (0.0, 0.0)
    new_cmds = []
    for cmd in cmds:
        if cmd[0].lower() == cmd[0]:
            new_cmd = cmd
        elif cmd[0].lower() == 'z':
            new_cmd = [cmd[0].lower()]
        else:
            new_cmd = [cmd[0].lower()] + _convert_args(cmd[1:], curr_pos, cmd=cmd[0])
        new_cmds.append(new_cmd)
        curr_pos, start_of_path = _update_curr_pos(curr_pos, new_cmd, start_of_path)
    return new_cmds


def _is_to_left_of(pt1, pt2):
    pt1_norm = (pt1[0]**2 + pt1[1]**2)
    pt2_norm = (pt2[0]**2 + pt2[1]**2)
    return pt1[1] < pt2[1] or (pt1_norm == pt2_norm and pt1[0] < pt2[0])


def _get_leftmost_point(path):
    """Returns the leftmost, topmost point of the path."""
    leftmost = (float('inf'), float('inf'))
    idx = -1

    for i, cmd in enumerate(path):
        if len(cmd) > 1:
            endpoint = cmd[-2:]
            if _is_to_left_of(endpoint, leftmost):
                leftmost = endpoint
                idx = i

    return leftmost, idx


def _separate_substructures(path):
    """Returns a list of subpaths, each representing substructures the glyph."""
    substructures = []
    curr = []
    for cmd in path:
        if cmd[0] in 'mM' and curr:
            substructures.append(curr)
            curr = []
        curr.append(cmd)
    if curr:
        substructures.append(curr)
    return substructures


def _is_clockwise(subpath):
    """Returns whether the given subpath is clockwise-oriented."""
    pts = [cmd[-2:] for cmd in subpath]
    det = 0
    for i in range(len(pts) - 1):
        det += np.linalg.det(pts[i:i + 2])
    return det > 0


def _make_clockwise(subpath):
    """Inverts the cardinality of the given subpath."""
    new_path = [subpath[0]]
    other_cmds = list(reversed(subpath[1:]))
    for i, cmd in enumerate(other_cmds):
        if i + 1 == len(other_cmds):
            where_we_were = subpath[0][-2:]
        else:
            where_we_were = other_cmds[i + 1][-2:]

        if len(cmd) > 3:
            new_cmd = [cmd[0], cmd[3], cmd[4], cmd[1], cmd[2],
                       where_we_were[0], where_we_were[1]]
        else:
            new_cmd = [cmd[0], where_we_were[0], where_we_were[1]]

        new_path.append(new_cmd)
    return new_path


def _canonicalize(path):
    """Makes all paths start at top left, and go clockwise first."""
    # convert args to floats
    #print(len(path),path)
    
    path = [[x[0]] + list(map(float, x[1:])) for x in path]
   # print(len(path),path)
    
    # _canonicalize each subpath separately
    #pdb.set_trace()
    
    new_substructures = []
    for subpath in _separate_substructures(path):
      #  print(subpath,"\n")
        leftmost_point, leftmost_idx = _get_leftmost_point(subpath)
        reordered = ([['M', leftmost_point[0], leftmost_point[1]]] + subpath[leftmost_idx + 1:] + subpath[1:leftmost_idx + 1])
        new_substructures.append((reordered, leftmost_point))
        
   # sys.exit()
    new_path = []
    first_substructure_done = False
    should_flip_cardinality = False
    for sp, _ in sorted(new_substructures, key=lambda x: (x[1][1], x[1][0])):
        if not first_substructure_done:
            # we're looking at the first substructure now, we can determine whether we
            # will flip the cardniality of the whole icon or not
            should_flip_cardinality = not _is_clockwise(sp)
            first_substructure_done = True

        if should_flip_cardinality:
            sp = _make_clockwise(sp)

        new_path.extend(sp)

    # convert args to strs
    path = [[x[0]] + list(map(str, x[1:])) for x in new_path]
    return path


# ######### UTILS FOR CONVERTING TOKENIZED PATHS TO VECTORS ###########
def _path_to_vector(path, categorical=False):
    """Converts path's commands to a series of vectors."""
    # Notes:
    #   - The SimpleSVG dataset does not have any 't', 'q', 'Z', 'T', or 'Q'.
    #     Thus, we don't handle those here.
    #   - We also removed all 'z's.
    #   - The x-axis-rotation argument to a commands is always 0 in this
    #     dataset, so we ignore it

