import numpy as np
import logging
import scipy.sparse
from typing import Optional, Text, Dict, Any, Union, List
from rasa.core.constants import DIALOGUE
from rasa.nlu.constants import TEXT
from rasa.nlu.tokenizers.tokenizer import Token
import rasa.utils.io as io_utils
from rasa.utils.tensorflow.constants import (
    LABEL,
    HIDDEN_LAYERS_SIZES,
    NUM_TRANSFORMER_LAYERS,
    NUM_HEADS,
    DENSE_DIMENSION,
    LOSS_TYPE,
    SIMILARITY_TYPE,
    NUM_NEG,
    EVAL_NUM_EXAMPLES,
    EVAL_NUM_EPOCHS,
    REGULARIZATION_CONSTANT,
    USE_MAX_NEG_SIM,
    MAX_NEG_SIM,
    MAX_POS_SIM,
    EMBEDDING_DIMENSION,
    DROP_RATE_DIALOGUE,
    DROP_RATE_LABEL,
    NEGATIVE_MARGIN_SCALE,
    DROP_RATE,
    EPOCHS,
    SOFTMAX,
    MARGIN,
    AUTO,
    INNER,
    COSINE,
)


logger = logging.getLogger(__name__)


def normalize(values: np.ndarray, ranking_length: Optional[int] = 0) -> np.ndarray:
    """Normalizes an array of positive numbers over the top `ranking_length` values.
    Other values will be set to 0.
    """

    new_values = values.copy()  # prevent mutation of the input
    if 0 < ranking_length < len(new_values):
        ranked = sorted(new_values, reverse=True)
        new_values[new_values < ranked[ranking_length - 1]] = 0

    if np.sum(new_values) > 0:
        new_values = new_values / np.sum(new_values)

    return new_values


def update_similarity_type(config: Dict[Text, Any]) -> Dict[Text, Any]:
    """
    If SIMILARITY_TYPE is set to 'auto', update the SIMILARITY_TYPE depending
    on the LOSS_TYPE.
    Args:
        config: model configuration

    Returns: updated model configuration
    """
    if config.get(SIMILARITY_TYPE) == AUTO:
        if config[LOSS_TYPE] == SOFTMAX:
            config[SIMILARITY_TYPE] = INNER
        elif config[LOSS_TYPE] == MARGIN:
            config[SIMILARITY_TYPE] = COSINE

    return config


def align_tokens(
    tokens_in: List[Text], token_end: int, token_start: int
) -> List[Token]:
    """Align sub-tokens of Language model with tokens return by the WhitespaceTokenizer.

    As a language model might split a single word into multiple tokens, we need to make
    sure that the start and end value of first and last sub-token matches the
    start and end value of the token return by the WhitespaceTokenizer as the
    entities are using those start and end values.
    """

    tokens_out = []

    current_token_offset = token_start

    for index, string in enumerate(tokens_in):
        if index == 0:
            if index == len(tokens_in) - 1:
                s_token_end = token_end
            else:
                s_token_end = current_token_offset + len(string)
            tokens_out.append(Token(string, token_start, end=s_token_end))
        elif index == len(tokens_in) - 1:
            tokens_out.append(Token(string, current_token_offset, end=token_end))
        else:
            tokens_out.append(
                Token(
                    string, current_token_offset, end=current_token_offset + len(string)
                )
            )

        current_token_offset += len(string)

    return tokens_out


def sequence_to_sentence_features(
    features: Union[np.ndarray, scipy.sparse.spmatrix]
) -> Optional[Union[np.ndarray, scipy.sparse.spmatrix]]:
    """Extract the CLS token vector as sentence features.
    Features is a sequence. The last token is the CLS token. The feature vector of
    this token contains the sentence features."""

    if features is None:
        return None

    if isinstance(features, scipy.sparse.spmatrix):
        return scipy.sparse.coo_matrix(features.tocsr()[-1])

    return np.expand_dims(features[-1], axis=0)


def update_evaluation_parameters(config: Dict[Text, Any]) -> Dict[Text, Any]:
    """
    If EVAL_NUM_EPOCHS is set to -1, evaluate at the end of the training.

