tokenization_chexagent.py

import json
from functools import lru_cache
from typing import TYPE_CHECKING

import regex as re
from transformers.tokenization_utils_base import TextInput
from transformers.utils import is_tf_available, is_torch_available, to_py_obj

if TYPE_CHECKING:
    if is_torch_available():
        import torch
    if is_tf_available():
        import tensorflow as tf

import os
import random
from typing import Dict, List, Tuple, Union, Any, Callable, Optional

import matplotlib as mpl
import matplotlib.colors as mcolors
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import requests
import torch
from PIL import Image
from matplotlib.backends.backend_agg import FigureCanvasAgg
from transformers import PreTrainedTokenizer, AddedToken
from transformers.utils import logging

logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
    },
    "merges_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
    },
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "Salesforce/codegen-350M-mono": 2048,
}

IMG_TOKEN_SPAN = 1024

DEFAULT_CHAT_TEMPLATE = "{% for message in messages %}\n{% if message['from'] == 'human' %}\n{{ '<|user|>\n' + message['value'] + eos_token }}\n{% elif message['from'] == 'system' %}\n{{ '<|system|>\n' + message['value'] + eos_token }}\n{% elif message['from'] == 'gpt' %}\n{{ '<|assistant|>\n'  + message['value'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}"


@lru_cache()
def bytes_to_unicode():
    """
    Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
    characters the bpe code barfs on.

    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 significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
    tables between utf-8 bytes and unicode strings.
    """
    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 _list_find(
        input_list: List[Any],
        candidates: Tuple[Any],
        start: int = 0,
):
    for i in range(start, len(input_list)):
        if input_list[i] in candidates:
            return i
    return -1


def _replace_closed_tag(
        input_tokens: List[Any],
        start_tags: Union[Any, Tuple[Any]],
        end_tags: Union[Any, Tuple[Any]],
        inclusive_replace_func: Callable,
        exclusive_replace_func: Callable = lambda x: x,
):
    if isinstance(start_tags, (str, int)):
        start_tags = (start_tags,)
    if isinstance(end_tags, (str, int)):
        end_tags = (end_tags,)
    assert len(start_tags) == len(end_tags)

    output_tokens = []
    end = 0
    while True:
        start = _list_find(input_tokens, start_tags, end)
        if start == -1:
            break
        output_tokens.extend(exclusive_replace_func(input_tokens[end: start]))
        tag_idx = start_tags.index(input_tokens[start])
        end = _list_find(input_tokens, (end_tags[tag_idx],), start)
        if end == -1:
            raise ValueError("Unclosed image token")
        output_tokens.extend(inclusive_replace_func(input_tokens[start: end + 1]))
        end += 1
    output_tokens.extend(exclusive_replace_func(input_tokens[end:]))
    return output_tokens


class CheXagentTokenizer(PreTrainedTokenizer):
    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
            self,
            vocab_file,
            merges_file,
            errors="replace",
            unk_token="<|endoftext|>",
            bos_token="<|endoftext|>",
            eos_token="<|endoftext|>",
            pad_token=None,
            add_prefix_space=False,
            add_bos_token=False,
            image_start_tag='<|img|>',
            image_end_tag='<|/img|>',
            image_pad_tag='<|imgpad|>',
            ref_start_tag='<|ref|>',
            ref_end_tag='<|/ref|>',
            box_start_tag='<|box|>',
            box_end_tag='<|/box|>',
            quad_start_tag='<|quad|>',
            quad_end_tag='<|/quad|>',
            **kwargs,
    ):
        bos_token = AddedToken(bos_token, special=True) if isinstance(bos_token, str) else bos_token
        eos_token = AddedToken(eos_token, special=True) if isinstance(eos_token, str) else eos_token
        unk_token = AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token
        pad_token = AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token
        self.add_bos_token = add_bos_token

