import tensorflow as tf

from detext.utils.model_utils import create_rnn_cell, init_word_embedding


class LstmLmModel():
    def __init__(self,
                 query,
                 doc_fields,
                 usr_fields,
                 hparams,
                 mode):
        """
        Applies LSTM (language modeling version) to convert text to a fix length embedding

        :param query: Tensor(dtype=tf.int)  Shape=[batch_size, query_length]
        :param doc_fields: list(Tensor(dtype=int))/Tensor  A list of document fields. Each has shape=
            [batch_size, max_group_size, doc_field_length]. For online scoring, these fields may be precomputed and
            input as Tensor
        :param usr_fields: list(Tensor(dtype=int))/Tensor  A list of user fields. Each has shape=
            [batch_size, usr_field_length]. For online scoring, these fields may be precomputed and
            input as Tensor
        :param hparams: HParams
        :param mode: TRAIN/EVAL/INFER
        """
        self._query = query
        self._doc_fields = doc_fields
        self._usr_fields = usr_fields
        self._hparams = hparams
        self._mode = mode
        self.text_ftr_size = 1

        # Initialize embedding
        self.embedding = init_word_embedding(self._hparams, self._mode)
        # NOTE: when normalized_lm is True, this embedding is not used because normalized_lm use a different
        #   implementation (dense layer) for embedding interaction
        if hparams.normalized_lm is False:
            self.nextw_embedding = init_word_embedding(self._hparams, self._mode, name_prefix="nextw")

        with tf.variable_scope("lstm_lm", dtype=tf.float32):
            # RNN cell setup
            self.cell_dtype = tf.float32
            self.cell = create_rnn_cell(
                unit_type=hparams.unit_type,
                num_units=hparams.num_units,
                num_layers=hparams.num_layers,
                num_residual_layers=hparams.num_residual_layers,
                forget_bias=hparams.forget_bias,
                dropout=hparams.rnn_dropout,
                mode=tf.contrib.learn.ModeKeys.TRAIN,
            )
            self.length_bias = tf.get_variable("length_bias", shape=[1], dtype=tf.float32)
            if hparams.bidirectional:
                self.cell_fw = create_rnn_cell(
                    unit_type=hparams.unit_type,
                    num_units=hparams.num_units / 2,
                    num_layers=hparams.num_layers,
                    num_residual_layers=hparams.num_residual_layers,
                    forget_bias=hparams.forget_bias,
                    dropout=hparams.rnn_dropout,
                    mode=tf.contrib.learn.ModeKeys.TRAIN,
                )
                self.cell_bw = create_rnn_cell(
                    unit_type=hparams.unit_type,
                    num_units=hparams.num_units / 2,
                    num_layers=hparams.num_layers,
                    num_residual_layers=hparams.num_residual_layers,
                    forget_bias=hparams.forget_bias,
                    dropout=hparams.rnn_dropout,
                    mode=tf.contrib.learn.ModeKeys.TRAIN,
                )

            # Apply LSTM on text fields
            self.query_ftrs = self.apply_lstm_on_query() if query is not None else None
            self.usr_ftrs = self.apply_lstm_on_usr() if usr_fields is not None else None
            self.doc_ftrs = self.apply_lstm_on_doc()

    def apply_lstm_on_query(self):
        """
        Applies LSTM on query

        :return Tensor  Query features. Shape=[batch_size, 1]
        """
        return self.apply_lstm_on_text(self._query)

    def apply_lstm_on_usr(self):
        """
        Applies LSTM on user fields

        :return Tensor  User features. Shape=[batch_size, num_usr_fields, 1]
        """
        if type(self._usr_fields) is not list:
            return self._usr_fields

        usr_ftrs = []
        for i, usr_field in enumerate(self._usr_fields):
            usr_field_ftrs = self.apply_lstm_on_text(usr_field)
            usr_ftrs.append(usr_field_ftrs)
        usr_ftrs = tf.stack(usr_ftrs, axis=1)  # shape=[batch_size, num_usr_fields, 1]
        return usr_ftrs

    def apply_lstm_on_doc(self):
        """Applies LSTM on documents

        :return Tensor  Document features. Shape=[batch_size, max_group_size, num_doc_fields, 1]
        """

        # doc_fields should be the doc embeddings
        if type(self._doc_fields) is not list:
            return self._doc_fields

        doc_ftrs = []
        for i, doc_field in enumerate(self._doc_fields):
            doc_shape = tf.shape(doc_field)  # Shape=[batch_size, group_size, sent_length]
            doc_field = tf.reshape(doc_field, shape=[doc_shape[0] * doc_shape[1], doc_shape[2]])

