import copy
import json
import os.path
import tempfile
import threading
import time
import warnings
from collections import Counter
from math import sqrt, ceil, floor
from typing import Optional

import numpy as np

from ...helper import deprecate_by


class PlotMixin:
    """Helper functions for plotting the arrays. """

    def _ipython_display_(self):
        """Displays the object in IPython as a side effect"""
        self.summary()

    def summary(self):
        """Print the structure and attribute summary of this DocumentArray object.

        .. warning::
            Calling {meth}`.summary` on large DocumentArray can be slow.

        """

        from rich.table import Table
        from rich.console import Console
        from rich import box

        all_attrs = self._get_attributes('non_empty_fields')
        attr_counter = Counter(all_attrs)

        table = Table(box=box.SIMPLE, title='Documents Summary')
        table.show_header = False
        table.add_row('Length', str(len(self)))
        is_homo = len(attr_counter) == 1
        table.add_row('Homogenous Documents', str(is_homo))

        all_attrs_names = set(v for k in all_attrs for v in k)
        _nested_in = []
        if 'chunks' in all_attrs_names:
            _nested_in.append('chunks')

        if 'matches' in all_attrs_names:
            _nested_in.append('matches')

        if _nested_in:
            table.add_row('Has nested Documents in', str(tuple(_nested_in)))

        if is_homo:
            table.add_row('Common Attributes', str(list(attr_counter.items())[0][0]))
        else:
            for _a, _n in attr_counter.most_common():
                if _n <= 1:
                    _doc_text = f'{_n} Document has'
                else:
                    _doc_text = f'{_n} Documents have'
                if len(_a) == 1:
                    _text = f'{_doc_text} one attribute'
                elif len(_a) == 0:
                    _text = f'{_doc_text} no attribute'
                else:
                    _text = f'{_doc_text} attributes'
                table.add_row(_text, str(_a))

        console = Console()
        all_attrs_names = tuple(sorted(all_attrs_names))
        if not all_attrs_names:
            console.print(table)
            return

        attr_table = Table(box=box.SIMPLE, title='Attributes Summary')
        attr_table.add_column('Attribute')
        attr_table.add_column('Data type')
        attr_table.add_column('#Unique values')
        attr_table.add_column('Has empty value')

        all_attrs_values = self._get_attributes(*all_attrs_names)
        if len(all_attrs_names) == 1:
            all_attrs_values = [all_attrs_values]
        for _a, _a_name in zip(all_attrs_values, all_attrs_names):
            try:
                _a = set(_a)
            except:
                pass  # intentional ignore as some fields are not hashable
            _set_type_a = set(type(_aa).__name__ for _aa in _a)
            attr_table.add_row(
                _a_name,
                str(tuple(_set_type_a)),
                str(len(_a)),
                str(any(_aa is None for _aa in _a)),
            )
        console.print(table, attr_table)

    def plot_embeddings(
        self,
        title: str = 'MyDocumentArray',
        path: Optional[str] = None,
        image_sprites: bool = False,
        min_image_size: int = 16,
        channel_axis: int = -1,
        start_server: bool = True,
        port: Optional[int] = None,
    ) -> str:
        """Interactively visualize :attr:`.embeddings` using the Embedding Projector.

        :param title: the title of this visualization. If you want to compare multiple embeddings at the same time,
                make sure to give different names each time and set ``path`` to the same value.
        :param port: if set, run the embedding-projector frontend at given port. Otherwise a random port is used.
        :param image_sprites: if set, visualize the dots using :attr:`.uri` and :attr:`.blob`.
        :param path: if set, then append the visualization to an existing folder, where you can compare multiple
            embeddings at the same time. Make sure to use a different ``title`` each time .
        :param min_image_size: only used when `image_sprites=True`. the minimum size of the image
        :param channel_axis: only used when `image_sprites=True`. the axis id of the color channel, ``-1`` indicates the color channel info at the last axis
        :param start_server: if set, start a HTTP server and open the frontend directly. Otherwise, you need to rely on ``return`` path and serve by yourself.
        :return: the path to the embeddings visualization info.
        """
        from ...helper import random_port, __resources_path__

