# Copyright 2021 Google AI The HuggingFace Inc. team. All rights reserved.
#
# 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 CANINE model."""

import copy
import math
from dataclasses import dataclass

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ... import initialization as init
from ...activations import ACT2FN
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import (
    BaseModelOutput,
    ModelOutput,
    MultipleChoiceModelOutput,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutput,
    TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward
from ...utils import auto_docstring, logging
from .configuration_canine import CanineConfig


logger = logging.get_logger(__name__)


# Support up to 16 hash functions.
_PRIMES = [31, 43, 59, 61, 73, 97, 103, 113, 137, 149, 157, 173, 181, 193, 211, 223]


@dataclass
@auto_docstring(
    custom_intro="""
    Output type of [`CanineModel`]. Based on [`~modeling_outputs.BaseModelOutputWithPooling`], but with slightly
    different `hidden_states` and `attentions`, as these also include the hidden states and attentions of the shallow
    Transformer encoders.
    """
)
class CanineModelOutputWithPooling(ModelOutput):
    r"""
    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 (i.e. the output of the final
        shallow Transformer encoder).
    pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
        Hidden-state of the first token of the sequence (classification token) at the last layer of the deep
        Transformer encoder, further processed by a Linear layer and a Tanh activation function. The Linear layer
        weights are trained from the next sentence prediction (classification) objective during pretraining.
    hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
        Tuple of `torch.FloatTensor` (one for the input to each encoder + one for the output of each layer of each
        encoder) of shape `(batch_size, sequence_length, hidden_size)` and `(batch_size, sequence_length //
        config.downsampling_rate, hidden_size)`. Hidden-states of the model at the output of each layer plus the
        initial input to each Transformer encoder. The hidden states of the shallow encoders have length
        `sequence_length`, but the hidden states of the deep encoder have length `sequence_length` //
        `config.downsampling_rate`.
    attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
        Tuple of `torch.FloatTensor` (one for each layer) of the 3 Transformer encoders of shape `(batch_size,
        num_heads, sequence_length, sequence_length)` and `(batch_size, num_heads, sequence_length //
        config.downsampling_rate, sequence_length // config.downsampling_rate)`. Attentions weights after the
        attention softmax, used to compute the weighted average in the self-attention heads.
    """

    last_hidden_state: torch.FloatTensor | None = None
    pooler_output: torch.FloatTensor | None = None
    hidden_states: tuple[torch.FloatTensor] | None = None
    attentions: tuple[torch.FloatTensor] | None = None


class CanineEmbeddings(nn.Module):
    """Construct the character, position and token_type embeddings."""

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

        self.config = config

        # character embeddings
        shard_embedding_size = config.hidden_size // config.num_hash_functions
        for i in range(config.num_hash_functions):
            name = f"HashBucketCodepointEmbedder_{i}"
            setattr(self, name, nn.Embedding(config.num_hash_buckets, shard_embedding_size))
        self.char_position_embeddings = nn.Embedding(config.num_hash_buckets, config.hidden_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)

        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
        self.register_buffer(
            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
        )

    def _hash_bucket_tensors(self, input_ids, num_hashes: int, num_buckets: int):
        """
        Converts ids to hash bucket ids via multiple hashing.

        Args:
            input_ids: The codepoints or other IDs to be hashed.
            num_hashes: The number of hash functions to use.
            num_buckets: The number of hash buckets (i.e. embeddings in each table).

        Returns:
            A list of tensors, each of which is the hash bucket IDs from one hash function.
        """
        if num_hashes > len(_PRIMES):
            raise ValueError(f"`num_hashes` must be <= {len(_PRIMES)}")

        primes = _PRIMES[:num_hashes]

        result_tensors = []
        for prime in primes:
            hashed = ((input_ids + 1) * prime) % num_buckets
            result_tensors.append(hashed)
        return result_tensors

    def _embed_hash_buckets(self, input_ids, embedding_size: int, num_hashes: int, num_buckets: int):
        """Converts IDs (e.g. codepoints) into embeddings via multiple hashing."""
        if embedding_size % num_hashes != 0:
            raise ValueError(f"Expected `embedding_size` ({embedding_size}) % `num_hashes` ({num_hashes}) == 0")

        hash_bucket_tensors = self._hash_bucket_tensors(input_ids, num_hashes=num_hashes, num_buckets=num_buckets)
        embedding_shards = []
        for i, hash_bucket_ids in enumerate(hash_bucket_tensors):
            name = f"HashBucketCodepointEmbedder_{i}"
            shard_embeddings = getattr(self, name)(hash_bucket_ids)
            embedding_shards.append(shard_embeddings)

        return torch.cat(embedding_shards, dim=-1)

