openrec/modeling/decoders/dptr_parseq_clip_b_decoder.py

# Scene Text Recognition Model Hub
# Copyright 2022 Darwin Bautista
#
# 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
#
#     https://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 math
from itertools import permutations
from collections import OrderedDict
import hashlib
import os
import gzip
import html
import urllib
import warnings
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torch.nn.modules import transformer
from typing import Any, Optional, Tuple, List, Union
from pkg_resources import packaging
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from tqdm import tqdm
from functools import lru_cache

import ftfy
import regex as re

try:
    from torchvision.transforms import InterpolationMode
    BICUBIC = InterpolationMode.BICUBIC
except ImportError:
    BICUBIC = Image.BICUBIC


@lru_cache()
def default_bpe():
    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")


@lru_cache()
def bytes_to_unicode():
    """

    Returns list of utf-8 byte and a corresponding list of unicode strings.

    The reversible bpe codes work on unicode strings.

    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.

    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.

    This is a signficant percentage of your normal, say, 32K bpe vocab.

    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.

    And avoids mapping to whitespace/control characters the bpe code barfs on.

    """
    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
    cs = bs[:]
    n = 0
    for b in range(2**8):
        if b not in bs:
            bs.append(b)
            cs.append(2**8+n)
            n += 1
    cs = [chr(n) for n in cs]
    return dict(zip(bs, cs))


def get_pairs(word):
    """Return set of symbol pairs in a word.

    Word is represented as tuple of symbols (symbols being variable-length strings).

    """
    pairs = set()
    prev_char = word[0]
    for char in word[1:]:
        pairs.add((prev_char, char))
        prev_char = char
    return pairs


def basic_clean(text):
    text = ftfy.fix_text(text)
    text = html.unescape(html.unescape(text))
    return text.strip()


def whitespace_clean(text):
    text = re.sub(r'\s+', ' ', text)
    text = text.strip()
    return text


class SimpleTokenizer(object):
    def __init__(self, bpe_path: str = default_bpe()):
        self.byte_encoder = bytes_to_unicode()
        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
        merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
        merges = merges[1:49152-256-2+1]
        merges = [tuple(merge.split()) for merge in merges]
        vocab = list(bytes_to_unicode().values())
        vocab = vocab + [v+'</w>' for v in vocab]
        for merge in merges:
            vocab.append(''.join(merge))
        vocab.extend(['<|startoftext|>', '<|endoftext|>'])
        self.encoder = dict(zip(vocab, range(len(vocab))))
        self.decoder = {v: k for k, v in self.encoder.items()}
        self.bpe_ranks = dict(zip(merges, range(len(merges))))
        self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
        self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)

    def bpe(self, token):
        if token in self.cache:
            return self.cache[token]
        word = tuple(token[:-1]) + ( token[-1] + '</w>',)
        pairs = get_pairs(word)

        if not pairs:
            return token+'</w>'

        while True:
            bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
            if bigram not in self.bpe_ranks:
                break
            first, second = bigram
            new_word = []
            i = 0
            while i < len(word):
                try:
                    j = word.index(first, i)
                    new_word.extend(word[i:j])
                    i = j
                except:
                    new_word.extend(word[i:])
                    break

                if word[i] == first and i < len(word)-1 and word[i+1] == second:
                    new_word.append(first+second)
                    i += 2
                else:
                    new_word.append(word[i])
                    i += 1
            new_word = tuple(new_word)
            word = new_word
            if len(word) == 1:
                break
            else:
                pairs = get_pairs(word)
        word = ' '.join(word)
        self.cache[token] = word
        return word

    def encode(self, text):
        bpe_tokens = []
        text = whitespace_clean(basic_clean(text)).lower()
        for token in re.findall(self.pat, text):
            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
        return bpe_tokens

    def decode(self, tokens):
        text = ''.join([self.decoder[token] for token in tokens])
        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
        return text


if packaging.version.parse(torch.__version__) < packaging.version.parse("1.7.1"):
    warnings.warn("PyTorch version 1.7.1 or higher is recommended")


__all__ = ["available_models", "load", "tokenize"]
_tokenizer = SimpleTokenizer()

_MODELS = {
    "RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt",
    "RN101": "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt",
    "RN50x4": "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt",
    "RN50x16": "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt",
    "RN50x64": "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt",
    "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
    "ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt",
    "ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt",
    "ViT-L/14@336px": "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt",
}


def convert_weights(model: nn.Module):
    """Convert applicable model parameters to fp16"""

    def _convert_weights_to_fp16(l):
        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
            l.weight.data = l.weight.data.half()
            if l.bias is not None:
                l.bias.data = l.bias.data.half()

        if isinstance(l, nn.MultiheadAttention):
            for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
                tensor = getattr(l, attr)
                if tensor is not None:
                    tensor.data = tensor.data.half()

        for name in ["text_projection", "proj"]:
            if hasattr(l, name):
                attr = getattr(l, name)
                if attr is not None:
                    attr.data = attr.data.half()

    model.apply(_convert_weights_to_fp16)


def build_model(state_dict: dict):
    vit = "visual.proj" in state_dict

    if vit:
        vision_width = state_dict["visual.conv1.weight"].shape[0]
        vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
        vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
        grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
        image_resolution = vision_patch_size * grid_size
    else:
        counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
        vision_layers = tuple(counts)
        vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
        output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
        vision_patch_size = None
        assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
        image_resolution = output_width * 32