        with open(vocab_file, encoding="utf-8") as vocab_handle:
            self.encoder = json.load(vocab_handle)
        self.decoder = {v: k for k, v in self.encoder.items()}
        self.errors = errors  # how to handle errors in decoding
        self.byte_encoder = bytes_to_unicode()
        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
        with open(merges_file, encoding="utf-8") as merges_handle:
            bpe_merges = merges_handle.read().split("\n")[1:-1]
        bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
        self.cache = {}
        self.add_prefix_space = add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
        super().__init__(
            errors=errors,
            unk_token=unk_token,
            bos_token=bos_token,
            eos_token=eos_token,
            pad_token=pad_token,
            add_prefix_space=add_prefix_space,
            add_bos_token=add_bos_token,
            **kwargs,
        )

        self.image_start_tag = image_start_tag
        self.image_end_tag = image_end_tag
        self.image_pad_tag = image_pad_tag
        self.ref_start_tag = ref_start_tag
        self.ref_end_tag = ref_end_tag
        self.box_start_tag = box_start_tag
        self.box_end_tag = box_end_tag
        self.quad_start_tag = quad_start_tag
        self.quad_end_tag = quad_end_tag
        self.IMAGE_ST = (
            image_start_tag, image_end_tag, image_pad_tag,
            ref_start_tag, ref_end_tag, box_start_tag, box_end_tag,
            quad_start_tag, quad_end_tag,
        )
        for special_token in self.IMAGE_ST:
            if special_token not in self.get_vocab():
                self.add_special_tokens({"additional_special_tokens": [special_token]})
        for coordinate in range(10):
            if f"<{coordinate}>" not in self.get_vocab():
                self.add_special_tokens({"additional_special_tokens": [f"<|coord_{coordinate}|>"]})
        if len(self) % 64 != 0:
            for extra in range(((len(self) // 64) + 1) * 64 - len(self)):
                if f"<extra_{extra}>" not in self.get_vocab():
                    self.add_special_tokens({"additional_special_tokens": [f"<|extra_{extra}|>"]})
        self.img_start_id = self.convert_tokens_to_ids(self.image_start_tag)
        self.img_end_id = self.convert_tokens_to_ids(self.image_end_tag)
        self.img_pad_id = self.convert_tokens_to_ids(self.image_pad_tag)
        self.ref_start_id = self.convert_tokens_to_ids(self.ref_start_tag)
        self.ref_end_id = self.convert_tokens_to_ids(self.ref_end_tag)
        self.box_start_id = self.convert_tokens_to_ids(self.box_start_tag)
        self.box_end_id = self.convert_tokens_to_ids(self.box_end_tag)
        self.quad_start_id = self.convert_tokens_to_ids(self.quad_start_tag)
        self.quad_end_id = self.convert_tokens_to_ids(self.quad_end_tag)
        self.chat_template = DEFAULT_CHAT_TEMPLATE

    @property
    def vocab_size(self):
        return len(self.encoder)

    def get_vocab(self):
        return dict(self.encoder, **self.added_tokens_encoder)

    def bpe(self, token):
        if token in self.cache:
            return self.cache[token]
        word = tuple(token)
        pairs = get_pairs(word)

        if not pairs:
            return token

        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)
                except ValueError:
                    new_word.extend(word[i:])
                    break
                else:
                    new_word.extend(word[i:j])
                    i = j

                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 build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        if self.add_bos_token:
            bos_token_ids = [self.bos_token_id]
        else:
            bos_token_ids = []

        output = bos_token_ids + token_ids_0

        if token_ids_1 is None:
            return output

        return output + bos_token_ids + token_ids_1

    def tokenize(self, text: TextInput, **kwargs) -> List[str]:
        def _encode_imgurl(img_tokens):
            assert img_tokens[0] == self.image_start_tag and img_tokens[-1] == self.image_end_tag
            img_tokens = img_tokens[1:-1]
            img_url = ''.join(img_tokens)
            out_img_tokens = list(img_url)
            if len(out_img_tokens) > IMG_TOKEN_SPAN:
                raise ValueError("The content in {}..{} is too long".format(self.image_start_tag, self.image_end_tag))
            out_img_tokens.extend([self.image_pad_tag] * (IMG_TOKEN_SPAN - len(out_img_tokens)))
            out_img_tokens = [self.image_start_tag] + out_img_tokens + [self.image_end_tag]
            return out_img_tokens

        tokens = super().tokenize(text, **kwargs)
        tokens = _replace_closed_tag(tokens, self.image_start_tag, self.image_end_tag, _encode_imgurl)
        return tokens

    def _tokenize(self, text):
        """Tokenize a string."""