    # Many commands have args that correspond to args in other commands.
    #   v  __,__ _______________ ______________,_________ __,__ __,__ _,y
    #   h  __,__ _______________ ______________,_________ __,__ __,__ x,_
    #   z  __,__ _______________ ______________,_________ __,__ __,__ _,_
    #   a  rx,ry x-axis-rotation large-arc-flag,sweepflag __,__ __,__ x,y
    #   l  __,__ _______________ ______________,_________ __,__ __,__ x,y
    #   c  __,__ _______________ ______________,_________ x1,y1 x2,y2 x,y
    #   m  __,__ _______________ ______________,_________ __,__ __,__ x,y
    #   s  __,__ _______________ ______________,_________ __,__ x2,y2 x,y

    # So each command will be converted to a vector where the dimension is the
    # minimal number of arguments to all commands:
    #   [rx, ry, large-arc-flag, sweepflag, x1, y1, x2, y2, x, y]
    # If a command does not output a certain arg, it is set to 0.
    #   "l 5,5" becomes [0, 0, 0, 0, 0, 0, 0, 0, 5, 5]

    # Also note, as of now we also output an extra dimension at index 0, which
    # indicates which command is being outputted (integer).
    new_path = []
    for cmd in path:
        new_path.append(_cmd_to_vector(cmd, categorical=categorical))
    return new_path


def _cmd_to_vector(cmd_list, categorical=False):
    """Converts the given command (given as a list) into a vector.
    UM_ARGS = {'v': 1, 'V': 1, 'h': 1, 'H': 1, 'a': 7, 'A': 7, 'l': 2, 'L': 2,
            't': 2, 'T': 2, 'c': 6, 'C': 6, 'm': 2, 'M': 2, 's': 4, 'S': 4,
            'q': 4, 'Q': 4, 'z': 0}
            
    CMDS_LIST = 'zhvmltsqcaHVMLTSQCA'
    CMD_MAPPING = {cmd: i for i, cmd in enumerate(CMDS_LIST)}
    """
    # For description of how this conversion happens, see
    # _path_to_vector docstring.
    cmd = cmd_list[0]
    args = cmd_list[1:]

    if not categorical:
        # integer, for MSE
        command = [float(CMD_MAPPING[cmd])]
    else:
        # one hot + 1 dim for EOS.
        command = [0.0] * (len(CMDS_LIST) + 1) # 大概有19个commands?
        command[CMD_MAPPING[cmd] + 1] = 1.0

    arguments = [0.0] * 10
    if cmd in 'hH':
        arguments[8] = float(args[0])  # x
    elif cmd in 'vV':
        arguments[9] = float(args[0])  # y
    elif cmd in 'mMlLtT':
        arguments[8] = float(args[0])  # x
        arguments[9] = float(args[1])  # y
    elif cmd in 'sSqQ':
        arguments[6] = float(args[0])  # x2
        arguments[7] = float(args[1])  # y2
        arguments[8] = float(args[2])  # x
        arguments[9] = float(args[3])  # y
    elif cmd in 'cC':
        arguments[4] = float(args[0])  # x1
        arguments[5] = float(args[1])  # y1
        arguments[6] = float(args[2])  # x2
        arguments[7] = float(args[3])  # y2
        arguments[8] = float(args[4])  # x
        arguments[9] = float(args[5])  # y
    elif cmd in 'aA':
        arguments[0] = float(args[0])  # rx
        arguments[1] = float(args[1])  # ry
        # we skip x-axis-rotation
        arguments[2] = float(args[3])  # large-arc-flag
        arguments[3] = float(args[4])  # sweep-flag
        # a does not have x1, y1, x2, y2 args
        arguments[8] = float(args[5])  # x
        arguments[9] = float(args[6])  # y

    return command + arguments


################## UTILS FOR RENDERING PATH INTO IMAGE #################
def _cubicbezier(x0, y0, x1, y1, x2, y2, x3, y3, n=40):
    """Return n points along cubiz bezier with given control points."""
    # from http://rosettacode.org/wiki/Bitmap/B%C3%A9zier_curves/Cubic
    pts = []
    for i in range(n + 1):
        t = float(i) / float(n)
        a = (1. - t)**3
        b = 3. * t * (1. - t)**2
        c = 3.0 * t**2 * (1.0 - t)
        d = t**3

        x = float(a * x0 + b * x1 + c * x2 + d * x3)
        y = float(a * y0 + b * y1 + c * y2 + d * y3)
        pts.append((x, y))
    return list(zip(*pts))


def _update_pos(curr_pos, end_pos, absolute):
    if absolute:
        return end_pos
    return curr_pos[0] + end_pos[0], curr_pos[1] + end_pos[1]


