# 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, 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 ...cache_utils import Cache, DynamicCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import (
    BaseModelOutputWithPastAndCrossAttentions,
    CausalLMOutputWithCrossAttentions,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutputWithPast,
    TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import auto_docstring, logging
from .configuration_mpt import MptConfig


logger = logging.get_logger(__name__)


def build_mpt_alibi_tensor(num_heads, sequence_length, alibi_bias_max=8, device=None):
    r"""
    Link to paper: https://huggingface.co/papers/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 implementation enables user to also use additive bias.
    """

    def __init__(self, config: MptConfig, layer_idx: Optional[int] = None):
        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)
        self.layer_idx = layer_idx

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_bias: torch.Tensor,
        past_key_value: Optional[Cache] = None,
        attention_mask: Optional[torch.Tensor] = None,
        cache_position: 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:
            cache_kwargs = {"cache_position": cache_position}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        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.get_seq_length()

        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


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(GradientCheckpointingLayer):
    def __init__(self, config: MptConfig, layer_idx: Optional[int] = None):
        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, layer_idx)

        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[Cache] = None,
        use_cache: bool = False,
        output_attentions: bool = False,
        cache_position: Optional[torch.Tensor] = None,
    ):
        # 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 = self.attn(
            layernorm_output,
            position_bias=position_bias,
            attention_mask=attention_mask,
            past_key_value=layer_past,
            cache_position=cache_position,
        )

        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)
        return output, attn_weights


@auto_docstring
class MptPreTrainedModel(PreTrainedModel):
    config: 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
        )


@auto_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, layer_idx=i) for i 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

    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Union[tuple[tuple[torch.Tensor, torch.Tensor], ...], Cache]] = 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,
        cache_position: Optional[torch.Tensor] = None,
        **kwargs,  # NOOP kwargs, for now
    ) -> Union[tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
        r"""
        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.get_seq_length()`
            (`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)
        """
        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 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

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

        return_legacy_cache = False
        if use_cache and not isinstance(past_key_values, Cache):
            return_legacy_cache = True
            logger.warning_once(
                "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. "
                "You should pass an instance of `DynamicCache` instead, e.g. "
                "`past_key_values=DynamicCache.from_legacy_cache(past_key_values)`."
            )
            past_key_values = DynamicCache.from_legacy_cache(past_key_values)

        hidden_states = inputs_embeds

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

        # Compute alibi tensor: check build_alibi_tensor documentation
        past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
        seq_length_with_past = seq_length + 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 in self.blocks:
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            outputs = block(
                hidden_states,
                layer_past=past_key_values,
                attention_mask=causal_mask,
                use_cache=use_cache,
                output_attentions=output_attentions,
                position_bias=alibi,
                cache_position=cache_position,
            )

            hidden_states = outputs[0]
            if output_attentions:
                all_self_attentions = all_self_attentions + (outputs[1],)

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

        if return_legacy_cache:
            past_key_values = past_key_values.to_legacy_cache()

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

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

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


@auto_docstring(
    custom_intro="""
    The MPT Model transformer with a language modeling head on top (linear layer with weights tied to the input
    embeddings).
    """
)
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 set_output_embeddings(self, new_embeddings: torch.Tensor):
        self.lm_head = new_embeddings

    @auto_docstring
    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,
        cache_position: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> Union[tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
        r"""
        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.get_seq_length()`
            (`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)
        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,
            cache_position=cache_position,
        )
        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)
            # Flatten the tokens
            loss = self.loss_function(
                lm_logits,
                labels,
                vocab_size=self.config.vocab_size,
                **kwargs,
            )

        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,
        )


@auto_docstring(
    custom_intro="""
    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).
    """
)
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()

    @auto_docstring
    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"""
        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.get_seq_length()`
            (`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)
        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:
            last_non_pad_token = -1
        elif input_ids is not None:
            # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
            non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
            token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
            last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
        else:
            last_non_pad_token = -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), last_non_pad_token]

        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,
        )


@auto_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()

    @auto_docstring
    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"""
        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.get_seq_length()`
            (`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)
        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,
        )


@auto_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()

    @auto_docstring
    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"""
        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.get_seq_length()`
            (`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)
        """
        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,
        )


__all__ = [
    "MptForCausalLM",
    "MptModel",
    "MptPreTrainedModel",
    "MptForSequenceClassification",
    "MptForTokenClassification",
    "MptForQuestionAnswering",
]