""" TF 2.0 DistilBERT model
"""

import logging
import math

import numpy as np
import tensorflow as tf

from .configuration_distilbert import DistilBertConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, get_initializer, shape_list
from .tokenization_utils import BatchEncoding

logger = logging.getLogger(__name__)

TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
    "distilbert-base-uncased": "http://example.com",
    "distilbert-base-uncased-distilled-squad": "http://example.com",
    "distilbert-base-cased": "http://example.com",
    "distilbert-base-cased-distilled-squad": "http://example.com",
    "distilbert-base-multilingual-cased": "http://example.com",
    "distilbert-base-uncased-finetuned-sst-2-english": "http://example.com",
}

# UTILS AND BUILDING BLOCKS OF THE ARCHITECTURE #
def gelu(x):
    """ Gaussian Error Linear Unit.
    Original Implementation of the gelu activation function in Google Bert repo when initially created.
        For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
        Also see http://example.com
    """
    cdf = 0.5 * (1.0 + tf.math.erf(x / tf.math.sqrt(2.0)))
    return x * cdf

def gelu_new(x):
    """Gaussian Error Linear Unit.
    This is a smoother version of the RELU.
    Original paper: https://arxiv.org/abs/1606.08415
    Args:
        x: float Tensor to perform activation.
    Returns:
        `x` with the GELU activation applied.
    """
    cdf = 0.5 * (1.0 + tf.tanh((np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
    return x * cdf

class TFEmbeddings(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.vocab_size = config.vocab_size
        self.dim = config.dim
        self.initializer_range = config.initializer_range
        self.word_embeddings = TFSharedEmbeddings(
            config.vocab_size, config.dim, initializer_range=config.initializer_range, name="word_embeddings"
        )  # padding_idx=0)
        self.position_embeddings = tf.keras.layers.Embedding(
            config.max_position_embeddings,
            config.dim,
            embeddings_initializer=get_initializer(config.initializer_range),
            name="position_embeddings",
        )

        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="LayerNorm")
        self.dropout = tf.keras.layers.Dropout(config.dropout)

    def build(self, input_shape):
        """Build shared word embedding layer """
        with tf.name_scope("word_embeddings"):
            # Create and initialize weights. The random normal initializer was chosen
            self.word_embeddings = self.add_weight(
                "weight", shape=[self.vocab_size, self.dim], initializer=get_initializer(self.initializer_range)
            )
        super().build(input_shape)

    def call(self, inputs, inputs_embeds=None, mode="embedding", training=False):
        """Get token embeddings of inputs.
        Args:
            inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
            mode: string, a valid value is one of "embedding" and "linear".
        Returns:
            outputs: (1) If mode == "embedding", output embedding tensor, float32 with
                shape [batch_size, length, embedding_size]; (2) mode == "linear", output
                linear tensor, float32 with shape [batch_size, length, vocab_size].
        Raises:
            ValueError: if mode is not valid.

        Shared weights logic adapted from
            http://example.com
        """
        if mode == "embedding":
            return self._embedding(inputs, inputs_embeds=inputs_embeds, training=training)
        elif mode == "linear":
            return self._linear(inputs)
        else:
            raise ValueError("mode {} is not valid.".format(mode))

    def _embedding(self, inputs, inputs_embeds=None, training=False):
        """
        Parameters
        ----------
        input_ids: tf.Tensor(bs, max_seq_length)
            The token ids to embed.

        Outputs
        -------
        embeddings: tf.Tensor(bs, max_seq_length, dim)
            The embedded tokens (plus position embeddings, no token_type embeddings)
        """
        if not isinstance(inputs, (tuple, list)):
            input_ids = inputs
            position_ids = None
        else:
            input_ids, position_ids = inputs

        if input_ids is not None:
            seq_length = shape_list(input_ids)[1]
        else:
            seq_length = shape_list(inputs_embeds)[1]

        if position_ids is None:
            position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]

        if inputs_embeds is None:
            inputs_embeds = tf.gather(self.word_embeddings, input_ids)
        position_embeddings = self.position_embeddings(position_ids)  # (bs, max_seq_length, dim)

        embeddings = inputs_embeds + position_embeddings  # (bs, max_seq_length, dim)
        embeddings = self.LayerNorm(embeddings)  # (bs, max_seq_length, dim)
        embeddings = self.dropout(embeddings, training=training)  # (bs, max_seq_length, dim)
        return embeddings

    def _linear(self, inputs):
        """Computes logits by running inputs through a linear layer.
            Args:
                inputs: A float32 tensor with shape [batch_size, length, hidden_size]
            Returns:
                float32 tensor with shape [batch_size, length, vocab_size].
        """
        batch_size = shape_list(inputs)[0]
        length = shape_list(inputs)[1]

        x = tf.reshape(inputs, [-1, self.dim])
        logits = tf.matmul(x, self.word_embeddings, transpose_b=True)

        return tf.reshape(logits, [batch_size, length, self.vocab_size])

class TFMultiHeadSelfAttention(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

        self.n_heads = config.n_heads
        self.dim = config.dim
        self.dropout = tf.keras.layers.Dropout(config.attention_dropout)
        self.output_attentions = config.output_attentions

        assert self.dim % self.n_heads == 0

        self.q_lin = tf.keras.layers.Dense(
            config.dim, kernel_initializer=get_initializer(config.initializer_range), name="q_lin"
        )
        self.k_lin = tf.keras.layers.Dense(
            config.dim, kernel_initializer=get_initializer(config.initializer_range), name="k_lin"
        )
        self.v_lin = tf.keras.layers.Dense(
            config.dim, kernel_initializer=get_initializer(config.initializer_range), name="v_lin"
        )
        self.out_lin = tf.keras.layers.Dense(
            config.dim, kernel_initializer=get_initializer(config.initializer_range), name="out_lin"
        )

        self.pruned_heads = set()

    def prune_heads(self, heads):
        raise NotImplementedError

    def call(self, inputs, training=False):
        """
        Parameters
        ----------
        query: tf.Tensor(bs, seq_length, dim)
        key: tf.Tensor(bs, seq_length, dim)
        value: tf.Tensor(bs, seq_length, dim)
        mask: tf.Tensor(bs, seq_length)

        Outputs
        -------
        weights: tf.Tensor(bs, n_heads, seq_length, seq_length)
            Attention weights
        context: tf.Tensor(bs, seq_length, dim)
            Contextualized layer. Optional: only if `output_attentions=True`
        """
        query, key, value, mask, head_mask = inputs
        bs, q_length, dim = shape_list(query)
        k_length = shape_list(key)[1]

        dim_per_head = self.dim // self.n_heads

        mask_reshape = [bs, 1, 1, k_length]

        def shape(x):
            """ separate heads """
            return tf.transpose(tf.reshape(x, (bs, -1, self.n_heads, dim_per_head)), perm=(0, 2, 1, 3))

        def unshape(x):
            """ group heads """
            return tf.reshape(tf.transpose(x, perm=(0, 2, 1, 3)), (bs, -1, self.n_heads * dim_per_head))

        q = shape(self.q_lin(query))  # (bs, n_heads, q_length, dim_per_head)
        k = shape(self.k_lin(key))  # (bs, n_heads, k_length, dim_per_head)
        v = shape(self.v_lin(value))  # (bs, n_heads, k_length, dim_per_head)

        q = q / math.sqrt(dim_per_head)  # (bs, n_heads, q_length, dim_per_head)
        scores = tf.matmul(q, k, transpose_b=True)  # (bs, n_heads, q_length, k_length)
        mask = tf.reshape(mask, mask_reshape)  # (bs, n_heads, qlen, klen)
        scores = scores - 1e30 * (1.0 - mask)

        weights = tf.nn.softmax(scores, axis=-1)  # (bs, n_heads, qlen, klen)
        weights = self.dropout(weights, training=training)  # (bs, n_heads, qlen, klen)