    Args:
        config: model configuration

    Returns: updated model configuration
    """

    if config[EVAL_NUM_EPOCHS] == -1:
        config[EVAL_NUM_EPOCHS] = config[EPOCHS]
    elif config[EVAL_NUM_EPOCHS] < 1:
        raise ValueError(
            f"'{EVAL_NUM_EXAMPLES}' is set to "
            f"'{config[EVAL_NUM_EPOCHS]}'. "
            f"Only values > 1 are allowed for this configuration value."
        )

    return config


def load_tf_hub_model(model_url: Text) -> Any:
    """Load model from cache if possible, otherwise from TFHub"""

    import tensorflow_hub as tfhub

    # needed to load the ConveRT model
    # noinspection PyUnresolvedReferences
    import tensorflow_text
    import os

    # required to take care of cases when other files are already
    # stored in the default TFHUB_CACHE_DIR
    try:
        return tfhub.load(model_url)
    except OSError:
        directory = io_utils.create_temporary_directory()
        os.environ["TFHUB_CACHE_DIR"] = directory
        return tfhub.load(model_url)


def _replace_deprecated_option(
    old_option: Text, new_option: Union[Text, List[Text]], config: Dict[Text, Any]
) -> Dict[Text, Any]:
    if old_option in config:
        if isinstance(new_option, str):
            logger.warning(
                f"Option '{old_option}' got renamed to '{new_option}'. "
                f"Please update your configuration file."
            )
            config[new_option] = config[old_option]
        else:
            logger.warning(
                f"Option '{old_option}' got renamed to "
                f"a dictionary '{new_option[0]}' with a key '{new_option[1]}'. "
                f"Please update your configuration file."
            )
            option_dict = config.get(new_option[0], {})
            option_dict[new_option[1]] = config[old_option]
            config[new_option[0]] = option_dict

    return config


def check_deprecated_options(config: Dict[Text, Any]) -> Dict[Text, Any]:
    """
    If old model configuration parameters are present in the provided config, replace
    them with the new parameters and log a warning.
    Args:
        config: model configuration

    Returns: updated model configuration
    """

    config = _replace_deprecated_option(
        "hidden_layers_sizes_pre_dial", [HIDDEN_LAYERS_SIZES, DIALOGUE], config
    )
    config = _replace_deprecated_option(
        "hidden_layers_sizes_bot", [HIDDEN_LAYERS_SIZES, LABEL], config
    )
    config = _replace_deprecated_option("droprate", DROP_RATE, config)
    config = _replace_deprecated_option("droprate_a", DROP_RATE_DIALOGUE, config)
    config = _replace_deprecated_option("droprate_b", DROP_RATE_LABEL, config)
    config = _replace_deprecated_option(
        "hidden_layers_sizes_a", [HIDDEN_LAYERS_SIZES, TEXT], config
    )
    config = _replace_deprecated_option(
        "hidden_layers_sizes_b", [HIDDEN_LAYERS_SIZES, LABEL], config
    )
    config = _replace_deprecated_option(
        "num_transformer_layers", NUM_TRANSFORMER_LAYERS, config
    )
    config = _replace_deprecated_option("num_heads", NUM_HEADS, config)
    config = _replace_deprecated_option("dense_dim", DENSE_DIMENSION, config)
    config = _replace_deprecated_option("embed_dim", EMBEDDING_DIMENSION, config)
    config = _replace_deprecated_option("num_neg", NUM_NEG, config)
    config = _replace_deprecated_option("mu_pos", MAX_POS_SIM, config)
    config = _replace_deprecated_option("mu_neg", MAX_NEG_SIM, config)
    config = _replace_deprecated_option("use_max_sim_neg", USE_MAX_NEG_SIM, config)
    config = _replace_deprecated_option("C2", REGULARIZATION_CONSTANT, config)
    config = _replace_deprecated_option("C_emb", NEGATIVE_MARGIN_SCALE, config)
    config = _replace_deprecated_option(
        "evaluate_every_num_epochs", EVAL_NUM_EPOCHS, config
    )
    config = _replace_deprecated_option(
        "evaluate_on_num_examples", EVAL_NUM_EXAMPLES, config
    )