    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        token_type_ids: torch.LongTensor | None = None,
        position_ids: torch.LongTensor | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
    ) -> torch.FloatTensor:
        if input_ids is not None:
            input_shape = input_ids.size()
        else:
            input_shape = inputs_embeds.size()[:-1]

        seq_length = input_shape[1]

        if position_ids is None:
            position_ids = self.position_ids[:, :seq_length]

        if token_type_ids is None:
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)

        if inputs_embeds is None:
            inputs_embeds = self._embed_hash_buckets(
                input_ids, self.config.hidden_size, self.config.num_hash_functions, self.config.num_hash_buckets
            )

        token_type_embeddings = self.token_type_embeddings(token_type_ids)
        embeddings = inputs_embeds + token_type_embeddings

        position_embeddings = self.char_position_embeddings(position_ids)
        embeddings += position_embeddings

        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings


class CharactersToMolecules(nn.Module):
    """Convert character sequence to initial molecule sequence (i.e. downsample) using strided convolutions."""

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

        self.conv = nn.Conv1d(
            in_channels=config.hidden_size,
            out_channels=config.hidden_size,
            kernel_size=config.downsampling_rate,
            stride=config.downsampling_rate,
        )
        self.activation = ACT2FN[config.hidden_act]

        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

    def forward(self, char_encoding: torch.Tensor) -> torch.Tensor:
        # `cls_encoding`: [batch, 1, hidden_size]
        cls_encoding = char_encoding[:, 0:1, :]

        # char_encoding has shape [batch, char_seq, hidden_size]
        # We transpose it to be [batch, hidden_size, char_seq]
        char_encoding = torch.transpose(char_encoding, 1, 2)
        downsampled = self.conv(char_encoding)
        downsampled = torch.transpose(downsampled, 1, 2)
        downsampled = self.activation(downsampled)

        # Truncate the last molecule in order to reserve a position for [CLS].
        # Often, the last position is never used (unless we completely fill the
        # text buffer). This is important in order to maintain alignment on TPUs
        # (i.e. a multiple of 128).
        downsampled_truncated = downsampled[:, 0:-1, :]

        # We also keep [CLS] as a separate sequence position since we always
        # want to reserve a position (and the model capacity that goes along
        # with that) in the deep BERT stack.
        # `result`: [batch, molecule_seq, molecule_dim]
        result = torch.cat([cls_encoding, downsampled_truncated], dim=1)

        result = self.LayerNorm(result)

        return result


class ConvProjection(nn.Module):
    """
    Project representations from hidden_size*2 back to hidden_size across a window of w = config.upsampling_kernel_size
    characters.
    """

    def __init__(self, config):
        super().__init__()
        self.config = config
        self.conv = nn.Conv1d(
            in_channels=config.hidden_size * 2,
            out_channels=config.hidden_size,
            kernel_size=config.upsampling_kernel_size,
            stride=1,
        )
        self.activation = ACT2FN[config.hidden_act]
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(
        self,
        inputs: torch.Tensor,
        final_seq_char_positions: torch.Tensor | None = None,
    ) -> torch.Tensor:
        # inputs has shape [batch, mol_seq, molecule_hidden_size+char_hidden_final]
        # we transpose it to be [batch, molecule_hidden_size+char_hidden_final, mol_seq]
        inputs = torch.transpose(inputs, 1, 2)

        # PyTorch < 1.9 does not support padding="same" (which is used in the original implementation),
        # so we pad the tensor manually before passing it to the conv layer
        # based on https://github.com/google-research/big_transfer/blob/49afe42338b62af9fbe18f0258197a33ee578a6b/bit_tf2/models.py#L36-L38
        pad_total = self.config.upsampling_kernel_size - 1
        pad_beg = pad_total // 2
        pad_end = pad_total - pad_beg

        pad = nn.ConstantPad1d((pad_beg, pad_end), 0)
        # `result`: shape (batch_size, char_seq_len, hidden_size)
        result = self.conv(pad(inputs))
        result = torch.transpose(result, 1, 2)
        result = self.activation(result)
        result = self.LayerNorm(result)
        result = self.dropout(result)
        final_char_seq = result

        if final_seq_char_positions is not None:
            # Limit transformer query seq and attention mask to these character
            # positions to greatly reduce the compute cost. Typically, this is just
            # done for the MLM training task.
            # TODO add support for MLM
            raise NotImplementedError("CanineForMaskedLM is currently not supported")
        else:
            query_seq = final_char_seq

        return query_seq


class CanineSelfAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
            raise ValueError(
                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
                f"heads ({config.num_attention_heads})"
            )

        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        self.key = nn.Linear(config.hidden_size, self.all_head_size)
        self.value = nn.Linear(config.hidden_size, self.all_head_size)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)

    def forward(
        self,
        from_tensor: torch.Tensor,
        to_tensor: torch.Tensor,
        attention_mask: torch.FloatTensor | None = None,
        output_attentions: bool | None = False,
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        batch_size, seq_length, _ = from_tensor.shape

        # If this is instantiated as a cross-attention module, the keys
        # and values come from an encoder; the attention mask needs to be
        # such that the encoder's padding tokens are not attended to.

        key_layer = (
            self.key(to_tensor)
            .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
            .transpose(1, 2)
        )
        value_layer = (
            self.value(to_tensor)
            .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
            .transpose(1, 2)
        )
        query_layer = (
            self.query(from_tensor)
            .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
            .transpose(1, 2)
        )

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None:
            if attention_mask.ndim == 3:
                # if attention_mask is 3D, do the following:
                attention_mask = torch.unsqueeze(attention_mask, dim=1)
                # 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 the dtype's smallest value for masked positions.
                attention_mask = (1.0 - attention_mask.float()) * torch.finfo(attention_scores.dtype).min
            # Apply the attention mask (precomputed for all layers in CanineModel forward() function)
            attention_scores = attention_scores + attention_mask

        # Normalize the attention scores to probabilities.
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs)

        context_layer = torch.matmul(attention_probs, value_layer)

        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(*new_context_layer_shape)

        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

        return outputs


class CanineSelfOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(
        self, hidden_states: tuple[torch.FloatTensor], input_tensor: torch.FloatTensor
    ) -> tuple[torch.FloatTensor, torch.FloatTensor]:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class CanineAttention(nn.Module):
    """
    Additional arguments related to local attention:

        - **local** (`bool`, *optional*, defaults to `False`) -- Whether to apply local attention.
        - **always_attend_to_first_position** (`bool`, *optional*, defaults to `False`) -- Should all blocks be able to
          attend
        to the `to_tensor`'s first position (e.g. a [CLS] position)? - **first_position_attends_to_all** (`bool`,
        *optional*, defaults to `False`) -- Should the *from_tensor*'s first position be able to attend to all
        positions within the *from_tensor*? - **attend_from_chunk_width** (`int`, *optional*, defaults to 128) -- The
        width of each block-wise chunk in `from_tensor`. - **attend_from_chunk_stride** (`int`, *optional*, defaults to
        128) -- The number of elements to skip when moving to the next block in `from_tensor`. -
        **attend_to_chunk_width** (`int`, *optional*, defaults to 128) -- The width of each block-wise chunk in
        *to_tensor*. - **attend_to_chunk_stride** (`int`, *optional*, defaults to 128) -- The number of elements to
        skip when moving to the next block in `to_tensor`.
    """

    def __init__(
        self,
        config,
        local=False,
        always_attend_to_first_position: bool = False,
        first_position_attends_to_all: bool = False,
        attend_from_chunk_width: int = 128,
        attend_from_chunk_stride: int = 128,
        attend_to_chunk_width: int = 128,
        attend_to_chunk_stride: int = 128,
    ):
        super().__init__()
        self.self = CanineSelfAttention(config)
        self.output = CanineSelfOutput(config)

        # additional arguments related to local attention
        self.local = local
        if attend_from_chunk_width < attend_from_chunk_stride:
            raise ValueError(
                "`attend_from_chunk_width` < `attend_from_chunk_stride` would cause sequence positions to get skipped."
            )
        if attend_to_chunk_width < attend_to_chunk_stride:
            raise ValueError(
                "`attend_to_chunk_width` < `attend_to_chunk_stride`would cause sequence positions to get skipped."
            )
        self.always_attend_to_first_position = always_attend_to_first_position
        self.first_position_attends_to_all = first_position_attends_to_all
        self.attend_from_chunk_width = attend_from_chunk_width
        self.attend_from_chunk_stride = attend_from_chunk_stride
        self.attend_to_chunk_width = attend_to_chunk_width
        self.attend_to_chunk_stride = attend_to_chunk_stride

    def forward(
        self,
        hidden_states: tuple[torch.FloatTensor],
        attention_mask: torch.FloatTensor | None = None,
        output_attentions: bool | None = False,
    ) -> tuple[torch.FloatTensor, torch.FloatTensor | None]:
        if not self.local:
            self_outputs = self.self(hidden_states, hidden_states, attention_mask, output_attentions)
            attention_output = self_outputs[0]
        else:
            from_seq_length = to_seq_length = hidden_states.shape[1]
            from_tensor = to_tensor = hidden_states

            # Create chunks (windows) that we will attend *from* and then concatenate them.
            from_chunks = []
            if self.first_position_attends_to_all:
                from_chunks.append((0, 1))
                # We must skip this first position so that our output sequence is the
                # correct length (this matters in the *from* sequence only).
                from_start = 1
            else:
                from_start = 0
            for chunk_start in range(from_start, from_seq_length, self.attend_from_chunk_stride):
                chunk_end = min(from_seq_length, chunk_start + self.attend_from_chunk_width)
                from_chunks.append((chunk_start, chunk_end))

            # Determine the chunks (windows) that will attend *to*.
            to_chunks = []
            if self.first_position_attends_to_all:
                to_chunks.append((0, to_seq_length))
            for chunk_start in range(0, to_seq_length, self.attend_to_chunk_stride):
                chunk_end = min(to_seq_length, chunk_start + self.attend_to_chunk_width)
                to_chunks.append((chunk_start, chunk_end))

            if len(from_chunks) != len(to_chunks):
                raise ValueError(
                    f"Expected to have same number of `from_chunks` ({from_chunks}) and "
                    f"`to_chunks` ({from_chunks}). Check strides."
                )

            # next, compute attention scores for each pair of windows and concatenate
            attention_output_chunks = []
            attention_probs_chunks = []
            for (from_start, from_end), (to_start, to_end) in zip(from_chunks, to_chunks):
                from_tensor_chunk = from_tensor[:, from_start:from_end, :]
                to_tensor_chunk = to_tensor[:, to_start:to_end, :]
                # `attention_mask`: <float>[batch_size, from_seq, to_seq]
                # `attention_mask_chunk`: <float>[batch_size, from_seq_chunk, to_seq_chunk]
                attention_mask_chunk = attention_mask[:, from_start:from_end, to_start:to_end]
                if self.always_attend_to_first_position:
                    cls_attention_mask = attention_mask[:, from_start:from_end, 0:1]
                    attention_mask_chunk = torch.cat([cls_attention_mask, attention_mask_chunk], dim=2)

                    cls_position = to_tensor[:, 0:1, :]
                    to_tensor_chunk = torch.cat([cls_position, to_tensor_chunk], dim=1)

                attention_outputs_chunk = self.self(
                    from_tensor_chunk, to_tensor_chunk, attention_mask_chunk, output_attentions
                )
                attention_output_chunks.append(attention_outputs_chunk[0])
                if output_attentions:
                    attention_probs_chunks.append(attention_outputs_chunk[1])

            attention_output = torch.cat(attention_output_chunks, dim=1)

        attention_output = self.output(attention_output, hidden_states)
        outputs = (attention_output,)
        if not self.local:
            outputs = outputs + self_outputs[1:]  # add attentions if we output them
        else:
            outputs = outputs + tuple(attention_probs_chunks)  # add attentions if we output them
        return outputs


class CanineIntermediate(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states


class CanineOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: tuple[torch.FloatTensor], input_tensor: torch.FloatTensor) -> torch.FloatTensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class CanineLayer(GradientCheckpointingLayer):
    def __init__(
        self,
        config,
        local,
        always_attend_to_first_position,
        first_position_attends_to_all,
        attend_from_chunk_width,
        attend_from_chunk_stride,
        attend_to_chunk_width,
        attend_to_chunk_stride,
    ):
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = CanineAttention(
            config,
            local,
            always_attend_to_first_position,
            first_position_attends_to_all,
            attend_from_chunk_width,
            attend_from_chunk_stride,
            attend_to_chunk_width,
            attend_to_chunk_stride,
        )
        self.intermediate = CanineIntermediate(config)
        self.output = CanineOutput(config)

    def forward(
        self,
        hidden_states: tuple[torch.FloatTensor],
        attention_mask: torch.FloatTensor | None = None,
        output_attentions: bool | None = False,
    ) -> tuple[torch.FloatTensor, torch.FloatTensor | None]:
        self_attention_outputs = self.attention(
            hidden_states,
            attention_mask,
            output_attentions=output_attentions,
        )
        attention_output = self_attention_outputs[0]

        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights

        layer_output = apply_chunking_to_forward(
            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
        )
        outputs = (layer_output,) + outputs

        return outputs

    def feed_forward_chunk(self, attention_output):
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output


class CanineEncoder(nn.Module):
    def __init__(
        self,
        config,
        local=False,
        always_attend_to_first_position=False,
        first_position_attends_to_all=False,
        attend_from_chunk_width=128,
        attend_from_chunk_stride=128,
        attend_to_chunk_width=128,
        attend_to_chunk_stride=128,
    ):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList(
            [
                CanineLayer(
                    config,
                    local,
                    always_attend_to_first_position,
                    first_position_attends_to_all,
                    attend_from_chunk_width,
                    attend_from_chunk_stride,
                    attend_to_chunk_width,
                    attend_to_chunk_stride,
                )
                for _ in range(config.num_hidden_layers)
            ]
        )
        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: tuple[torch.FloatTensor],
        attention_mask: torch.FloatTensor | None = None,
        output_attentions: bool | None = False,
        output_hidden_states: bool | None = False,
        return_dict: bool | None = True,
    ) -> tuple | BaseModelOutput:
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

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

            layer_outputs = layer_module(hidden_states, attention_mask, output_attentions)

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

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
        return BaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )


class CaninePooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()

    def forward(self, hidden_states: tuple[torch.FloatTensor]) -> torch.FloatTensor:
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output


class CaninePredictionHeadTransform(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        if isinstance(config.hidden_act, str):
            self.transform_act_fn = ACT2FN[config.hidden_act]
        else:
            self.transform_act_fn = config.hidden_act
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

    def forward(self, hidden_states: tuple[torch.FloatTensor]) -> torch.FloatTensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states


class CanineLMPredictionHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.transform = CaninePredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=True)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`

    def forward(self, hidden_states: tuple[torch.FloatTensor]) -> torch.FloatTensor:
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states


class CanineOnlyMLMHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.predictions = CanineLMPredictionHead(config)

    def forward(
        self,
        sequence_output: tuple[torch.Tensor],
    ) -> tuple[torch.Tensor]:
        prediction_scores = self.predictions(sequence_output)
        return prediction_scores


@auto_docstring
class CaninePreTrainedModel(PreTrainedModel):
    config: CanineConfig
    base_model_prefix = "canine"
    supports_gradient_checkpointing = True

    def _init_weights(self, module):
        super()._init_weights(module)
        if isinstance(module, CanineEmbeddings):
            init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1)))


@auto_docstring
class CanineModel(CaninePreTrainedModel):
    def __init__(self, config, add_pooling_layer=True):
        r"""
        add_pooling_layer (bool, *optional*, defaults to `True`):
            Whether to add a pooling layer
        """
        super().__init__(config)
        self.config = config
        shallow_config = copy.deepcopy(config)
        shallow_config.num_hidden_layers = 1

        self.char_embeddings = CanineEmbeddings(config)
        # shallow/low-dim transformer encoder to get a initial character encoding
        self.initial_char_encoder = CanineEncoder(
            shallow_config,
            local=True,
            always_attend_to_first_position=False,
            first_position_attends_to_all=False,
            attend_from_chunk_width=config.local_transformer_stride,
            attend_from_chunk_stride=config.local_transformer_stride,
            attend_to_chunk_width=config.local_transformer_stride,
            attend_to_chunk_stride=config.local_transformer_stride,
        )
        self.chars_to_molecules = CharactersToMolecules(config)
        # deep transformer encoder
        self.encoder = CanineEncoder(config)
        self.projection = ConvProjection(config)
        # shallow/low-dim transformer encoder to get a final character encoding
        self.final_char_encoder = CanineEncoder(shallow_config)

        self.pooler = CaninePooler(config) if add_pooling_layer else None

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

    def _create_3d_attention_mask_from_input_mask(self, from_tensor, to_mask):
        """
        Create 3D attention mask from a 2D tensor mask.

        Args:
            from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...].
            to_mask: int32 Tensor of shape [batch_size, to_seq_length].

        Returns:
            float Tensor of shape [batch_size, from_seq_length, to_seq_length].
        """
        batch_size, from_seq_length = from_tensor.shape[0], from_tensor.shape[1]

        to_seq_length = to_mask.shape[1]

        to_mask = torch.reshape(to_mask, (batch_size, 1, to_seq_length)).float()

        # We don't assume that `from_tensor` is a mask (although it could be). We
        # don't actually care if we attend *from* padding tokens (only *to* padding)
        # tokens so we create a tensor of all ones.
        broadcast_ones = torch.ones(size=(batch_size, from_seq_length, 1), dtype=torch.float32, device=to_mask.device)

        # Here we broadcast along two dimensions to create the mask.
        mask = broadcast_ones * to_mask

        return mask

    def _downsample_attention_mask(self, char_attention_mask: torch.Tensor, downsampling_rate: int):
        """Downsample 2D character attention mask to 2D molecule attention mask using MaxPool1d layer."""

        # first, make char_attention_mask 3D by adding a channel dim
        batch_size, char_seq_len = char_attention_mask.shape
        poolable_char_mask = torch.reshape(char_attention_mask, (batch_size, 1, char_seq_len))

        # next, apply MaxPool1d to get pooled_molecule_mask of shape (batch_size, 1, mol_seq_len)
        pooled_molecule_mask = torch.nn.MaxPool1d(kernel_size=downsampling_rate, stride=downsampling_rate)(
            poolable_char_mask.float()
        )

        # finally, squeeze to get tensor of shape (batch_size, mol_seq_len)
        molecule_attention_mask = torch.squeeze(pooled_molecule_mask, dim=-1)

        return molecule_attention_mask

    def _repeat_molecules(self, molecules: torch.Tensor, char_seq_length: int) -> torch.Tensor:
        """Repeats molecules to make them the same length as the char sequence."""

        rate = self.config.downsampling_rate

        molecules_without_extra_cls = molecules[:, 1:, :]
        # `repeated`: [batch_size, almost_char_seq_len, molecule_hidden_size]
        repeated = torch.repeat_interleave(molecules_without_extra_cls, repeats=rate, dim=-2)

        # So far, we've repeated the elements sufficient for any `char_seq_length`
        # that's a multiple of `downsampling_rate`. Now we account for the last
        # n elements (n < `downsampling_rate`), i.e. the remainder of floor
        # division. We do this by repeating the last molecule a few extra times.
        last_molecule = molecules[:, -1:, :]
        remainder_length = char_seq_length % rate
        remainder_repeated = torch.repeat_interleave(
            last_molecule,
            # +1 molecule to compensate for truncation.
            repeats=remainder_length + rate,
            dim=-2,
        )