        # Mask heads if we want to
        if head_mask is not None:
            weights = weights * head_mask

        context = tf.matmul(weights, v)  # (bs, n_heads, qlen, dim_per_head)
        context = unshape(context)  # (bs, q_length, dim)
        context = self.out_lin(context)  # (bs, q_length, dim)

        if self.output_attentions:
            return (context, weights)
        else:
            return (context,)

class TFFFN(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.dropout = tf.keras.layers.Dropout(config.dropout)
        self.lin1 = tf.keras.layers.Dense(
            config.hidden_dim, kernel_initializer=get_initializer(config.initializer_range), name="lin1"
        )
        self.lin2 = tf.keras.layers.Dense(
            config.dim, kernel_initializer=get_initializer(config.initializer_range), name="lin2"
        )
        assert config.activation in ["relu", "gelu"], "activation ({}) must be in ['relu', 'gelu']".format(
            config.activation
        )
        self.activation = (
            tf.keras.layers.Activation(gelu) if config.activation == "gelu" else tf.keras.activations.relu
        )

    def call(self, input, training=False):
        x = self.lin1(input)
        x = self.activation(x)
        x = self.lin2(x)
        x = self.dropout(x, training=training)
        return x

class TFTransformerBlock(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

        self.n_heads = config.n_heads
        self.dim = config.dim
        self.hidden_dim = config.hidden_dim
        self.dropout = tf.keras.layers.Dropout(config.dropout)
        self.activation = config.activation
        self.output_attentions = config.output_attentions

        assert config.dim % config.n_heads == 0

        self.attention = TFMultiHeadSelfAttention(config, name="attention")
        self.sa_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="sa_layer_norm")

        self.ffn = TFFFN(config, name="ffn")
        self.output_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="output_layer_norm")

    def call(self, inputs, training=False):  # removed: src_enc=None, src_len=None
        """
        Parameters
        ----------
        x: tf.Tensor(bs, seq_length, dim)
        attn_mask: tf.Tensor(bs, seq_length)

        Outputs
        -------
        sa_weights: tf.Tensor(bs, n_heads, seq_length, seq_length)
            The attention weights
        ffn_output: tf.Tensor(bs, seq_length, dim)
            The output of the transformer block contextualization.
        """
        x, attn_mask, head_mask = inputs

        # Self-Attention
        sa_output = self.attention([x, x, x, attn_mask, head_mask], training=training)
        if self.output_attentions:
            sa_output, sa_weights = sa_output  # (bs, seq_length, dim), (bs, n_heads, seq_length, seq_length)
        else:  # To handle these `output_attention` or `output_hidden_states` cases returning tuples
            sa_output = sa_output[0]
        sa_output = self.sa_layer_norm(sa_output + x)  # (bs, seq_length, dim)

        # Feed Forward Network
        ffn_output = self.ffn(sa_output, training=training)  # (bs, seq_length, dim)
        ffn_output = self.output_layer_norm(ffn_output + sa_output)  # (bs, seq_length, dim)

        output = (ffn_output,)
        if self.output_attentions:
            output = (sa_weights,) + output
        return output

class TFTransformer(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.n_layers = config.n_layers
        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states

        self.layer = [TFTransformerBlock(config, name="layer_._{}".format(i)) for i in range(config.n_layers)]

    def call(self, inputs, training=False):
        """
        Parameters
        ----------
        x: tf.Tensor(bs, seq_length, dim)
            Input sequence embedded.
        attn_mask: tf.Tensor(bs, seq_length)
            Attention mask on the sequence.

        Outputs
        -------
        hidden_state: tf.Tensor(bs, seq_length, dim)
            Sequence of hiddens states in the last (top) layer
        all_hidden_states: Tuple[tf.Tensor(bs, seq_length, dim)]
            Tuple of length n_layers with the hidden states from each layer.
            Optional: only if output_hidden_states=True
        all_attentions: Tuple[tf.Tensor(bs, n_heads, seq_length, seq_length)]
            Tuple of length n_layers with the attention weights from each layer
            Optional: only if output_attentions=True
        """
        x, attn_mask, head_mask = inputs

        all_hidden_states = ()
        all_attentions = ()

        hidden_state = x
        for i, layer_module in enumerate(self.layer):
            if self.output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_state,)

            layer_outputs = layer_module([hidden_state, attn_mask, head_mask[i]], training=training)
            hidden_state = layer_outputs[-1]

            if self.output_attentions:
                assert len(layer_outputs) == 2
                attentions = layer_outputs[0]
                all_attentions = all_attentions + (attentions,)
            else:
                assert len(layer_outputs) == 1

        # Add last layer
        if self.output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_state,)

        outputs = (hidden_state,)
        if self.output_hidden_states:
            outputs = outputs + (all_hidden_states,)
        if self.output_attentions:
            outputs = outputs + (all_attentions,)
        return outputs  # last-layer hidden state, (all hidden states), (all attentions)

class TFDistilBertMainLayer(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.num_hidden_layers = config.num_hidden_layers

        self.embeddings = TFEmbeddings(

  # Embeddings
        self.transformer = TFTransformer(config, name="transformer")  # Encoder

    def get_input_embeddings(self):
        return self.embeddings

    def _resize_token_embeddings(self, new_num_tokens):
        raise NotImplementedError

    def _prune_heads(self, heads_to_prune):
        raise NotImplementedError

    def call(self, inputs, attention_mask=None, head_mask=None, inputs_embeds=None, training=False):
        if isinstance(inputs, (tuple, list)):
            input_ids = inputs[0]
            attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
            head_mask = inputs[2] if len(inputs) > 2 else head_mask
            inputs_embeds = inputs[3] if len(inputs) > 3 else inputs_embeds
            assert len(inputs) <= 4, "Too many inputs."
        elif isinstance(inputs, (dict, BatchEncoding)):
            input_ids = inputs.get("input_ids")
            attention_mask = inputs.get("attention_mask", attention_mask)
            head_mask = inputs.get("head_mask", head_mask)
            inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
            assert len(inputs) <= 4, "Too many inputs."
        else:
            input_ids = inputs

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = shape_list(input_ids)
        elif inputs_embeds is not None:
            input_shape = shape_list(inputs_embeds)[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if attention_mask is None:
            attention_mask = tf.ones(input_shape)  # (bs, seq_length)
        attention_mask = tf.cast(attention_mask, dtype=tf.float32)

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        if head_mask is not None:
            raise NotImplementedError
        else:
            head_mask = [None] * self.num_hidden_layers

        embedding_output = self.embeddings(input_ids, inputs_embeds=inputs_embeds)  # (bs, seq_length, dim)
        tfmr_output = self.transformer([embedding_output, attention_mask, head_mask], training=training)

        return tfmr_output  # last-layer hidden-state, (all hidden_states), (all attentions)

# INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL #
class TFDistilBertPreTrainedModel(TFPreTrainedModel):
    """ An abstract class to handle weights initialization and
        a simple interface for downloading and loading pretrained models.
    """

    config_class = DistilBertConfig
    pretrained_model_archive_map = TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP
    base_model_prefix = "distilbert"

DISTILBERT_START_DOCSTRING = r"""
    This model is a `tf.keras.Model <http://example.com>`__ sub-class.
    Use it as a regular TF 2.0 Keras Model and
    refer to the TF 2.0 documentation for all matter related to general usage and behavior.

    .. note::

        TF 2.0 models accepts two formats as inputs:

            - having all inputs as keyword arguments (like PyTorch models), or
            - having all inputs as a list, tuple or dict in the first positional arguments.

        This second option is useful when using :obj:`tf.keras.Model.fit()` method which currently requires having
        all the tensors in the first argument of the model call function: :obj:`model(inputs)`.

        If you choose this second option, there are three possibilities you can use to gather all the input Tensors
        in the first positional argument :

        - a single Tensor with input_ids only and nothing else: :obj:`model(inputs_ids)`
        - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
          :obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
        - a dictionary with one or several input Tensors associated to the input names given in the docstring:
          :obj:`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`

    Parameters:
        config (:class:`~transformers.DistilBertConfig`): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the configuration.
            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""

DISTILBERT_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using :class:`transformers.BertTokenizer`.
            See :func:`transformers.PreTrainedTokenizer.encode` and
            :func:`transformers.PreTrainedTokenizer.encode_plus` for details.

            `What are input IDs? <../glossary.html#input-ids>`__
        attention_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Mask to avoid performing attention on padding token indices.
            Mask values selected in ``[0, 1]``:
            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.

            `What are attention masks? <../glossary.html#attention-mask>`__
        head_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
            Mask to nullify selected heads of the self-attention modules.
            Mask values selected in ``[0, 1]``:
            :obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
        inputs_embeds (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, embedding_dim)`, `optional`, defaults to :obj:`None`):
            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `input_ids` indices into associated vectors
            than the model's internal embedding lookup matrix.
        training (:obj:`boolean`, `optional`, defaults to :obj:`False`):
            Whether to activate dropout modules (if set to :obj:`True`) during training or to de-activate them
            (if set to :obj:`False`) for evaluation.

"""

@add_start_docstrings(
    "The bare DistilBERT encoder/transformer outputing raw hidden-states without any specific head on top.",
    DISTILBERT_START_DOCSTRING,
)
class TFDistilBertModel(TFDistilBertPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.distilbert = TFDistilBertMainLayer(config, name="distilbert")  # Embeddings

    @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING)
    def call(self, inputs, **kwargs):
        r"""
    Returns:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
        last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
            tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
            tuple of :obj:`tf.Tensor` (one for each layer) of shape
            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`:

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

    Examples::

        import tensorflow as tf
        from transformers import DistilBertTokenizer, TFDistilBertModel

        tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-cased')
        model = TFDistilBertModel.from_pretrained('distilbert-base-cased')
        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
        outputs = model(input_ids)
        last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
        """
        outputs = self.distilbert(inputs, **kwargs)
        return outputs

class TFDistilBertLMHead(tf.keras.layers.Layer):
    def __init__(self, config, input_embeddings, **kwargs):
        super().__init__(**kwargs)
        self.vocab_size = config.vocab_size

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.input_embeddings = input_embeddings

    def build(self, input_shape):
        self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
        super().build(input_shape)

    def call(self, hidden_states):
        hidden_states = self.input_embeddings(hidden_states, mode="linear")
        hidden_states = hidden_states + self.bias
        return hidden_states

@add_start_docstrings(
    """DistilBert Model with a `masked language modeling` head on top. """, DISTILBERT_START_DOCSTRING,
)
class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states
        self.vocab_size = config.vocab_size

        self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
        self.vocab_transform = tf.keras.layers.Dense(
            config.dim, kernel_initializer=get_initializer(config.initializer_range), name="vocab_transform"
        )
        self.act = tf.keras.layers.Activation(gelu)
        self.vocab_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="vocab_layer_norm")
        self.vocab_projector = TFDistilBertLMHead(config, self.distilbert.embeddings, name="vocab_projector")

    def get_output_embeddings(self):
        return self.vocab_projector.input_embeddings

    @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING)
    def call(self, inputs, **kwargs):
        r"""

    Returns:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
        prediction_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
            tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
            tuple of :obj:`tf.Tensor` (one for each layer) of shape
            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`:

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

    Examples::

        import tensorflow as tf
        from transformers import DistilBertTokenizer, TFDistilBertForMaskedLM

        tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-cased')
        model = TFDistilBertForMaskedLM.from_pretrained('distilbert-base-cased')
        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
        outputs = model(input_ids)
        prediction_scores = outputs[0]

        """
        distilbert_output = self.distilbert(inputs, **kwargs)

        hidden_states = distilbert_output[0]  # (bs, seq_length, dim)
        prediction_logits = self.vocab_transform(hidden_states)  # (bs, seq_length, dim)
        prediction_logits = self.act(prediction_logits)  # (bs, seq_length, dim)
        prediction_logits = self.vocab_layer_norm(prediction_logits)  # (bs, seq_length, dim)
        prediction_logits = self.vocab_projector(prediction_logits)

        outputs = (prediction_logits,) + distilbert_output[1:]
        return outputs  # logits, (hidden_states), (attentions)

@add_start_docstrings(
    """DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. """,
    DISTILBERT_START_DOCSTRING,
)
class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels

        self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
        self.pre_classifier = tf.keras.layers.Dense(
            config.dim,
            kernel_initializer=get_initializer(config.initializer_range),
            activation="relu",
            name="pre_classifier",
        )
        self.classifier = tf.keras.layers.Dense(
            config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
        )
        self.dropout = tf.keras.layers.Dropout(config.seq_classif_dropout)

    @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING)
    def call(self, inputs, **kwargs):
        r"""
    Returns:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
        logits (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
            Classification (or regression if config.num_labels==1) scores (before SoftMax).
        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
            tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
            tuple of :obj:`tf.Tensor` (one for each layer) of shape
            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`:

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

    Examples::

        import tensorflow as tf
        from transformers import DistilBertTokenizer, TFDistilBertForSequenceClassification

        tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-cased')
        model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-cased')
        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
        outputs = model(input_ids)
        logits = outputs[0]

        """
        distilbert_output = self.distilbert(inputs, **kwargs)

        hidden_state = distilbert_output[0]  # (bs, seq_len, dim)
        pooled_output = hidden_state[:, 0]  # (bs, dim)
        pooled_output = self.pre_classifier(pooled_output)  # (bs, dim)
        pooled_output = self.dropout(pooled_output, training=kwargs.get("training", False))  # (bs, dim)
        logits = self.classifier(pooled_output)  # (bs, dim)

        outputs = (logits,) + distilbert_output[1:]
        return outputs  # logits, (hidden_states), (attentions)

@add_start_docstrings(
    """DistilBert Model with a token classification head on top (a linear layer on top of
    the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
    DISTILBERT_START_DOCSTRING,
)
class TFDistilBertForTokenClassification(TFDistilBertPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels

        self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
        self.dropout = tf.keras.layers.Dropout(config.dropout)
        self.classifier = tf.keras.layers.Dense(
            config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
        )

    @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING)
    def call(self, inputs, **kwargs):
        r"""
    Returns:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
        scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
            Classification scores (before SoftMax).
        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
            tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
            tuple of :obj:`tf.Tensor` (one for each layer) of shape
            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`:

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

    Examples::

        import tensorflow as tf
        from transformers import DistilBertTokenizer, TFDistilBertForTokenClassification

        tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-cased')
        model = TFDistilBertForTokenClassification.from_pretrained('distilbert-base-cased')
        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
        outputs = model(input_ids)
        scores = outputs[0]
        """
        outputs = self.distilbert(inputs, **kwargs)

        sequence_output = outputs[0]

        sequence_output = self.dropout(sequence_output, training=kwargs.get("training", False))
        logits = self.classifier(sequence_output)

        outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here

        return outputs  # scores, (hidden_states), (attentions)

@add_start_docstrings(
    """DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
    the hidden-states output to compute `span start logits` and `span end logits`). """,
    DISTILBERT_START_DOCSTRING,
)
class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
        self.qa_outputs = tf.keras.layers.Dense(
            config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
        )
        assert config.num_labels == 2
        self.dropout = tf.keras.layers.Dropout(config.qa_dropout)

    @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING)
    def call(self, inputs, **kwargs):
        r"""
    Return:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
        start_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
            Span-start scores (before SoftMax).
        end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
            Span-end scores (before SoftMax).
        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
            tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
            tuple of :obj:`tf.Tensor` (one for each layer) of shape
            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`:

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

    Examples::

        import tensorflow as tf
        from transformers import DistilBertTokenizer, TFDistilBertForQuestionAnswering

        tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-cased')
        model = TFDistilBertForQuestionAnswering.from_pretrained('distilbert-base-cased')
        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
        outputs = model(input_ids)
        start_scores, end_scores = outputs[:2]

        """
        distilbert_output = self.distilbert(inputs, **kwargs)

        hidden_states = distilbert_output[0]  # (bs, max_query_len, dim)
        hidden_states = self.dropout(hidden_states, training=kwargs.get("training", False))  # (bs, max_query_len, dim)
        logits = self.qa_outputs(hidden_states)  # (bs, max_query_len, 2)
        start_logits, end_logits = tf.split(logits, 2, axis=-1)
        start_logits = tf.squeeze(start_logits, axis=-1)
        end_logits = tf.squeeze(end_logits, axis=-1)

        outputs = (start_logits, end_logits,) + distilbert_output[1:]
        return outputs  # start_logits, end_logits, (hidden_states), (attentions)


        batch_size = shape_list(inputs)[0]
        length = shape_list(inputs)[1]

        x = tf.reshape(inputs, [-1, self.dim])
        logits = tf.matmul(x, self.word_embeddings, transpose_b=True)

        return tf.reshape(logits, [batch_size, length, self.vocab_size])

""" TF 2.0 DistilBERT model
"""

import logging
import math

import numpy as np
import tensorflow as tf

from .configuration_distilbert import DistilBertConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, get_initializer, shape_list
from .tokenization_utils import BatchEncoding

logger = logging.getLogger(__name__)

TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
    "distilbert-base-uncased": "http://example.com",
    "distilbert-base-uncased-distilled-squad": "http://example.com",
    "distilbert-base-cased": "http://example.com",
    "distilbert-base-cased-distilled-squad": "http://example.com",
    "distilbert-base-multilingual-cased": "http://example.com",
    "distilbert-base-uncased-finetuned-sst-2-english": "http://example.com",
}

# UTILS AND BUILDING BLOCKS OF THE ARCHITECTURE #
def gelu(x):
    """ Gaussian Error Linear Unit.
    Original Implementation of the gelu activation function in Google Bert repo when initially created.
        For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
        Also see http://example.com
    """
    cdf = 0.5 * (1.0 + tf.math.erf(x / tf.math.sqrt(2.0)))
    return x * cdf

def gelu_new(x):
    """Gaussian Error Linear Unit.
    This is a smoother version of the RELU.
    Original paper: https://arxiv.org/abs/1606.08415
    Args:
        x: float Tensor to perform activation.
    Returns:
        `x` with the GELU activation applied.
    """
    cdf = 0.5 * (1.0 + tf.tanh((np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
    return x * cdf

class TFEmbeddings(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.vocab_size = config.vocab_size
        self.dim = config.dim
        self.initializer_range = config.initializer_range
        self.word_embeddings = TFSharedEmbeddings(
            config.vocab_size, config.dim, initializer_range=config.initializer_range, name="word_embeddings"
        )  # padding_idx=0)
        self.position_embeddings = tf.keras.layers.Embedding(
            config.max_position_embeddings,
            config.dim,
            embeddings_initializer=get_initializer(config.initializer_range),
            name="position_embeddings",
        )

        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="LayerNorm")
        self.dropout = tf.keras.layers.Dropout(config.dropout)

    def build(self, input_shape):
        """Build shared word embedding layer """
        with tf.name_scope("word_embeddings"):
            # Create and initialize weights. The random normal initializer was chosen
            self.word_embeddings = self.add_weight(
                "weight", shape=[self.vocab_size, self.dim], initializer=get_initializer(self.initializer_range)
            )
        super().build(input_shape)

    def call(self, inputs, inputs_embeds=None, mode="embedding", training=False):
        """Get token embeddings of inputs.
        Args:
            inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
            mode: string, a valid value is one of "embedding" and "linear".
        Returns:
            outputs: (1) If mode == "embedding", output embedding tensor, float32 with
                shape [batch_size, length, embedding_size]; (2) mode == "linear", output
                linear tensor, float32 with shape [batch_size, length, vocab_size].
        Raises:
            ValueError: if mode is not valid.

        Shared weights logic adapted from
            http://example.com
        """
        if mode == "embedding":
            return self._embedding(inputs, inputs_embeds=inputs_embeds, training=training)
        elif mode == "linear":
            return self._linear(inputs)
        else:
            raise ValueError("mode {} is not valid.".format(mode))

    def _embedding(self, inputs, inputs_embeds=None, training=False):
        """
        Parameters
        ----------
        input_ids: tf.Tensor(bs, max_seq_length)
            The token ids to embed.

        Outputs
        -------
        embeddings: tf.Tensor(bs, max_seq_length, dim)
            The embedded tokens (plus position embeddings, no token_type embeddings)
        """
        if not isinstance(inputs, (tuple, list)):
            input_ids = inputs
            position_ids = None
        else:
            input_ids, position_ids = inputs

        if input_ids is not None:
            seq_length = shape_list(input_ids)[1]
        else:
            seq_length = shape_list(inputs_embeds)[1]

        if position_ids is None:
            position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]

        if inputs_embeds is None:
            inputs_embeds = tf.gather(self.word_embeddings, input_ids)
        position_embeddings = self.position_embeddings(position_ids)  # (bs, max_seq_length, dim)

        embeddings = inputs_embeds + position_embeddings  # (bs, max_seq_length, dim)
        embeddings = self.LayerNorm(embeddings)  # (bs, max_seq_length, dim)
        embeddings = self.dropout(embeddings, training=training)  # (bs, max_seq_length, dim)
        return embeddings

    def _linear(self, inputs):
        """Computes logits by running inputs through a linear layer.
            Args:
                inputs: A float32 tensor with shape [batch_size, length, hidden_size]
            Returns:
                float32 tensor with shape [batch_size, length, vocab_size].
        """

class TFMultiHeadSelfAttention(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

        self.n_heads = config.n_heads
        self.dim = config.dim
        self.dropout = tf.keras.layers.Dropout(config.attention_dropout)
        self.output_attentions = config.output_attentions

        assert self.dim % self.n_heads == 0

        self.q_lin = tf.keras.layers.Dense(
            config.dim, kernel_initializer=get_initializer(config.initializer_range), name="q_lin"
        )
        self.k_lin = tf.keras.layers.Dense(
            config.dim, kernel_initializer=get_initializer(config.initializer_range), name="k_lin"
        )
        self.v_lin = tf.keras.layers.Dense(
            config.dim, kernel_initializer=get_initializer(config.initializer_range), name="v_lin"
        )
        self.out_lin = tf.keras.layers.Dense(
            config.dim, kernel_initializer=get_initializer(config.initializer_range), name="out_lin"
        )

        self.pruned_heads = set()

    def prune_heads(self, heads):
        raise NotImplementedError

    def call(self, inputs, training=False):
        """
        Parameters
        ----------
        query: tf.Tensor(bs, seq_length, dim)
        key: tf.Tensor(bs, seq_length, dim)
        value: tf.Tensor(bs, seq_length, dim)
        mask: tf.Tensor(bs, seq_length)

        Outputs
        -------
        weights: tf.Tensor(bs, n_heads, seq_length, seq_length)
            Attention weights
        context: tf.Tensor(bs, seq_length, dim)
            Contextualized layer. Optional: only if `output_attentions=True`
        """
        query, key, value, mask, head_mask = inputs
        bs, q_length, dim = shape_list(query)
        k_length = shape_list(key)[1]

        dim_per_head = self.dim // self.n_heads

        mask_reshape = [bs, 1, 1, k_length]

        def shape(x):
            """ separate heads """
            return tf.transpose(tf.reshape(x, (bs, -1, self.n_heads, dim_per_head)), perm=(0, 2, 1, 3))

        def unshape(x):
            """ group heads """
            return tf.reshape(tf.transpose(x, perm=(0, 2, 1, 3)), (bs, -1, self.n_heads * dim_per_head))

        q = shape(self.q_lin(query))  # (bs, n_heads, q_length, dim_per_head)
        k = shape(self.k_lin(key))  # (bs, n_heads, k_length, dim_per_head)
        v = shape(self.v_lin(value))  # (bs, n_heads, k_length, dim_per_head)

        q = q / math.sqrt(dim_per_head)  # (bs, n_heads, q_length, dim_per_head)
        scores = tf.matmul(q, k, transpose_b=True)  # (bs, n_heads, q_length, k_length)
        mask = tf.reshape(mask, mask_reshape)  # (bs, n_heads, qlen, klen)
        scores = scores - 1e30 * (1.0 - mask)

        weights = tf.nn.softmax(scores, axis=-1)  # (bs, n_heads, qlen, klen)
        weights = self.dropout(weights, training=training)  # (bs, n_heads, qlen, klen)

        # Mask heads if we want to
        if head_mask is not None:
            weights = weights * head_mask

        context = tf.matmul(weights, v)  # (bs, n_heads, qlen, dim_per_head)
        context = unshape(context)  # (bs, q_length, dim)
        context = self.out_lin(context)  # (bs, q_length, dim)

        if self.output_attentions:
            return (context, weights)
        else:
            return (context,)

class TFFFN(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.dropout = tf.keras.layers.Dropout(config.dropout)
        self.lin1 = tf.keras.layers.Dense(
            config.hidden_dim, kernel_initializer=get_initializer(config.initializer_range), name="lin1"
        )
        self.lin2 = tf.keras.layers.Dense(
            config.dim, kernel_initializer=get_initializer(config.initializer_range), name="lin2"
        )
        assert config.activation in ["relu", "gelu"], "activation ({}) must be in ['relu', 'gelu']".format(
            config.activation
        )
        self.activation = (
            tf.keras.layers.Activation(gelu) if config.activation == "gelu" else tf.keras.activations.relu
        )

    def call(self, input, training=False):
        x = self.lin1(input)
        x = self.activation(x)
        x = self.lin2(x)
        x = self.dropout(x, training=training)
        return x

class TFTransformerBlock(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

        self.n_heads = config.n_heads
        self.dim = config.dim
        self.hidden_dim = config.hidden_dim
        self.dropout = tf.keras.layers.Dropout(config.dropout)
        self.activation = config.activation
        self.output_attentions = config.output_attentions

        assert config.dim % config.n_heads == 0

        self.attention = TFMultiHeadSelfAttention(config, name="attention")
        self.sa_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="sa_layer_norm")

        self.ffn = TFFFN(config, name="ffn")
        self.output_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="output_layer_norm")

    def call(self, inputs, training=False):  # removed: src_enc=None, src_len=None
        """
        Parameters
        ----------
        x: tf.Tensor(bs, seq_length, dim)
        attn_mask: tf.Tensor(bs, seq_length)

        Outputs
        -------
        sa_weights: tf.Tensor(bs, n_heads, seq_length, seq_length)
            The attention weights
        ffn_output: tf.Tensor(bs, seq_length, dim)
            The output of the transformer block contextualization.
        """
        x, attn_mask, head_mask = inputs

        # Self-Attention
        sa_output = self.attention([x, x, x, attn_mask, head_mask], training=training)
        if self.output_attentions:
            sa_output, sa_weights = sa_output  # (bs, seq_length, dim), (bs, n_heads, seq_length, seq_length)
        else:  # To handle these `output_attention` or `output_hidden_states` cases returning tuples
            sa_output = sa_output[0]
        sa_output = self.sa_layer_norm(sa_output + x)  # (bs, seq_length, dim)

        # Feed Forward Network
        ffn_output = self.ffn(sa_output, training=training)  # (bs, seq_length, dim)
        ffn_output = self.output_layer_norm(ffn_output + sa_output)  # (bs, seq_length, dim)

        output = (ffn_output,)
        if self.output_attentions:
            output = (sa_weights,) + output
        return output

class TFTransformer(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.n_layers = config.n_layers
        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states

        self.layer = [TFTransformerBlock(config, name="layer_._{}".format(i)) for i in range(config.n_layers)]

    def call(self, inputs, training=False):
        """
        Parameters
        ----------
        x: tf.Tensor(bs, seq_length, dim)
            Input sequence embedded.
        attn_mask: tf.Tensor(bs, seq_length)
            Attention mask on the sequence.

        Outputs
        -------
        hidden_state: tf.Tensor(bs, seq_length, dim)
            Sequence of hiddens states in the last (top) layer
        all_hidden_states: Tuple[tf.Tensor(bs, seq_length, dim)]
            Tuple of length n_layers with the hidden states from each layer.
            Optional: only if output_hidden_states=True
        all_attentions: Tuple[tf.Tensor(bs, n_heads, seq_length, seq_length)]
            Tuple of length n_layers with the attention weights from each layer
            Optional: only if output_attentions=True
        """
        x, attn_mask, head_mask = inputs

        all_hidden_states = ()
        all_attentions = ()

        hidden_state = x
        for i, layer_module in enumerate(self.layer):
            if self.output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_state,)

            layer_outputs = layer_module([hidden_state, attn_mask, head_mask[i]], training=training)
            hidden_state = layer_outputs[-1]

            if self.output_attentions:
                assert len(layer_outputs) == 2
                attentions = layer_outputs[0]
                all_attentions = all_attentions + (attentions,)
            else:
                assert len(layer_outputs) == 1

        # Add last layer
        if self.output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_state,)

        outputs = (hidden_state,)
        if self.output_hidden_states:
            outputs = outputs + (all_hidden_states,)
        if self.output_attentions:
            outputs = outputs + (all_attentions,)
        return outputs  # last-layer hidden state, (all hidden states), (all attentions)

class TFDistilBertMainLayer(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.num_hidden_layers = config.num_hidden_layers

        self.embeddings = TFEmbeddings(config, name="embeddings")  # Embeddings
        self.transformer = TFTransformer(config, name="transformer")  # Encoder

    def get_input_embeddings(self):
        return self.embeddings

    def _resize_token_embeddings(self, new_num_tokens):
        raise NotImplementedError

    def _prune_heads(self, heads_to_prune):
        raise NotImplementedError

    def call(self, inputs, attention_mask=None, head_mask=None, inputs_embeds=None, training=False):
        if isinstance(inputs, (tuple, list)):
            input_ids = inputs[0]
            attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
            head_mask = inputs[2] if len(inputs) > 2 else head_mask
            inputs_embeds = inputs[3] if len(inputs) > 3 else inputs_embeds
            assert len(inputs) <= 4, "Too many inputs."
        elif isinstance(inputs, (dict, BatchEncoding)):
            input_ids = inputs.get("input_ids")
            attention_mask = inputs.get("attention_mask", attention_mask)
            head_mask = inputs.get("head_mask", head_mask)
            inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
            assert len(inputs) <= 4, "Too many inputs."
        else:
            input_ids = inputs

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = shape_list(input_ids)
        elif inputs_embeds is not None:
            input_shape = shape_list(inputs_embeds)[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if attention_mask is None:
            attention_mask = tf.ones(input_shape)  # (bs, seq_length)
        attention_mask = tf.cast(attention_mask, dtype=tf.float32)

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        if head_mask is not None:
            raise NotImplementedError
        else:
            head_mask = [None] * self.num_hidden_layers

        embedding_output = self.embeddings(input_ids, inputs_embeds=inputs_embeds)  # (bs, seq_length, dim)
        tfmr_output = self.transformer([embedding_output, attention_mask, head_mask], training=training)

        return tfmr_output  # last-layer hidden-state, (all hidden_states), (all attentions)

# INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL #
class TFDistilBertPreTrainedModel(TFPreTrainedModel):
    """ An abstract class to handle weights initialization and
        a simple interface for downloading and loading pretrained models.
    """

    config_class = DistilBertConfig
    pretrained_model_archive_map = TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP
    base_model_prefix = "distilbert"

DISTILBERT_START_DOCSTRING = r"""
    This model is a `tf.keras.Model <http://example.com>`__ sub-class.
    Use it as a regular TF 2.0 Keras Model and
    refer to the TF 2.0 documentation for all matter related to general usage and behavior.

    .. note::

        TF 2.0 models accepts two formats as inputs:

            - having all inputs as keyword arguments (like PyTorch models), or
            - having all inputs as a list, tuple or dict in the first positional arguments.

        This second option is useful when using :obj:`tf.keras.Model.fit()` method which currently requires having
        all the tensors in the first argument of the model call function: :obj:`model(inputs)`.

        If you choose this second option, there are three possibilities you can use to gather all the input Tensors
        in the first positional argument :

        - a single Tensor with input_ids only and nothing else: :obj:`model(inputs_ids)`
        - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
          :obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
        - a dictionary with one or several input Tensors associated to the input names given in the docstring:
          :obj:`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`

    Parameters:
        config (:class:`~transformers.DistilBertConfig`): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the configuration.
            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""

DISTILBERT_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using :class:`transformers.BertTokenizer`.
            See :func:`transformers.PreTrainedTokenizer.encode` and
            :func:`transformers.PreTrainedTokenizer.encode_plus` for details.

            `What are input IDs? <../glossary.html#input-ids>`__
        attention_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Mask to avoid performing attention on padding token indices.
            Mask values selected in ``[0, 1]``:
            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.

            `What are attention masks? <../glossary.html#attention-mask>`__
        head_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
            Mask to nullify selected heads of the self-attention modules.
            Mask values selected in ``[0, 1]``:
            :obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
        inputs_embeds (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, embedding_dim)`, `optional`, defaults to :obj:`None`):
            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `input_ids` indices into associated vectors
            than the model's internal embedding lookup matrix.
        training (:obj:`boolean`, `optional`, defaults to :obj:`False`):
            Whether to activate dropout modules (if set to :obj:`True`) during training or to de-activate them
            (if set to :obj:`False`) for evaluation.

"""

@add_start_docstrings(
    "The bare DistilBERT encoder/transformer outputing raw hidden-states without any specific head on top.",
    DISTILBERT_START_DOCSTRING,
)
class TFDistilBertModel(TFDistilBertPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.distilbert = TFDistilBertMainLayer(config, name="distilbert")  # Embeddings

    @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING)
    def call(self, inputs, **kwargs):
        r"""
    Returns:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
        last_hidden_state (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
            tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
            tuple of :obj:`tf.Tensor` (one for each layer) of shape
            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`:

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

    Examples::

        import tensorflow as tf
        from transformers import DistilBertTokenizer, TFDistilBertModel

        tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-cased')
        model = TFDistilBertModel.from_pretrained('distilbert-base-cased')
        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
        outputs = model(input_ids)
        last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
        """
        outputs = self.distilbert(inputs, **kwargs)
        return outputs

class TFDistilBertLMHead(tf.keras.layers.Layer):
    def __init__(self, config, input_embeddings, **kwargs):
        super().__init__(**kwargs)
        self.vocab_size = config.vocab_size

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.input_embeddings = input_embeddings

    def build(self, input_shape):
        self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
        super().build(input_shape)

    def call(self, hidden_states):
        hidden_states = self.input_embeddings(hidden_states, mode="linear")
        hidden_states = hidden_states + self.bias
        return hidden_states

@add_start_docstrings(
    """DistilBert Model with a `masked language modeling` head on top. """, DISTILBERT_START_DOCSTRING,
)
class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states
        self.vocab_size = config.vocab_size

        self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
        self.vocab_transform = tf.keras.layers.Dense(
            config.dim, kernel_initializer=get_initializer(config.initializer_range), name="vocab_transform"
        )
        self.act = tf.keras.layers.Activation(gelu)
        self.vocab_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="vocab_layer_norm")
        self.vocab_projector = TFDistilBertLMHead(config, self.distilbert.embeddings, name="vocab_projector")

    def get_output_embeddings(self):
        return self.vocab_projector.input_embeddings

    @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING)
    def call(self, inputs, **kwargs):
        r"""

    Returns:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
        prediction_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
            tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
            tuple of :obj:`tf.Tensor` (one for each layer) of shape
            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`:

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

    Examples::

        import tensorflow as tf
        from transformers import DistilBertTokenizer, TFDistilBertForMaskedLM

        tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-cased')
        model = TFDistilBertForMaskedLM.from_pretrained('distilbert-base-cased')
        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
        outputs = model(input_ids)
        prediction_scores = outputs[0]

        """
        distilbert_output = self.distilbert(inputs, **kwargs)

        hidden_states = distilbert_output[0]  # (bs, seq_length, dim)
        prediction_logits = self.vocab_transform(hidden_states)  # (bs, seq_length, dim)
        prediction_logits = self.act(prediction_logits)  # (bs, seq_length, dim)
        prediction_logits = self.vocab_layer_norm(prediction_logits)  # (bs, seq_length, dim)
        prediction_logits = self.vocab_projector(prediction_logits)

        outputs = (prediction_logits,) + distilbert_output[1:]
        return outputs  # logits, (hidden_states), (attentions)

@add_start_docstrings(
    """DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. """,
    DISTILBERT_START_DOCSTRING,
)
class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels

        self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
        self.pre_classifier = tf.keras.layers.Dense(
            config.dim,
            kernel_initializer=get_initializer(config.initializer_range),
            activation="relu",
            name="pre_classifier",
        )
        self.classifier = tf.keras.layers.Dense(
            config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
        )
        self.dropout = tf.keras.layers.Dropout(config.seq_classif_dropout)

    @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING)
    def call(self, inputs, **kwargs):
        r"""
    Returns:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
        logits (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, config.num_labels)`):
            Classification (or regression if config.num_labels==1) scores (before SoftMax).
        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
            tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
            tuple of :obj:`tf.Tensor` (one for each layer) of shape
            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`:

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

    Examples::

        import tensorflow as tf
        from transformers import DistilBertTokenizer, TFDistilBertForSequenceClassification

        tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-cased')
        model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-cased')
        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
        outputs = model(input_ids)
        logits = outputs[0]

        """
        distilbert_output = self.distilbert(inputs, **kwargs)

        hidden_state = distilbert_output[0]  # (bs, seq_len, dim)
        pooled_output = hidden_state[:, 0]  # (bs, dim)
        pooled_output = self.pre_classifier(pooled_output)  # (bs, dim)
        pooled_output = self.dropout(pooled_output, training=kwargs.get("training", False))  # (bs, dim)
        logits = self.classifier(pooled_output)  # (bs, dim)

        outputs = (logits,) + distilbert_output[1:]
        return outputs  # logits, (hidden_states), (attentions)

@add_start_docstrings(
    """DistilBert Model with a token classification head on top (a linear layer on top of
    the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
    DISTILBERT_START_DOCSTRING,
)
class TFDistilBertForTokenClassification(TFDistilBertPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels

        self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
        self.dropout = tf.keras.layers.Dropout(config.dropout)
        self.classifier = tf.keras.layers.Dense(
            config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
        )

    @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING)
    def call(self, inputs, **kwargs):
        r"""
    Returns:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
        scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.num_labels)`):
            Classification scores (before SoftMax).
        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
            tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
            tuple of :obj:`tf.Tensor` (one for each layer) of shape
            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`:

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

    Examples::

        import tensorflow as tf
        from transformers import DistilBertTokenizer, TFDistilBertForTokenClassification

        tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-cased')
        model = TFDistilBertForTokenClassification.from_pretrained('distilbert-base-cased')
        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
        outputs = model(input_ids)
        scores = outputs[0]
        """
        outputs = self.distilbert(inputs, **kwargs)

        sequence_output = outputs[0]

        sequence_output = self.dropout(sequence_output, training=kwargs.get("training", False))
        logits = self.classifier(sequence_output)

        outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here

        return outputs  # scores, (hidden_states), (attentions)

@add_start_docstrings(
    """DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
    the hidden-states output to compute `span start logits` and `span end logits`). """,
    DISTILBERT_START_DOCSTRING,
)
class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
        self.qa_outputs = tf.keras.layers.Dense(
            config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
        )
        assert config.num_labels == 2
        self.dropout = tf.keras.layers.Dropout(config.qa_dropout)

    @add_start_docstrings_to_callable(DISTILBERT_INPUTS_DOCSTRING)
    def call(self, inputs, **kwargs):
        r"""
    Return:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers,DistilBertConfig`) and inputs:
        start_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
            Span-start scores (before SoftMax).
        end_scores (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length,)`):
            Span-end scores (before SoftMax).
        hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when :obj:`config.output_hidden_states=True`):
            tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``config.output_attentions=True``):
            tuple of :obj:`tf.Tensor` (one for each layer) of shape
            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`:

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

    Examples::

        import tensorflow as tf
        from transformers import DistilBertTokenizer, TFDistilBertForQuestionAnswering

        tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-cased')
        model = TFDistilBertForQuestionAnswering.from_pretrained('distilbert-base-cased')
        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
        outputs = model(input_ids)
        start_scores, end_scores = outputs[:2]

        """
        distilbert_output = self.distilbert(inputs, **kwargs)

        hidden_states = distilbert_output[0]  # (bs, max_query_len, dim)
        hidden_states = self.dropout(hidden_states, training=kwargs.get("training", False))  # (bs, max_query_len, dim)
        logits = self.qa_outputs(hidden_states)  # (bs, max_query_len, 2)
        start_logits, end_logits = tf.split(logits, 2, axis=-1)
        start_logits = tf.squeeze(start_logits, axis=-1)
        end_logits = tf.squeeze(end_logits, axis=-1)

        outputs = (start_logits, end_logits,) + distilbert_output[1:]
        return outputs  # start_logits, end_logits, (hidden_states), (attentions)


"""

