# coding=utf-8
# Copyright 2022 SwitchTransformers Authors and HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch SwitchTransformers model."""


import copy
import math
import warnings
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from torch.utils.checkpoint import checkpoint

from ...activations import ACT2FN
from ...modeling_outputs import (
    MoEModelOutput,
    MoEModelOutputWithPastAndCrossAttentions,
    Seq2SeqMoEModelOutput,
    Seq2SeqMoEOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import ALL_LAYERNORM_LAYERS, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
    DUMMY_INPUTS,
    DUMMY_MASK,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_torch_fx_proxy,
    logging,
    replace_return_docstrings,
)
from .configuration_switch_transformers import SwitchTransformersConfig


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "SwitchTransformersConfig"
_CHECKPOINT_FOR_DOC = "google/switch-base-8"

####################################################
# This dict contains ids and associated url
# for the pretrained weights provided with the models
####################################################
SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "google/switch-base-8",
    "google/switch-base-16",
    "google/switch-base-32",
    "google/switch-base-64",
    "google/switch-base-128",
    "google/switch-base-256",
    "google/switch-large-128",
    "google/switch-xxl-128",
    "google/switch-c-2048",
    # See all SwitchTransformers models at https://huggingface.co/models?filter=switch_transformers
]


def router_z_loss_func(router_logits: torch.Tensor) -> float:
    r"""
    Compute the router z-loss implemented in PyTorch.

    The router z-loss was introduced in [Designing Effective Sparse Expert Models](https://arxiv.org/abs/2202.08906).
    It encourages router logits to remain small in an effort to improve stability.

    Args:
        router_logits (`float`):
            Input logits of shape [batch_size, sequence_length, num_experts]

    Returns:
        Scalar router z-loss.
    """
    num_groups, tokens_per_group, _ = router_logits.shape
    log_z = torch.logsumexp(router_logits, dim=-1)
    z_loss = log_z**2
    return torch.sum(z_loss) / (num_groups * tokens_per_group)


def load_balancing_loss_func(router_probs: torch.Tensor, expert_indices: torch.Tensor) -> float:
    r"""
    Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.

    See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
    function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
    experts is too unbalanced.

    Args:
        router_probs (`torch.Tensor`):
            Probability assigned to each expert per token. Shape: [batch_size, seqeunce_length, num_experts].
        expert_indices (`torch.Tensor`):
            Indices tensor of shape [batch_size, seqeunce_length] identifying the selected expert for a given token.

    Returns:
        The auxiliary loss.
    """
    num_experts = router_probs.shape[-1]

    # cast the expert indices to int64, otherwise one-hot encoding will fail
    if expert_indices.dtype != torch.int64:
        expert_indices = expert_indices.to(torch.int64)

    if len(expert_indices.shape) == 2:
        expert_indices = expert_indices.unsqueeze(2)

    expert_mask = torch.nn.functional.one_hot(expert_indices, num_experts)

    # For a given token, determine if it was routed to a given expert.
    expert_mask = torch.max(expert_mask, axis=-2).values

    # cast to float32 otherwise mean will fail
    expert_mask = expert_mask.to(torch.float32)
    tokens_per_group_and_expert = torch.mean(expert_mask, axis=-2)

    router_prob_per_group_and_expert = torch.mean(router_probs, axis=-2)
    return torch.mean(tokens_per_group_and_expert * router_prob_per_group_and_expert) * (num_experts**2)


class SwitchTransformersTop1Router(nn.Module):
    """
    Router using tokens choose top-1 experts assignment.

    This router uses the same mechanism as in Switch Transformer (https://arxiv.org/abs/2101.03961) and V-MoE
    (https://arxiv.org/abs/2106.05974): tokens choose their top experts. Items are sorted by router_probs and then
    routed to their choice of expert until the expert's expert_capacity is reached. **There is no guarantee that each
    token is processed by an expert**, or that each expert receives at least one token.

    """

    def __init__(self, config: SwitchTransformersConfig):
        super().__init__()
        self.num_experts = config.num_experts
        self.expert_capacity = config.expert_capacity
        self.classifier = nn.Linear(config.hidden_size, self.num_experts, bias=config.router_bias)
        self.jitter_noise = config.router_jitter_noise
        self.ignore_padding_tokens = config.router_ignore_padding_tokens
        self.dtype = getattr(torch, config.router_dtype)

    def _compute_router_probabilities(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        r"""
        Computes router probabilities from input hidden states.