    return config

import numpy as np
import logging
import scipy.sparse
from typing import Optional, Text, Dict, Any, Union, List
from rasa.core.constants import DIALOGUE
from rasa.nlu.constants import TEXT
from rasa.nlu.tokenizers.tokenizer import Token
import rasa.utils.io as io_utils
from rasa.utils.tensorflow.constants import (
    LABEL,
    HIDDEN_LAYERS_SIZES,
    NUM_TRANSFORMER_LAYERS,
    NUM_HEADS,
    DENSE_DIMENSION,
    LOSS_TYPE,
    SIMILARITY_TYPE,
    NUM_NEG,
    EVAL_NUM_EXAMPLES,
    EVAL_NUM_EPOCHS,
    REGULARIZATION_CONSTANT,
    USE_MAX_NEG_SIM,
    MAX_NEG_SIM,
    MAX_POS_SIM,
    EMBEDDING_DIMENSION,
    DROP_RATE_DIALOGUE,
    DROP_RATE_LABEL,
    NEGATIVE_MARGIN_SCALE,
    DROP_RATE,
    EPOCHS,
    SOFTMAX,
    MARGIN,
    AUTO,
    INNER,
    COSINE,
)


logger = logging.getLogger(__name__)


def normalize(values: np.ndarray, ranking_length: Optional[int] = 0) -> np.ndarray:
    """Normalizes an array of positive numbers over the top `ranking_length` values.
    Other values will be set to 0.
    """

    new_values = values.copy()  # prevent mutation of the input
    if 0 < ranking_length < len(new_values):
        ranked = sorted(new_values, reverse=True)
        new_values[new_values < ranked[ranking_length - 1]] = 0

    if np.sum(new_values) > 0:
        new_values = new_values / np.sum(new_values)

    return new_values


def update_similarity_type(config: Dict[Text, Any]) -> Dict[Text, Any]:
    """
    If SIMILARITY_TYPE is set to 'auto', update the SIMILARITY_TYPE depending
    on the LOSS_TYPE.
    Args:
        config: model configuration

    Returns: updated model configuration
    """
    if config.get(SIMILARITY_TYPE) == AUTO:
        if config[LOSS_TYPE] == SOFTMAX:
            config[SIMILARITY_TYPE] = INNER
        elif config[LOSS_TYPE] == MARGIN:
            config[SIMILARITY_TYPE] = COSINE

    return config


def align_tokens(
    tokens_in: List[Text], token_end: int, token_start: int
) -> List[Token]:
    """Align sub-tokens of Language model with tokens return by the WhitespaceTokenizer.

    As a language model might split a single word into multiple tokens, we need to make
    sure that the start and end value of first and last sub-token matches the
    start and end value of the token return by the WhitespaceTokenizer as the
    entities are using those start and end values.
    """

    tokens_out = []

    current_token_offset = token_start

    for index, string in enumerate(tokens_in):
        if index == 0:
            if index == len(tokens_in) - 1:
                s_token_end = token_end
            else:
                s_token_end = current_token_offset + len(string)
            tokens_out.append(Token(string, token_start, end=s_token_end))
        elif index == len(tokens_in) - 1:
            tokens_out.append(Token(string, current_token_offset, end=token_end))
        else:
            tokens_out.append(
                Token(
                    string, current_token_offset, end=current_token_offset + len(string)
                )
            )

        current_token_offset += len(string)

    return tokens_out


def sequence_to_sentence_features(
    features: Union[np.ndarray, scipy.sparse.spmatrix]
) -> Optional[Union[np.ndarray, scipy.sparse.spmatrix]]:
    """Extract the CLS token vector as sentence features.
    Features is a sequence. The last token is the CLS token. The feature vector of
    this token contains the sentence features."""

    if features is None:
        return None

    if isinstance(features, scipy.sparse.spmatrix):
        return scipy.sparse.coo_matrix(features.tocsr()[-1])

    return np.expand_dims(features[-1], axis=0)


def update_evaluation_parameters(config: Dict[Text, Any]) -> Dict[Text, Any]:
    """
    If EVAL_NUM_EPOCHS is set to -1, evaluate at the end of the training.

    Args:
        config: model configuration

    Returns: updated model configuration
    """

    if config[EVAL_NUM_EPOCHS] == -1:
        config[EVAL_NUM_EPOCHS] = config[EPOCHS]
    elif config[EVAL_NUM_EPOCHS] < 1:
        raise ValueError(
            f"'{EVAL_NUM_EXAMPLES}' is set to "
            f"'{config[EVAL_NUM_EPOCHS]}'. "
            f"Only values > 1 are allowed for this configuration value."
        )

    return config


def load_tf_hub_model(model_url: Text) -> Any:
    """Load model from cache if possible, otherwise from TFHub"""

    import tensorflow_hub as tfhub

    # needed to load the ConveRT model
    # noinspection PyUnresolvedReferences
    import tensorflow_text
    import os

    # required to take care of cases when other files are already
    # stored in the default TFHUB_CACHE_DIR
    try:
        return tfhub.load(model_url)
    except OSError:
        directory = io_utils.create_temporary_directory()
        os.environ["TFHUB_CACHE_DIR"] = directory
        return tfhub.load(model_url)


