# coding=utf-8
# Copyright 2018 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch T5 model. """

from __future__ import absolute_import, division, print_function, unicode_literals

import json
import logging
import math
import os
import math
import sys
import itertools
from io import open

import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss, MSELoss

from .modeling_utils import PreTrainedModel, prune_linear_layer
from .configuration_t5 import T5Config
from .file_utils import add_start_docstrings

logger = logging.getLogger(__name__)

####################################################
# This dict contrains shortcut names and associated url
# for the pretrained weights provided with the models
####################################################
T5_PRETRAINED_MODEL_ARCHIVE_MAP = {
    't5-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-uncased-pytorch_model.bin",
    't5-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-uncased-pytorch_model.bin",
}

####################################################
# This is a conversion method from TF 1.0 to PyTorch
# More details: https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28
####################################################
def load_tf_weights_in_t5(model, config, tf_checkpoint_path):
    """ Load tf checkpoints in a pytorch model.
    """
    try:
        import re
        import numpy as np
        import tensorflow as tf
    except ImportError:
        logger.error("Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
            "https://www.tensorflow.org/install/ for installation instructions.")
        raise
    tf_path = os.path.abspath(tf_checkpoint_path)
    logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
    # Load weights from TF model
    init_vars = tf.train.list_variables(tf_path)
    names = []
    arrays = []
    for name, shape in init_vars:
        logger.info("Loading TF weight {} with shape {}".format(name, shape))
        array = tf.train.load_variable(tf_path, name)
        names.append(name)
        arrays.append(array)

    for name, array in zip(names, arrays):
        name = name.split('/')
        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
        # which are not required for using pretrained model
        if any(n in ["adam_v", "adam_m", "global_step"] for n in name):
            logger.info("Skipping {}".format("/".join(name)))
            continue
        pointer = model
        for m_name in name:
            if re.fullmatch(r'[A-Za-z]+_\d+', m_name):
                l = re.split(r'_(\d+)', m_name)
            else:
                l = [m_name]
            if l[0] == 'kernel' or l[0] == 'gamma':
                pointer = getattr(pointer, 'weight')
            elif l[0] == 'output_bias' or l[0] == 'beta':
                pointer = getattr(pointer, 'bias')
            elif l[0] == 'output_weights':
                pointer = getattr(pointer, 'weight')
            elif l[0] == 'squad':
                pointer = getattr(pointer, 'classifier')
            else:
                try:
                    pointer = getattr(pointer, l[0])
                except AttributeError:
                    logger.info("Skipping {}".format("/".join(name)))
                    continue
            if len(l) >= 2:
                num = int(l[1])
                pointer = pointer[num]
        if m_name[-11:] == '_embeddings':
            pointer = getattr(pointer, 'weight')
        elif m_name == 'kernel':
            array = np.transpose(array)
        try:
            assert pointer.shape == array.shape
        except AssertionError as e:
            e.args += (pointer.shape, array.shape)
            raise
        logger.info("Initialize PyTorch weight {}".format(name))
        pointer.data = torch.from_numpy(array)
    return model


####################################################
# PyTorch Models are constructed by sub-classing
# - torch.nn.Module for the layers and
# - PreTrainedModel for the models (it-self a sub-class of torch.nn.Module)
####################################################

class T5DenseReluDense(nn.Module):
    def __init__(self, config):
        super(T5DenseReluDense, self).__init__()
        self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
        self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
        self.dropout = nn.Dropout(config.dropout)

    def forward(self, hidden_states):
        h = self.wi(hidden_states)
        h = F.relu(h)
        h = self.dropout(h)
        h = self.wo(h)
        return h


class T5LayerFF(nn.Module):
    def __init__(self, config):
        super(T5LayerFF, self).__init__()
        self.DenseReluDense = T5DenseReluDense(config)
        self.layer_norm = nn.LayerNorm(config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout)

    def forward(self, hidden_states):
        norm_x = self.layer_norm(hidden_states)
        y = self.DenseReluDense(norm_x)
        layer_output = hidden_states + self.dropout(y)
        return layer_output


class T5Attention(nn.Module):
    NEW_ID = itertools.count()