        Args:
            hidden_states (`torch.Tensor`):
                (batch_size, sequence_length, hidden_dim) from which router probabilities are computed.
        Returns:
            router_probabilities (`torch.Tensor`):
                Tensor of shape (batch_size, sequence_length, num_experts) corresponding to the probabilities for each
                token and expert. Used for routing tokens to experts.
            router_logits (`torch.Tensor`):
                Logits tensor of shape (batch_size, sequence_length, num_experts) corresponding to raw router logits.
                This is used later for computing router z-loss.
        """
        # float32 is used to ensure stability. See the discussion of "selective precision" in
        # https://arxiv.org/abs/2101.03961.
        # We also store the previous dtype to cast back the output to the previous dtype
        self.input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(self.dtype)

        if self.jitter_noise > 0:
            # Get the lower and upper bound of the uniform distribution
            # Adapted from: https://stackoverflow.com/questions/44328530/how-to-get-a-uniform-distribution-in-a-range-r1-r2-in-pytorch
            distrib_lower_bound = 1.0 - self.jitter_noise
            distrib_upper_bound = 1.0 + self.jitter_noise

            uniform_distrib = torch.rand(hidden_states.shape, device=hidden_states.device, dtype=self.dtype)
            uniform_distrib = uniform_distrib * (distrib_lower_bound - distrib_upper_bound)

            uniform_distrib = uniform_distrib + distrib_upper_bound
            # Multiply the token inputs by the uniform distribution - adding some noise
            hidden_states *= uniform_distrib

        # Shape: [num_groups, tokens_per_group, num_experts]
        self._cast_classifier()
        router_logits = self.classifier(hidden_states)

        # Apply Softmax and cast back to the original `dtype`
        router_probabilities = nn.functional.softmax(router_logits, dim=-1, dtype=self.dtype).to(self.input_dtype)
        return router_probabilities, router_logits

    def _cast_classifier(self):
        r"""
        `bitsandbytes` `Linear8bitLt` layers does not support manual casting Therefore we need to check if they are an
        instance of the `Linear8bitLt` class by checking special attributes.
        """
        if not (hasattr(self.classifier, "SCB") or hasattr(self.classifier, "CB")):
            self.classifier = self.classifier.to(self.dtype)

    def forward(self, hidden_states: torch.Tensor) -> Tuple:
        r"""
        Generic forward function for every Router class. Each Router expects to have the same input hidden states
        (`hidden_states`) corresponding to the hidden states for each token, the `expert_capacity` corresponding to the
        number of tokens the Router will send to each expert, some Routers can send up to few tokens to each expert.

        Each Router works as the following: it expects the hidden states for each token, gets the `router_probs` and
        `router_logits` from the `router_weights`. This will assign for each token, the raw probability to be assigned
        to an expert. Then each Router class will have to define its own `_compute_routing_instructions`.

        Args:
            hidden_states (`torch.Tensor`) :
                [num_groups, tokens_per_group, hidden_dim] inputs to send to experts.
        Returns:
            Tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`] Tuple containing the expert index, the router probs
            and the router logits. The router probabilities and logits are required to compute the loss.
        """
        router_probs, router_logits = self._compute_router_probabilities(hidden_states)

        expert_index = torch.argmax(router_probs, dim=-1)
        expert_index = torch.nn.functional.one_hot(expert_index, num_classes=self.num_experts)

        # Mask tokens outside expert capacity. Sum over each sequence
        token_priority = torch.cumsum(expert_index, dim=-2)
        # mask if the token routed to to the expert will overflow
        expert_capacity_mask = token_priority <= self.expert_capacity
        expert_index = expert_index * expert_capacity_mask

        router_probs = torch.max(router_probs, dim=-1).values.unsqueeze(-1)
        return expert_index, router_probs, router_logits


# Copied from transformers.models.t5.modeling_t5.T5LayerNorm with T5->SwitchTransformers
class SwitchTransformersLayerNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        Construct a layernorm module in the SwitchTransformers style. No bias and no subtraction of mean.
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):

        # SwitchTransformers uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated
        # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
        # half-precision inputs is done in fp32

        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)

        # convert into half-precision if necessary
        if self.weight.dtype in [torch.float16, torch.bfloat16]:
            hidden_states = hidden_states.to(self.weight.dtype)

        return self.weight * hidden_states


ALL_LAYERNORM_LAYERS.append(SwitchTransformersLayerNorm)


# Copied from transformers.models.t5.modeling_t5.T5DenseActDense with T5->SwitchTransformers
class SwitchTransformersDenseActDense(nn.Module):
    def __init__(self, config: SwitchTransformersConfig):
        super().__init__()
        self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
        self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
        self.dropout = nn.Dropout(config.dropout_rate)
        self.act = ACT2FN[config.dense_act_fn]

    def forward(self, hidden_states):
        hidden_states = self.wi(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.wo(hidden_states)
        return hidden_states


