# coding: utf-8
""" BigGAN utilities to prepare truncated noise samples and convert/save/display output images.
    Also comprise ImageNet utilities to prepare one hot input vectors for ImageNet classes.
    We use Wordnet so you can just input a name in a string and automatically get a corresponding
    imagenet class if it exists (or a hypo/hypernym exists in imagenet).
"""
from __future__ import absolute_import, division, print_function, unicode_literals

import json
import logging
from io import BytesIO

import numpy as np
from scipy.stats import truncnorm

logger = logging.getLogger(__name__)

NUM_CLASSES = 1000


def truncated_noise_sample(batch_size=1, dim_z=128, truncation=1., seed=None):
    """ Create a truncated noise vector.
        Params:
            batch_size: batch size.
            dim_z: dimension of z
            truncation: truncation value to use
            seed: seed for the random generator
        Output:
            array of shape (batch_size, dim_z)
    """
    state = None if seed is None else np.random.RandomState(seed)
    values = truncnorm.rvs(-2, 2, size=(batch_size, dim_z), random_state=state).astype(np.float32)
    return truncation * values


def convert_to_images(obj):
    """ Convert an output tensor from BigGAN in a list of images.
        Params:
            obj: tensor or numpy array of shape (batch_size, channels, height, width)
        Output:
            list of Pillow Images of size (height, width)
    """
    try:
        import PIL
    except ImportError:
        raise ImportError("Please install Pillow to use images: pip install Pillow")

    if not isinstance(obj, np.ndarray):
        obj = obj.detach().numpy()

    obj = obj.transpose((0, 2, 3, 1))
    obj = np.clip(((obj + 1) / 2.0) * 256, 0, 255)

    img = []
    for i, out in enumerate(obj):
        out_array = np.asarray(np.uint8(out), dtype=np.uint8)
        img.append(PIL.Image.fromarray(out_array))
    return img


def save_as_images(obj, file_name='output'):
    """ Convert and save an output tensor from BigGAN in a list of saved images.
        Params:
            obj: tensor or numpy array of shape (batch_size, channels, height, width)
            file_name: path and beggingin of filename to save.
                Images will be saved as `file_name_{image_number}.png`
    """
    img = convert_to_images(obj)

    for i, out in enumerate(img):
        current_file_name = file_name + '_%d.png' % i
        logger.info("Saving image to {}".format(current_file_name))
        out.save(current_file_name, 'png')


def display_in_terminal(obj):
    """ Convert and display an output tensor from BigGAN in the terminal.
        This function use `libsixel` and will only work in a libsixel-compatible terminal.
        Please refer to https://github.com/saitoha/libsixel for more details.

        Params:
            obj: tensor or numpy array of shape (batch_size, channels, height, width)
            file_name: path and beggingin of filename to save.
                Images will be saved as `file_name_{image_number}.png`
    """
    try:
        import PIL
        from libsixel import (sixel_output_new, sixel_dither_new, sixel_dither_initialize,
                              sixel_dither_set_palette, sixel_dither_set_pixelformat,
                              sixel_dither_get, sixel_encode, sixel_dither_unref,
                              sixel_output_unref, SIXEL_PIXELFORMAT_RGBA8888,
                              SIXEL_PIXELFORMAT_RGB888, SIXEL_PIXELFORMAT_PAL8,
                              SIXEL_PIXELFORMAT_G8, SIXEL_PIXELFORMAT_G1)
    except ImportError:
        raise ImportError("Display in Terminal requires Pillow, libsixel "
                          "and a libsixel compatible terminal. "
                          "Please read info at https://github.com/saitoha/libsixel "
                          "and install with pip install Pillow libsixel-python")

    s = BytesIO()

    images = convert_to_images(obj)
    widths, heights = zip(*(i.size for i in images))

    output_width = sum(widths)
    output_height = max(heights)

    output_image = PIL.Image.new('RGB', (output_width, output_height))

    x_offset = 0
    for im in images:
        output_image.paste(im, (x_offset,0))
        x_offset += im.size[0]

    try:
        data = output_image.tobytes()
    except NotImplementedError:
        data = output_image.tostring()
    output = sixel_output_new(lambda data, s: s.write(data), s)