    def __init__(self, config, has_relative_attention_bias=False):
        super(T5Attention, self).__init__()
        self.layer_id = next(T5Attention.NEW_ID)
        self.has_relative_attention_bias = has_relative_attention_bias

        self.output_attentions = config.output_attentions
        self.relative_attention_num_buckets = config.relative_attention_num_buckets
        self.dim = config.d_model
        self.n_heads = config.num_heads
        self.dropout = config.dropout_rate
        assert self.dim % self.n_heads == 0

        self.q = nn.Linear(self.dim, self.dim, bias=False)
        self.k = nn.Linear(self.dim, self.dim, bias=False)
        self.v = nn.Linear(self.dim, self.dim, bias=False)
        self.o = nn.Linear(self.dim, self.dim, bias=False)

        if self.has_relative_attention_bias:
            self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
        self.pruned_heads = set()

    def prune_heads(self, heads):
        attention_head_size = self.dim // self.n_heads
        if len(heads) == 0:
            return
        mask = torch.ones(self.n_heads, attention_head_size)
        heads = set(heads) - self.pruned_heads
        for head in heads:
            head -= sum(1 if h < head else 0 for h in self.pruned_heads)
            mask[head] = 0
        mask = mask.view(-1).contiguous().eq(1)
        index = torch.arange(len(mask))[mask].long()
        # Prune linear layers
        self.q = prune_linear_layer(self.q, index)
        self.k = prune_linear_layer(self.k, index)
        self.v = prune_linear_layer(self.v, index)
        self.o = prune_linear_layer(self.o, index, dim=1)
        # Update hyper params
        self.n_heads = self.n_heads - len(heads)
        self.dim = attention_head_size * self.n_heads
        self.pruned_heads = self.pruned_heads.union(heads)

    @staticmethod
    def _relative_position_bucket(relative_position,
                                  bidirectional=True,
                                  num_buckets=32,
                                  max_distance=128):
        """
        Adapted from Mesh Tensorflow:
        https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593

        Translate relative position to a bucket number for relative attention.
        The relative position is defined as memory_position - query_position, i.e.
        the distance in tokens from the attending position to the attended-to
        position.  If bidirectional=False, then positive relative positions are
        invalid.
        We use smaller buckets for small absolute relative_position and larger buckets
        for larger absolute relative_positions.  All relative positions >=max_distance
        map to the same bucket.  All relative positions <=-max_distance map to the
        same bucket.  This should allow for more graceful generalization to longer
        sequences than the model has been trained on.
        Args:
            relative_position: an int32 Tensor
            bidirectional: a boolean - whether the attention is bidirectional
            num_buckets: an integer
            max_distance: an integer
        Returns:
            a Tensor with the same shape as relative_position, containing int32
            values in the range [0, num_buckets)
        """
        ret = 0
        n = -relative_position
        if bidirectional:
            num_buckets //= 2
            ret += (n < 0).to(torch.long) * num_buckets  # mtf.to_int32(mtf.less(n, 0)) * num_buckets
            n = torch.abs(n)
        else:
            n = torch.max(n, 0)
        # now n is in the range [0, inf)

        # half of the buckets are for exact increments in positions
        max_exact = num_buckets // 2
        is_small = (n < max_exact)

        # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
        val_if_large = max_exact + (
            torch.log(n.float() / max_exact)
            / math.log(max_distance / max_exact) * (num_buckets - max_exact)).to(torch.long)
        val_if_large = torch.min(val_if_large, num_buckets - 1)

        ret += torch.where(is_small, n, val_if_large)
        return ret

    def compute_bias(self, qlen, klen):
        """ Compute binned relative position bias """
        context_position = torch.arange(qlen, dtype=torch.long)[:, None]
        memory_position = torch.arange(klen, dtype=torch.long)[None, :]
        relative_position = memory_position - context_position  # shape (qlen, klen)
        rp_bucket = self._relative_position_bucket(relative_position,
                                                   bidirectional=not self.is_decoder,
                                                   num_buckets=self.relative_attention_num_buckets)
        values = self.relative_attention_bias(rp_bucket)  # shape (qlen, klen, num_heads)
        values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, qlen, klen)
        return values