            # Apply LSTM
            doc_field_ftrs = self.apply_lstm_on_text(doc_field)

            # Restore batch_size and group_size
            doc_field_ftrs = tf.reshape(doc_field_ftrs, shape=[doc_shape[0], doc_shape[1], self.text_ftr_size])
            doc_ftrs.append(doc_field_ftrs)
        doc_ftrs = tf.stack(doc_ftrs, axis=2)  # Shape=[batch_size, max_group_size, num_doc_fields, 1]
        return doc_ftrs

    def apply_lstm_on_text(self, text):
        """
        Applies LSTM (language modeling) on text

        :param text: Tensor Shape=[batch_size, seq_len]
        :return Tensor Shape=[batch_size, 1]
        """
        hparams = self._hparams

        # Shape=[batch_size]
        seq_len = tf.reduce_sum(tf.cast(tf.not_equal(text, hparams.pad_id), dtype=tf.int32), axis=-1)
        max_seq_len = tf.shape(text)[1]

        input_seq = text[:, :-1]  # [batch_size, seq_len - 1]
        input_emb = tf.nn.embedding_lookup(self.embedding, input_seq)  # [batch_size, seq_len - 1, num_units]
        output_seq = text[:, 1:]  # [batch_size, seq_len - 1]

        # [batch_size, seq_len - 1, num_units]
        seq_outputs, _ = tf.nn.dynamic_rnn(
            self.cell,
            input_emb,
            dtype=self.cell_dtype,
            sequence_length=seq_len - 1,
            time_major=False,
            swap_memory=True)

        # Whether to use bidirectional RNN
        if hparams.bidirectional:
            seq_outputs, _ = tf.nn.bidirectional_dynamic_rnn(
                self.cell_fw,
                self.cell_bw,
                input_emb,
                dtype=self.cell_dtype,
                sequence_length=seq_len - 1,
                time_major=False,
                swap_memory=True)
            seq_outputs = tf.concat(seq_outputs, axis=2)

        # Whether to use normalized language model
        if not hparams.normalized_lm:
            output_emb = tf.nn.embedding_lookup(self.nextw_embedding, output_seq)  # [batch_size, seq_len - 1, num_units]
            # If do reduce_sum for the result, it will approximate normalized log_p(w_i+1|w_i, w_i-1, ..., w_0)
            word_scores = seq_outputs * output_emb

            # Approximate normalized log_p(word sequence)
            word_scores = tf.reduce_sum(word_scores, axis=-1)  # [batch_size, max_seq_len - 1]
            # Use bias to approximate sum(dot(curr_embedding, all_embedding))
            word_scores += self.length_bias
        else:
            # Compute h_i*w_i+1 - log_sum_k_softmax(h_i*w_k)
            seq_outputs = tf.layers.dense(
                seq_outputs, units=hparams.vocab_size, use_bias=False, name="dot_prod_nextw",
                reuse=tf.AUTO_REUSE)  # [batch_size, seq_len - 1, vocab_size]
            word_scores = tf.nn.sparse_softmax_cross_entropy_with_logits(
                labels=output_seq, logits=seq_outputs)

        # Mask out padding scores
        mask = tf.sequence_mask(seq_len - 1, max_seq_len - 1, dtype=tf.float32)
        word_scores *= mask
        word_scores = tf.reduce_sum(word_scores, axis=-1, keepdims=True)  # [batch_size, 1]
        return word_scores

import tensorflow as tf

from detext.utils.model_utils import create_rnn_cell, init_word_embedding


class LstmLmModel():
    def __init__(self,
                 query,
                 doc_fields,
                 usr_fields,
                 hparams,
                 mode):
        """
        Applies LSTM (language modeling version) to convert text to a fix length embedding

        :param query: Tensor(dtype=tf.int)  Shape=[batch_size, query_length]
        :param doc_fields: list(Tensor(dtype=int))/Tensor  A list of document fields. Each has shape=
            [batch_size, max_group_size, doc_field_length]. For online scoring, these fields may be precomputed and
            input as Tensor
        :param usr_fields: list(Tensor(dtype=int))/Tensor  A list of user fields. Each has shape=
            [batch_size, usr_field_length]. For online scoring, these fields may be precomputed and
            input as Tensor
        :param hparams: HParams
        :param mode: TRAIN/EVAL/INFER
        """
        self._query = query
        self._doc_fields = doc_fields
        self._usr_fields = usr_fields
        self._hparams = hparams
        self._mode = mode
        self.text_ftr_size = 1