"""
from programy.utils.logging.ylogger import YLogger
from programy.config.container import BaseContainerConfigurationData
from programy.config.bot.bot import BotConfiguration
from programy.scheduling.config import SchedulerConfiguration
from programy.storage.config import StorageConfiguration
from programy.utils.substitutions.substitues import Substitutions
from programy.utils.email.config import EmailConfiguration
from programy.triggers.config import TriggerConfiguration
from programy.clients.ping.config import PingResponderConfig
from programy.config.brain.brain import BrainConfiguration

class ClientConfigurationData(BaseContainerConfigurationData):

    def __init__(self, name):
        BaseContainerConfigurationData.__init__(self, name)
        self._description = 'ProgramY AIML2.0 Client'
        self._renderer = self._get_renderer_class()
        self._scheduler = SchedulerConfiguration()
        self._storage = StorageConfiguration()
        self._email = EmailConfiguration()
        self._triggers = TriggerConfiguration()
        self._responder = PingResponderConfig()

        self._bot_selector = self._get_bot_selector_class()
        bot_config = BotConfiguration('bot')
        self._bot_configs = [bot_config]

    def _get_renderer_class(self):
        return "programy.clients.render.text.TextRenderer"

    def _get_bot_selector_class(self):
        return "programy.clients.botfactory.DefaultBotSelector"

    @property
    def description(self):
        return self._description

    @property
    def configurations(self):
        return self._bot_configs

    @property
    def bot_selector(self):
        return self._bot_selector

    @property

return self._scheduler

    @property
    def storage(self):
        return self._storage

    @property
    def renderer(self):
        return self._renderer

    @property
    def email(self):
        return self._email

    @property
    def triggers(self):
        return self._triggers

    @property
    def responder(self):
        return self._responder

    def load_configuration(self, configuration_file, bot_root, subs: Substitutions = None):
        client = configuration_file.get_section(self.section_name)
        if client is None:
            YLogger.error(None, 'Client section named [%s] not found, trying "client"', self.section_name)
            client = configuration_file.get_section('client')

        if client is not None:
            self.load_configuration_section(configuration_file, client, bot_root, subs)

        else:
            YLogger.error(None, 'No client section not found!')

    def load_configuration_section(self, configuration_file, section, bot_root, subs: Substitutions = None):

        assert configuration_file is not None
        assert section is not None

        self._description = configuration_file.get_option(section, "description",
                                                          missing_value='ProgramY AIML2.0 Client', subs=subs)

        self._scheduler.load_config_section(configuration_file, section, bot_root, subs=subs)

        self._storage.load_config_section(configuration_file, section, bot_root, subs=subs)

        self._renderer = configuration_file.get_option(section, "renderer", subs=subs)

        self._email.load_config_section(configuration_file, section, bot_root, subs=subs)

        self._triggers.load_config_section(configuration_file, section, bot_root, subs=subs)

        self._responder.load_config_section(configuration_file, section, bot_root, subs=subs)

        self._load_bot_configurations(configuration_file, section, bot_root, subs)

    def _load_bot_configurations(self, configuration_file, section, bot_root, subs):
        bots = configuration_file.get_section("bots", section)
        if bots is not None:
            bot_keys = configuration_file.get_keys(bots)
            first = True
            for name in bot_keys:
                if first is True:
                    self._bot_configs.clear()
                    first = False
                bot_config = configuration_file.get_section(name, bots)
                config = BotConfiguration(name)
                config.load_configuration_section(configuration_file, bot_config, bot_root, subs=subs)
                self._bot_configs.append(config)

        if len(self._bot_configs) == 0:
            YLogger.warning(self, "No bot name defined for client [%s], defaulting to 'bot'.", self.section_name)
            self._bot_configs.append(BotConfiguration("bot"))
            self._bot_configs[0].configurations.append(BrainConfiguration("brain"))

        self._bot_selector = configuration_file.get_option(section, "bot_selector",
                       missing_value="programy.clients.botfactory.DefaultBotSelector",
                       subs=subs)

    def to_yaml(self, data, defaults=True):

        assert data is not None

        if defaults is True:
            data['description'] = 'ProgramY AIML2.0 Client'
            data['bot_selector'] = "programy.clients.botfactory.DefaultBotSelector"

            data['scheduler'] = {}
            self._scheduler.to_yaml(data['scheduler'], defaults)

            data['email'] = {}
            self._email.to_yaml(data['email'], defaults)

            data['triggers'] = {}
            self._triggers.to_yaml(data['triggers'], defaults)

            data['responder'] = {}
            self._responder.to_yaml(data['responder'], defaults)

            data['renderer'] = "programy.clients.render.text.TextRenderer"

            data['storage'] = {}
            self._storage.to_yaml(data['storage'], defaults)

            data['bots'] = {}
            data['bots']['bot'] = {}
            bot_config = BotConfiguration('bot')
            bot_config.to_yaml(data['bots']['bot'], defaults)

        else:
            data['description'] = self._description
            data['bot_selector'] = self.bot_selector

            data[self._scheduler.id] = {}
            self._scheduler.to_yaml(data[self._scheduler.id], defaults)

            data['email'] = {}
            self._email.to_yaml(data['email'], defaults)

            data['triggers'] = {}
            self._triggers.to_yaml(data['triggers'], defaults)

            data['responder'] = {}
            self._responder.to_yaml(data['responder'], defaults)

            data['renderer'] = self.renderer

            data['storage'] = {}
            self._storage.to_yaml(data['storage'], defaults)

            data['bots'] = {}
            for botconfig in self._bot_configs:
                data['bots'][botconfig.id] = {}
                botconfig.to_yaml(data['bots'][botconfig.id], defaults)


"""

"""
from programy.utils.logging.ylogger import YLogger
from programy.config.container import BaseContainerConfigurationData
from programy.config.bot.bot import BotConfiguration
from programy.scheduling.config import SchedulerConfiguration
from programy.storage.config import StorageConfiguration
from programy.utils.substitutions.substitues import Substitutions
from programy.utils.email.config import EmailConfiguration
from programy.triggers.config import TriggerConfiguration
from programy.clients.ping.config import PingResponderConfig
from programy.config.brain.brain import BrainConfiguration

class ClientConfigurationData(BaseContainerConfigurationData):

    def __init__(self, name):
        BaseContainerConfigurationData.__init__(self, name)
        self._description = 'ProgramY AIML2.0 Client'
        self._renderer = self._get_renderer_class()
        self._scheduler = SchedulerConfiguration()
        self._storage = StorageConfiguration()
        self._email = EmailConfiguration()
        self._triggers = TriggerConfiguration()
        self._responder = PingResponderConfig()

        self._bot_selector = self._get_bot_selector_class()
        bot_config = BotConfiguration('bot')
        self._bot_configs = [bot_config]

    def _get_renderer_class(self):
        return "programy.clients.render.text.TextRenderer"

    def _get_bot_selector_class(self):

    @property
    def description(self):
        return self._description

    @property
    def configurations(self):
        return self._bot_configs

    @property
    def bot_selector(self):
        return self._bot_selector

    @property
    def scheduler(self):
        return self._scheduler

    @property
    def storage(self):
        return self._storage

    @property
    def renderer(self):
        return self._renderer

    @property
    def email(self):
        return self._email

    @property
    def triggers(self):
        return self._triggers

    @property
    def responder(self):
        return self._responder

    def load_configuration(self, configuration_file, bot_root, subs: Substitutions = None):
        client = configuration_file.get_section(self.section_name)
        if client is None:
            YLogger.error(None, 'Client section named [%s] not found, trying "client"', self.section_name)
            client = configuration_file.get_section('client')

        if client is not None:
            self.load_configuration_section(configuration_file, client, bot_root, subs)

        else:
            YLogger.error(None, 'No client section not found!')

    def load_configuration_section(self, configuration_file, section, bot_root, subs: Substitutions = None):

        assert configuration_file is not None
        assert section is not None

        self._description = configuration_file.get_option(section, "description",
                                                          missing_value='ProgramY AIML2.0 Client', subs=subs)

        self._scheduler.load_config_section(configuration_file, section, bot_root, subs=subs)

        self._storage.load_config_section(configuration_file, section, bot_root, subs=subs)

        self._renderer = configuration_file.get_option(section, "renderer", subs=subs)

        self._email.load_config_section(configuration_file, section, bot_root, subs=subs)

        self._triggers.load_config_section(configuration_file, section, bot_root, subs=subs)

        self._responder.load_config_section(configuration_file, section, bot_root, subs=subs)

        self._load_bot_configurations(configuration_file, section, bot_root, subs)

    def _load_bot_configurations(self, configuration_file, section, bot_root, subs):
        bots = configuration_file.get_section("bots", section)
        if bots is not None:
            bot_keys = configuration_file.get_keys(bots)
            first = True
            for name in bot_keys:
                if first is True:
                    self._bot_configs.clear()
                    first = False
                bot_config = configuration_file.get_section(name, bots)
                config = BotConfiguration(name)
                config.load_configuration_section(configuration_file, bot_config, bot_root, subs=subs)
                self._bot_configs.append(config)

        if len(self._bot_configs) == 0:
            YLogger.warning(self, "No bot name defined for client [%s], defaulting to 'bot'.", self.section_name)
            self._bot_configs.append(BotConfiguration("bot"))
            self._bot_configs[0].configurations.append(BrainConfiguration("brain"))

        self._bot_selector = configuration_file.get_option(section, "bot_selector",
                       missing_value="programy.clients.botfactory.DefaultBotSelector",
                       subs=subs)

    def to_yaml(self, data, defaults=True):

        assert data is not None

        if defaults is True:
            data['description'] = 'ProgramY AIML2.0 Client'
            data['bot_selector'] = "programy.clients.botfactory.DefaultBotSelector"

            data['scheduler'] = {}
            self._scheduler.to_yaml(data['scheduler'], defaults)

            data['email'] = {}
            self._email.to_yaml(data['email'], defaults)

            data['triggers'] = {}
            self._triggers.to_yaml(data['triggers'], defaults)

            data['responder'] = {}
            self._responder.to_yaml(data['responder'], defaults)

            data['renderer'] = "programy.clients.render.text.TextRenderer"

            data['storage'] = {}
            self._storage.to_yaml(data['storage'], defaults)

            data['bots'] = {}
            data['bots']['bot'] = {}
            bot_config = BotConfiguration('bot')
            bot_config.to_yaml(data['bots']['bot'], defaults)

        else:
            data['description'] = self._description
            data['bot_selector'] = self.bot_selector

            data[self._scheduler.id] = {}
            self._scheduler.to_yaml(data[self._scheduler.id], defaults)

            data['email'] = {}
            self._email.to_yaml(data['email'], defaults)

            data['triggers'] = {}
            self._triggers.to_yaml(data['triggers'], defaults)

            data['responder'] = {}
            self._responder.to_yaml(data['responder'], defaults)

            data['renderer'] = self.renderer

            data['storage'] = {}
            self._storage.to_yaml(data['storage'], defaults)

            data['bots'] = {}
            for botconfig in self._bot_configs:
                data['bots'][botconfig.id] = {}
                botconfig.to_yaml(data['bots'][botconfig.id], defaults)


"""

"""
from programy.utils.logging.ylogger import YLogger
from programy.config.container import BaseContainerConfigurationData
from programy.config.bot.bot import BotConfiguration
from programy.scheduling.config import SchedulerConfiguration
from programy.storage.config import StorageConfiguration
from programy.utils.substitutions.substitues import Substitutions
from programy.utils.email.config import EmailConfiguration
from programy.triggers.config import TriggerConfiguration
from programy.clients.ping.config import PingResponderConfig
from programy.config.brain.brain import BrainConfiguration

class ClientConfigurationData(BaseContainerConfigurationData):

    def __init__(self, name):
        BaseContainerConfigurationData.__init__(self, name)
        self._description = 'ProgramY AIML2.0 Client'
        self._renderer = self._get_renderer_class()
        self._scheduler = SchedulerConfiguration()
        self._storage = StorageConfiguration()
        self._email = EmailConfiguration()
        self._triggers = TriggerConfiguration()
        self._responder = PingResponderConfig()

        self._bot_selector = self._get_bot_selector_class()
        bot_config = BotConfiguration('bot')
        self._bot_configs = [bot_config]

    def _get_renderer_class(self):
        return "programy.clients.render.text.TextRenderer"

    def _get_bot_selector_class(self):
        return "programy.clients.botfactory.DefaultBotSelector"

    @property
    def description(self):
        return self._description

    @property
    def configurations(self):
        return self._bot_configs

    @property
    def bot_selector(self):
        return self._bot_selector

    @property
    def scheduler(self):
        return self._scheduler

    @property
    def storage(self):
        return self._storage

    @property
    def renderer(self):
        return self._renderer

    @property
    def email(self):
        return self._email

    @property
    def triggers(self):
        return self._triggers

    @property
    def responder(self):
        return self._resp

    def load_configuration(self, configuration_file, bot_root, subs: Substitutions = None):
        client = configuration_file.get_section(self.section_name)
        if client is None:
            YLogger.error(None, 'Client section named [%s] not found, trying "client"', self.section_name)
            client = configuration_file.get_section('client')

        if client is not None:
            self.load_configuration_section(configuration_file, client, bot_root, subs)

        else:
            YLogger.error(None, 'No client section not found!')

    def load_configuration_section(self, configuration_file, section, bot_root, subs: Substitutions = None):

        assert configuration_file is not None
        assert section is not None

        self._description = configuration_file.get_option(section, "description",
                                                          missing_value='ProgramY AIML2.0 Client', subs=subs)

        self._scheduler.load_config_section(configuration_file, section, bot_root, subs=subs)

        self._storage.load_config_section(configuration_file, section, bot_root, subs=subs)

        self._renderer = configuration_file.get_option(section, "renderer", subs=subs)

        self._email.load_config_section(configuration_file, section, bot_root, subs=subs)

        self._triggers.load_config_section(configuration_file, section, bot_root, subs=subs)

        self._responder.load_config_section(configuration_file, section, bot_root, subs=subs)

        self._load_bot_configurations(configuration_file, section, bot_root, subs)

    def _load_bot_configurations(self, configuration_file, section, bot_root, subs):
        bots = configuration_file.get_section("bots", section)
        if bots is not None:
            bot_keys = configuration_file.get_keys(bots)
            first = True
            for name in bot_keys:
                if first is True:
                    self._bot_configs.clear()
                    first = False
                bot_config = configuration_file.get_section(name, bots)
                config = BotConfiguration(name)
                config.load_configuration_section(configuration_file, bot_config, bot_root, subs=subs)
                self._bot_configs.append(config)

        if len(self._bot_configs) == 0:
            YLogger.warning(self, "No bot name defined for client [%s], defaulting to 'bot'.", self.section_name)
            self._bot_configs.append(BotConfiguration("bot"))
            self._bot_configs[0].configurations.append(BrainConfiguration("brain"))

        self._bot_selector = configuration_file.get_option(section, "bot_selector",
                       missing_value="programy.clients.botfactory.DefaultBotSelector",
                       subs=subs)

    def to_yaml(self, data, defaults=True):

        assert data is not None

        if defaults is True:
            data['description'] = 'ProgramY AIML2.0 Client'
            data['bot_selector'] = "programy.clients.botfactory.DefaultBotSelector"

            data['scheduler'] = {}
            self._scheduler.to_yaml(data['scheduler'], defaults)

            data['email'] = {}
            self._email.to_yaml(data['email'], defaults)

            data['triggers'] = {}
            self._triggers.to_yaml(data['triggers'], defaults)

            data['responder'] = {}
            self._responder.to_yaml(data['responder'], defaults)

            data['renderer'] = "programy.clients.render.text.TextRenderer"

            data['storage'] = {}
            self._storage.to_yaml(data['storage'], defaults)

            data['bots'] = {}
            data['bots']['bot'] = {}
            bot_config = BotConfiguration('bot')
            bot_config.to_yaml(data['bots']['bot'], defaults)

        else:
            data['description'] = self._description
            data['bot_selector'] = self.bot_selector

            data[self._scheduler.id] = {}
            self._scheduler.to_yaml(data[self._scheduler.id], defaults)

            data['email'] = {}
            self._email.to_yaml(data['email'], defaults)

            data['triggers'] = {}
            self._triggers.to_yaml(data['triggers'], defaults)

            data['responder'] = {}
            self._responder.to_yaml(data['responder'], defaults)

            data['renderer'] = self.renderer

            data['storage'] = {}
            self._storage.to_yaml(data['storage'], defaults)

            data['bots'] = {}
            for botconfig in self._bot_configs:
                data['bots'][botconfig.id] = {}
                botconfig.to_yaml(data['bots'][botconfig.id], defaults)