        # `repeated`: [batch_size, char_seq_len, molecule_hidden_size]
        return torch.cat([repeated, remainder_repeated], dim=-2)

    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.FloatTensor | None = None,
        token_type_ids: torch.LongTensor | None = None,
        position_ids: torch.LongTensor | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> tuple | CanineModelOutputWithPooling:
        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
        )
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        return_dict = return_dict if return_dict is not None else self.config.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:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.size()
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        batch_size, seq_length = input_shape
        device = input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
            attention_mask = torch.ones(((batch_size, seq_length)), device=device)
        if token_type_ids is None:
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=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.
        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
        molecule_attention_mask = self._downsample_attention_mask(
            attention_mask, downsampling_rate=self.config.downsampling_rate
        )
        extended_molecule_attention_mask: torch.Tensor = self.get_extended_attention_mask(
            molecule_attention_mask, (batch_size, molecule_attention_mask.shape[-1])
        )

        # `input_char_embeddings`: shape (batch_size, char_seq, char_dim)
        input_char_embeddings = self.char_embeddings(
            input_ids=input_ids,
            position_ids=position_ids,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
        )

        # Contextualize character embeddings using shallow Transformer.
        # We use a 3D attention mask for the local attention.
        # `input_char_encoding`: shape (batch_size, char_seq_len, char_dim)
        char_attention_mask = self._create_3d_attention_mask_from_input_mask(
            input_ids if input_ids is not None else inputs_embeds, attention_mask
        )
        init_chars_encoder_outputs = self.initial_char_encoder(
            input_char_embeddings,
            attention_mask=char_attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
        )
        input_char_encoding = init_chars_encoder_outputs.last_hidden_state

        # Downsample chars to molecules.
        # The following lines have dimensions: [batch, molecule_seq, molecule_dim].
        # In this transformation, we change the dimensionality from `char_dim` to
        # `molecule_dim`, but do *NOT* add a resnet connection. Instead, we rely on
        # the resnet connections (a) from the final char transformer stack back into
        # the original char transformer stack and (b) the resnet connections from
        # the final char transformer stack back into the deep BERT stack of
        # molecules.
        #
        # Empirically, it is critical to use a powerful enough transformation here:
        # mean pooling causes training to diverge with huge gradient norms in this
        # region of the model; using a convolution here resolves this issue. From
        # this, it seems that molecules and characters require a very different
        # feature space; intuitively, this makes sense.
        init_molecule_encoding = self.chars_to_molecules(input_char_encoding)

        # Deep BERT encoder
        # `molecule_sequence_output`: shape (batch_size, mol_seq_len, mol_dim)
        encoder_outputs = self.encoder(
            init_molecule_encoding,
            attention_mask=extended_molecule_attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        molecule_sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(molecule_sequence_output) if self.pooler is not None else None

        # Upsample molecules back to characters.
        # `repeated_molecules`: shape (batch_size, char_seq_len, mol_hidden_size)
        repeated_molecules = self._repeat_molecules(molecule_sequence_output, char_seq_length=input_shape[-1])

        # Concatenate representations (contextualized char embeddings and repeated molecules):
        # `concat`: shape [batch_size, char_seq_len, molecule_hidden_size+char_hidden_final]
        concat = torch.cat([input_char_encoding, repeated_molecules], dim=-1)

        # Project representation dimension back to hidden_size
        # `sequence_output`: shape (batch_size, char_seq_len, hidden_size])
        sequence_output = self.projection(concat)

        # Apply final shallow Transformer
        # `sequence_output`: shape (batch_size, char_seq_len, hidden_size])
        final_chars_encoder_outputs = self.final_char_encoder(
            sequence_output,
            attention_mask=extended_attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
        )
        sequence_output = final_chars_encoder_outputs.last_hidden_state

        if output_hidden_states:
            deep_encoder_hidden_states = encoder_outputs.hidden_states if return_dict else encoder_outputs[1]
            all_hidden_states = (
                all_hidden_states
                + init_chars_encoder_outputs.hidden_states
                + deep_encoder_hidden_states
                + final_chars_encoder_outputs.hidden_states
            )

        if output_attentions:
            deep_encoder_self_attentions = encoder_outputs.attentions if return_dict else encoder_outputs[-1]
            all_self_attentions = (
                all_self_attentions
                + init_chars_encoder_outputs.attentions
                + deep_encoder_self_attentions
                + final_chars_encoder_outputs.attentions
            )

        if not return_dict:
            output = (sequence_output, pooled_output)
            output += tuple(v for v in [all_hidden_states, all_self_attentions] if v is not None)
            return output

        return CanineModelOutputWithPooling(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )


@auto_docstring(
    custom_intro="""
    CANINE Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
    output) e.g. for GLUE tasks.
    """
)
class CanineForSequenceClassification(CaninePreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.canine = CanineModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        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: torch.LongTensor | None = None,
        attention_mask: torch.FloatTensor | None = None,
        token_type_ids: torch.LongTensor | None = None,
        position_ids: torch.LongTensor | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        labels: torch.LongTensor | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> tuple | SequenceClassifierOutput:
        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.return_dict

        outputs = self.canine(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        pooled_output = outputs[1]

        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)

        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(logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@auto_docstring
class CanineForMultipleChoice(CaninePreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.canine = CanineModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, 1)

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

    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.FloatTensor | None = None,
        token_type_ids: torch.LongTensor | None = None,
        position_ids: torch.LongTensor | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        labels: torch.LongTensor | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> tuple | MultipleChoiceModelOutput:
        r"""
        input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

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

            [What are input IDs?](../glossary#input-ids)
        token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:

            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.

            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.

            [What are position IDs?](../glossary#position-ids)
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, 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.
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
            num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
            `input_ids` above)
        """
        return_dict = return_dict if return_dict is not None else self.config.return_dict
        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]

        input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
        attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
        token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
        position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
        inputs_embeds = (
            inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
            if inputs_embeds is not None
            else None
        )

        outputs = self.canine(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        pooled_output = outputs[1]

        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)
        reshaped_logits = logits.view(-1, num_choices)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(reshaped_logits, labels)

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

        return MultipleChoiceModelOutput(
            loss=loss,
            logits=reshaped_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@auto_docstring
class CanineForTokenClassification(CaninePreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.canine = CanineModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        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: torch.LongTensor | None = None,
        attention_mask: torch.FloatTensor | None = None,
        token_type_ids: torch.LongTensor | None = None,
        position_ids: torch.LongTensor | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        labels: torch.LongTensor | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> tuple | TokenClassifierOutput:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.

        Example:

        ```python
        >>> from transformers import AutoTokenizer, CanineForTokenClassification
        >>> import torch

        >>> tokenizer = AutoTokenizer.from_pretrained("google/canine-s")
        >>> model = CanineForTokenClassification.from_pretrained("google/canine-s")

        >>> inputs = tokenizer(
        ...     "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
        ... )

        >>> with torch.no_grad():
        ...     logits = model(**inputs).logits

        >>> predicted_token_class_ids = logits.argmax(-1)

        >>> # Note that tokens are classified rather then input words which means that
        >>> # there might be more predicted token classes than words.
        >>> # Multiple token classes might account for the same word
        >>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
        >>> predicted_tokens_classes  # doctest: +SKIP
        ```

        ```python
        >>> labels = predicted_token_class_ids
        >>> loss = model(**inputs, labels=labels).loss
        >>> round(loss.item(), 2)  # doctest: +SKIP
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        outputs = self.canine(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

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

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


@auto_docstring
class CanineForQuestionAnswering(CaninePreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.canine = CanineModel(config)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

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

    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.FloatTensor | None = None,
        token_type_ids: torch.LongTensor | None = None,
        position_ids: torch.LongTensor | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        start_positions: torch.LongTensor | None = None,
        end_positions: torch.LongTensor | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> tuple | QuestionAnsweringModelOutput:
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        outputs = self.canine(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            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)
        end_logits = end_logits.squeeze(-1)

        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.clamp_(0, ignored_index)
            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__ = [
    "CanineForMultipleChoice",
    "CanineForQuestionAnswering",
    "CanineForSequenceClassification",
    "CanineForTokenClassification",
    "CanineLayer",
    "CanineModel",
    "CaninePreTrainedModel",
]