        # Initialize embedding
        self.embedding = init_word_embedding(self._hparams, self._mode)
        # NOTE: when normalized_lm is True, this embedding is not used because normalized_lm use a different
        #   implementation (dense layer) for embedding interaction
        if hparams.normalized_lm is False:
            self.nextw_embedding = init_word_embedding(self._hparams, self._mode, name_prefix="nextw")

        with tf.variable_scope("lstm_lm", dtype=tf.float32):
            # RNN cell setup
            self.cell_dtype = tf.float32
            self.cell = create_rnn_cell(
                unit_type=hparams.unit_type,
                num_units=hparams.num_units,
                num_layers=hparams.num_layers,
                num_residual_layers=hparams.num_residual_layers,
                forget_bias=hparams.forget_bias,
                dropout=hparams.rnn_dropout,
                mode=tf.contrib.learn.ModeKeys.TRAIN,
            )
            self.length_bias = tf.get_variable("length_bias", shape=[1], dtype=tf.float32)
            if hparams.bidirectional:
                self.cell_fw = create_rnn_cell(
                    unit_type=hparams.unit_type,
                    num_units=hparams.num_units / 2,
                    num_layers=hparams.num_layers,
                    num_residual_layers=hparams.num_residual_layers,
                    forget_bias=hparams.forget_bias,
                    dropout=hparams.rnn_dropout,
                    mode=tf.contrib.learn.ModeKeys.TRAIN,
                )
                self.cell_bw = create_rnn_cell(
                    unit_type=hparams.unit_type,
                    num_units=hparams.num_units / 2,
                    num_layers=hparams.num_layers,
                    num_residual_layers=hparams.num_residual_layers,
                    forget_bias=hparams.forget_bias,
                    dropout=hparams.rnn_dropout,
                    mode=tf.contrib.learn.ModeKeys.TRAIN,
                )

            # Apply LSTM on text fields
            self.query_ftrs = self.apply_lstm_on_query() if query is not None else None
            self.usr_ftrs = self.apply_lstm_on_usr() if usr_fields is not None else None
            self.doc_ftrs = self.apply_lstm_on_doc()

    def apply_lstm_on_query(self):
        """
        Applies LSTM on query

        :return Tensor  Query features. Shape=[batch_size, 1]
        """
        return self.apply_lstm_on_text(self._query)

    def apply_lstm_on_usr(self):
        """
        Applies LSTM on user fields

        :return Tensor  User features. Shape=[batch_size, num_usr_fields, 1]
        """
        if type(self._usr_fields) is not list:
            return self._usr_fields

        usr_ftrs = []
        for i, usr_field in enumerate(self._usr_fields):
            usr_field_ftrs = self.apply_lstm_on_text(usr_field)
            usr_ftrs.append(usr_field_ftrs)
        usr_ftrs = tf.stack(usr_ftrs, axis=1)  # shape=[batch_size, num_usr_fields, 1]
        return usr_ftrs

    def apply_lstm_on_doc(self):
        """Applies LSTM on documents

        :return Tensor  Document features. Shape=[batch_size, max_group_size, num_doc_fields, 1]
        """

        # doc_fields should be the doc embeddings
        if type(self._doc_fields) is not list:
            return self._doc_fields

        doc_ftrs = []
        for i, doc_field in enumerate(self._doc_fields):
            doc_shape = tf.shape(doc_field)  # Shape=[batch_size, group_size, sent_length]
            doc_field = tf.reshape(doc_field, shape=[doc_shape[0] * doc_shape[1], doc_shape[2]])

            # Apply LSTM
            doc_field_ftrs = self.apply_lstm_on_text(doc_field)

            # Restore batch_size and group_size
            doc_field_ftrs = tf.reshape(doc_field_ftrs, shape=[doc_shape[0], doc_shape[1], self.text_ftr_size])
            doc_ftrs.append(doc_field_ftrs)
        doc_ftrs = tf.stack(doc_ftrs, axis=2)  # Shape=[batch_size, max_group_size, num_doc_fields, 1]
        return doc_ftrs

    def apply_lstm_on_text(self, text):
        """
        Applies LSTM (language modeling) on text

        :param text: Tensor Shape=[batch_size, seq_len]
        :return Tensor Shape=[batch_size, 1]
        """
        hparams = self._hparams

        # Shape=[batch_size]
        seq_len = tf.reduce_sum(tf.cast(tf.not_equal(text, hparams.pad_id), dtype=tf.int32), axis=-1)
        max_seq_len = tf.shape(text)[1]

        input_seq = text[:, :-1]  # [batch_size, seq_len - 1]
        input_emb = tf.nn.embedding_lookup(self.embedding, input_seq)  # [batch_size, seq_len - 1, num_units]
        output_seq = text[:, 1:]  # [batch_size, seq_len - 1]

        # [batch_size, seq_len - 1, num_units]
        seq_outputs, _ = tf.nn.dynamic_rnn(
            self.cell,
            input_emb,
            dtype=self.cell_dtype,
            sequence_length=seq_len - 1,
            time_major=False,
            swap_memory=True)

        # Whether to use bidirectional RNN
        if hparams.bidirectional:
            seq_outputs, _ = tf.nn.bidirectional_dynamic_rnn(
                self.cell_fw,
                self.cell_bw,
                input_emb,
                dtype=self.cell_dtype,
                sequence_length=seq_len - 1,
                time_major=False,
                swap_memory=True)
            seq_outputs = tf.concat(seq_outputs, axis=2)

        # Whether to use normalized language model
        if not hparams.normalized_lm:
            output_emb = tf.nn.embedding_lookup(self.nextw_embedding, output_seq)  # [batch_size, seq_len - 1, num_units]
            # If do reduce_sum for the result, it will approximate normalized log_p(w_i+1|w_i, w_i-1, ..., w_0)
            word_scores = seq_outputs * output_emb

            # Approximate normalized log_p(word sequence)
            word_scores = tf.reduce_sum(word_scores, axis=-1)  # [batch_size, max_seq_len - 1]
            # Use bias to approximate sum(dot(curr_embedding, all_embedding))
            word_scores += self.length_bias
        else:
            # Compute h_i*w_i+1 - log_sum_k_softmax(h_i*w_k)
            seq_outputs = tf.layers.dense(
                seq_outputs, units=hparams.vocab_size, use_bias=False, name="dot_prod_nextw",
                reuse=tf.AUTO_REUSE)  # [batch_size, seq_len - 1, vocab_size]
            word_scores = tf.nn.sparse_softmax_cross_entropy_with_logits(
                labels=output_seq, logits=seq_outputs)

        # Mask out padding scores
        mask = tf.sequence_mask(seq_len - 1, max_seq_len - 1, dtype=tf.float32)
        word_scores *= mask
        word_scores = tf.reduce_sum(word_scores, axis=-1, keepdims=True)  # [batch_size, 1]
        return word_scores

Applies LSTM (language modeling version) to convert text to a fix length embedding          :param query: Tensor(dtype=tf.int)  Shape=[batch_size, query_length]         :param doc_fields: list(Tensor(dtype=int))/Tensor  A list of document fields. Each has shape=             [batch_size, max_group_size, doc_field_length]. For online scoring, these fields may be precomputed and             input as Tensor         :param usr_fields: list(Tensor(dtype=int))/Tensor  A list of user fields. Each has shape=             [batch_size, usr_field_length]. For online scoring, these fields may be precomputed and             input as Tensor         :param hparams: HParams         :param mode: TRAIN/EVAL/INFER # Initialize embedding # NOTE: when normalized_lm is True, this embedding is not used because normalized_lm use a different #   implementation (dense layer) for embedding interaction # RNN cell setup # Apply LSTM on text fields Applies LSTM on query          :return Tensor  Query features. Shape=[batch_size, 1] Applies LSTM on user fields          :return Tensor  User features. Shape=[batch_size, num_usr_fields, 1] # shape=[batch_size, num_usr_fields, 1] Applies LSTM on documents          :return Tensor  Document features. Shape=[batch_size, max_group_size, num_doc_fields, 1] # doc_fields should be the doc embeddings # Shape=[batch_size, group_size, sent_length] # Apply LSTM # Restore batch_size and group_size # Shape=[batch_size, max_group_size, num_doc_fields, 1] Applies LSTM (language modeling) on text          :param text: Tensor Shape=[batch_size, seq_len]         :return Tensor Shape=[batch_size, 1] # Shape=[batch_size] # [batch_size, seq_len - 1] # [batch_size, seq_len - 1, num_units] # [batch_size, seq_len - 1] # [batch_size, seq_len - 1, num_units] # Whether to use bidirectional RNN # Whether to use normalized language model # [batch_size, seq_len - 1, num_units] # If do reduce_sum for the result, it will approximate normalized log_p(w_i+1|w_i, w_i-1, ..., w_0) # Approximate normalized log_p(word sequence) # [batch_size, max_seq_len - 1] # Use bias to approximate sum(dot(curr_embedding, all_embedding)) # Compute h_i*w_i+1 - log_sum_k_softmax(h_i*w_k) # [batch_size, seq_len - 1, vocab_size] # Mask out padding scores # [batch_size, 1]