    def forward(self, input, mask, kv=None, position_bias=None, cache=None, head_mask=None):
        """
        Self-attention (if kv is None) or attention over source sentence (provided by kv).
        """
        # Input is (bs, qlen, dim)
        # Mask is (bs, klen) (non-causal) or (bs, klen, klen)
        bs, qlen, dim = input.size()
        if kv is None:
            klen = qlen if cache is None else cache['slen'] + qlen
        else:
            klen = kv.size(1)
        # assert dim == self.dim, 'Dimensions do not match: %s input vs %s configured' % (dim, self.dim)
        n_heads = self.n_heads
        dim_per_head = self.dim // n_heads
        mask_reshape = (bs, 1, qlen, klen) if mask.dim() == 3 else (bs, 1, 1, klen)

        def shape(x):
            """  projection """
            return x.view(bs, -1, self.n_heads, dim_per_head).transpose(1, 2)

        def unshape(x):
            """  compute context """
            return x.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * dim_per_head)

        q = shape(self.q(input))                                          # (bs, n_heads, qlen, dim_per_head)
        if kv is None:
            k = shape(self.k(input))                                      # (bs, n_heads, qlen, dim_per_head)
            v = shape(self.v(input))                                      # (bs, n_heads, qlen, dim_per_head)
        elif cache is None or self.layer_id not in cache:
            k = v = kv
            k = shape(self.k(k))                                          # (bs, n_heads, qlen, dim_per_head)
            v = shape(self.v(v))                                          # (bs, n_heads, qlen, dim_per_head)

        if cache is not None:
            if self.layer_id in cache:
                if kv is None:
                    k_, v_ = cache[self.layer_id]
                    k = torch.cat([k_, k], dim=2)                             # (bs, n_heads, klen, dim_per_head)
                    v = torch.cat([v_, v], dim=2)                             # (bs, n_heads, klen, dim_per_head)
                else:
                    k, v = cache[self.layer_id]
            cache[self.layer_id] = (k, v)

        # q = q / math.sqrt(dim_per_head)                                     # No scaling in T5
        scores = torch.matmul(q, k.transpose(2, 3))                           # (bs, n_heads, qlen, klen)

        if position_bias is None:
            if not self.has_relative_attention_bias:
                raise ValueError("No position_bias provided and no weights to compute position_bias")
            position_bias = self.compute_bias(qlen, klen)
        scores += position_bias

        mask = (mask == 0).view(mask_reshape).expand_as(scores)               # (bs, n_heads, qlen, klen)
        scores.masked_fill_(mask, -float('inf'))                              # (bs, n_heads, qlen, klen)

        weights = F.softmax(scores.float(), dim=-1).type_as(scores)           # (bs, n_heads, qlen, klen)
        weights = F.dropout(weights, p=self.dropout, training=self.training)  # (bs, n_heads, qlen, klen)

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

        context = torch.matmul(weights, v)                                    # (bs, n_heads, qlen, dim_per_head)
        context = unshape(context)                                            # (bs, qlen, dim)

        context = self.o(context)

        outputs = (context,)
        if self.output_attentions:
            outputs = outputs + (weights,)
        if self.has_relative_attention_bias:
            outputs = outputs + (position_bias,)
        return outputs


class T5LayerSelfAttention(nn.Module):
    def __init__(self, config, has_relative_attention_bias=False):
        super(T5LayerSelfAttention, self).__init__()
        self.SelfAttention = T5Attention(config, has_relative_attention_bias=has_relative_attention_bias)
        self.layer_norm = nn.LayerNorm(config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout)

    def forward(self, hidden_states, attention_mask=None, position_bias=None, head_mask=None):
        norm_x = self.layer_norm(hidden_states)
        attention_output = self.SelfAttention(norm_x,
                                              attention_mask=attention_mask,
                                              position_bias=position_bias,
                                              head_mask=head_mask)
        y = attention_output[0]
        layer_output = hidden_states + self.dropout(y)
        outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
        return outputs


class T5LayerCrossAttention(nn.Module):
    def __init__(self, config, has_relative_attention_bias=False):
        super(T5LayerCrossAttention, self).__init__()
        self.EncDecAttention = T5Attention(config, has_relative_attention_bias=has_relative_attention_bias)
        self.layer_norm = nn.LayerNorm(config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout)