def constant_color(*unused_args):
    return np.array([255, 255, 255])


def _render_cubic(canvas, curr_pos, c_args, absolute, color):
    """Renders a cubic bezier curve in the given canvas."""
    if not absolute:
        c_args[0] += curr_pos[0]
        c_args[1] += curr_pos[1]
        c_args[2] += curr_pos[0]
        c_args[3] += curr_pos[1]
        c_args[4] += curr_pos[0]
        c_args[5] += curr_pos[1]
    x, y = _cubicbezier(curr_pos[0], curr_pos[1],
                        c_args[0], c_args[1],
                        c_args[2], c_args[3],
                        c_args[4], c_args[5])
    max_possible = len(canvas)
    x = [int(round(x_)) for x_ in x]
    y = [int(round(y_)) for y_ in y]

    def within_range(x):
        return 0 <= x < max_possible

    filtered = [(x_, y_) for x_, y_ in zip(x, y)
                if within_range(x_) and within_range(y_)]
    if not filtered:
        return
    x, y = list(zip(*filtered))
    canvas[y, x, :] = color


def _render_line(canvas, curr_pos, l_args, absolute, color):
    """Renders a line in the given canvas."""
    end_point = l_args
    if not absolute:
        end_point[0] += curr_pos[0]
        end_point[1] += curr_pos[1]
    rr, cc, val = draw.line_aa(int(curr_pos[0]), int(curr_pos[1]),
                               int(end_point[0]), int(end_point[1]))

    max_possible = len(canvas)

    def within_range(x):
        return 0 <= x < max_possible

    filtered = [(x, y, v) for x, y, v in zip(rr, cc, val)
                if within_range(x) and within_range(y)]
    if not filtered:
        return
    rr, cc, val = list(zip(*filtered))
    val = [(v * color) for v in val]
    canvas[cc, rr, :] = val


def _per_step_render(path, absolute=False, color=constant_color):
    """Render the icon's edges, given its path."""
    def to_canvas_size(l):
        return [float(f) * (64. / 24.) for f in l]

    canvas = np.zeros((64, 64, 3))
    curr_pos = (0.0, 0.0)
    for i, cmd in enumerate(path):
        if not cmd:
            continue
        if cmd[0] in 'mM':
            curr_pos = _update_pos(curr_pos, to_canvas_size(cmd[-2:]), absolute)
        elif cmd[0] in 'cC':
            _render_cubic(canvas, curr_pos, to_canvas_size(cmd[1:]), absolute, color(i, 55))
            curr_pos = _update_pos(curr_pos, to_canvas_size(cmd[-2:]), absolute)
        elif cmd[0] in 'lL':
            _render_line(canvas, curr_pos, to_canvas_size(cmd[1:]), absolute, color(i, 55))
            curr_pos = _update_pos(curr_pos, to_canvas_size(cmd[1:]), absolute)

    return canvas


def _zoom_out(path_list, add_baseline=0., per=22):
    """Makes glyph slightly smaller in viewbox, makes some descenders visible."""
    # assumes tensor is already unnormalized, and in long form
    new_path = []
    for command in path_list:
        args = []
        is_even = False
        for arg in command[1:]:
            if is_even:
                args.append(str(float(arg) - ((24. - per) / 24.) * 64. / 4.))
                is_even = False
            else:
                args.append(str(float(arg) - add_baseline))
                is_even = True
        new_path.append([command[0]] + args)
    return new_path


##################### UTILS FOR PROCESSING VECTORS ################
def _append_eos(sample, categorical, feature_dim):
    if not categorical:
        eos = -1 * np.ones(feature_dim)
    else:
        eos = np.zeros(feature_dim)
        eos[0] = 1.0
    sample.append(eos)
    return sample


def _make_simple_cmds_long(out):
    """Converts svg decoder output to format required by some render functions."""
    # out has 10 dims
    # the first 4 are respectively dims 0, 4, 5, 9 of the full 20-dim onehot vec
    # the latter 6 are the 6 last dims of the 10-dim arg vec
    shape_minus_dim = list(np.shape(out))[:-1]
   # print("make?  ",shape_minus_dim ) # [51]
                              
    return np.concatenate([out[..., :1], # [51,1]  51个steps的第1维特征
                           np.zeros(shape_minus_dim + [3]),# [51,3]
                           out[..., 1:3], #[51,2]
                           np.zeros(shape_minus_dim + [3]),# [51,3]
                           out[..., 3:4],# [51,1]
                           np.zeros(shape_minus_dim + [14]),# [51,14]
                           out[..., 4:]], -1)# [51,6] # 最后的6个绘制参数

def render(tensor, data_dir=None):
    """Converts SVG decoder output into HTML svg."""
    # undo normalization
    # mean_npz, stdev_npz = get_means_stdevs(data_dir)
    # tensor = (tensor * stdev_npz) + mean_npz

    # convert to html
    #print("before",tensor.shape)# 51, 10)
    tensor = _make_simple_cmds_long(tensor)
   # print("after",tensor.shape)#(51, 30)
    # vector = np.squeeze(np.squeeze(tensor, 0), 2)
   # print("1",tensor[0,:5])# (51, 30)
    html = _vector_to_svg(tensor, stop_at_eos=True, categorical=True)
   # print(html.shape)
    # some aesthetic postprocessing
    html = postprocess(html)
    html = html.replace('256px', '50px')

    return html

################# UTILS FOR CONVERTING VECTORS TO SVGS ########################
#note: transform the decoded trg_seq into the common svg format.把decode出来的seq转成html的svg，命令有前后关系，也都是相对位置。
def _vector_to_svg(vectors, stop_at_eos=False, categorical=False):
    """Tranforms a given vector to an svg string.
    
    """
    new_path = []
    for vector in vectors:
        if stop_at_eos:
            if categorical:
                try:
                    is_eos = np.argmax(vector[:len(CMDS_LIST) + 1]) == 0
                except Exception:
                    raise Exception(vector)
            else:
                is_eos = vector[0] < -0.5

            if is_eos:
                break
        new_path.append(' '.join(_vector_to_cmd(vector, categorical=categorical))) #
    new_path = ' '.join(new_path) # 加入new_path，每个path都以空格分隔
    return SVG_PREFIX_BIG + PATH_PREFIX_1 + new_path + PATH_POSFIX_1 + SVG_POSFIX

def _vector_to_path(vectors):
    new_path = []
    for vector in vectors:
        #print(vector,"???")
        new_path.append(_vector_to_cmd(vector,categorical=True)) #
        #print(_vector_to_cmd(vector),"hhh")
   # new_path = ' '.join(new_path) # 加入new_path，每个path都以空格分隔
    return new_path

def _vector_to_cmd(vector, categorical=False, return_floats=False):
    """Does the inverse transformation as _cmd_to_vector().
        UM_ARGS = {'v': 1, 'V': 1, 'h': 1, 'H': 1, 'a': 7, 'A': 7, 'l': 2, 'L': 2,
            't': 2, 'T': 2, 'c': 6, 'C': 6, 'm': 2, 'M': 2, 's': 4, 'S': 4,
            'q': 4, 'Q': 4, 'z': 0}
            
    CMDS_LIST = 'zhvmltsqcaHVMLTSQCA'
    CMD_MAPPING = {cmd: i for i, cmd in enumerate(CMDS_LIST)}

    """
    cast_fn = float if return_floats else str
    if categorical:
       # print(vector.shape,vector)# 30
        #print("??",len(CMDS_LIST)) # 19
        command = vector[:len(CMDS_LIST) + 1],# 前20维
        arguments = vector[len(CMDS_LIST) + 1:]# 后10维
        cmd_idx = np.argmax(command) - 1 # 看当前绘制命令属于哪一类
       
    else:
        
        command, arguments = vector[:1], vector[1:]
        cmd_idx = int(round(command[0]))

    if cmd_idx < -0.5:
        # EOS
        return []
    if cmd_idx >= len(CMDS_LIST):
        cmd_idx = len(CMDS_LIST) - 1

    cmd = CMDS_LIST[cmd_idx]
    cmd = cmd.upper()
    cmd_list = [cmd]

