# Copyright (C) 2021-2024, Mindee.

# This program is licensed under the Apache License 2.0.
# See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.

from copy import deepcopy
from typing import Any

import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.models import Model, Sequential

from doctr.datasets import VOCABS

from ...classification import mobilenet_v3_large_r, mobilenet_v3_small_r, vgg16_bn_r
from ...utils.tensorflow import _bf16_to_float32, _build_model, load_pretrained_params
from ..core import RecognitionModel, RecognitionPostProcessor

__all__ = ["CRNN", "crnn_vgg16_bn", "crnn_mobilenet_v3_small", "crnn_mobilenet_v3_large"]

default_cfgs: dict[str, dict[str, Any]] = {
    "crnn_vgg16_bn": {
        "mean": (0.694, 0.695, 0.693),
        "std": (0.299, 0.296, 0.301),
        "input_shape": (32, 128, 3),
        "vocab": VOCABS["legacy_french"],
        "url": "https://doctr-static.mindee.com/models?id=v0.9.0/crnn_vgg16_bn-9c188f45.weights.h5&src=0",
    },
    "crnn_mobilenet_v3_small": {
        "mean": (0.694, 0.695, 0.693),
        "std": (0.299, 0.296, 0.301),
        "input_shape": (32, 128, 3),
        "vocab": VOCABS["french"],
        "url": "https://doctr-static.mindee.com/models?id=v0.9.0/crnn_mobilenet_v3_small-54850265.weights.h5&src=0",
    },
    "crnn_mobilenet_v3_large": {
        "mean": (0.694, 0.695, 0.693),
        "std": (0.299, 0.296, 0.301),
        "input_shape": (32, 128, 3),
        "vocab": VOCABS["french"],
        "url": "https://doctr-static.mindee.com/models?id=v0.9.0/crnn_mobilenet_v3_large-c64045e5.weights.h5&src=0",
    },
}


class CTCPostProcessor(RecognitionPostProcessor):
    """Postprocess raw prediction of the model (logits) to a list of words using CTC decoding

    Args:
        vocab: string containing the ordered sequence of supported characters
        ignore_case: if True, ignore case of letters
        ignore_accents: if True, ignore accents of letters
    """

    def __call__(
        self,
        logits: tf.Tensor,
        beam_width: int = 1,
        top_paths: int = 1,
    ) -> list[tuple[str, float]] | list[tuple[list[str] | list[float]]]:
        """Performs decoding of raw output with CTC and decoding of CTC predictions
        with label_to_idx mapping dictionnary

        Args:
            logits: raw output of the model, shape BATCH_SIZE X SEQ_LEN X NUM_CLASSES + 1
            beam_width: An int scalar >= 0 (beam search beam width).
            top_paths: An int scalar >= 0, <= beam_width (controls output size).

        Returns:
            A list of decoded words of length BATCH_SIZE


        """
        # Decode CTC
        _decoded, _log_prob = tf.nn.ctc_beam_search_decoder(
            tf.transpose(logits, perm=[1, 0, 2]),
            tf.fill(tf.shape(logits)[:1], tf.shape(logits)[1]),
            beam_width=beam_width,
            top_paths=top_paths,
        )

        _decoded = tf.sparse.concat(
            1,
            [tf.sparse.expand_dims(dec, axis=1) for dec in _decoded],
            expand_nonconcat_dims=True,
        )  # dim : batchsize x beamwidth x actual_max_len_predictions
        out_idxs = tf.sparse.to_dense(_decoded, default_value=len(self.vocab))