    def forward(self, hidden_states, kv, attention_mask=None, position_bias=None, head_mask=None):
        norm_x = self.layer_norm(hidden_states)
        attention_output = self.EncDecAttention(norm_x,
                                                kv=kv,
                                                attention_mask=attention_mask,
                                                position_bias=position_bias,
                                                head_mask=head_mask)
        y = attention_output[0]
        layer_output = hidden_states + self.dropout(y)
        outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
        return outputs


class T5Block(nn.Module):
    def __init__(self, config, has_relative_attention_bias=False):
        super(T5Block, self).__init__()
        self.is_decoder = config.is_decoder
        self.layer_000 = T5LayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias)
        if self.is_decoder:
            self.layer_001 = T5LayerCrossAttention(config, has_relative_attention_bias=has_relative_attention_bias)
            self.layer_002 = T5LayerFF(config)
        else:
            self.layer_001 = T5LayerFF(config)

    def forward(self, hidden_states, attention_mask=None, position_bias=None,
                encoder_hidden_states=None, encoder_attention_mask=None, encoder_decoder_position_bias=None,
                head_mask=None):
        self_attention_outputs = self.layer_000(hidden_states,
                                                attention_mask=attention_mask,
                                                position_bias=position_bias,
                                                head_mask=head_mask)
        hidden_states = self_attention_outputs[0]
        outputs = self_attention_outputs[1:]

        if self.is_decoder:
            cross_attention_outputs = self.layer_001(hidden_states,
                                                     kv=encoder_hidden_states,
                                                     attention_mask=encoder_attention_mask,
                                                     position_bias=encoder_decoder_position_bias,
                                                     head_mask=head_mask)
            hidden_states = cross_attention_outputs[0]
            outputs = cross_attention_outputs[1:] + outputs
            hidden_states = self.layer_002(hidden_states)
        else:
            hidden_states = self.layer_001(hidden_states)

        outputs = (hidden_states,) + outputs  # add attentions if we output them
        return outputs


class T5Stack(nn.Module):
    def __init__(self, config):
        super(T5Stack, self).__init__()
        self.blocks = nn.ModuleList([T5Block(config, has_relative_attention_bias=bool(i == 0))
                                     for i in range(config.num_layers)])
        self.final_layer_norm = nn.LayerNorm(config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout)

    def forward(self,
                hidden_states,
                attention_mask=None,
                encoder_hidden_states=None,
                encoder_attention_mask=None,
                head_mask=None):

        batch_size, seq_length = hidden_states.shape[0], hidden_states.shape[1]
        encoder_seq_length = encoder_hidden_states.shape[1] if encoder_hidden_states is not None else 0
        if attention_mask is None:
            attention_mask = torch.ones(batch_size, seq_length).to(hidden_states.device)
        if encoder_attention_mask is None:
            encoder_attention_mask = torch.ones(batch_size, encoder_seq_length).to(hidden_states.device)

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        if attention_mask.dim() == 3:
            extended_attention_mask = attention_mask[:, None, :, :]

        # Provided a padding mask of dimensions [batch_size, seq_length]
        # - if the model is a decoder, apply a causal mask in addition to the padding mask
        # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if attention_mask.dim() == 2:
            if self.config.is_decoder:
                seq_ids = torch.arange(seq_length, device=hidden_states.device)
                causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
                extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
            else:
                extended_attention_mask = attention_mask[:, None, None, :]

        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
        # masked positions, this operation will create a tensor which is 0.0 for
        # positions we want to attend and -10000.0 for masked positions.
        # Since we are adding it to the raw scores before the softmax, this is
        # effectively the same as removing these entirely.
        extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype)  # fp16 compatibility
        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0

        # If a 2D ou 3D attention mask is provided for the cross-attention
        # we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length]
        if encoder_attention_mask.dim() == 3:
            encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
        if encoder_attention_mask.dim() == 2:
            encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]

        encoder_extended_attention_mask = encoder_extended_attention_mask.to(dtype=next(self.parameters()).dtype)  # fp16 compatibility
        encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0

        # 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:
            if head_mask.dim() == 1:
                head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
                head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1)
            elif head_mask.dim() == 2:
                head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)  # We can specify head_mask for each layer
            head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility
        else:
            head_mask = [None] * self.config.num_hidden_layers

        all_hidden_states = ()
        all_attentions = ()
        position_bias = None
        encoder_decoder_position_bias = None
        for i, layer_module in enumerate(self.layer):
            if self.output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            layer_outputs = layer_module(hidden_states,
                                         attention_mask=extended_attention_mask,
                                         position_bias=position_bias,
                                         encoder_hidden_states=encoder_hidden_states,
                                         encoder_attention_mask=encoder_extended_attention_mask,
                                         encoder_decoder_position_bias=encoder_decoder_position_bias,
                                         head_mask=head_mask[i])
            hidden_states = layer_outputs[0]
            if i == 0:
                position_bias = layer_outputs[2] if len(layer_outputs) > 3 else None
                encoder_decoder_position_bias = layer_outputs[4] if len(layer_outputs) > 5 else None

            if self.output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)

        hidden_states = self.final_layer_norm(hidden_states)
        layer_output = self.dropout(hidden_states)

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

        outputs = (hidden_states,)
        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 T5PreTrainedModel(PreTrainedEncoderDecoder):
    """ An abstract class to handle weights initialization and
        a simple interface for dowloading and loading pretrained models.
    """
    config_class = T5Config
    pretrained_model_archive_map = T5_PRETRAINED_MODEL_ARCHIVE_MAP
    load_tf_weights = load_tf_weights_in_t5

    def _init_weights(self, module):
        """ Initialize the weights """
        if isinstance(module, (nn.Linear, nn.Embedding)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        if isinstance(module, nn.Linear) and module.bias is not None:
            module.bias.data.zero_()


T5_START_DOCSTRING = r"""    The T5 model was proposed in
    `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer`_
    by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.
    It's an encoder decoder pre-trained transformer.

    This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and
    refer to the PyTorch documentation for all matter related to general usage and behavior.

    .. _`Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer`:
        https://arxiv.org/abs/1910.10683

    .. _`torch.nn.Module`:
        https://pytorch.org/docs/stable/nn.html#module

    Parameters:
        config (:class:`~transformers.T5Config`): 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.
"""

T5_INPUTS_DOCSTRING = r"""
    Inputs:
        **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
            Indices of input sequence tokens in the vocabulary.
            To match pre-training, T5 input sequence should be formatted with [CLS] and [SEP] tokens as follows:

            (a) For sequence pairs:

                ``tokens:         [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]``

            (b) For single sequences:

                ``tokens:         [CLS] the dog is hairy . [SEP]``

            T5 is a model with relative position embeddings so you should be able to pad the inputs on
            the right or the left.

            Indices can be obtained using :class:`transformers.T5Tokenizer`.
            See :func:`transformers.PreTrainedTokenizer.encode` and
            :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
        **attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
            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.
        **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
            Indices of positions of each input sequence tokens in the position embeddings.
            Selected in the range ``[0, config.max_position_embeddings - 1]``.
        **head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
            Mask to nullify selected heads of the self-attention modules.
            Mask values selected in ``[0, 1]``:
            ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
"""

@add_start_docstrings("The bare single stack (encoder or decoder) of a T5 Model transformer outputting raw hidden-states"
                      "without any specific head on top.",
                      T5_START_DOCSTRING, T5_INPUTS_DOCSTRING)
class T5Model(T5PreTrainedModel):
    r"""
    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
        **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
            Sequence of hidden-states at the output of the last layer of the model.
        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
            of shape ``(batch_size, sequence_length, hidden_size)``:
            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
            list of ``torch.FloatTensor`` (one for each layer) of shape ``(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::

        tokenizer = T5Tokenizer.from_pretrained('t5-base-uncased')
        model = T5Model.from_pretrained('t5-base-uncased')
        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
        outputs = model(input_ids)
        last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple

    """
    def __init__(self, config):
        super(T5Model, self).__init__(config)
        self.shared = nn.Embeddings(config.vocab_size, config.d_model)

        encoder_config = copy.deepcopy(config)
        self.encoder = T5Stack(encoder_config)

        decoder_config = copy.deepcopy(config)
        decoder_config.is_decoder = True
        self.decoder = T5Stack(decoder_config)

        self.init_weights()

    @property
    def get_input_embeddings(self):
        return self.shared

    def set_input_embeddings(self, new_embeddings):
        self.shared = new_embeddings

    def _prune_heads(self, heads_to_prune):
        """ Prunes heads of the model.
            heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
            See base class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    def forward(self, encoder_input_ids, decoder_input_ids, **kwargs):
        # keyword arguments come in 3 flavors: encoder-specific (prefixed by
        # `encoder_`), decoder-specific (prefixed by `decoder_`) and those
        # that apply to the model as whole.
        # We let the specific kwargs override the common ones in case of conflict.
        kwargs_common = dict((k, v) for k, v in kwargs.items()
                             if not k.startswith("encoder_") and not k.startswith("decoder_"))
        kwargs_decoder = kwargs_common.copy()
        kwargs_encoder = kwargs_common.copy()
        kwargs_encoder.update(dict((k[len("encoder_") :], v) for k, v in kwargs.items() if k.startswith("encoder_")))
        kwargs_decoder.update(dict((k[len("decoder_") :], v) for k, v in kwargs.items() if k.startswith("decoder_")))

        # Encode if needed (training, first prediction pass)
        encoder_hidden_states = kwargs_encoder.pop("hidden_states", None)
        if encoder_hidden_states is None:
            encoder_inputs_ids = kwargs_encoder.pop("input_ids")
            hidden_states = self.shared(encoder_inputs_ids)  # Convert inputs in embeddings
            encoder_outputs = self.encoder(hidden_states, **kwargs_encoder)
            encoder_hidden_states = encoder_outputs[0]
        else:
            encoder_outputs = ()

        # Decode
        decoder_inputs_ids = kwargs_decoder.pop("input_ids")
        hidden_states = self.shared(decoder_inputs_ids)  # Convert inputs in embeddings
        kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states
        kwargs_decoder["encoder_attention_mask"] = kwargs_encoder.get("attention_mask", None)
        decoder_outputs = self.decoder(hidden_states, **kwargs_decoder)

        return decoder_outputs + encoder_outputs


@add_start_docstrings("""T5 Model with a `language modeling` head on top. """,
    T5_START_DOCSTRING, T5_INPUTS_DOCSTRING)
class T5WithLMHead(T5PreTrainedModel):
    r"""
        **lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
            Labels for computing the masked language modeling loss.
            Indices should be in ``[-1, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
            Tokens with indices set to ``-1`` are ignored (masked), the loss is only computed for the tokens with labels
            in ``[0, ..., config.vocab_size]``

    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
        **loss**: (`optional`, returned when ``lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
            Masked language modeling loss.
        **prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
            of shape ``(batch_size, sequence_length, hidden_size)``:
            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
            list of ``torch.FloatTensor`` (one for each layer) of shape ``(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::

        tokenizer = T5Tokenizer.from_pretrained('t5-base-uncased')
        model = T5ForMaskedLM.from_pretrained('t5-base-uncased')
        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
        outputs = model(input_ids, lm_labels=input_ids)
        loss, prediction_scores = outputs[:2]

    """
    def __init__(self, config):
        super(T5ForMaskedLM, self).__init__(config)

        self.transformer = T5Model(config)
        self.lm_head = nn.Linear(config.d_model, config.vocab_size)

        self.init_weights()

    def get_output_embeddings(self):
        return self.lm_head

    def forward(self, encoder_input_ids, decoder_input_ids, **kwargs):
        outputs = self.transformer(encoder_input_ids, decoder_input_ids, **kwargs)

        sequence_output = outputs[0]
        lm_logits = self.cls(sequence_output)

        outputs = (lm_logits,) + outputs[2:]  # Add hidden states and attention if they are here
        if lm_labels is not None:
            shift_logits = lm_logits[..., :-1, :].contiguous()
            shift_labels = lm_labels[..., 1:].contiguous()
            loss_fct = CrossEntropyLoss(ignore_index=-1)
            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
                            shift_labels.view(-1))
            outputs = (loss,) + outputs

        return outputs  # (lm_loss), lm_logits, (hidden_states), (attentions)
