# coding=utf-8
# Copyright 2023 HuggingFace Inc. team and MosaicML NLP 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 MPT model."""

import math
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
from torch.nn import functional as F

from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
from ...modeling_outputs import (
    BaseModelOutputWithPastAndCrossAttentions,
    CausalLMOutputWithCrossAttentions,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutputWithPast,
    TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_mpt import MptConfig


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "mosaicml/mpt-7b"
_CONFIG_FOR_DOC = "MptConfig"


def build_mpt_alibi_tensor(num_heads, sequence_length, alibi_bias_max=8, device=None):
    r"""
    Link to paper: https://arxiv.org/abs/2108.12409 - Alibi tensor is not causal as the original paper mentions, it
    relies on a translation invariance of softmax for quick implementation. This implementation has been copied from
    the alibi implementation of MPT source code that led to slightly different results than the Bloom alibi:
    https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L292
    """
    alibi = torch.arange(1 - sequence_length, 1, dtype=torch.int32, device=device).view(1, 1, 1, sequence_length)
    num_heads_power_of_2 = 2 ** math.ceil(math.log2(num_heads))

    base = torch.arange(1, num_heads_power_of_2 + 1, dtype=torch.int64, device=device).float()
    base = base * (alibi_bias_max / num_heads_power_of_2)

    slopes = 1.0 / torch.pow(2, base)
    slopes = slopes.view(1, num_heads_power_of_2, 1, 1)

    if num_heads_power_of_2 != num_heads:
        slopes = torch.concat([slopes[:, 1::2, ...], slopes[:, ::2, ...]], dim=1)[:, :num_heads, ...]

    alibi = alibi * slopes
    return alibi.squeeze(0)


class MptAttention(nn.Module):
    """Multi-head self attention.
    Using torch or triton attention implemetation enables user to also use additive bias.
    """

    def __init__(self, config: MptConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.n_heads = config.n_heads
        self.max_seq_length = config.max_seq_len
        self.head_dim = self.hidden_size // self.n_heads
        self.softmax_scale = config.attn_config.softmax_scale
        if self.softmax_scale is None:
            self.softmax_scale = 1 / math.sqrt(self.hidden_size / self.n_heads)

        self.attn_dropout_p = config.attn_config.attn_pdrop
        self.clip_qkv = config.attn_config.clip_qkv
        self.Wqkv = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
        self.out_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_bias: torch.Tensor,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.Tensor] = None,
    ):
        batch_size, seq_length = hidden_states.shape[:2]

        mixed_qkv = self.Wqkv(hidden_states)
        if self.clip_qkv:
            mixed_qkv = mixed_qkv.clamp(min=-self.clip_qkv, max=self.clip_qkv)

        query_states, key_states, value_states = mixed_qkv.chunk(3, dim=2)
        query_states = query_states.reshape(batch_size, seq_length, self.n_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.reshape(batch_size, seq_length, self.n_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.reshape(batch_size, seq_length, self.n_heads, self.head_dim).transpose(1, 2)

        if past_key_value is not None:
            if len(past_key_value) != 0:
                key_states = torch.cat([past_key_value[0], key_states], dim=2)
                value_states = torch.cat([past_key_value[1], value_states], dim=2)
            past_key_value = (key_states, value_states)
        else:
            past_key_value = (key_states, value_states)

        attention_scores = torch.matmul(query_states, key_states.transpose(-1, -2)) * self.softmax_scale

        query_length = seq_length if past_key_value is None else seq_length + past_key_value[0].shape[2]

        if position_bias is not None:
            if len(position_bias.shape) != 3:
                raise ValueError(f"Expecting position_bias shape to be 3 dimensions, got {len(position_bias.shape)}")
            key_length = key_states.shape[-2]

            position_bias_query_index = max(0, position_bias.size(1) - query_length)
            position_bias_key_index = max(0, position_bias.size(2) - key_length)

            position_bias = position_bias[:, position_bias_query_index:, position_bias_key_index:]

            attention_scores = attention_scores + position_bias

        if attention_mask is not None:
            attention_scores = attention_scores.masked_fill(attention_mask, torch.finfo(query_states.dtype).min)