    try:
        if output_image.mode == 'RGBA':
            dither = sixel_dither_new(256)
            sixel_dither_initialize(dither, data, output_width, output_height, SIXEL_PIXELFORMAT_RGBA8888)
        elif output_image.mode == 'RGB':
            dither = sixel_dither_new(256)
            sixel_dither_initialize(dither, data, output_width, output_height, SIXEL_PIXELFORMAT_RGB888)
        elif output_image.mode == 'P':
            palette = output_image.getpalette()
            dither = sixel_dither_new(256)
            sixel_dither_set_palette(dither, palette)
            sixel_dither_set_pixelformat(dither, SIXEL_PIXELFORMAT_PAL8)
        elif output_image.mode == 'L':
            dither = sixel_dither_get(SIXEL_BUILTIN_G8)
            sixel_dither_set_pixelformat(dither, SIXEL_PIXELFORMAT_G8)
        elif output_image.mode == '1':
            dither = sixel_dither_get(SIXEL_BUILTIN_G1)
            sixel_dither_set_pixelformat(dither, SIXEL_PIXELFORMAT_G1)
        else:
            raise RuntimeError('unexpected output_image mode')
        try:
            sixel_encode(data, output_width, output_height, 1, dither, output)
            print(s.getvalue().decode('ascii'))
        finally:
            sixel_dither_unref(dither)
    finally:
        sixel_output_unref(output)


def one_hot_from_int(int_or_list, batch_size=1):
    """ Create a one-hot vector from a class index or a list of class indices.
        Params:
            int_or_list: int, or list of int, of the imagenet classes (between 0 and 999)
            batch_size: batch size.
                If int_or_list is an int create a batch of identical classes.
                If int_or_list is a list, we should have `len(int_or_list) == batch_size`
        Output:
            array of shape (batch_size, 1000)
    """
    if isinstance(int_or_list, int):
        int_or_list = [int_or_list]

    if len(int_or_list) == 1 and batch_size > 1:
        int_or_list = [int_or_list[0]] * batch_size

    assert batch_size == len(int_or_list)

    array = np.zeros((batch_size, NUM_CLASSES), dtype=np.float32)
    for i, j in enumerate(int_or_list):
        array[i, j] = 1.0
    return array


def one_hot_from_names(class_name_or_list, batch_size=1):
    """ Create a one-hot vector from the name of an imagenet class ('tennis ball', 'daisy', ...).
        We use NLTK's wordnet search to try to find the relevant synset of ImageNet and take the first one.
        If we can't find it direcly, we look at the hyponyms and hypernyms of the class name.

        Params:
            class_name_or_list: string containing the name of an imagenet object or a list of such strings (for a batch).
        Output:
            array of shape (batch_size, 1000)
    """
    try:
        from nltk.corpus import wordnet as wn
    except ImportError:
        raise ImportError("You need to install nltk to use this function")

    if not isinstance(class_name_or_list, (list, tuple)):
        class_name_or_list = [class_name_or_list]
    else:
        batch_size = max(batch_size, len(class_name_or_list))

    classes = []
    for class_name in class_name_or_list:
        class_name = class_name.replace(" ", "_")

        original_synsets = wn.synsets(class_name)
        original_synsets = list(filter(lambda s: s.pos() == 'n', original_synsets))  # keep only names
        if not original_synsets:
            return None

        possible_synsets = list(filter(lambda s: s.offset() in IMAGENET, original_synsets))
        if possible_synsets:
            classes.append(IMAGENET[possible_synsets[0].offset()])
        else:
            # try hypernyms and hyponyms
            possible_synsets = sum([s.hypernyms() + s.hyponyms() for s in original_synsets], [])
            possible_synsets = list(filter(lambda s: s.offset() in IMAGENET, possible_synsets))
            if possible_synsets:
                classes.append(IMAGENET[possible_synsets[0].offset()])

    return one_hot_from_int(classes, batch_size=batch_size)