        bpe_tokens = []
        for token in re.findall(self.pat, text):
            token = "".join(
                self.byte_encoder[b] for b in token.encode("utf-8")
            )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
        return bpe_tokens

    def _convert_token_to_id(self, token):
        """Converts a token (str) in an id using the vocab."""
        return self.encoder.get(token, self.encoder.get(self.unk_token))

    def _convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the vocab."""
        return self.decoder.get(index)

    def convert_tokens_to_string(self, tokens):
        """Converts a sequence of tokens (string) in a single string."""
        text = "".join(tokens)
        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
        return text

    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        if not os.path.isdir(save_directory):
            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
            return
        vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )
        merge_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
        )

        with open(vocab_file, "w", encoding="utf-8") as f:
            f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")

        index = 0
        with open(merge_file, "w", encoding="utf-8") as writer:
            writer.write("#version: 0.2\n")
            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
                if index != token_index:
                    logger.warning(
                        f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                        " Please check that the tokenizer is not corrupted!"
                    )
                    index = token_index
                writer.write(" ".join(bpe_tokens) + "\n")
                index += 1

        return vocab_file, merge_file

    def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
        add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
        if is_split_into_words or add_prefix_space:
            text = " " + text
        return (text, kwargs)

    def decode(
            self,
            token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
            skip_special_tokens: bool = False,
            clean_up_tokenization_spaces: bool = None,
            truncate_before_pattern: Optional[List[str]] = None,
            **kwargs,
    ) -> str:
        """
        Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
        tokens and clean up tokenization spaces.

        Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.

        Args:
            token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
                List of tokenized input ids. Can be obtained using the `__call__` method.
            skip_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not to remove special tokens in the decoding.
            clean_up_tokenization_spaces (`bool`, *optional*):
                Whether or not to clean up the tokenization spaces. If `None`, will default to
                `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`).
            truncate_before_pattern (`List[str]`, *optional*, defaults to `None`):
                A list of regular expression strings that will be used to truncate the returned string. This can be
                used to remove extra pieces of code (e.g. truncate if observing a comment symbol "#" at the beginning
                of a new line). An example pattern could be `["^#", re.escape("<|endoftext|>"), "^'''", "\n\n\n"]`.
            kwargs (additional keyword arguments, *optional*):
                Will be passed to the underlying model specific decode method.

        Returns:
            `str`: The decoded sentence.
        """

        token_ids = to_py_obj(token_ids)

        decoded_text = self._decode(
            token_ids=token_ids,
            skip_special_tokens=skip_special_tokens,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            **kwargs,
        )

        if truncate_before_pattern is not None and len(truncate_before_pattern) > 0:
            decoded_text = self.truncate(decoded_text, truncate_before_pattern)

        return decoded_text

    def _decode(
            self,
            token_ids: List[int],
            skip_special_tokens: bool = False,
            clean_up_tokenization_spaces: bool = None,
            spaces_between_special_tokens: bool = True,
            **kwargs,
    ) -> str:

        def _decode_imgurl(img_token_ids):
            assert img_token_ids[0] == self.img_start_id and img_token_ids[-1] == self.img_end_id
            img_token_ids = img_token_ids[1:-1]
            img_token_ids = img_token_ids[: img_token_ids.index(self.img_pad_id)]
            return [self.img_start_id] + img_token_ids + [self.img_end_id]

        token_ids = _replace_closed_tag(token_ids, self.img_start_id, self.img_end_id, _decode_imgurl)