    embed_dim = state_dict["text_projection"].shape[1]
    context_length = state_dict["positional_embedding"].shape[0]
    vocab_size = state_dict["token_embedding.weight"].shape[0]
    transformer_width = state_dict["ln_final.weight"].shape[0]
    transformer_heads = transformer_width // 64
    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith("transformer.resblocks")))

    model = CLIP(
        embed_dim,
        image_resolution, vision_layers, vision_width, vision_patch_size,
        context_length, vocab_size, transformer_width, transformer_heads, transformer_layers
    )

    for key in ["input_resolution", "context_length", "vocab_size"]:
        if key in state_dict:
            del state_dict[key]

    convert_weights(model)
    model.load_state_dict(state_dict)
    return model.eval()


def _download(url: str, root: str):
    os.makedirs(root, exist_ok=True)
    filename = os.path.basename(url)

    expected_sha256 = url.split("/")[-2]
    download_target = os.path.join(root, filename)

    if os.path.exists(download_target) and not os.path.isfile(download_target):
        raise RuntimeError(f"{download_target} exists and is not a regular file")

    if os.path.isfile(download_target):
        if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256:
            return download_target
        else:
            warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")

    with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
        with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop:
            while True:
                buffer = source.read(8192)
                if not buffer:
                    break

                output.write(buffer)
                loop.update(len(buffer))

    if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256:
        raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match")

    return download_target


def _convert_image_to_rgb(image):
    return image.convert("RGB")


def _transform(n_px):
    return Compose([
        Resize(n_px, interpolation=BICUBIC),
        CenterCrop(n_px),
        _convert_image_to_rgb,
        ToTensor(),
        Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
    ])


def available_models() -> List[str]:
    """Returns the names of available CLIP models"""
    return list(_MODELS.keys())


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1):
        super().__init__()

        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu1 = nn.ReLU(inplace=True)

        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.relu2 = nn.ReLU(inplace=True)

        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()

        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu3 = nn.ReLU(inplace=True)

        self.downsample = None
        self.stride = stride

        if stride > 1 or inplanes != planes * Bottleneck.expansion:
            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
            self.downsample = nn.Sequential(OrderedDict([
                ("-1", nn.AvgPool2d(stride)),
                ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
                ("1", nn.BatchNorm2d(planes * self.expansion))
            ]))

    def forward(self, x: torch.Tensor):
        identity = x

        out = self.relu1(self.bn1(self.conv1(x)))
        out = self.relu2(self.bn2(self.conv2(out)))
        out = self.avgpool(out)
        out = self.bn3(self.conv3(out))

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu3(out)
        return out


class AttentionPool2d(nn.Module):
    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
        super().__init__()
        self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
        self.k_proj = nn.Linear(embed_dim, embed_dim)
        self.q_proj = nn.Linear(embed_dim, embed_dim)
        self.v_proj = nn.Linear(embed_dim, embed_dim)
        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
        self.num_heads = num_heads

    def forward(self, x):
        x = x.flatten(start_dim=2).permute(2, 0, 1)  # NCHW -> (HW)NC
        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
        x, _ = F.multi_head_attention_forward(
            query=x[:1], key=x, value=x,
            embed_dim_to_check=x.shape[-1],
            num_heads=self.num_heads,
            q_proj_weight=self.q_proj.weight,
            k_proj_weight=self.k_proj.weight,
            v_proj_weight=self.v_proj.weight,
            in_proj_weight=None,
            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
            bias_k=None,
            bias_v=None,
            add_zero_attn=False,
            dropout_p=0,
            out_proj_weight=self.c_proj.weight,
            out_proj_bias=self.c_proj.bias,
            use_separate_proj_weight=True,
            training=self.training,
            need_weights=False
        )
        return x.squeeze(0)


class ModifiedResNet(nn.Module):
    """

    A ResNet class that is similar to torchvision's but contains the following changes:

    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.

    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1

    - The final pooling layer is a QKV attention instead of an average pool

    """

    def __init__(self, layers, output_dim, heads, input_resolution=224, width=64):
        super().__init__()
        self.output_dim = output_dim
        self.input_resolution = input_resolution

        # the 3-layer stem
        self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(width // 2)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(width // 2)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
        self.bn3 = nn.BatchNorm2d(width)
        self.relu3 = nn.ReLU(inplace=True)
        self.avgpool = nn.AvgPool2d(2)

        # residual layers
        self._inplanes = width  # this is a *mutable* variable used during construction
        self.layer1 = self._make_layer(width, layers[0])
        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
        self.layer4 = self._make_layer(width * 8, layers[3], stride=2)

        embed_dim = width * 32  # the ResNet feature dimension
        self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim)

    def _make_layer(self, planes, blocks, stride=1):
        layers = [Bottleneck(self._inplanes, planes, stride)]

        self._inplanes = planes * Bottleneck.expansion
        for _ in range(1, blocks):
            layers.append(Bottleneck(self._inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        def stem(x):
            x = self.relu1(self.bn1(self.conv1(x)))
            x = self.relu2(self.bn2(self.conv2(x)))
            x = self.relu3(self.bn3(self.conv3(x)))
            x = self.avgpool(x)
            return x

        x = x.type(self.conv1.weight.dtype)
        x = stem(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.attnpool(x)

        return x


class LayerNorm(nn.LayerNorm):
    """Subclass torch's LayerNorm to handle fp16."""

    def forward(self, x: torch.Tensor):
        orig_type = x.dtype
        ret = super().forward(x.type(torch.float32))
        return ret.type(orig_type)


class QuickGELU(nn.Module):
    def forward(self, x: torch.Tensor):
        return x * torch.sigmoid(1.702 * x)


class ResidualAttentionBlock(nn.Module):
    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
        super().__init__()

        self.attn = nn.MultiheadAttention(d_model, n_head)
        self.ln_1 = LayerNorm(d_model)
        self.mlp = nn.Sequential(OrderedDict([
            ("c_fc", nn.Linear(d_model, d_model * 4)),
            ("gelu", QuickGELU()),
            ("c_proj", nn.Linear(d_model * 4, d_model))
        ]))
        self.ln_2 = LayerNorm(d_model)
        self.attn_mask = attn_mask

    def attention(self, x: torch.Tensor):
        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]

    def forward(self, x: torch.Tensor):
        x = x + self.attention(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x


class Transformer(nn.Module):
    def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
        super().__init__()
        self.width = width
        self.layers = layers
        self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])

    def forward(self, x: torch.Tensor):
        return self.resblocks(x)


class VisionTransformer(nn.Module):
    def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int):
        super().__init__()
        self.input_resolution = input_resolution
        self.output_dim = output_dim
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)

        scale = width ** -0.5
        self.class_embedding = nn.Parameter(scale * torch.randn(width))
        self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
        self.ln_pre = LayerNorm(width)

        self.transformer = Transformer(width, layers, heads)

        self.ln_post = LayerNorm(width)
        self.proj = nn.Parameter(scale * torch.randn(width, output_dim))

    def forward(self, x: torch.Tensor):
        x = self.conv1(x)  # shape = [*, width, grid, grid]
        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
        x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
        x = x + self.positional_embedding.to(x.dtype)
        x = self.ln_pre(x)

        x = x.permute(1, 0, 2)  # NLD -> LND
        x = self.transformer(x)
        x = x.permute(1, 0, 2)  # LND -> NLD

        x = self.ln_post(x)
        if self.proj is not None:
            x = x @ self.proj

        return x


class CLIP(nn.Module):
    def __init__(self,

                 embed_dim: int,

                 # vision

                 image_resolution: int,

                 vision_layers: Union[Tuple[int, int, int, int], int],

                 vision_width: int,

                 vision_patch_size: int,

                 # text

                 context_length: int,

                 vocab_size: int,

                 transformer_width: int,

                 transformer_heads: int,

                 transformer_layers: int

                 ):
        super().__init__()

        self.context_length = context_length

        if isinstance(vision_layers, (tuple, list)):
            vision_heads = vision_width * 32 // 64
            self.visual = ModifiedResNet(
                layers=vision_layers,
                output_dim=embed_dim,
                heads=vision_heads,
                input_resolution=image_resolution,
                width=vision_width
            )
        else:
            vision_heads = vision_width // 64
            self.visual = VisionTransformer(
                input_resolution=image_resolution,
                patch_size=vision_patch_size,
                width=vision_width,
                layers=vision_layers,
                heads=vision_heads,
                output_dim=embed_dim
            )

        self.transformer = Transformer(
            width=transformer_width,
            layers=transformer_layers,
            heads=transformer_heads,
            attn_mask=self.build_attention_mask()
        )

        self.vocab_size = vocab_size
        self.token_embedding = nn.Embedding(vocab_size, transformer_width)
        self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
        self.ln_final = LayerNorm(transformer_width)

        self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))

        self.initialize_parameters()

    def initialize_parameters(self):
        nn.init.normal_(self.token_embedding.weight, std=0.02)
        nn.init.normal_(self.positional_embedding, std=0.01)

        if isinstance(self.visual, ModifiedResNet):
            if self.visual.attnpool is not None:
                std = self.visual.attnpool.c_proj.in_features ** -0.5
                nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
                nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
                nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
                nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)

            for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
                for name, param in resnet_block.named_parameters():
                    if name.endswith("bn3.weight"):
                        nn.init.zeros_(param)

        proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
        attn_std = self.transformer.width ** -0.5
        fc_std = (2 * self.transformer.width) ** -0.5
        for block in self.transformer.resblocks:
            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)

        if self.text_projection is not None:
            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)

    def build_attention_mask(self):
        # lazily create causal attention mask, with full attention between the vision tokens
        # pytorch uses additive attention mask; fill with -inf
        mask = torch.empty(self.context_length, self.context_length)
        mask.fill_(float("-inf"))
        mask.triu_(1)  # zero out the lower diagonal
        return mask

    @property
    def dtype(self):
        return self.visual.conv1.weight.dtype

    def encode_image(self, image):
        return self.visual(image.type(self.dtype))

    def encode_text(self, text):
        x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]

        x = x + self.positional_embedding.type(self.dtype)
        x = x.permute(1, 0, 2)  # NLD -> LND
        x = self.transformer(x)
        x = x.permute(1, 0, 2)  # LND -> NLD
        x = self.ln_final(x).type(self.dtype)

        # take features from the eot embedding (eot_token is the highest number in each sequence)
        output = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
        output = torch.cat([output.unsqueeze(1), x], dim=1)

        return output

    def forward(self, image, text):
        image_features = self.encode_image(image)
        text_features = self.encode_text(text)

        # normalized features
        image_features = image_features / image_features.norm(dim=1, keepdim=True)
        text_features = text_features / text_features.norm(dim=1, keepdim=True)

        # cosine similarity as logits
        logit_scale = self.logit_scale.exp()
        logits_per_image = logit_scale * image_features @ text_features.t()
        logits_per_text = logits_per_image.t()

        # shape = [global_batch_size, global_batch_size]
        return logits_per_image, logits_per_text


class FMU(nn.Module):
    """A Transformer decoder layer supporting two-stream attention (XLNet)

       This implements a pre-LN decoder, as opposed to the post-LN default in PyTorch."""

    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation='gelu',

                 layer_norm_eps=1e-5):
        super().__init__()
        self.cross_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=True)
        # Implementation of Feedforward model
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.linear2 = nn.Linear(dim_feedforward, d_model)

        self.norm = nn.LayerNorm(d_model, eps=layer_norm_eps)

        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.dropout3 = nn.Dropout(dropout)

        self.activation = transformer._get_activation_fn(activation)

    def __setstate__(self, state):
        if 'activation' not in state:
            state['activation'] = F.gelu
        super().__setstate__(state)

    def forward(self, query: Tensor, memory: Tensor):
        """Forward pass for a single stream (i.e. content or query)

        tgt_norm is just a LayerNorm'd tgt. Added as a separate parameter for efficiency.

        Both tgt_kv and memory are expected to be LayerNorm'd too.

        memory is LayerNorm'd by ViT.

        """
        query1, ca_weights = self.cross_attn(query, memory, memory)
        query = query + self.dropout1(query1)

        query2 = self.linear2(self.dropout2(self.activation(self.linear1(self.norm(query)))))
        query = query + self.dropout3(query2)

        return query


class DecoderLayer(nn.Module):
    """A Transformer decoder layer supporting two-stream attention (XLNet) This

    implements a pre-LN decoder, as opposed to the post-LN default in

    PyTorch."""

    def __init__(

            self,

            d_model,

            nhead,

            dim_feedforward=2048,

            dropout=0.1,

            activation='gelu',

            layer_norm_eps=1e-5,

    ):
        super().__init__()
        self.self_attn = nn.MultiheadAttention(d_model,
                                               nhead,
                                               dropout=dropout,
                                               batch_first=True)
        self.cross_attn = nn.MultiheadAttention(d_model,
                                                nhead,
                                                dropout=dropout,
                                                batch_first=True)
        # Implementation of Feedforward model
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)

        self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps)
        self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
        self.norm_q = nn.LayerNorm(d_model, eps=layer_norm_eps)
        self.norm_c = nn.LayerNorm(d_model, eps=layer_norm_eps)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.dropout3 = nn.Dropout(dropout)

        self.activation = transformer._get_activation_fn(activation)

    def __setstate__(self, state):
        if 'activation' not in state:
            state['activation'] = F.gelu
        super().__setstate__(state)

    def forward_stream(

            self,

            tgt: Tensor,

            tgt_norm: Tensor,

            tgt_kv: Tensor,

            memory: Tensor,

            tgt_mask: Optional[Tensor],

            tgt_key_padding_mask: Optional[Tensor],

    ):
        """Forward pass for a single stream (i.e. content or query) tgt_norm is

        just a LayerNorm'd tgt.



        Added as a separate parameter for efficiency. Both tgt_kv and memory

        are expected to be LayerNorm'd too. memory is LayerNorm'd by ViT.

        """
        tgt2, sa_weights = self.self_attn(
        tgt_norm,
        tgt_kv,
        tgt_kv,
        attn_mask=tgt_mask,
        key_padding_mask=tgt_key_padding_mask)

        tgt = tgt + self.dropout1(tgt2)

        tgt2, ca_weights = self.cross_attn(self.norm1(tgt), memory, memory)
        self.attn_map = ca_weights
        tgt = tgt + self.dropout2(tgt2)

        tgt2 = self.linear2(
            self.dropout(self.activation(self.linear1(self.norm2(tgt)))))
        tgt = tgt + self.dropout3(tgt2)
        return tgt, sa_weights, ca_weights

    def forward(

            self,

            query,

            content,

            memory,

            query_mask: Optional[Tensor] = None,

            content_mask: Optional[Tensor] = None,

            content_key_padding_mask: Optional[Tensor] = None,

            update_content: bool = True,

    ):
        query_norm = self.norm_q(query)
        content_norm = self.norm_c(content)
        query = self.forward_stream(query, query_norm, content_norm, memory,
                                    query_mask, content_key_padding_mask)[0]
        if update_content:
            content = self.forward_stream(content, content_norm, content_norm,
                                          memory, content_mask,
                                          content_key_padding_mask)[0]
        return query, content


class Decoder(nn.Module):
    __constants__ = ['norm']

    def __init__(self, decoder_layer, num_layers, norm):
        super().__init__()
        self.layers = transformer._get_clones(decoder_layer, num_layers)
        self.num_layers = num_layers
        self.norm = norm

    def forward(

            self,

            query,

            content,

            memory,

            query_mask: Optional[Tensor] = None,

            content_mask: Optional[Tensor] = None,

            content_key_padding_mask: Optional[Tensor] = None,

    ):
        for i, mod in enumerate(self.layers):
            last = i == len(self.layers) - 1
            query, content = mod(
                query,
                content,
                memory,
                query_mask,
                content_mask,
                content_key_padding_mask,
                update_content=not last,
            )
        query = self.norm(query)
        return query


class TokenEmbedding(nn.Module):

    def __init__(self, charset_size: int, embed_dim: int):
        super().__init__()
        self.embedding = nn.Embedding(charset_size, embed_dim)
        self.embed_dim = embed_dim

    def forward(self, tokens: torch.Tensor):
        return math.sqrt(self.embed_dim) * self.embedding(tokens)


def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit: bool = False, download_root: str = None):
    """Load a CLIP model



    Parameters

    ----------

    name : str

        A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict



    device : Union[str, torch.device]

        The device to put the loaded model



    jit : bool

        Whether to load the optimized JIT model or more hackable non-JIT model (default).



    download_root: str

        path to download the model files; by default, it uses "~/.cache/clip"



    Returns

    -------

    model : torch.nn.Module

        The CLIP model



    preprocess : Callable[[PIL.Image], torch.Tensor]

        A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input

    """
    if name in _MODELS:
        model_path = _download(_MODELS[name], download_root or os.path.expanduser("~/.cache/clip"))
    elif os.path.isfile(name):
        model_path = name
    else:
        raise RuntimeError(f"Model {name} not found; available models = {available_models()}")

    with open(model_path, 'rb') as opened_file:
        try:
            # loading JIT archive
            model = torch.jit.load(opened_file, map_location=device if jit else "cpu").eval()
            state_dict = None
        except RuntimeError:
            # loading saved state dict
            if jit:
                warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
                jit = False
            state_dict = torch.load(opened_file, map_location="cpu")

    if not jit:
        model = build_model(state_dict or model.state_dict()).to(device)
        if str(device) == "cpu":
            model.float()
        return model, _transform(model.visual.input_resolution)

    # patch the device names
    device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
    device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]

    def patch_device(module):
        try:
            graphs = [module.graph] if hasattr(module, "graph") else []
        except RuntimeError:
            graphs = []

        if hasattr(module, "forward1"):
            graphs.append(module.forward1.graph)

        for graph in graphs:
            for node in graph.findAllNodes("prim::Constant"):
                if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"):
                    node.copyAttributes(device_node)

    model.apply(patch_device)
    patch_device(model.encode_image)
    patch_device(model.encode_text)

    # patch dtype to float32 on CPU
    if str(device) == "cpu":
        float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])
        float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
        float_node = float_input.node()

        def patch_float(module):
            try:
                graphs = [module.graph] if hasattr(module, "graph") else []
            except RuntimeError:
                graphs = []

            if hasattr(module, "forward1"):
                graphs.append(module.forward1.graph)

            for graph in graphs:
                for node in graph.findAllNodes("aten::to"):
                    inputs = list(node.inputs())
                    for i in [1, 2]:  # dtype can be the second or third argument to aten::to()
                        if inputs[i].node()["value"] == 5:
                            inputs[i].node().copyAttributes(float_node)

        model.apply(patch_float)
        patch_float(model.encode_image)
        patch_float(model.encode_text)

        model.float()

    return model, _transform(model.input_resolution.item())

def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> Union[torch.IntTensor, torch.LongTensor]:
    """

    Returns the tokenized representation of given input string(s)



    Parameters

    ----------

    texts : Union[str, List[str]]

        An input string or a list of input strings to tokenize



    context_length : int

        The context length to use; all CLIP models use 77 as the context length



    truncate: bool

        Whether to truncate the text in case its encoding is longer than the context length



    Returns

    -------

    A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length].

    We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long.

    """
    if isinstance(texts, str):
        texts = [texts]

    sot_token = _tokenizer.encoder["<|startoftext|>"]
    eot_token = _tokenizer.encoder["<|endoftext|>"]
    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
    if packaging.version.parse(torch.__version__) < packaging.version.parse("1.8.0"):
        result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
    else:
        result = torch.zeros(len(all_tokens), context_length, dtype=torch.int)

    for i, tokens in enumerate(all_tokens):
        if len(tokens) > context_length:
            if truncate:
                tokens = tokens[:context_length]
                tokens[-1] = eot_token
            else:
                raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
        result[i, :len(tokens)] = torch.tensor(tokens)

    return result

class DptrParseq(nn.Module):

    def __init__(self,

                 in_channels,

                 out_channels,

                 max_label_length=25,

                 embed_dim=512,

                 dec_num_heads=8,

                 dec_mlp_ratio=4,

                 dec_depth=6,

                 perm_num=6,

                 perm_forward=True,

                 perm_mirrored=True,

                 decode_ar=True,

                 refine_iters=1,

                 dropout=0.1,

                 is_pretrain=True,

                 ORP_path=None,

                 **kwargs: Any) -> None:
        super().__init__()
        self.pad_id = out_channels - 1
        self.eos_id = 0
        self.bos_id = out_channels - 2
        self.max_label_length = max_label_length
        self.decode_ar = decode_ar
        self.refine_iters = refine_iters
        self.is_pretrain = is_pretrain
        if not is_pretrain:
            self.token_query = nn.Parameter(torch.Tensor(1, 26, embed_dim))
            self.fmu = FMU(embed_dim, dec_num_heads, embed_dim * dec_mlp_ratio, dropout)

        decoder_layer = DecoderLayer(embed_dim, dec_num_heads, embed_dim * dec_mlp_ratio, dropout)
        self.decoder = Decoder(decoder_layer,
                               num_layers=dec_depth,
                               norm=nn.LayerNorm(embed_dim))

        # Perm/attn mask stuff
        self.rng = np.random.default_rng()
        self.max_gen_perms = perm_num // 2 if perm_mirrored else perm_num
        self.perm_forward = perm_forward
        self.perm_mirrored = perm_mirrored

        # We don't predict <bos> nor <pad>
        self.head = nn.Linear(embed_dim, out_channels - 2)
        self.text_embed = TokenEmbedding(out_channels, embed_dim)

        # +1 for <eos>
        self.pos_queries = nn.Parameter(
            torch.Tensor(1, max_label_length + 1, embed_dim))
        self.dropout = nn.Dropout(p=dropout)
        # Encoder has its own init.
        self.apply(self._init_weights)
        nn.init.trunc_normal_(self.pos_queries, std=0.02)

        if is_pretrain:
            self.clip_encoder, preprocess = load("ViT-B/16")
            for p in self.clip_encoder.parameters():
                p.requires_grad = False
            if ORP_path is None:
                background_image_folder_path = 'background_mages_folder/path'
                self.background_features = self.get_noise(background_image_folder_path, preprocess)
                torch.save(self.background_features, 'save/noise/to/ORP_path')
            else:
                self.background_features = torch.load(ORP_path, map_location='cpu')

    def _init_weights(self, module: nn.Module):
        """Initialize the weights using the typical initialization schemes used

        in SOTA models."""

        if isinstance(module, nn.Linear):
            nn.init.trunc_normal_(module.weight, std=0.02)
            if module.bias is not None:
                nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            nn.init.trunc_normal_(module.weight, std=0.02)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.Conv2d):
            nn.init.kaiming_normal_(module.weight,
                                    mode='fan_out',
                                    nonlinearity='relu')
            if module.bias is not None:
                nn.init.zeros_(module.bias)
        elif isinstance(module, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm)):
            nn.init.ones_(module.weight)
            nn.init.zeros_(module.bias)

    @torch.jit.ignore
    def no_weight_decay(self):
        param_names = {'text_embed.embedding.weight', 'pos_queries'}
        return param_names

    def get_noise(self, background_image_path, preprocess):
        image_paths = [os.path.join(background_image_path, filename) for filename in os.listdir(image_folder_path) if
                       filename.endswith(('.png', '.jpg', '.jpeg'))]
        features = []
        for image_path in image_paths:
            image = Image.open(image_path)
            input = preprocess(image).unsqueeze(0).to(self._device)
            with torch.no_grad():
                feature = self.clip_encoder.encode_image(input)
            features.append(feature)
            image.close()
        return torch.cat(features).cpu().numpy()

    def clip_encode(self, labels):
        text_inputs = torch.cat([tokenize(f"a photo of a '{c}'") for c in labels]).to(self._device)

        return self.clip_encoder.encode_text(text_inputs)

    def decode(

            self,

            tgt: torch.Tensor,

            memory: torch.Tensor,

            tgt_mask: Optional[Tensor] = None,

            tgt_padding_mask: Optional[Tensor] = None,

            tgt_query: Optional[Tensor] = None,

            tgt_query_mask: Optional[Tensor] = None,

            pos_query: torch.Tensor = None,

    ):
        N, L = tgt.shape
        # <bos> stands for the null context. We only supply position information for characters after <bos>.
        null_ctx = self.text_embed(tgt[:, :1])

        if tgt_query is None:
            tgt_query = pos_query[:, :L]
        tgt_emb = pos_query[:, :L - 1] + self.text_embed(tgt[:, 1:])
        tgt_emb = self.dropout(torch.cat([null_ctx, tgt_emb], dim=1))

        tgt_query = self.dropout(tgt_query)
        return self.decoder(tgt_query, tgt_emb, memory, tgt_query_mask,
                            tgt_mask, tgt_padding_mask)

    def forward(self, memory, data=None, pos_query=None):
        # print(memory.shape, data[0].shape)
        if self.training:
            if self.is_pretrain:
                return self.training_step(None, pos_query, data[0], memory)
            return self.training_step(memory, pos_query, data[0], None)
        else:
            if self.is_pretrain:
                return self.forward_test(None, memory, pos_query)
            return self.forward_test(memory, None, pos_query)

    def forward_test(self,

                     memory: Tensor, clip_ids,

                     pos_query: Tensor = None,

                     max_length: Optional[int] = None) -> Tensor:
        testing = max_length is None
        max_length = (self.max_label_length if max_length is None else min(
            max_length, self.max_label_length))

        if self.is_pretrain:
            memory = self.clip_encoder.encode_text(clip_ids)
        else:
            bs = memory.shape[0]
            token_query = self.token_query.expand(bs, -1, -1)
            memory = self.fmu(token_query, memory)
        _device = memory.get_device()
        bs = memory.shape[0]
        # +1 for <eos> at end of sequence.
        num_steps = max_length + 1
        # memory = self.encode(images)

        # Query positions up to `num_steps`
        if pos_query is None:
            pos_queries = self.pos_queries[:, :num_steps].expand(bs, -1, -1)
        else:
            pos_queries = pos_query

        # Special case for the forward permutation. Faster than using `generate_attn_masks()`
        tgt_mask = query_mask = torch.triu(
            torch.full((num_steps, num_steps), float('-inf'), device=_device),
            1)
        self.attn_maps = []
        if self.decode_ar:
            tgt_in = torch.full((bs, num_steps),
                                self.pad_id,
                                dtype=torch.long,
                                device=_device)
            tgt_in[:, 0] = self.bos_id

            logits = []
            for i in range(num_steps):
                j = i + 1  # next token index
                # Efficient decoding:
                # Input the context up to the ith token. We use only one query (at position = i) at a time.
                # This works because of the lookahead masking effect of the canonical (forward) AR context.
                # Past tokens have no access to future tokens, hence are fixed once computed.
                tgt_out = self.decode(
                    tgt_in[:, :j],
                    memory,
                    tgt_mask[:j, :j],
                    tgt_query=pos_queries[:, i:j],
                    tgt_query_mask=query_mask[i:j, :j],
                    pos_query=pos_queries,
                )
                self.attn_maps.append(self.decoder.layers[-1].attn_map)
                # the next token probability is in the output's ith token position
                p_i = self.head(tgt_out)
                logits.append(p_i)
                if j < num_steps:
                    # greedy decode. add the next token index to the target input
                    tgt_in[:, j] = p_i.squeeze().argmax(-1)
                    # Efficient batch decoding: If all output words have at least one EOS token, end decoding.
                    if testing and (tgt_in == self.eos_id).any(dim=-1).all():
                        break

            logits = torch.cat(logits, dim=1)
        else:
            # No prior context, so input is just <bos>. We query all positions.
            tgt_in = torch.full((bs, 1),
                                self.bos_id,
                                dtype=torch.long,
                                device=_device)
            tgt_out = self.decode(tgt_in,
                                  memory,
                                  tgt_query=pos_queries,
                                  pos_query=pos_queries)
            logits = self.head(tgt_out)

        if self.refine_iters:
            # For iterative refinement, we always use a 'cloze' mask.
            # We can derive it from the AR forward mask by unmasking the token context to the right.
            query_mask[torch.triu(
                torch.ones(num_steps,
                           num_steps,
                           dtype=torch.bool,
                           device=_device), 2)] = 0
            bos = torch.full((bs, 1),
                             self.bos_id,
                             dtype=torch.long,
                             device=_device)
            for i in range(self.refine_iters):
                # Prior context is the previous output.
                tgt_in = torch.cat([bos, logits[:, :-1].argmax(-1)], dim=1)
                tgt_len = tgt_in.shape[1]
                tgt_padding_mask = (tgt_in == self.eos_id).int().cumsum(
                    -1) > 0  # mask tokens beyond the first EOS token.
                tgt_out = self.decode(
                    tgt_in,
                    memory,
                    tgt_mask[:tgt_len, :tgt_len],
                    tgt_padding_mask,
                    tgt_query=pos_queries,
                    tgt_query_mask=query_mask[:, :tgt_len],
                    pos_query=pos_queries,
                )
                logits = self.head(tgt_out)

        return F.softmax(logits, -1)

    def gen_tgt_perms(self, tgt, _device):
        """Generate shared permutations for the whole batch.



        This works because the same attention mask can be used for the shorter

        sequences because of the padding mask.