IMAGENET = {1440764: 0, 1443537: 1, 1484850: 2, 1491361: 3, 1494475: 4, 1496331: 5, 1498041: 6, 1514668: 7, 1514859: 8, 1518878: 9, 1530575: 10, 1531178: 11, 1532829: 12, 1534433: 13, 1537544: 14, 1558993: 15, 1560419: 16, 1580077: 17, 1582220: 18, 1592084: 19, 1601694: 20, 1608432: 21, 1614925: 22, 1616318: 23, 1622779: 24, 1629819: 25, 1630670: 26, 1631663: 27, 1632458: 28, 1632777: 29, 1641577: 30, 1644373: 31, 1644900: 32, 1664065: 33, 1665541: 34, 1667114: 35, 1667778: 36, 1669191: 37, 1675722: 38, 1677366: 39, 1682714: 40, 1685808: 41, 1687978: 42, 1688243: 43, 1689811: 44, 1692333: 45, 1693334: 46, 1694178: 47, 1695060: 48, 1697457: 49, 1698640: 50, 1704323: 51, 1728572: 52, 1728920: 53, 1729322: 54, 1729977: 55, 1734418: 56, 1735189: 57, 1737021: 58, 1739381: 59, 1740131: 60, 1742172: 61, 1744401: 62, 1748264: 63, 1749939: 64, 1751748: 65, 1753488: 66, 1755581: 67, 1756291: 68, 1768244: 69, 1770081: 70, 1770393: 71, 1773157: 72, 1773549: 73, 1773797: 74, 1774384: 75, 1774750: 76, 1775062: 77, 1776313: 78, 1784675: 79, 1795545: 80, 1796340: 81, 1797886: 82, 1798484: 83, 1806143: 84, 1806567: 85, 1807496: 86, 1817953: 87, 1818515: 88, 1819313: 89, 1820546: 90, 1824575: 91, 1828970: 92, 1829413: 93, 1833805: 94, 1843065: 95, 1843383: 96, 1847000: 97, 1855032: 98, 1855672: 99, 1860187: 100, 1871265: 101, 1872401: 102, 1873310: 103, 1877812: 104, 1882714: 105, 1883070: 106, 1910747: 107, 1914609: 108, 1917289: 109, 1924916: 110, 1930112: 111, 1943899: 112, 1944390: 113, 1945685: 114, 1950731: 115, 1955084: 116, 1968897: 117, 1978287: 118, 1978455: 119, 1980166: 120, 1981276: 121, 1983481: 122, 1984695: 123, 1985128: 124, 1986214: 125, 1990800: 126, 2002556: 127, 2002724: 128, 2006656: 129, 2007558: 130, 2009229: 131, 2009912: 132, 2011460: 133, 2012849: 134, 2013706: 135, 2017213: 136, 2018207: 137, 2018795: 138, 2025239: 139, 2027492: 140, 2028035: 141, 2033041: 142, 2037110: 143, 2051845: 144, 2056570: 145, 2058221: 146, 2066245: 147, 2071294: 148, 2074367: 149, 2077923: 150, 2085620: 151, 2085782: 152, 2085936: 153, 2086079: 154, 2086240: 155, 2086646: 156, 2086910: 157, 2087046: 158, 2087394: 159, 2088094: 160, 2088238: 161, 2088364: 162, 2088466: 163, 2088632: 164, 2089078: 165, 2089867: 166, 2089973: 167, 2090379: 168, 2090622: 169, 2090721: 170, 2091032: 171, 2091134: 172, 2091244: 173, 2091467: 174, 2091635: 175, 2091831: 176, 2092002: 177, 2092339: 178, 2093256: 179, 2093428: 180, 2093647: 181, 2093754: 182, 2093859: 183, 2093991: 184, 2094114: 185, 2094258: 186, 2094433: 187, 2095314: 188, 2095570: 189, 2095889: 190, 2096051: 191, 2096177: 192, 2096294: 193, 2096437: 194, 2096585: 195, 2097047: 196, 2097130: 197, 2097209: 198, 2097298: 199, 2097474: 200, 2097658: 201, 2098105: 202, 2098286: 203, 2098413: 204, 2099267: 205, 2099429: 206, 2099601: 207, 2099712: 208, 2099849: 209, 2100236: 210, 2100583: 211, 2100735: 212, 2100877: 213, 2101006: 214, 2101388: 215, 2101556: 216, 2102040: 217, 2102177: 218, 2102318: 219, 2102480: 220, 2102973: 221, 2104029: 222, 2104365: 223, 2105056: 224, 2105162: 225, 2105251: 226, 2105412: 227, 2105505: 228, 2105641: 229, 2105855: 230, 2106030: 231, 2106166: 232, 2106382: 233, 2106550: 234, 2106662: 235, 2107142: 236, 2107312: 237, 2107574: 238, 2107683: 239, 2107908: 240, 2108000: 241, 2108089: 242, 2108422: 243, 2108551: 244, 