        # Map it to characters
        _decoded_strings_pred = tf.strings.reduce_join(
            inputs=tf.nn.embedding_lookup(tf.constant(self._embedding, dtype=tf.string), out_idxs),
            axis=-1,
        )
        _decoded_strings_pred = tf.strings.split(_decoded_strings_pred, "<eos>")
        decoded_strings_pred = tf.sparse.to_dense(_decoded_strings_pred.to_sparse(), default_value="not valid")[
            :, :, 0
        ]  # dim : batch_size x beam_width

        if top_paths == 1:
            probs = tf.math.exp(tf.squeeze(_log_prob, axis=1))  # dim : batchsize
            decoded_strings_pred = tf.squeeze(decoded_strings_pred, axis=1)
            word_values = [word.decode() for word in decoded_strings_pred.numpy().tolist()]
        else:
            probs = tf.math.exp(_log_prob)  # dim : batchsize x beamwidth
            word_values = [[word.decode() for word in words] for words in decoded_strings_pred.numpy().tolist()]
        return list(zip(word_values, probs.numpy().tolist()))


class CRNN(RecognitionModel, Model):
    """Implements a CRNN architecture as described in `"An End-to-End Trainable Neural Network for Image-based
    Sequence Recognition and Its Application to Scene Text Recognition" <https://arxiv.org/pdf/1507.05717.pdf>`_.

    Args:
        feature_extractor: the backbone serving as feature extractor
        vocab: vocabulary used for encoding
        rnn_units: number of units in the LSTM layers
        exportable: onnx exportable returns only logits
        beam_width: beam width for beam search decoding
        top_paths: number of top paths for beam search decoding
        cfg: configuration dictionary
    """

    _children_names: list[str] = ["feat_extractor", "decoder", "postprocessor"]

    def __init__(
        self,
        feature_extractor: Model,
        vocab: str,
        rnn_units: int = 128,
        exportable: bool = False,
        beam_width: int = 1,
        top_paths: int = 1,
        cfg: dict[str, Any] | None = None,
    ) -> None:
        # Initialize kernels
        h, w, c = feature_extractor.output_shape[1:]

        super().__init__()
        self.vocab = vocab
        self.max_length = w
        self.cfg = cfg
        self.exportable = exportable
        self.feat_extractor = feature_extractor

        self.decoder = Sequential([
            layers.Bidirectional(layers.LSTM(units=rnn_units, return_sequences=True)),
            layers.Bidirectional(layers.LSTM(units=rnn_units, return_sequences=True)),
            layers.Dense(units=len(vocab) + 1),
        ])
        self.decoder.build(input_shape=(None, w, h * c))

        self.postprocessor = CTCPostProcessor(vocab=vocab)

        self.beam_width = beam_width
        self.top_paths = top_paths

    def compute_loss(
        self,
        model_output: tf.Tensor,
        target: list[str],
    ) -> tf.Tensor:
        """Compute CTC loss for the model.

        Args:
            model_output: predicted logits of the model
            target: lengths of each gt word inside the batch

        Returns:
            The loss of the model on the batch
        """
        gt, seq_len = self.build_target(target)
        batch_len = model_output.shape[0]
        input_length = tf.fill((batch_len,), model_output.shape[1])
        ctc_loss = tf.nn.ctc_loss(
            gt, model_output, seq_len, input_length, logits_time_major=False, blank_index=len(self.vocab)
        )
        return ctc_loss

    def call(
        self,
        x: tf.Tensor,
        target: list[str] | None = None,
        return_model_output: bool = False,
        return_preds: bool = False,
        beam_width: int = 1,
        top_paths: int = 1,
        **kwargs: Any,
    ) -> dict[str, Any]:
        if kwargs.get("training", False) and target is None:
            raise ValueError("Need to provide labels during training")

        features = self.feat_extractor(x, **kwargs)
        # B x H x W x C --> B x W x H x C
        transposed_feat = tf.transpose(features, perm=[0, 2, 1, 3])
        w, h, c = transposed_feat.get_shape().as_list()[1:]
        # B x W x H x C --> B x W x H * C
        features_seq = tf.reshape(transposed_feat, shape=(-1, w, h * c))
        logits = _bf16_to_float32(self.decoder(features_seq, **kwargs))

        out: dict[str, tf.Tensor] = {}
        if self.exportable:
            out["logits"] = logits
            return out

        if return_model_output:
            out["out_map"] = logits

        if target is None or return_preds:
            # Post-process boxes
            out["preds"] = self.postprocessor(logits, beam_width=beam_width, top_paths=top_paths)

        if target is not None:
            out["loss"] = self.compute_loss(logits, target)

        return out


def _crnn(
    arch: str,
    pretrained: bool,
    backbone_fn,
    pretrained_backbone: bool = True,
    input_shape: tuple[int, int, int] | None = None,
    **kwargs: Any,
) -> CRNN:
    pretrained_backbone = pretrained_backbone and not pretrained

    kwargs["vocab"] = kwargs.get("vocab", default_cfgs[arch]["vocab"])