# Copied from transformers.models.longt5.modeling_longt5.LongT5DenseGatedActDense with LongT5->SwitchTransformers
class SwitchTransformersDenseGatedActDense(nn.Module):
    def __init__(self, config: SwitchTransformersConfig):
        super().__init__()
        self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False)
        self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False)
        self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
        self.dropout = nn.Dropout(config.dropout_rate)
        self.act = ACT2FN[config.dense_act_fn]

    def forward(self, hidden_states):
        hidden_gelu = self.act(self.wi_0(hidden_states))
        hidden_linear = self.wi_1(hidden_states)
        hidden_states = hidden_gelu * hidden_linear
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.wo(hidden_states)
        return hidden_states


class SwitchTransformersSparseMLP(nn.Module):
    r"""
    Implementation of the Switch Transformers Sparse MLP module.
    """

    def __init__(self, config: SwitchTransformersConfig, expert_class: nn.Module = SwitchTransformersDenseActDense):
        super().__init__()
        # Step 1: Get the correct router according to its class
        self.router = SwitchTransformersTop1Router(config)

        # Step 2: Get the experts
        self.experts = nn.ModuleDict()
        for idx in range(config.num_experts):
            self.experts[f"expert_{idx}"] = expert_class(config)

    def forward(self, hidden_states):
        r"""
        Hold on, this will be slightly tricky to understand In the correct order, a MoE layer does the following:

        1- Gets the `router_mask` from the router. The shape of the mask is `(batch_size, sequence_length, num_expert)`
        and corresponds to the argmax of the `router_probs`. The probabilities are needed in the computation of the
        hidden states : they are broadcasted to the hidden states values (can be interpreted as a scaling factor).

        2- Dispatch the tokens to its associated experts. We do a classic for loop over the experts and assign for each
        expert the corresponding hidden states.

        """
        # Step 1: Get the router_mask from the router as wel as the probabilities
        router_mask, router_probs, router_logits = self.router(hidden_states)
        expert_index = torch.argmax(router_mask, dim=-1)

        # The routers introduced might not always map all the tokens, to a router, which means that some hidden states
        # can be unchanged from one layer to another. That is why the hidden states are cloned before updating only the seleced ones.

        next_states = hidden_states.clone()
        for idx, expert in enumerate(self.experts.values()):

            token_indices = router_mask[:, :, idx].bool()
            next_states[token_indices] = expert(hidden_states[token_indices])

        hidden_states = router_probs * next_states
        return hidden_states, (router_logits, expert_index)


class SwitchTransformersLayerFF(nn.Module):
    r"""
    Switch Transformers Feed Forward layer module. This is a wrapper around the Mixture of Experts module.

    Parameters:
        config : ([`SwitchTransformersConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
        is_sparse (`bool`):
            Whether the MLP layer is a `Sparse` layer (contains a Mixture of Experts) or not
    """

    def __init__(self, config: SwitchTransformersConfig, is_sparse=False):
        super().__init__()
        self.is_sparse = is_sparse

        # Check if it is a sparse layer, if not then it is a dense layer
        if not self.is_sparse:
            self.mlp = SwitchTransformersDenseActDense(config)
        else:
            self.mlp = SwitchTransformersSparseMLP(config)

        self.layer_norm = SwitchTransformersLayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(self, hidden_states, output_router_logits):
        forwarded_states = self.layer_norm(hidden_states)
        forwarded_states = self.mlp(forwarded_states)

        if isinstance(forwarded_states, tuple):
            forwarded_states, router_tuple = forwarded_states
        else:
            router_tuple = None

        output = hidden_states + self.dropout(forwarded_states)

        if output_router_logits and router_tuple is not None:
            output = (output, router_tuple)

        return output


# Copied from transformers.models.t5.modeling_t5.T5Attention with T5->SwitchTransformers
class SwitchTransformersAttention(nn.Module):
    def __init__(self, config: SwitchTransformersConfig, has_relative_attention_bias=False):
        super().__init__()
        self.is_decoder = config.is_decoder
        self.has_relative_attention_bias = has_relative_attention_bias
        self.relative_attention_num_buckets = config.relative_attention_num_buckets
        self.relative_attention_max_distance = config.relative_attention_max_distance
        self.d_model = config.d_model
        self.key_value_proj_dim = config.d_kv
        self.n_heads = config.num_heads
        self.dropout = config.dropout_rate
        self.inner_dim = self.n_heads * self.key_value_proj_dim

        # Mesh TensorFlow initialization to avoid scaling before softmax
        self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
        self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
        self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
        self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)