    if cmd in 'hH': # 如果是画线，而且是x轴
        cmd_list.append(cast_fn(arguments[8]))  # x
    elif cmd in 'vV': # 如果是画线，而且是y轴
        cmd_list.append(cast_fn(arguments[9]))  # y
    elif cmd in 'mMlLtT':
        cmd_list.append(cast_fn(arguments[8]))  # x
        cmd_list.append(cast_fn(arguments[9]))  # y
    elif cmd in 'sSqQ':
        cmd_list.append(cast_fn(arguments[6]))  # x2
        cmd_list.append(cast_fn(arguments[7]))  # y2
        cmd_list.append(cast_fn(arguments[8]))  # x
        cmd_list.append(cast_fn(arguments[9]))  # y
    elif cmd in 'cC':
        cmd_list.append(cast_fn(arguments[4]))  # x1
        cmd_list.append(cast_fn(arguments[5]))  # y1
        cmd_list.append(cast_fn(arguments[6]))  # x2
        cmd_list.append(cast_fn(arguments[7]))  # y2
        cmd_list.append(cast_fn(arguments[8]))  # x
        cmd_list.append(cast_fn(arguments[9]))  # y
    elif cmd in 'aA':
        cmd_list.append(cast_fn(arguments[0]))  # rx
        cmd_list.append(cast_fn(arguments[1]))  # ry
        # x-axis-rotation is always 0
        cmd_list.append(cast_fn('0'))
        # the following two flags are binary.
        cmd_list.append(cast_fn(1 if arguments[2] > 0.5 else 0))  # large-arc-flag
        cmd_list.append(cast_fn(1 if arguments[3] > 0.5 else 0))  # sweep-flag
        cmd_list.append(cast_fn(arguments[8]))  # x
        cmd_list.append(cast_fn(arguments[9]))  # y
   
    return cmd_list


############## UTILS FOR CONVERTING SVGS/VECTORS TO IMAGES ###################

# From Infer notebook
start = ("""<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www."""
         """w3.org/1999/xlink" width="256px" height="256px" style="-ms-trans"""
         """form: rotate(360deg); -webkit-transform: rotate(360deg); transfo"""
         """rm: rotate(360deg);" preserveAspectRatio="xMidYMid meet" viewBox"""
         """="0 0 24 24"><path d=\"""")
end = """\" fill="currentColor"/></svg>"""

COMMAND_RX = re.compile("([MmLlHhVvCcSsQqTtAaZz])")
FLOAT_RX = re.compile("[-+]?[0-9]*\.?[0-9]+(?:[eE][-+]?[0-9]+)?")  # noqa


def svg_html_to_path_string(svg):
    return svg.replace(start, '').replace(end, '')


def _tokenize(pathdef):
    """Returns each svg token from path list."""
    # e.g.: 'm0.1-.5c0,6' -> m', '0.1, '-.5', 'c', '0', '6'
    for x in COMMAND_RX.split(pathdef):
        if x != '' and x in 'MmLlHhVvCcSsQqTtAaZz':
            yield x
        for token in FLOAT_RX.findall(x):
            yield token


def path_string_to_tokenized_commands(path):
    """Tokenizes the given path string.

    E.g.:
        Given M 0.5 0.5 l 0.25 0.25 z
        Returns [['M', '0.5', '0.5'], ['l', '0.25', '0.25'], ['z']]
    """
    new_path = []
    current_cmd = []
    for token in _tokenize(path):
        if len(current_cmd) > 0:
            if token in 'MmLlHhVvCcSsQqTtAaZz':
                # cmd ended, convert to vector and add to new_path
                new_path.append(current_cmd)
                current_cmd = [token]
            else:
                # add arg to command
                current_cmd.append(token)
        else:
            # add to start new cmd
            current_cmd.append(token)

    if current_cmd:
        # process command still unprocessed
        new_path.append(current_cmd)

    return new_path


def separate_substructures(tokenized_commands):
  """Returns a list of SVG substructures."""
  # every moveTo command starts a new substructure
  # an SVG substructure is a subpath that closes on itself
  # such as the outter and the inner edge of the character `o`
  substructures = []
  curr = []
  for cmd in tokenized_commands:
    if cmd[0] in 'mM' and len(curr) > 0:
      substructures.append(curr)
      curr = []
    curr.append(cmd)
  if len(curr) > 0:
    substructures.append(curr)
  return substructures


def postprocess(svg, dist_thresh=2., skip=False):
    path = svg_html_to_path_string(svg)
    #print(svg)
    svg_template = svg.replace(path, '{}')
    tokenized_commands = path_string_to_tokenized_commands(path)

    def dist(a, b):
        return np.sqrt((float(a[0]) - float(b[0]))**2 + (float(a[1]) - float(b[1]))**2)

    def are_close_together(a, b, t):
        return dist(a, b) < t

    # first, go through each start/end point and merge if they're close enough
    # together (that is, make end point the same as the start point).
    # TODO: there are better ways of doing this, in a way that propagates errors. 
    # back (so if total error is 0.2, go through all N commands in this
    # substructure and fix each by 0.2/N (unless they have 0 vertical change))
    # NOTE: this is the same.  
    substructures = separate_substructures(tokenized_commands)
   # print(len(substructures))# 7578

    previous_substructure_endpoint = (0., 0.,)
    for substructure in substructures:
        # first, if the last substructure's endpoint was updated, we must update
        # the start point of this one to reflect the opposite update
        substructure[0][-2] = str(float(substructure[0][-2]) -
                                  previous_substructure_endpoint[0])
        substructure[0][-1] = str(float(substructure[0][-1]) -
                                  previous_substructure_endpoint[1])

        start = list(map(float, substructure[0][-2:]))
        curr_pos = (0., 0.)
        for cmd in substructure:
            curr_pos, _ = _update_curr_pos(curr_pos, cmd, (0., 0.))
        if are_close_together(start, curr_pos, dist_thresh):
            new_point = np.array(start)
            previous_substructure_endpoint = ((new_point[0] - curr_pos[0]),
                                              (new_point[1] - curr_pos[1]))
            substructure[-1][-2] = str(float(substructure[-1][-2]) +
                                       (new_point[0] - curr_pos[0]))
            substructure[-1][-1] = str(float(substructure[-1][-1]) +
                                       (new_point[1] - curr_pos[1]))
            if substructure[-1][0] in 'cC':
                substructure[-1][-4] = str(float(substructure[-1][-4]) +
                                           (new_point[0] - curr_pos[0]))
                substructure[-1][-3] = str(float(substructure[-1][-3]) +
                                           (new_point[1] - curr_pos[1]))

    if skip:
        return svg_template.format(' '.join([' '.join(' '.join(cmd) for cmd in s)
                                             for s in substructures]))

    def cosa(x, y):
        return (x[0] * y[0] + x[1] * y[1]) / ((np.sqrt(x[0]**2 + x[1]**2) * np.sqrt(y[0]**2 + y[1]**2)))

    def rotate(a, x, y):
        return (x * np.cos(a) - y * np.sin(a), y * np.cos(a) + x * np.sin(a))
    # second, gotta find adjacent bezier curves and, if their control points
    # are well enough aligned, fully align them
    for substructure in substructures:
        curr_pos = (0., 0.)
        new_curr_pos, _ = _update_curr_pos((0., 0.,), substructure[0], (0., 0.))

        for cmd_idx in range(1, len(substructure)):
            prev_cmd = substructure[cmd_idx-1]
            cmd = substructure[cmd_idx]

            new_new_curr_pos, _ = _update_curr_pos(new_curr_pos, cmd, (0., 0.))

            if cmd[0] == 'c':
                if prev_cmd[0] == 'c':
                    # check the vectors and update if needed
                    # previous control pt wrt new curr point
                    prev_ctr_point = (curr_pos[0] + float(prev_cmd[3]) - new_curr_pos[0],
                                      curr_pos[1] + float(prev_cmd[4]) - new_curr_pos[1])
                    ctr_point = (float(cmd[1]), float(cmd[2]))

                    if -1. < cosa(prev_ctr_point, ctr_point) < -0.95:
                        # calculate exact angle between the two vectors
                        angle_diff = (np.pi - np.arccos(cosa(prev_ctr_point, ctr_point)))/2

                        # rotate each vector by angle/2 in the correct direction for each.
                        sign = np.sign(np.cross(prev_ctr_point, ctr_point))
                        new_ctr_point = rotate(sign * angle_diff, *ctr_point)
                        new_prev_ctr_point = rotate(-sign * angle_diff, *prev_ctr_point)

                        # override the previous control points
                        # (which has to be wrt previous curr position)
                        substructure[cmd_idx-1][3] = str(new_prev_ctr_point[0] -
                                                         curr_pos[0] + new_curr_pos[0])
                        substructure[cmd_idx-1][4] = str(new_prev_ctr_point[1] -
                                                         curr_pos[1] + new_curr_pos[1])
                        substructure[cmd_idx][1] = str(new_ctr_point[0])
                        substructure[cmd_idx][2] = str(new_ctr_point[1])

            curr_pos = new_curr_pos
            new_curr_pos = new_new_curr_pos
        
   # print('0',substructures)
    return svg_template.format(' '.join([' '.join(' '.join(cmd) for cmd in s)
                                         for s in substructures]))





# def get_means_stdevs(data_dir):
#     """Returns the means and stdev saved in data_dir."""
#     if data_dir not in means_stdevs:
#         with tf.gfile.Open(os.path.join(data_dir, 'mean.npz'), 'r') as f:
#             mean_npz = np.load(f)
#         with tf.gfile.Open(os.path.join(data_dir, 'stdev.npz'), 'r') as f:
#             stdev_npz = np.load(f)
#         means_stdevs[data_dir] = (mean_npz, stdev_npz)
#     return means_stdevs[data_dir]




###############


def convert_to_svg(decoder_output, categorical=False):
    converted = []
    for example in decoder_output:
        converted.append(_vector_to_svg(example, True, categorical=categorical))
    return np.array(converted)


def create_image_conversion_fn(max_outputs, categorical=False):
    """Binds the number of outputs to the image conversion fn (to svg or png)."""
    def convert_to_svg(decoder_output):
        converted = []
        for example in decoder_output:
            if len(converted) == max_outputs:
                break
            converted.append(_vector_to_svg(example, True, categorical=categorical))
        return np.array(converted)

    return convert_to_svg


################### UTILS FOR CREATING TF SUMMARIES ##########################
def _make_encoded_image(img_tensor):
    pil_img = Image.fromarray(np.squeeze(img_tensor * 255).astype(np.uint8), mode='L')
    buff = io.BytesIO()
    pil_img.save(buff, format='png')
    encoded_image = buff.getvalue()
    return encoded_image


################### CHECK GLYPH/PATH VALID ##############################################
def is_valid_glyph(g):
    is_09 = 48 <= g['uni'] <= 57
    is_capital_az = 65 <= g['uni'] <= 90
    is_az = 97 <= g['uni'] <= 122
    is_valid_dims = g['width'] != 0 and g['vwidth'] != 0
    return (is_09 or is_capital_az or is_az) and is_valid_dims


def is_valid_path(pathunibfp):
   # print(len(pathunibfp[0]))
    if len(pathunibfp[0])>70:
        print("!!!more than 400",len(pathunibfp[0]))
       # sys.exit()
    return pathunibfp[0] and len(pathunibfp[0]) <= 70,len(pathunibfp[0])


################### DATASET PROCESSING #######################################
def convert_to_path(g):
    """Converts SplineSet in SFD font to str path."""
    path = _sfd_to_path_list(g)
    path = _add_missing_cmds(path, remove_zs=False)
    path = _normalize_based_on_viewbox(path, '0 0 {} {}'.format(g['width'], g['vwidth']))
    return path, g['uni'], g['binary_fp']
def convert_simple_vector_to_path(seq):
    path=[]
    for i in range(seq.shape[0]):
       # seq_i = seq[i]
        path_i=[]
        cmd  = np.argmax(seq[i][:4])
       # args = seq[i][4:]
        p0 = seq[i][4:6]
        p1 = seq[i][6:8]
        p2 = seq[i][8:10]
        if cmd == 0:
            break
        elif cmd==1:
            path_i.append('M')
            path_i.append(str(p2[0]))
            path_i.append(str(p2[1]))
        elif cmd==2:
            path_i.append('L')
            path_i.append(str(p2[0]))
            path_i.append(str(p2[1]))
        elif cmd==3:
            path_i.append('C')
            path_i.append(str(p0[0]))
            path_i.append(str(p0[1]))
            path_i.append(str(p1[0]))
            path_i.append(str(p1[1]))
            path_i.append(str(p2[0]))
            path_i.append(str(p2[1]))
        else:
            print("wrong!!! to path")
            sys.exit()
        path.append(path_i)
    return path
   # print("jjj")
def clockwise(seq):
    #pdb.set_trace()
    path=convert_simple_vector_to_path(seq)
    path = _canonicalize(path)
    final = {}
    final['rendered'] = _per_step_render(path, absolute=True)
    vector = _path_to_vector(path, categorical=True)
    vector = np.array(vector)
  #  print(vector.shape,vector[:,9])# note vector: 12,30
  
    vector = np.concatenate([np.take(vector, [0, 4, 5, 9], axis=-1), vector[..., -6:]], axis=-1)
    final['seq_len'] = np.shape(vector)[0]
    vector = _append_eos(vector.tolist(), True, 10)
    final['sequence'] = np.concatenate((vector, np.zeros(((70 - final['seq_len']), 10))), 0)
    final['rendered'] = np.reshape(final['rendered'][..., 0], [64 * 64]).astype(np.float32).tolist()
    return final
    
    
def create_example(pathunibfp):
    """Bulk of dataset processing. Converts str path to np array"""
    path, uni, binary_fp = pathunibfp
    final = {}

    # zoom out
    path = _zoom_out(path)
    # make clockwise
    path = _canonicalize(path)

    # render path for training
    final['rendered'] = _per_step_render(path, absolute=True)

    # make path relative
    #path = _make_relative(path) # note 不rela 直接是绝对的
    # convert to vector
    vector = _path_to_vector(path, categorical=True)



   # path2 = _vector_to_path(vector)# note vector转成path
    #print(path2)
    #print(path==path2)
    
    vector = np.array(vector)
  #  print(vector.shape,vector[:,9])# note vector: 12,30
    vector = np.concatenate([np.take(vector, [0, 4, 5, 9], axis=-1), vector[..., -6:]], axis=-1)
    
    

    #path2 = _vector_to_path(vector)
    #print(path,"\nhhh",path2)
    
    
    # print("hhh",vector)
    # print(render(vector))
    # sys.exit()
    # count some stats
    final['seq_len'] = np.shape(vector)[0]
    # final['class'] = int(_map_uni_to_alphanum(uni))
    final['class'] = int(_map_uni_to_alpha(uni))
    final['binary_fp'] = str(binary_fp)

    # append eos
    vector = _append_eos(vector.tolist(), True, 10)

    # pad path to 51 (with eos)
#    pdb.set_trace()
    # if final['seq_len']>50:
        
   # print( final['seq_len'])
    final['sequence'] = np.concatenate((vector, np.zeros(((70 - final['seq_len']), 10))), 0)
    #seq = final['sequence']
    
    # new_path = convert_simple_vector_to_path(seq)
    # print(new_path,path2==new_path)
    
   # sys.exit()
    # make pure list:
    # use last channel only
    final['rendered'] = np.reshape(final['rendered'][..., 0], [64 * 64]).astype(np.float32).tolist()
    final['sequence'] = np.reshape(final['sequence'], [71 * 10]).astype(np.float32).tolist()
    final['class'] = np.reshape(final['class'], [1]).astype(np.int64).tolist()
    final['seq_len'] = np.reshape(final['seq_len'], [1]).astype(np.int64).tolist()
    return final


def mean_to_example(mean_stdev):
    """Converts the found mean and stdev to example."""
    # mean_stdev is a dict
    mean_stdev['mean'] = np.reshape(mean_stdev['mean'], [10]).astype(np.float32).tolist()
    mean_stdev['variance'] = np.reshape(mean_stdev['variance'], [10]).astype(np.float32).tolist()
    mean_stdev['stddev'] = np.reshape(mean_stdev['stddev'], [10]).astype(np.float32).tolist()
    mean_stdev['count'] = np.reshape(mean_stdev['count'], [1]).astype(np.int64).tolist()
    return mean_stdev


################### CHECK VALID ##############################################
class MeanStddev:
    """Accumulator to compute the mean/stdev of svg commands."""

    def create_accumulator(self):
        curr_sum = np.zeros([10])
        sum_sq = np.zeros([10])
        return (curr_sum, sum_sq, 0)  # x, x^2, count

    def add_input(self, sum_count, new_input):
        (curr_sum, sum_sq, count) = sum_count
        # new_input is a dict with keys = ['seq_len', 'sequence']
        new_seq_len = new_input['seq_len'][0]  # Line #754 'seq_len' is a list of one int
        assert isinstance(new_seq_len, int), print(type(new_seq_len))

        # remove padding and eos from sequence
        assert isinstance(new_input['sequence'], list), print(type(new_input['sequence']))
        new_input_np = np.reshape(np.array(new_input['sequence']), [-1, 10])
        assert isinstance(new_input_np, np.ndarray), print(type())
        assert new_input_np.shape[0] >= new_seq_len
        new_input_np = new_input_np[:new_seq_len, :]

        # accumulate new_sum and new_sum_sq
        new_sum = np.sum([curr_sum, np.sum(new_input_np, axis=0)], axis=0)
        new_sum_sq = np.sum([sum_sq, np.sum(np.power(new_input_np, 2), axis=0)],
                            axis=0)
        return new_sum, new_sum_sq, count + new_seq_len

    def merge_accumulators(self, accumulators):
        curr_sums, sum_sqs, counts = list(zip(*accumulators))
        return np.sum(curr_sums, axis=0), np.sum(sum_sqs, axis=0), np.sum(counts)

    def extract_output(self, sum_count):
        (curr_sum, curr_sum_sq, count) = sum_count
        if count:
            mean = np.divide(curr_sum, count)
            variance = np.divide(curr_sum_sq, count) - np.power(mean, 2)
            # -ve value could happen due to rounding
            variance = np.max([variance, np.zeros(np.shape(variance))], axis=0)
            stddev = np.sqrt(variance)
            return {
                'mean': mean,
                'variance': variance,
                'stddev': stddev,
                'count': count
            }
        else:
            return {
                'mean': float('NaN'),
                'variance': float('NaN'),
                'stddev': float('NaN'),
                'count': 0
            }