        # (batch_size, n_heads, seq_length, key_length)
        attn_weights = nn.functional.softmax(attention_scores.float(), dim=-1).to(value_states.dtype)
        attn_weights = nn.functional.dropout(attn_weights, p=self.attn_dropout_p, training=self.training)

        context_states = torch.matmul(attn_weights, value_states)
        context_states = context_states.permute(0, 2, 1, 3).contiguous().view(batch_size, seq_length, -1)
        attn_output = self.out_proj(context_states)

        return attn_output, attn_weights, past_key_value


class MptMLP(nn.Module):
    def __init__(self, config: MptConfig):
        super().__init__()
        hidden_size = config.hidden_size

        self.up_proj = nn.Linear(hidden_size, 4 * hidden_size, bias=False)
        self.act = nn.GELU(approximate="none")
        self.down_proj = nn.Linear(4 * hidden_size, hidden_size, bias=False)
        self.hidden_dropout = config.attn_config.attn_pdrop

    def forward(self, hidden_states: torch.Tensor, residual: torch.Tensor) -> torch.Tensor:
        hidden_states = self.act(self.up_proj(hidden_states))

        intermediate_output = self.down_proj(hidden_states)

        output = F.dropout(intermediate_output, p=self.hidden_dropout, training=self.training)
        output = output + residual

        return output


class MptBlock(nn.Module):
    def __init__(self, config: MptConfig):
        super().__init__()
        hidden_size = config.hidden_size

        self.norm_1 = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        # backward compatibility with weights on the Hub
        self.norm_1.bias = None

        self.num_heads = config.n_heads
        self.attn = MptAttention(config)

        self.norm_2 = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        # backward compatibility with weights on the Hub
        self.norm_2.bias = None

        self.ffn = MptMLP(config)

        self.dropout_rate = config.attn_config.attn_pdrop
        self.resid_attn_dropout = nn.Dropout(self.dropout_rate)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_bias: torch.Tensor,
        attention_mask: torch.Tensor,
        layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        use_cache: bool = False,
        output_attentions: bool = False,
    ):
        # hidden_states: [batch_size, seq_length, hidden_size]
        # Layer norm at the beginning of the transformer layer.
        layernorm_output = self.norm_1(hidden_states)

        residual = hidden_states

        # Self attention.
        attn_outputs, attn_weights, past_key_value = self.attn(
            layernorm_output,
            position_bias=position_bias,
            attention_mask=attention_mask,
            past_key_value=layer_past,
        )

        hidden_states = self.resid_attn_dropout(attn_outputs) + residual

        layernorm_output = self.norm_2(hidden_states)

        # Get residual
        residual = hidden_states

        # MLP.
        output = self.ffn(layernorm_output, residual)
        outputs = (output,)

        if use_cache:
            outputs += (past_key_value,)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs  # hidden_states, present, attentions


class MptPreTrainedModel(PreTrainedModel):
    config_class = MptConfig
    base_model_prefix = "transformer"
    supports_gradient_checkpointing = True
    _no_split_modules = ["MptBlock"]
    _keys_to_ignore_on_load_missing = [r"lm_head.*."]

    def __init__(self, *inputs, **kwargs):
        super().__init__(*inputs, **kwargs)

    def _init_weights(self, module: nn.Module):
        """Initialize the weights."""
        if isinstance(module, nn.Linear):
            # 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)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, LayerNorm):
            if module.bias is not None:
                module.bias.data.zero_()
            module.weight.data.fill_(1.0)

    @staticmethod
    def _convert_to_mpt_cache(
        past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]],
    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
        """
        Converts the cache to the format expected by Mpt, i.e. to tuple(tuple([batch_size * num_heads, ...]))
        """
        batch_size, num_heads, head_dim, seq_length = past_key_value[0][0].shape
        batch_size_times_num_heads = batch_size * num_heads
        # key:  [batch_size, num_heads, head_dim, seq_length] -> [batch_size * num_heads, head_dim, seq_length]
        # value: [batch_size, num_heads, seq_length, head_dim] -> [batch_size * num_heads, seq_length, head_dim]
        return tuple(
            (
                layer_past[0].reshape(batch_size_times_num_heads, head_dim, seq_length),
                layer_past[1].reshape(batch_size_times_num_heads, seq_length, head_dim),
            )
            for layer_past in past_key_value
        )


MPT_START_DOCSTRING = r"""

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`MptConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

MPT_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
            `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0][0].shape[2]`
            (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.

            If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
            `input_ids`.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.n_layers`):
            Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
            `past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have
            their past given to this model should not be passed as `input_ids` as they have already been computed.