    _cfg = deepcopy(default_cfgs[arch])
    _cfg["vocab"] = kwargs["vocab"]
    _cfg["input_shape"] = input_shape or default_cfgs[arch]["input_shape"]

    feat_extractor = backbone_fn(
        input_shape=_cfg["input_shape"],
        include_top=False,
        pretrained=pretrained_backbone,
    )

    # Build the model
    model = CRNN(feat_extractor, cfg=_cfg, **kwargs)
    _build_model(model)
    # Load pretrained parameters
    if pretrained:
        # The given vocab differs from the pretrained model => skip the mismatching layers for fine tuning
        load_pretrained_params(model, _cfg["url"], skip_mismatch=kwargs["vocab"] != default_cfgs[arch]["vocab"])

    return model


def crnn_vgg16_bn(pretrained: bool = False, **kwargs: Any) -> CRNN:
    """CRNN with a VGG-16 backbone as described in `"An End-to-End Trainable Neural Network for Image-based
    Sequence Recognition and Its Application to Scene Text Recognition" <https://arxiv.org/pdf/1507.05717.pdf>`_.

    >>> import tensorflow as tf
    >>> from doctr.models import crnn_vgg16_bn
    >>> model = crnn_vgg16_bn(pretrained=True)
    >>> input_tensor = tf.random.uniform(shape=[1, 32, 128, 3], maxval=1, dtype=tf.float32)
    >>> out = model(input_tensor)

    Args:
        pretrained (bool): If True, returns a model pre-trained on our text recognition dataset
        **kwargs: keyword arguments of the CRNN architecture

    Returns:
        text recognition architecture
    """
    return _crnn("crnn_vgg16_bn", pretrained, vgg16_bn_r, **kwargs)


def crnn_mobilenet_v3_small(pretrained: bool = False, **kwargs: Any) -> CRNN:
    """CRNN with a MobileNet V3 Small backbone as described in `"An End-to-End Trainable Neural Network for Image-based
    Sequence Recognition and Its Application to Scene Text Recognition" <https://arxiv.org/pdf/1507.05717.pdf>`_.

    >>> import tensorflow as tf
    >>> from doctr.models import crnn_mobilenet_v3_small
    >>> model = crnn_mobilenet_v3_small(pretrained=True)
    >>> input_tensor = tf.random.uniform(shape=[1, 32, 128, 3], maxval=1, dtype=tf.float32)
    >>> out = model(input_tensor)

    Args:
        pretrained (bool): If True, returns a model pre-trained on our text recognition dataset
        **kwargs: keyword arguments of the CRNN architecture

    Returns:
        text recognition architecture
    """
    return _crnn("crnn_mobilenet_v3_small", pretrained, mobilenet_v3_small_r, **kwargs)


def crnn_mobilenet_v3_large(pretrained: bool = False, **kwargs: Any) -> CRNN:
    """CRNN with a MobileNet V3 Large backbone as described in `"An End-to-End Trainable Neural Network for Image-based
    Sequence Recognition and Its Application to Scene Text Recognition" <https://arxiv.org/pdf/1507.05717.pdf>`_.

    >>> import tensorflow as tf
    >>> from doctr.models import crnn_mobilenet_v3_large
    >>> model = crnn_mobilenet_v3_large(pretrained=True)
    >>> input_tensor = tf.random.uniform(shape=[1, 32, 128, 3], maxval=1, dtype=tf.float32)
    >>> out = model(input_tensor)

    Args:
        pretrained (bool): If True, returns a model pre-trained on our text recognition dataset
        **kwargs: keyword arguments of the CRNN architecture

    Returns:
        text recognition architecture
    """
    return _crnn("crnn_mobilenet_v3_large", pretrained, mobilenet_v3_large_r, **kwargs)