        return super()._decode(
            token_ids, skip_special_tokens, clean_up_tokenization_spaces, spaces_between_special_tokens, **kwargs
        )

    def truncate(self, completion, truncate_before_pattern):
        def find_re(string, pattern, start_pos):
            m = pattern.search(string, start_pos)
            return m.start() if m else -1

        terminals = [re.compile(pattern, re.MULTILINE) for pattern in truncate_before_pattern]

        prints = list(re.finditer("^print", completion, re.MULTILINE))

        if len(prints) > 1:
            completion = completion[: prints[1].start()]

        defs = list(re.finditer("^def", completion, re.MULTILINE))

        if len(defs) > 1:
            completion = completion[: defs[1].start()]

        start_pos = 0

        terminals_pos = [
            pos for pos in [find_re(completion, terminal, start_pos) for terminal in terminals] if pos != -1
        ]

        if len(terminals_pos) > 0:
            return completion[: min(terminals_pos)]
        else:
            return completion

    def from_list_format(self, list_format: List[Dict]):
        text = ''
        num_images = 0
        for ele in list_format:
            if 'image' in ele:
                num_images += 1
                text += f'Picture {num_images}:'
                text += self.image_start_tag + ele['image'] + self.image_end_tag
                text += '\n'
            elif 'text' in ele:
                text += ele['text']
            elif 'box' in ele:
                if 'ref' in ele:
                    text += self.ref_start_tag + ele['ref'] + self.ref_end_tag
                for box in ele['box']:
                    text += self.box_start_tag + '(%d,%d),(%d,%d)' % (box[0], box[1], box[2], box[3]) + self.box_end_tag
            else:
                raise ValueError("Unsupport element: " + str(ele))
        return text

    def to_list_format(self, text: str):
        token_ids = self.encode(text)

        def _encode_vl_info(tokens):
            if len(tokens) == 0:
                return []
            if tokens[0] == self.img_start_id and tokens[-1] == self.img_end_id:
                key = 'image'
            elif tokens[0] == self.ref_start_id and tokens[-1] == self.ref_end_id:
                key = 'ref'
            elif tokens[0] == self.box_start_id and tokens[-1] == self.box_end_id:
                key = 'box'
            elif tokens[0] == self.quad_start_id and tokens[-1] == self.quad_end_id:
                key = 'quad'
            else:
                val = self.decode(tokens)
                return [{'text': val}]
            tokens = [token for token in tokens[1:-1] if token != self.img_pad_id]
            val = self.decode(tokens, skip_special_tokens=True)
            return [{key: val}]

        return _replace_closed_tag(
            token_ids,
            (self.img_start_id, self.ref_start_id, self.box_start_id, self.quad_start_id),
            (self.img_end_id, self.ref_end_id, self.box_end_id, self.quad_end_id),
            _encode_vl_info,
            _encode_vl_info,
        )

    def _fetch_latest_picture(self, response, history):
        if history is None:
            history = []
        _history = history + [(response, None)]
        for q, r in _history[::-1]:
            for ele in self.to_list_format(q)[::-1]:
                if 'image' in ele:
                    return ele['image']
        return None

    def _fetch_all_box_with_ref(self, text):
        list_format = self.to_list_format(text)
        output = []
        for i, ele in enumerate(list_format):
            if 'box' in ele:
                bbox = tuple(map(int, ele['box'].replace('(', '').replace(')', '').split(',')))
                assert len(bbox) == 4
                output.append({'box': bbox})
                if i > 0 and 'ref' in list_format[i - 1]:
                    output[-1]['ref'] = list_format[i - 1]['ref'].strip()
        return output

    def draw_bbox_on_latest_picture(
            self,
            response,
            history=None,
    ) -> Optional[Image.Image]:
        image = self._fetch_latest_picture(response, history)
        if image is None:
            return None
        if image.startswith("http://") or image.startswith("https://"):
            image = Image.open(requests.get(image, stream=True).raw).convert("RGB")
            h, w = image.height, image.width
        else:
            image = np.asarray(Image.open(image).convert("RGB"))
            h, w = image.shape[0], image.shape[1]
        visualizer = Visualizer(image)

        boxes = self._fetch_all_box_with_ref(response)
        if not boxes:
            return None
        color = random.choice([_ for _ in mcolors.TABLEAU_COLORS.keys()])  # init color
        for box in boxes:
            if 'ref' in box:  # random new color for new refexps
                color = random.choice([_ for _ in mcolors.TABLEAU_COLORS.keys()])
            x1, y1, x2, y2 = box['box']
            x1, y1, x2, y2 = (int(x1 / 1000 * w), int(y1 / 1000 * h), int(x2 / 1000 * w), int(y2 / 1000 * h))
            visualizer.draw_box((x1, y1, x2, y2), alpha=1, edge_color=color)
            if 'ref' in box:
                visualizer.draw_text(box['ref'], (x1, y1), color=color, horizontal_alignment="left")
        return visualizer.output


class VisImage:
    def __init__(self, img, scale=1.0):
        self.img = img
        self.scale = scale
        self.width, self.height = img.shape[1], img.shape[0]
        self._setup_figure(img)

    def _setup_figure(self, img):
        fig = mplfigure.Figure(frameon=False)
        self.dpi = fig.get_dpi()
        # add a small 1e-2 to avoid precision lost due to matplotlib's truncation
        # (https://github.com/matplotlib/matplotlib/issues/15363)
        fig.set_size_inches(
            (self.width * self.scale + 1e-2) / self.dpi,
            (self.height * self.scale + 1e-2) / self.dpi,
        )
        self.canvas = FigureCanvasAgg(fig)
        # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)
        ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])
        ax.axis("off")
        self.fig = fig
        self.ax = ax
        self.reset_image(img)

    def reset_image(self, img):
        img = img.astype("uint8")
        self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation="nearest")

    def save(self, filepath):
        self.fig.savefig(filepath)

    def get_image(self):
        canvas = self.canvas
        s, (width, height) = canvas.print_to_buffer()

        buffer = np.frombuffer(s, dtype="uint8")

        img_rgba = buffer.reshape(height, width, 4)
        rgb, alpha = np.split(img_rgba, [3], axis=2)
        return rgb.astype("uint8")


class Visualizer:
    def __init__(self, img_rgb, metadata=None, scale=1.0):
        self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)
        self.output = VisImage(self.img, scale=scale)
        self.cpu_device = torch.device("cpu")

        # too small texts are useless, therefore clamp to 14
        self._default_font_size = max(
            np.sqrt(self.output.height * self.output.width) // 30, 15 // scale
        )

    def draw_text(
            self,
            text,
            position,
            *,
            font_size=None,
            color="g",
            horizontal_alignment="center",
            rotation=0,
    ):
        if not font_size:
            font_size = self._default_font_size

        # since the text background is dark, we don't want the text to be dark
        color = np.maximum(list(mplc.to_rgb(color)), 0.2)
        color[np.argmax(color)] = max(0.8, np.max(color))

        x, y = position
        self.output.ax.text(
            x,
            y,
            text,
            size=font_size * self.output.scale,
            bbox={"facecolor": "black", "alpha": 0.8, "pad": 0.7, "edgecolor": "none"},
            verticalalignment="top",
            horizontalalignment=horizontal_alignment,
            color=color,
            zorder=10,
            rotation=rotation,
        )
        return self.output

    def draw_box(self, box_coord, alpha=0.5, edge_color="g", line_style="-"):
        x0, y0, x1, y1 = box_coord
        width = x1 - x0
        height = y1 - y0

        linewidth = max(self._default_font_size / 4, 1)

        self.output.ax.add_patch(
            mpl.patches.Rectangle(
                (x0, y0),
                width,
                height,
                fill=False,
                edgecolor=edge_color,
                linewidth=linewidth * self.output.scale,
                alpha=alpha,
                linestyle=line_style,
            )
        )
        return self.output

    def get_output(self):
        return self.output