def _replace_deprecated_option(
    old_option: Text, new_option: Union[Text, List[Text]], config: Dict[Text, Any]
) -> Dict[Text, Any]:
    if old_option in config:
        if isinstance(new_option, str):
            logger.warning(
                f"Option '{old_option}' got renamed to '{new_option}'. "
                f"Please update your configuration file."
            )
            config[new_option] = config[old_option]
        else:
            logger.warning(
                f"Option '{old_option}' got renamed to "
                f"a dictionary '{new_option[0]}' with a key '{new_option[1]}'. "
                f"Please update your configuration file."
            )
            option_dict = config.get(new_option[0], {})
            option_dict[new_option[1]] = config[old_option]
            config[new_option[0]] = option_dict

    return config


def check_deprecated_options(config: Dict[Text, Any]) -> Dict[Text, Any]:
    """
    If old model configuration parameters are present in the provided config, replace
    them with the new parameters and log a warning.
    Args:
        config: model configuration

    Returns: updated model configuration
    """

    config = _replace_deprecated_option(
        "hidden_layers_sizes_pre_dial", [HIDDEN_LAYERS_SIZES, DIALOGUE], config
    )
    config = _replace_deprecated_option(
        "hidden_layers_sizes_bot", [HIDDEN_LAYERS_SIZES, LABEL], config
    )
    config = _replace_deprecated_option("droprate", DROP_RATE, config)
    config = _replace_deprecated_option("droprate_a", DROP_RATE_DIALOGUE, config)
    config = _replace_deprecated_option("droprate_b", DROP_RATE_LABEL, config)
    config = _replace_deprecated_option(
        "hidden_layers_sizes_a", [HIDDEN_LAYERS_SIZES, TEXT], config
    )
    config = _replace_deprecated_option(
        "hidden_layers_sizes_b", [HIDDEN_LAYERS_SIZES, LABEL], config
    )
    config = _replace_deprecated_option(
        "num_transformer_layers", NUM_TRANSFORMER_LAYERS, config
    )
    config = _replace_deprecated_option("num_heads", NUM_HEADS, config)
    config = _replace_deprecated_option("dense_dim", DENSE_DIMENSION, config)
    config = _replace_deprecated_option("embed_dim", EMBEDDING_DIMENSION, config)
    config = _replace_deprecated_option("num_neg", NUM_NEG, config)
    config = _replace_deprecated_option("mu_pos", MAX_POS_SIM, config)
    config = _replace_deprecated_option("mu_neg", MAX_NEG_SIM, config)
    config = _replace_deprecated_option("use_max_sim_neg", USE_MAX_NEG_SIM, config)
    config = _replace_deprecated_option("C2", REGULARIZATION_CONSTANT, config)
    config = _replace_deprecated_option("C_emb", NEGATIVE_MARGIN_SCALE, config)
    config = _replace_deprecated_option(
        "evaluate_every_num_epochs", EVAL_NUM_EPOCHS, config
    )
    config = _replace_deprecated_option(
        "evaluate_on_num_examples", EVAL_NUM_EXAMPLES, config
    )

    return config

Normalizes an array of positive numbers over the top `ranking_length` values.     Other values will be set to 0. # prevent mutation of the input If SIMILARITY_TYPE is set to 'auto', update the SIMILARITY_TYPE depending     on the LOSS_TYPE.     Args:         config: model configuration      Returns: updated model configuration Align sub-tokens of Language model with tokens return by the WhitespaceTokenizer.      As a language model might split a single word into multiple tokens, we need to make     sure that the start and end value of first and last sub-token matches the     start and end value of the token return by the WhitespaceTokenizer as the     entities are using those start and end values. Extract the CLS token vector as sentence features.     Features is a sequence. The last token is the CLS token. The feature vector of     this token contains the sentence features. If EVAL_NUM_EPOCHS is set to -1, evaluate at the end of the training.      Args:         config: model configuration      Returns: updated model configuration Load model from cache if possible, otherwise from TFHub # needed to load the ConveRT model # noinspection PyUnresolvedReferences # required to take care of cases when other files are already # stored in the default TFHUB_CACHE_DIR If old model configuration parameters are present in the provided config, replace     them with the new parameters and log a warning.     Args:         config: model configuration      Returns: updated model configuration