2108915: 245, 2109047: 246, 2109525: 247, 2109961: 248, 2110063: 249, 2110185: 250, 2110341: 251, 2110627: 252, 2110806: 253, 2110958: 254, 2111129: 255, 2111277: 256, 2111500: 257, 2111889: 258, 2112018: 259, 2112137: 260, 2112350: 261, 2112706: 262, 2113023: 263, 2113186: 264, 2113624: 265, 2113712: 266, 2113799: 267, 2113978: 268, 2114367: 269, 2114548: 270, 2114712: 271, 2114855: 272, 2115641: 273, 2115913: 274, 2116738: 275, 2117135: 276, 2119022: 277, 2119789: 278, 2120079: 279, 2120505: 280, 2123045: 281, 2123159: 282, 2123394: 283, 2123597: 284, 2124075: 285, 2125311: 286, 2127052: 287, 2128385: 288, 2128757: 289, 2128925: 290, 2129165: 291, 2129604: 292, 2130308: 293, 2132136: 294, 2133161: 295, 2134084: 296, 2134418: 297, 2137549: 298, 2138441: 299, 2165105: 300, 2165456: 301, 2167151: 302, 2168699: 303, 2169497: 304, 2172182: 305, 2174001: 306, 2177972: 307, 2190166: 308, 2206856: 309, 2219486: 310, 2226429: 311, 2229544: 312, 2231487: 313, 2233338: 314, 2236044: 315, 2256656: 316, 2259212: 317, 2264363: 318, 2268443: 319, 2268853: 320, 2276258: 321, 2277742: 322, 2279972: 323, 2280649: 324, 2281406: 325, 2281787: 326, 2317335: 327, 2319095: 328, 2321529: 329, 2325366: 330, 2326432: 331, 2328150: 332, 2342885: 333, 2346627: 334, 2356798: 335, 2361337: 336, 2363005: 337, 2364673: 338, 2389026: 339, 2391049: 340, 2395406: 341, 2396427: 342, 2397096: 343, 2398521: 344, 2403003: 345, 2408429: 346, 2410509: 347, 2412080: 348, 2415577: 349, 2417914: 350, 2422106: 351, 2422699: 352, 2423022: 353, 2437312: 354, 2437616: 355, 2441942: 356, 2442845: 357, 2443114: 358, 2443484: 359, 2444819: 360, 2445715: 361, 2447366: 362, 2454379: 363, 2457408: 364, 2480495: 365, 2480855: 366, 2481823: 367, 2483362: 368, 2483708: 369, 2484975: 370, 2486261: 371, 2486410: 372, 2487347: 373, 2488291: 374, 2488702: 375, 2489166: 376, 2490219: 377, 2492035: 378, 2492660: 379, 2493509: 380, 2493793: 381, 2494079: 382, 2497673: 383, 2500267: 384, 2504013: 385, 2504458: 386, 2509815: 387, 2510455: 388, 2514041: 389, 2526121: 390, 2536864: 391, 2606052: 392, 2607072: 393, 2640242: 394, 2641379: 395, 2643566: 396, 2655020: 397, 2666196: 398, 2667093: 399, 2669723: 400, 2672831: 401, 2676566: 402, 2687172: 403, 2690373: 404, 2692877: 405, 2699494: 406, 2701002: 407, 2704792: 408, 2708093: 409, 2727426: 410, 2730930: 411, 2747177: 412, 2749479: 413, 2769748: 414, 2776631: 415, 2777292: 416, 2782093: 417, 2783161: 418, 2786058: 419, 2787622: 420, 2788148: 421, 2790996: 422, 2791124: 423, 2791270: 424, 2793495: 425, 2794156: 426, 2795169: 427, 2797295: 428, 2799071: 429, 2802426: 430, 2804414: 431, 2804610: 432, 2807133: 433, 2808304: 434, 2808440: 435, 2814533: 436, 2814860: 437, 2815834: 438, 2817516: 439, 2823428: 440, 2823750: 441, 2825657: 442, 2834397: 443, 2835271: 444, 2837789: 445, 2840245: 446, 2841315: 447, 2843684: 448, 2859443: 449, 2860847: 450, 2865351: 451, 2869837: 452, 2870880: 453, 2871525: 454, 2877765: 455, 2879718: 456, 2883205: 457, 2892201: 458, 2892767: 459, 2894605: 460, 2895154: 461, 2906734: 462, 2909870: 463, 2910353: 464, 2916936: 465, 2917067: 466, 2927161: 467, 2930766: 468, 2939185: 469, 2948072: 470, 2950826: 471, 2951358: 472, 2951585: 473, 2963159: 474, 2965783: 475, 2966193: 476, 2966687: 477, 2971356: 478, 2974003: 479, 2977058: 480, 2978881: 481, 2979186: 482, 2980441: 