        """
        # We don't permute the position of BOS, we permute EOS separately
        max_num_chars = tgt.shape[1] - 2
        # Special handling for 1-character sequences
        if max_num_chars == 1:
            return torch.arange(3, device=_device).unsqueeze(0)
        perms = [torch.arange(max_num_chars, device=_device)
                 ] if self.perm_forward else []
        # Additional permutations if needed
        max_perms = math.factorial(max_num_chars)
        if self.perm_mirrored:
            max_perms //= 2
        num_gen_perms = min(self.max_gen_perms, max_perms)
        # For 4-char sequences and shorter, we generate all permutations and sample from the pool to avoid collisions
        # Note that this code path might NEVER get executed since the labels in a mini-batch typically exceed 4 chars.
        if max_num_chars < 5:
            # Pool of permutations to sample from. We only need the first half (if complementary option is selected)
            # Special handling for max_num_chars == 4 which correctly divides the pool into the flipped halves
            if max_num_chars == 4 and self.perm_mirrored:
                selector = [0, 3, 4, 6, 9, 10, 12, 16, 17, 18, 19, 21]
            else:
                selector = list(range(max_perms))
            perm_pool = torch.as_tensor(list(
                permutations(range(max_num_chars), max_num_chars)),
                device=_device)[selector]
            # If the forward permutation is always selected, no need to add it to the pool for sampling
            if self.perm_forward:
                perm_pool = perm_pool[1:]
            perms = torch.stack(perms)
            if len(perm_pool):
                i = self.rng.choice(len(perm_pool),
                                    size=num_gen_perms - len(perms),
                                    replace=False)
                perms = torch.cat([perms, perm_pool[i]])
        else:
            perms.extend([
                torch.randperm(max_num_chars, device=_device)
                for _ in range(num_gen_perms - len(perms))
            ])
            perms = torch.stack(perms)
        if self.perm_mirrored:
            # Add complementary pairs
            comp = perms.flip(-1)
            # Stack in such a way that the pairs are next to each other.
            perms = torch.stack([perms, comp
                                 ]).transpose(0, 1).reshape(-1, max_num_chars)
        # NOTE:
        # The only meaningful way of permuting the EOS position is by moving it one character position at a time.
        # However, since the number of permutations = T! and number of EOS positions = T + 1, the number of possible EOS
        # positions will always be much less than the number of permutations (unless a low perm_num is set).
        # Thus, it would be simpler to just train EOS using the full and null contexts rather than trying to evenly
        # distribute it across the chosen number of permutations.
        # Add position indices of BOS and EOS
        bos_idx = perms.new_zeros((len(perms), 1))
        eos_idx = perms.new_full((len(perms), 1), max_num_chars + 1)
        perms = torch.cat([bos_idx, perms + 1, eos_idx], dim=1)
        # Special handling for the reverse direction. This does two things:
        # 1. Reverse context for the characters
        # 2. Null context for [EOS] (required for learning to predict [EOS] in NAR mode)
        if len(perms) > 1:
            perms[1, 1:] = max_num_chars + 1 - torch.arange(max_num_chars + 1,
                                                            device=_device)
        return perms

    def generate_attn_masks(self, perm, _device):
        """Generate attention masks given a sequence permutation (includes pos.

        for bos and eos tokens)



        :param perm: the permutation sequence. i = 0 is always the BOS

        :return: lookahead attention masks

        """
        sz = perm.shape[0]
        mask = torch.zeros((sz, sz), device=_device)
        for i in range(sz):
            query_idx = perm[i]
            masked_keys = perm[i + 1:]
            mask[query_idx, masked_keys] = float('-inf')
        content_mask = mask[:-1, :-1].clone()
        mask[torch.eye(sz, dtype=torch.bool,
                       device=_device)] = float('-inf')  # mask "self"
        query_mask = mask[1:, :-1]
        return content_mask, query_mask

    def training_step(self, memory, pos_query, tgt_ids, clip_ids):
        bs = tgt_ids.shape[0]
        if self.is_pretrain:
            memory = self.clip_encoder.encode_text(clip_ids)
            n = memory.shape[1]
            B, N, D = self.background_features.shape
            random_B = np.random.choice(B, bs, replace=False)
            random_N = np.random.choice(N, n, replace=False)
            noise = self.background_features[random_B][:, random_N]
            noise = torch.from_numpy(noise).to(memory.get_device())
            memory = memory + noise * 1e-1
        else:
            token_query = self.token_query.expand(bs, -1, -1)
            memory = self.fmu(token_query, memory)

        if pos_query is None:
            pos_query = self.pos_queries.expand(bs, -1, -1)
        # Prepare the target sequences (input and output)
        tgt_perms = self.gen_tgt_perms(tgt_ids, memory.get_device())
        tgt_in = tgt_ids[:, :-1]
        tgt_out = tgt_ids[:, 1:]

        # The [EOS] token is not depended upon by any other token in any permutation ordering
        tgt_padding_mask = (tgt_in == self.pad_id) | (tgt_in == self.eos_id)

        loss = 0
        loss_numel = 0
        n = (tgt_out != self.pad_id).sum().item()
        for i, perm in enumerate(tgt_perms):
            tgt_mask, query_mask = self.generate_attn_masks(
                perm, memory.get_device())
            # print("tgt_in:", tgt_in, "tgt_out:", tgt_out, "tgt_padding_mask:", tgt_padding_mask)
            # print('tgt_mask:', tgt_mask)
            # print('query_mask:', query_mask)
            # print(tgt_in.shape, memory.shape, tgt_mask.shape, tgt_padding_mask.shape, query_mask.shape, pos_query.shape)
            out = self.decode(
                tgt_in,
                memory,
                tgt_mask,
                tgt_padding_mask,
                tgt_query_mask=query_mask,
                pos_query=pos_query,
            )
            # print('out:', out)
            logits = self.head(out)
            # print('logits:', logits)
            if i == 0:
                final_out = logits
            loss += n * F.cross_entropy(logits.flatten(end_dim=1),
                                        tgt_out.flatten(),
                                        ignore_index=self.pad_id)
            loss_numel += n
            # After the second iteration (i.e. done with canonical and reverse orderings),
            # remove the [EOS] tokens for the succeeding perms
            if i == 1:
                tgt_out = torch.where(tgt_out == self.eos_id, self.pad_id,
                                      tgt_out)
                n = (tgt_out != self.pad_id).sum().item()
        loss /= loss_numel

        # self.log('loss', loss)
        return [loss, final_out]