            Each element of `past_key_values` is a tuple (past_key, past_value):
            - past_key: [batch_size * num_heads, head_dim, kv_length]
            - past_value: [batch_size * num_heads, kv_length, head_dim]
        attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)

        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `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.

            If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see
            `past_key_values`).
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""


@add_start_docstrings(
    "The bare Mpt Model transformer outputting raw hidden-states without any specific head on top.",
    MPT_START_DOCSTRING,
)
class MptModel(MptPreTrainedModel):
    def __init__(self, config: MptConfig):
        super().__init__(config)

        self.hidden_size = config.hidden_size
        self.num_heads = config.n_heads

        # Embedding + LN Embedding
        self.wte = nn.Embedding(config.vocab_size, self.hidden_size)

        # Transformer blocks
        self.blocks = nn.ModuleList([MptBlock(config) for _ in range(config.n_layers)])

        # Final Layer Norm
        self.norm_f = LayerNorm(self.hidden_size, eps=config.layer_norm_epsilon)
        # backward compatibility with weights on the Hub
        self.norm_f.bias = None

        self.gradient_checkpointing = False

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.wte

    def build_mpt_alibi_tensor(self, num_heads, sequence_length, alibi_bias_max=8, device=None):
        return build_mpt_alibi_tensor(num_heads, sequence_length, alibi_bias_max, device)

    def set_input_embeddings(self, new_embeddings: torch.Tensor):
        self.wte = new_embeddings

    @add_start_docstrings_to_model_forward(MPT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPastAndCrossAttentions,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        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:
            batch_size, seq_length = input_ids.shape
        elif inputs_embeds is not None:
            batch_size, seq_length, _ = inputs_embeds.shape
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if past_key_values is None:
            past_key_values = tuple([None] * len(self.blocks))

        if inputs_embeds is None:
            inputs_embeds = self.wte(input_ids)

        hidden_states = inputs_embeds

        presents = () if use_cache else None
        all_self_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False

        # Compute alibi tensor: check build_alibi_tensor documentation
        seq_length_with_past = seq_length
        past_key_values_length = 0
        if past_key_values[0] is not None:
            past_key_values_length = past_key_values[0][0].shape[2]
            seq_length_with_past = seq_length_with_past + past_key_values_length
        if attention_mask is None:
            attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
        else:
            attention_mask = attention_mask.to(hidden_states.device)

        alibi = self.build_mpt_alibi_tensor(self.num_heads, self.config.max_seq_len, device=hidden_states.device)

        causal_mask = _prepare_4d_causal_attention_mask(
            attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
        )
        causal_mask = causal_mask.bool()

        for block, layer_past in zip(self.blocks, past_key_values):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            if self.gradient_checkpointing and self.training:
                outputs = self._gradient_checkpointing_func(
                    block.__call__,
                    hidden_states,
                    alibi,
                    causal_mask,
                    layer_past,
                    use_cache,
                    output_attentions,
                )
            else:
                outputs = block(
                    hidden_states,
                    layer_past=layer_past,
                    attention_mask=causal_mask,
                    use_cache=use_cache,
                    output_attentions=output_attentions,
                    position_bias=alibi,
                )

            hidden_states = outputs[0]
            if use_cache is True:
                presents = presents + (outputs[1],)

            if output_attentions:
                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)

        # Add last hidden state
        hidden_states = self.norm_f(hidden_states)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)

        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=presents,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )


@add_start_docstrings(
    """
    The MPT Model transformer with a language modeling head on top (linear layer with weights tied to the input
    embeddings).
    """,
    MPT_START_DOCSTRING,
)
class MptForCausalLM(MptPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config: MptConfig):
        super().__init__(config)
        self.transformer = MptModel(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings: torch.Tensor):
        self.lm_head = new_embeddings

    @add_start_docstrings_to_model_forward(MPT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=CausalLMOutputWithCrossAttentions,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        transformer_outputs = self.transformer(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = transformer_outputs[0]

        lm_logits = self.lm_head(hidden_states)

        loss = None
        if labels is not None:
            # move labels to correct device to enable model parallelism
            labels = labels.to(lm_logits.device)
            # Shift so that tokens < n predict n
            shift_logits = lm_logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            batch_size, seq_length, vocab_size = shift_logits.shape
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(
                shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length)
            )

        if not return_dict:
            output = (lm_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return CausalLMOutputWithCrossAttentions(
            loss=loss,
            logits=lm_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )

    def _reorder_cache(
        self, past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
        """
        This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
        [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
        beam_idx at every generation step.