483, 2981792: 484, 2988304: 485, 2992211: 486, 2992529: 487, 2999410: 488, 3000134: 489, 3000247: 490, 3000684: 491, 3014705: 492, 3016953: 493, 3017168: 494, 3018349: 495, 3026506: 496, 3028079: 497, 3032252: 498, 3041632: 499, 3042490: 500, 3045698: 501, 3047690: 502, 3062245: 503, 3063599: 504, 3063689: 505, 3065424: 506, 3075370: 507, 3085013: 508, 3089624: 509, 3095699: 510, 3100240: 511, 3109150: 512, 3110669: 513, 3124043: 514, 3124170: 515, 3125729: 516, 3126707: 517, 3127747: 518, 3127925: 519, 3131574: 520, 3133878: 521, 3134739: 522, 3141823: 523, 3146219: 524, 3160309: 525, 3179701: 526, 3180011: 527, 3187595: 528, 3188531: 529, 3196217: 530, 3197337: 531, 3201208: 532, 3207743: 533, 3207941: 534, 3208938: 535, 3216828: 536, 3218198: 537, 3220513: 538, 3223299: 539, 3240683: 540, 3249569: 541, 3250847: 542, 3255030: 543, 3259280: 544, 3271574: 545, 3272010: 546, 3272562: 547, 3290653: 548, 3291819: 549, 3297495: 550, 3314780: 551, 3325584: 552, 3337140: 553, 3344393: 554, 3345487: 555, 3347037: 556, 3355925: 557, 3372029: 558, 3376595: 559, 3379051: 560, 3384352: 561, 3388043: 562, 3388183: 563, 3388549: 564, 3393912: 565, 3394916: 566, 3400231: 567, 3404251: 568, 3417042: 569, 3424325: 570, 3425413: 571, 3443371: 572, 3444034: 573, 3445777: 574, 3445924: 575, 3447447: 576, 3447721: 577, 3450230: 578, 3452741: 579, 3457902: 580, 3459775: 581, 3461385: 582, 3467068: 583, 3476684: 584, 3476991: 585, 3478589: 586, 3481172: 587, 3482405: 588, 3483316: 589, 3485407: 590, 3485794: 591, 3492542: 592, 3494278: 593, 3495258: 594, 3496892: 595, 3498962: 596, 3527444: 597, 3529860: 598, 3530642: 599, 3532672: 600, 3534580: 601, 3535780: 602, 3538406: 603, 3544143: 604, 3584254: 605, 3584829: 606, 3590841: 607, 3594734: 608, 3594945: 609, 3595614: 610, 3598930: 611, 3599486: 612, 3602883: 613, 3617480: 614, 3623198: 615, 3627232: 616, 3630383: 617, 3633091: 618, 3637318: 619, 3642806: 620, 3649909: 621, 3657121: 622, 3658185: 623, 3661043: 624, 3662601: 625, 3666591: 626, 3670208: 627, 3673027: 628, 3676483: 629, 3680355: 630, 3690938: 631, 3691459: 632, 3692522: 633, 3697007: 634, 3706229: 635, 3709823: 636, 3710193: 637, 3710637: 638, 3710721: 639, 3717622: 640, 3720891: 641, 3721384: 642, 3724870: 643, 3729826: 644, 3733131: 645, 3733281: 646, 3733805: 647, 3742115: 648, 3743016: 649, 3759954: 650, 3761084: 651, 3763968: 652, 3764736: 653, 3769881: 654, 3770439: 655, 3770679: 656, 3773504: 657, 3775071: 658, 3775546: 659, 3776460: 660, 3777568: 661, 3777754: 662, 3781244: 663, 3782006: 664, 3785016: 665, 3786901: 666, 3787032: 667, 3788195: 668, 3788365: 669, 3791053: 670, 3792782: 671, 3792972: 672, 3793489: 673, 3794056: 674, 3796401: 675, 3803284: 676, 3804744: 677, 3814639: 678, 3814906: 679, 3825788: 680, 3832673: 681, 3837869: 682, 3838899: 683, 3840681: 684, 3841143: 685, 3843555: 686, 3854065: 687, 3857828: 688, 3866082: 689, 3868242: 690, 3868863: 691, 3871628: 692, 3873416: 693, 3874293: 694, 3874599: 695, 3876231: 696, 3877472: 697, 3877845: 698, 3884397: 699, 3887697: 700, 3888257: 701, 3888605: 702, 3891251: 703, 3891332: 704, 3895866: 705, 3899768: 706, 3902125: 707, 3903868: 708, 3908618: 709, 3908714: 710, 3916031: 711, 3920288: 712, 3924679: 713, 3929660: 714, 3929855: 715, 3930313: 716, 3930630: 717, 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