        path = path or tempfile.mkdtemp()
        emb_fn = f'{title}.tsv'
        meta_fn = f'{title}.metas.tsv'
        config_fn = f'config.json'
        sprite_fn = f'{title}.png'

        if image_sprites:
            img_per_row = ceil(sqrt(len(self)))
            canvas_size = min(img_per_row * min_image_size, 8192)
            img_size = max(int(canvas_size / img_per_row), min_image_size)

            max_docs = ceil(canvas_size / img_size) ** 2
            if len(self) > max_docs:
                warnings.warn(
                    f'''
                    {self!r} has more than {max_docs} elements, which is the maximum number of image sprites can support. 
                    The resulting visualization may not be correct. You can do the following:
                    
                    - use fewer images: `da[:10000].plot_embeddings()`
                    - reduce the `min_image_size` to a smaller number, say 8 or 4 (but bear in mind you can hardly recognize anything with a 4x4 image)
                    - turn off `image_sprites` via `da.plot_embeddings(image_sprites=False)`
                    '''
                )

            self.plot_image_sprites(
                os.path.join(path, sprite_fn),
                canvas_size=canvas_size,
                min_size=min_image_size,
                channel_axis=channel_axis,
            )

        self.save_embeddings_csv(os.path.join(path, emb_fn), delimiter='\t')

        _exclude_fields = ('embedding', 'blob', 'scores')
        with_header = True
        if len(set(self[0].non_empty_fields).difference(set(_exclude_fields))) <= 1:
            with_header = False

        self.save_csv(
            os.path.join(path, meta_fn),
            exclude_fields=_exclude_fields,
            dialect='excel-tab',
            with_header=with_header,
        )

        _epj_config = {
            'embeddings': [
                {
                    'tensorName': title,
                    'tensorShape': list(self.embeddings.shape),
                    'tensorPath': f'/static/{emb_fn}',
                    'metadataPath': f'/static/{meta_fn}',
                    'sprite': {
                        'imagePath': f'/static/{sprite_fn}',
                        'singleImageDim': (img_size,) * 2,
                    }
                    if image_sprites
                    else {},
                }
            ]
        }

        if os.path.exists(os.path.join(path, config_fn)):
            with open(os.path.join(path, config_fn)) as fp:
                old_config = json.load(fp)
                _epj_config['embeddings'].extend(old_config.get('embeddings', []))

        with open(os.path.join(path, config_fn), 'w') as fp:
            json.dump(_epj_config, fp)

        import gzip

        with gzip.open(
            os.path.join(__resources_path__, 'embedding-projector/index.html.gz'), 'rt'
        ) as fr, open(os.path.join(path, 'index.html'), 'w') as fp:
            fp.write(fr.read())

        if start_server:

            def _get_fastapi_app():
                from fastapi import FastAPI
                from starlette.middleware.cors import CORSMiddleware
                from starlette.staticfiles import StaticFiles

                app = FastAPI()
                app.add_middleware(
                    CORSMiddleware,
                    allow_origins=['*'],
                    allow_credentials=True,
                    allow_methods=['*'],
                    allow_headers=['*'],
                )
                app.mount('/static', StaticFiles(directory=path), name='static')
                return app

            import uvicorn

            app = _get_fastapi_app()
            port = port or random_port()
            t_m = threading.Thread(
                target=uvicorn.run,
                kwargs=dict(app=app, port=port, log_level='error'),
                daemon=True,
            )
            url_html_path = (
                f'http://localhost:{port}/static/index.html?config={config_fn}'
            )
            t_m.start()
            try:
                _env = str(get_ipython())  # noqa
                if 'ZMQInteractiveShell' in _env:
                    _env = 'jupyter'
                elif 'google.colab' in _env:
                    _env = 'colab'
            except:
                _env = 'local'
            if _env == 'jupyter':
                time.sleep(
                    1
                )  # jitter is required otherwise encouter werid `strict-origin-when-cross-origin` error in browser
                from IPython.display import IFrame, display  # noqa

                display(IFrame(src=url_html_path, width="100%", height=600))
                warnings.warn(
                    f'Showing iframe in cell, you may want to open {url_html_path} in a new tab for better experience. '
                    f'Also, `localhost` may need to be changed to the IP address if your jupyter is running remotely. '
                    f'Click "stop" button in the toolbar to move to the next cell.'
                )
            elif _env == 'colab':
                from google.colab.output import eval_js  # noqa

                colab_url = eval_js(f'google.colab.kernel.proxyPort({port})')
                colab_url += f'/static/index.html?config={config_fn}'
                warnings.warn(
                    f'Showing iframe in cell, you may want to open {colab_url} in a new tab for better experience. '
                    f'Click "stop" button in the toolbar to move to the next cell.'
                )
                time.sleep(
                    1
                )  # jitter is required otherwise encouter werid `strict-origin-when-cross-origin` error in browser
                from IPython.display import IFrame, display

                display(IFrame(src=colab_url, width="100%", height=600))
            elif _env == 'local':
                try:
                    import webbrowser

                    webbrowser.open(url_html_path, new=2)
                except:
                    pass  # intentional pass, browser support isn't cross-platform
                finally:
                    print(
                        f'You should see a webpage opened in your browser, '
                        f'if not, you may open {url_html_path} manually'
                    )
            t_m.join()
        return path

    def plot_image_sprites(
        self,
        output: Optional[str] = None,
        canvas_size: int = 512,
        min_size: int = 16,
        channel_axis: int = -1,
    ) -> None:
        """Generate a sprite image for all image blobs in this DocumentArray-like object.

        An image sprite is a collection of images put into a single image. It is always square-sized.
        Each sub-image is also square-sized and equally-sized.

        :param output: Optional path to store the visualization. If not given, show in UI
        :param canvas_size: the size of the canvas
        :param min_size: the minimum size of the image
        :param channel_axis: the axis id of the color channel, ``-1`` indicates the color channel info at the last axis
        """
        if not self:
            raise ValueError(f'{self!r} is empty')

        import matplotlib.pyplot as plt

        img_per_row = ceil(sqrt(len(self)))
        img_size = int(canvas_size / img_per_row)

        if img_size < min_size:
            # image is too small, recompute the size
            img_size = min_size
            img_per_row = int(canvas_size / img_size)

        max_num_img = img_per_row ** 2
        sprite_img = np.zeros(
            [img_size * img_per_row, img_size * img_per_row, 3], dtype='uint8'
        )
        img_id = 0
        for d in self:
            _d = copy.deepcopy(d)
            if _d.content_type != 'blob':
                _d.load_uri_to_image_blob()
                channel_axis = -1

            _d.set_image_blob_channel_axis(channel_axis, -1).set_image_blob_shape(
                shape=(img_size, img_size)
            )

            row_id = floor(img_id / img_per_row)
            col_id = img_id % img_per_row
            sprite_img[
                (row_id * img_size) : ((row_id + 1) * img_size),
                (col_id * img_size) : ((col_id + 1) * img_size),
            ] = _d.blob

            img_id += 1
            if img_id >= max_num_img:
                break

        from PIL import Image

        im = Image.fromarray(sprite_img)

        if output:
            with open(output, 'wb') as fp:
                im.save(fp)
        else:
            plt.gca().set_axis_off()
            plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
            plt.margins(0, 0)
            plt.gca().xaxis.set_major_locator(plt.NullLocator())
            plt.gca().yaxis.set_major_locator(plt.NullLocator())
            plt.imshow(im)
            plt.show()