        Output shares the same memory storage as `past`.
        """
        # Get a copy of `beam_idx` on all the devices where we need those indices.
        device_to_beam_idx = {
            past_state.device: beam_idx.to(past_state.device) for layer_past in past for past_state in layer_past
        }
        reordered_past = tuple(
            (
                layer_past[0].index_select(0, device_to_beam_idx[layer_past[0].device]),
                layer_past[1].index_select(0, device_to_beam_idx[layer_past[0].device]),
            )
            for layer_past in past
        )
        return reordered_past


@add_start_docstrings(
    """
    The MPT Model transformer with a sequence classification head on top (linear layer).

    [`MptForSequenceClassification`] uses the last token in order to do the classification, as other causal models
    (e.g. GPT-1) do.

    Since it does classification on the last token, it requires to know the position of the last token. If a
    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
    each row of the batch).
    """,
    MPT_START_DOCSTRING,
)
class MptForSequenceClassification(MptPreTrainedModel):
    def __init__(self, config: MptConfig):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.transformer = MptModel(config)
        self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    @add_start_docstrings_to_model_forward(MPT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=SequenceClassifierOutputWithPast,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutputWithPast]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        transformer_outputs = self.transformer(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = transformer_outputs[0]
        logits = self.score(hidden_states)

        if input_ids is not None:
            batch_size = input_ids.shape[0]
        else:
            batch_size = inputs_embeds.shape[0]

        if self.config.pad_token_id is None and batch_size != 1:
            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
        if self.config.pad_token_id is None:
            sequence_lengths = -1
        else:
            if input_ids is not None:
                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
                sequence_lengths = sequence_lengths % input_ids.shape[-1]
                sequence_lengths = sequence_lengths.to(logits.device)
            else:
                sequence_lengths = -1
                logger.warning_once(
                    f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
                    "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
                )

        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]

        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(pooled_logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(pooled_logits, labels)
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(pooled_logits, labels)
        if not return_dict:
            output = (pooled_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutputWithPast(
            loss=loss,
            logits=pooled_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )


@add_start_docstrings(
    """
    MPT 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.
    """,
    MPT_START_DOCSTRING,
)
class MptForTokenClassification(MptPreTrainedModel):
    def __init__(self, config: MptConfig):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.transformer = MptModel(config)
        if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
            classifier_dropout = config.classifier_dropout
        elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
            classifier_dropout = config.hidden_dropout
        else:
            classifier_dropout = 0.1
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    @add_start_docstrings_to_model_forward(MPT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **deprecated_arguments,
    ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        transformer_outputs = self.transformer(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = transformer_outputs[0]
        hidden_states = self.dropout(hidden_states)
        logits = self.classifier(hidden_states)

        loss = None
        if labels is not None:
            # move labels to correct device to enable model parallelism
            labels = labels.to(logits.device)
            batch_size, seq_length = labels.shape
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(
                logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)
            )

        if not return_dict:
            output = (logits,) + transformer_outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return TokenClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )


@add_start_docstrings(
    """
    The MPT Model transformer 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`).
    """,
    MPT_START_DOCSTRING,
)
class MptForQuestionAnswering(MptPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.transformer = MptModel(config)
        self.qa_outputs = nn.Linear(config.hidden_size, 2)

        # Initialize weights and apply final processing
        self.post_init()

    @add_start_docstrings_to_model_forward(MPT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        start_positions: Optional[torch.LongTensor] = None,
        end_positions: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
        r"""
        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the start of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the end of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.transformer(
            input_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1).contiguous()
        end_logits = end_logits.squeeze(-1).contiguous()

        total_loss = None
        if start_positions is not None and end_positions is not None:
            # If we are on multi-GPU, split add a dimension
            if len(start_positions.size()) > 1:
                start_positions = start_positions.squeeze(-1)
            if len(end_positions.size()) > 1:
                end_positions = end_positions.squeeze(-1)
            # sometimes the start/end positions are outside our model inputs, we ignore these terms
            ignored_index = start_logits.size(1)
            start_positions = start_positions.clamp(0, ignored_index)
            end_positions = end_positions.clamp(0, ignored_index)

            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2

        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output

        return QuestionAnsweringModelOutput(
            loss=total_loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )