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

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads
        )
        # Prune linear layers
        self.q = prune_linear_layer(self.q, index)
        self.k = prune_linear_layer(self.k, index)
        self.v = prune_linear_layer(self.v, index)
        self.o = prune_linear_layer(self.o, index, dim=1)
        # Update hyper params
        self.n_heads = self.n_heads - len(heads)
        self.inner_dim = self.key_value_proj_dim * self.n_heads
        self.pruned_heads = self.pruned_heads.union(heads)

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

        Translate relative position to a bucket number for relative attention. The relative position is defined as
        memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
        position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
        small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
        positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
        This should allow for more graceful generalization to longer sequences than the model has been trained on

        Args:
            relative_position: an int32 Tensor
            bidirectional: a boolean - whether the attention is bidirectional
            num_buckets: an integer
            max_distance: an integer

        Returns:
            a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
        """
        relative_buckets = 0
        if bidirectional:
            num_buckets //= 2
            relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
            relative_position = torch.abs(relative_position)
        else:
            relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
        # now relative_position is in the range [0, inf)

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

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

        relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
        return relative_buckets

    def compute_bias(self, query_length, key_length, device=None):
        """Compute binned relative position bias"""
        if device is None:
            device = self.relative_attention_bias.weight.device
        context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
        memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
        relative_position = memory_position - context_position  # shape (query_length, key_length)
        relative_position_bucket = self._relative_position_bucket(
            relative_position,  # shape (query_length, key_length)
            bidirectional=(not self.is_decoder),
            num_buckets=self.relative_attention_num_buckets,
            max_distance=self.relative_attention_max_distance,
        )
        values = self.relative_attention_bias(relative_position_bucket)  # shape (query_length, key_length, num_heads)
        values = values.permute([2, 0, 1]).unsqueeze(0)  # shape (1, num_heads, query_length, key_length)
        return values

    def forward(
        self,
        hidden_states,
        mask=None,
        key_value_states=None,
        position_bias=None,
        past_key_value=None,
        layer_head_mask=None,
        query_length=None,
        use_cache=False,
        output_attentions=False,
    ):
        """
        Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
        """
        # Input is (batch_size, seq_length, dim)
        # Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length)
        # past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head)
        batch_size, seq_length = hidden_states.shape[:2]

        real_seq_length = seq_length

        if past_key_value is not None:
            assert (
                len(past_key_value) == 2
            ), f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states"
            real_seq_length += past_key_value[0].shape[2] if query_length is None else query_length

        key_length = real_seq_length if key_value_states is None else key_value_states.shape[1]

        def shape(states):
            """projection"""
            return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)

        def unshape(states):
            """reshape"""
            return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)

        def project(hidden_states, proj_layer, key_value_states, past_key_value):
            """projects hidden states correctly to key/query states"""
            if key_value_states is None:
                # self-attn
                # (batch_size, n_heads, seq_length, dim_per_head)
                hidden_states = shape(proj_layer(hidden_states))
            elif past_key_value is None:
                # cross-attn
                # (batch_size, n_heads, seq_length, dim_per_head)
                hidden_states = shape(proj_layer(key_value_states))

            if past_key_value is not None:
                if key_value_states is None:
                    # self-attn
                    # (batch_size, n_heads, key_length, dim_per_head)
                    hidden_states = torch.cat([past_key_value, hidden_states], dim=2)
                elif past_key_value.shape[2] != key_value_states.shape[1]:
                    # checking that the `sequence_length` of the `past_key_value` is the same as
                    # the provided `key_value_states` to support prefix tuning
                    # cross-attn
                    # (batch_size, n_heads, seq_length, dim_per_head)
                    hidden_states = shape(proj_layer(key_value_states))
                else:
                    # cross-attn
                    hidden_states = past_key_value
            return hidden_states

        # get query states
        query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)

        # get key/value states
        key_states = project(
            hidden_states, self.k, key_value_states, past_key_value[0] if past_key_value is not None else None
        )
        value_states = project(
            hidden_states, self.v, key_value_states, past_key_value[1] if past_key_value is not None else None
        )

        # compute scores
        scores = torch.matmul(
            query_states, key_states.transpose(3, 2)
        )  # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9

        if position_bias is None:
            if not self.has_relative_attention_bias:
                position_bias = torch.zeros(
                    (1, self.n_heads, real_seq_length, key_length), device=scores.device, dtype=scores.dtype
                )
                if self.gradient_checkpointing and self.training:
                    position_bias.requires_grad = True
            else:
                position_bias = self.compute_bias(real_seq_length, key_length, device=scores.device)

            # if key and values are already calculated
            # we want only the last query position bias
            if past_key_value is not None:
                position_bias = position_bias[:, :, -hidden_states.size(1) :, :]

            if mask is not None:
                position_bias = position_bias + mask  # (batch_size, n_heads, seq_length, key_length)

        if self.pruned_heads:
            mask = torch.ones(position_bias.shape[1])
            mask[list(self.pruned_heads)] = 0
            position_bias_masked = position_bias[:, mask.bool()]
        else:
            position_bias_masked = position_bias

        scores += position_bias_masked
        attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
            scores
        )  # (batch_size, n_heads, seq_length, key_length)
        attn_weights = nn.functional.dropout(
            attn_weights, p=self.dropout, training=self.training
        )  # (batch_size, n_heads, seq_length, key_length)

        # Mask heads if we want to
        if layer_head_mask is not None:
            attn_weights = attn_weights * layer_head_mask

        attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
        attn_output = self.o(attn_output)

        present_key_value_state = (key_states, value_states) if (self.is_decoder and use_cache) else None
        outputs = (attn_output,) + (present_key_value_state,) + (position_bias,)

        if output_attentions:
            outputs = outputs + (attn_weights,)
        return outputs


# Copied from transformers.models.t5.modeling_t5.T5LayerSelfAttention with T5->SwitchTransformers
class SwitchTransformersLayerSelfAttention(nn.Module):
    def __init__(self, config, has_relative_attention_bias=False):
        super().__init__()
        self.SelfAttention = SwitchTransformersAttention(
            config, has_relative_attention_bias=has_relative_attention_bias
        )
        self.layer_norm = SwitchTransformersLayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        position_bias=None,
        layer_head_mask=None,
        past_key_value=None,
        use_cache=False,
        output_attentions=False,
    ):
        normed_hidden_states = self.layer_norm(hidden_states)
        attention_output = self.SelfAttention(
            normed_hidden_states,
            mask=attention_mask,
            position_bias=position_bias,
            layer_head_mask=layer_head_mask,
            past_key_value=past_key_value,
            use_cache=use_cache,
            output_attentions=output_attentions,
        )
        hidden_states = hidden_states + self.dropout(attention_output[0])
        outputs = (hidden_states,) + attention_output[1:]  # add attentions if we output them
        return outputs


# Copied from transformers.models.t5.modeling_t5.T5LayerCrossAttention with T5->SwitchTransformers
class SwitchTransformersLayerCrossAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.EncDecAttention = SwitchTransformersAttention(config, has_relative_attention_bias=False)
        self.layer_norm = SwitchTransformersLayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(
        self,
        hidden_states,
        key_value_states,
        attention_mask=None,
        position_bias=None,
        layer_head_mask=None,
        past_key_value=None,
        use_cache=False,
        query_length=None,
        output_attentions=False,
    ):
        normed_hidden_states = self.layer_norm(hidden_states)
        attention_output = self.EncDecAttention(
            normed_hidden_states,
            mask=attention_mask,
            key_value_states=key_value_states,
            position_bias=position_bias,
            layer_head_mask=layer_head_mask,
            past_key_value=past_key_value,
            use_cache=use_cache,
            query_length=query_length,
            output_attentions=output_attentions,
        )
        layer_output = hidden_states + self.dropout(attention_output[0])
        outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
        return outputs


class SwitchTransformersBlock(nn.Module):
    def __init__(self, config, has_relative_attention_bias=False, is_sparse=False):
        super().__init__()
        self.is_decoder = config.is_decoder
        self.is_sparse = is_sparse
        self.layer = nn.ModuleList()
        self.layer.append(
            SwitchTransformersLayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias)
        )
        if self.is_decoder:
            self.layer.append(SwitchTransformersLayerCrossAttention(config))

        self.layer.append(SwitchTransformersLayerFF(config, is_sparse=self.is_sparse))

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        position_bias=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        encoder_decoder_position_bias=None,
        layer_head_mask=None,
        cross_attn_layer_head_mask=None,
        past_key_value=None,
        use_cache=False,
        output_attentions=False,
        output_router_logits=True,
        return_dict=True,
    ):

        if past_key_value is not None:
            if not self.is_decoder:
                logger.warning("`past_key_values` is passed to the encoder. Please make sure this is intended.")
            expected_num_past_key_values = 2 if encoder_hidden_states is None else 4

            if len(past_key_value) != expected_num_past_key_values:
                raise ValueError(
                    f"There should be {expected_num_past_key_values} past states. "
                    f"{'2 (past / key) for cross attention. ' if expected_num_past_key_values == 4 else ''}"
                    f"Got {len(past_key_value)} past key / value states"
                )

            self_attn_past_key_value = past_key_value[:2]
            cross_attn_past_key_value = past_key_value[2:]
        else:
            self_attn_past_key_value, cross_attn_past_key_value = None, None

        self_attention_outputs = self.layer[0](
            hidden_states,
            attention_mask=attention_mask,
            position_bias=position_bias,
            layer_head_mask=layer_head_mask,
            past_key_value=self_attn_past_key_value,
            use_cache=use_cache,
            output_attentions=output_attentions,
        )
        hidden_states, present_key_value_state = self_attention_outputs[:2]
        attention_outputs = self_attention_outputs[2:]  # Keep self-attention outputs and relative position weights

        # clamp inf values to enable fp16 training
        if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

        do_cross_attention = self.is_decoder and encoder_hidden_states is not None
        if do_cross_attention:
            # the actual query length is unknown for cross attention
            # if using past key value states. Need to inject it here
            if present_key_value_state is not None:
                query_length = present_key_value_state[0].shape[2]
            else:
                query_length = None

            cross_attention_outputs = self.layer[1](
                hidden_states,
                key_value_states=encoder_hidden_states,
                attention_mask=encoder_attention_mask,
                position_bias=encoder_decoder_position_bias,
                layer_head_mask=cross_attn_layer_head_mask,
                past_key_value=cross_attn_past_key_value,
                query_length=query_length,
                use_cache=use_cache,
                output_attentions=output_attentions,
            )
            hidden_states = cross_attention_outputs[0]

            # clamp inf values to enable fp16 training
            if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
                clamp_value = torch.finfo(hidden_states.dtype).max - 1000
                hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

            # Combine self attn and cross attn key value states
            if present_key_value_state is not None:
                present_key_value_state = present_key_value_state + cross_attention_outputs[1]

            # Keep cross-attention outputs and relative position weights
            attention_outputs = attention_outputs + cross_attention_outputs[2:]

        # Apply Feed Forward layer
        hidden_states = self.layer[-1](hidden_states, output_router_logits)

        if isinstance(hidden_states, tuple):
            hidden_states, router_tuple = hidden_states
        else:
            router_tuple = (None,)

        # clamp inf values to enable fp16 training
        if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

        outputs = (hidden_states,)

        if use_cache:
            outputs = outputs + (present_key_value_state,) + attention_outputs + (router_tuple,)
        else:
            outputs = outputs + attention_outputs + (router_tuple,)

        return outputs  # hidden-states, present_key_value_states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights), (router_tuple)


class SwitchTransformersPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = SwitchTransformersConfig
    base_model_prefix = "switch_transformers"
    supports_gradient_checkpointing = True
    _no_split_modules = ["SwitchTransformersBlock"]

    @property
    def dummy_inputs(self):
        input_ids = torch.tensor(DUMMY_INPUTS)
        input_mask = torch.tensor(DUMMY_MASK)
        dummy_inputs = {
            "decoder_input_ids": input_ids,
            "input_ids": input_ids,
            "decoder_attention_mask": input_mask,
        }
        return dummy_inputs

    def _init_weights(self, module):
        """Initialize the weights"""
        factor = self.config.initializer_factor  # Used for testing weights initialization
        if isinstance(module, SwitchTransformersLayerNorm):
            module.weight.data.fill_(factor * 1.0)
        elif isinstance(
            module,
            (SwitchTransformersModel, SwitchTransformersForConditionalGeneration, SwitchTransformersEncoderModel),
        ):
            # Mesh TensorFlow embeddings initialization
            # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624
            module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0)
            if hasattr(module, "lm_head") and not self.config.tie_word_embeddings:
                module.lm_head.weight.data.normal_(mean=0.0, std=factor * 1.0)
        elif isinstance(module, SwitchTransformersDenseActDense):
            # Mesh TensorFlow FF initialization
            # See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56
            # and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89
            module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
            if hasattr(module.wi, "bias") and module.wi.bias is not None:
                module.wi.bias.data.zero_()
            module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
            if hasattr(module.wo, "bias") and module.wo.bias is not None:
                module.wo.bias.data.zero_()
        elif isinstance(module, SwitchTransformersDenseGatedActDense):
            module.wi_0.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
            if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None:
                module.wi_0.bias.data.zero_()
            module.wi_1.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
            if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None:
                module.wi_1.bias.data.zero_()
            module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
            if hasattr(module.wo, "bias") and module.wo.bias is not None:
                module.wo.bias.data.zero_()
        elif isinstance(module, SwitchTransformersAttention):
            # Mesh TensorFlow attention initialization to avoid scaling before softmax
            # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136
            d_model = self.config.d_model
            key_value_proj_dim = self.config.d_kv
            n_heads = self.config.num_heads
            module.q.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5))
            module.k.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
            module.v.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
            module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5))
            if module.has_relative_attention_bias:
                module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5))
        elif isinstance(module, SwitchTransformersSparseMLP):
            # Mesh TensorFlow attention initialization to avoid scaling before softmax
            # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136
            d_model = self.config.d_model
            key_value_proj_dim = self.config.d_kv
            n_heads = self.config.num_heads
            module.router.classifier.weight.data.normal_(mean=0.0, std=factor * 1)
            for idx in range(self.config.num_experts):
                module.experts[f"expert_{idx}"].wi.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
                module.experts[f"expert_{idx}"].wo.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, (SwitchTransformersAttention, SwitchTransformersStack)):
            module.gradient_checkpointing = value

    def _shift_right(self, input_ids):
        decoder_start_token_id = self.config.decoder_start_token_id
        pad_token_id = self.config.pad_token_id

        if decoder_start_token_id is None:
            raise ValueError(
                "self.model.config.decoder_start_token_id has to be defined. In SwitchTransformers it is usually set"
                " to the pad_token_id. See SwitchTransformers docs for more information"
            )

        # shift inputs to the right
        if is_torch_fx_proxy(input_ids):
            # Item assignment is not supported natively for proxies.
            shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), decoder_start_token_id)
            shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1)
        else:
            shifted_input_ids = input_ids.new_zeros(input_ids.shape)
            shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
            shifted_input_ids[..., 0] = decoder_start_token_id

        if pad_token_id is None:
            raise ValueError("self.model.config.pad_token_id has to be defined.")
        # replace possible -100 values in labels by `pad_token_id`
        shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)

        return shifted_input_ids


class SwitchTransformersStack(SwitchTransformersPreTrainedModel):
    def __init__(self, config, embed_tokens=None):
        super().__init__(config)

        self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model)

        if embed_tokens is not None:
            self.embed_tokens.weight = embed_tokens.weight

        self.is_decoder = config.is_decoder

        sparse_step = config.decoder_sparse_step if self.is_decoder else config.encoder_sparse_step
        config.num_layers = config.num_decoder_layers if self.is_decoder else config.num_layers
        self.block = nn.ModuleList()
        for i in range(config.num_layers):

            is_sparse = (i % sparse_step == 1) if sparse_step > 0 else False

            self.block.append(
                SwitchTransformersBlock(config, has_relative_attention_bias=bool(i == 0), is_sparse=is_sparse)
            )

        self.final_layer_norm = SwitchTransformersLayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)

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

        self.device_map = None
        self.gradient_checkpointing = False

    def get_input_embeddings(self):
        return self.embed_tokens

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

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        inputs_embeds=None,
        head_mask=None,
        cross_attn_head_mask=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        output_router_logits=True,
        return_dict=None,
    ):
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        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
        )
        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:
            err_msg_prefix = "decoder_" if self.is_decoder else ""
            raise ValueError(
                f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time"
            )
        elif input_ids is not None:
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            err_msg_prefix = "decoder_" if self.is_decoder else ""
            raise ValueError(f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds")

        if inputs_embeds is None:
            if self.embed_tokens is None:
                raise ValueError("You have to initialize the model with valid token embeddings")
            inputs_embeds = self.embed_tokens(input_ids)

        batch_size, seq_length = input_shape

        # required mask seq length can be calculated via length of past
        mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length

        if use_cache is True:
            if not self.is_decoder:
                raise ValueError(f"`use_cache` can only be set to `True` if {self} is used as a decoder")

        if attention_mask is None:
            attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
        if self.is_decoder and encoder_attention_mask is None and encoder_hidden_states is not None:
            encoder_seq_length = encoder_hidden_states.shape[1]
            encoder_attention_mask = torch.ones(
                batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long
            )

        # initialize past_key_values with `None` if past does not exist
        if past_key_values is None:
            past_key_values = [None] * len(self.block)

        # 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 = self.get_extended_attention_mask(attention_mask, input_shape)

        # If a 2D or 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if self.is_decoder and encoder_hidden_states is not None:
            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
            if encoder_attention_mask is None:
                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device)
            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
        else:
            encoder_extended_attention_mask = None

        # Prepare head mask if needed
        head_mask = self.get_head_mask(head_mask, self.config.num_layers)
        cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers)
        present_key_value_states = () if use_cache else None
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        all_router_probs = () if output_router_logits else None
        all_cross_attentions = () if (output_attentions and self.is_decoder) else None
        position_bias = None
        encoder_decoder_position_bias = None

        hidden_states = self.dropout(inputs_embeds)

        for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)):
            layer_head_mask = head_mask[i]
            cross_attn_layer_head_mask = cross_attn_head_mask[i]

            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

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

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return tuple(module(*inputs, use_cache, output_attentions))

                    return custom_forward

                layer_outputs = checkpoint(
                    create_custom_forward(layer_module),
                    hidden_states,
                    extended_attention_mask,
                    position_bias,
                    encoder_hidden_states,
                    encoder_extended_attention_mask,
                    encoder_decoder_position_bias,
                    layer_head_mask,
                    cross_attn_layer_head_mask,
                    None,  # past_key_value is always None with gradient checkpointing
                )
            else:
                layer_outputs = layer_module(
                    hidden_states,
                    attention_mask=extended_attention_mask,
                    position_bias=position_bias,
                    encoder_hidden_states=encoder_hidden_states,
                    encoder_attention_mask=encoder_extended_attention_mask,
                    encoder_decoder_position_bias=encoder_decoder_position_bias,
                    layer_head_mask=layer_head_mask,
                    cross_attn_layer_head_mask=cross_attn_layer_head_mask,
                    past_key_value=past_key_value,
                    use_cache=use_cache,
                    output_attentions=output_attentions,
                    output_router_logits=output_router_logits,
                )

            router_probs = layer_outputs[-1]
            layer_outputs = layer_outputs[:-1]

            # layer_outputs is a tuple with:
            # hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
            if use_cache is False:
                layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:]

            hidden_states, present_key_value_state = layer_outputs[:2]

            # We share the position biases between the layers - the first layer store them
            # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
            # (cross-attention position bias), (cross-attention weights)
            position_bias = layer_outputs[2]
            if self.is_decoder and encoder_hidden_states is not None:
                encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3]
            # append next layer key value states
            if use_cache:
                present_key_value_states = present_key_value_states + (present_key_value_state,)

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[3],)
                if self.is_decoder:
                    all_cross_attentions = all_cross_attentions + (layer_outputs[5],)

            if output_router_logits:
                all_router_probs = all_router_probs + (router_probs,)

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

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

        if not return_dict:
            return tuple(
                v
                for v in [
                    hidden_states,
                    present_key_value_states,
                    all_hidden_states,
                    all_attentions,
                    all_cross_attentions,
                    all_router_probs,
                ]
                if v is not None
            )
        return MoEModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=present_key_value_states,
            hidden_states=all_hidden_states,
            attentions=all_attentions,
            cross_attentions=all_cross_attentions,
            router_probs=all_router_probs,
        )


SWITCH_TRANSFORMERS_START_DOCSTRING = r"""

    The SWITCH_TRANSFORMERS model was proposed in [Switch Transformers: Scaling to Trillion Parameter Models with
    Simple and Efficient Sparsity](https://arxiv.org/abs/2101.03961) by [William
    Fedus](https://arxiv.org/search/cs?searchtype=author&query=Fedus%2C+W), [Barret
    Zoph](https://arxiv.org/search/cs?searchtype=author&query=Zoph%2C+B), and [Noam
    Shazeer](https://arxiv.org/search/cs?searchtype=author&query=Shazeer%2C+N). It's an encoder-decoder T5-like model
    with sparse Feed Forward that stands for Mixture of Experts (MoE) architecture.

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

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

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

SWITCH_TRANSFORMERS_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. SWITCH_TRANSFORMERS is a model with relative position
            embeddings so you should be able to pad the inputs on both the right and the left.

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

            [What are input IDs?](../glossary#input-ids)

            To know more on how to prepare `input_ids` for pretraining take a look a [SWITCH_TRANSFORMERS
            Training](./switch_transformers#training).
        attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

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

            [What are attention masks?](../glossary#attention-mask)
        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Indices of decoder input sequence tokens in the vocabulary.

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

            [What are decoder input IDs?](../glossary#decoder-input-ids)

            SWITCH_TRANSFORMERS uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
            `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).

            To know more on how to prepare `decoder_input_ids` for pretraining take a look at [SWITCH_TRANSFORMERS
            Training](./switch_transformers#training).
        decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
            be used by default.
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in `[0,
            1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0,
            1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
                Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
                `[0, 1]`:

                - 1 indicates the head is **not masked**,
                - 0 indicates the head is **masked**.

        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
            Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*)
            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at
            the output of the last layer of the encoder. Used in the cross-attention of the decoder.
        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
            representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
            input (see `past_key_values`). This is useful if you want more control over how to convert
            `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.

            If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
            of `inputs_embeds`.

        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).

        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        output_router_logits (`bool`, *optional*):
            Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
            should not be returned during inference.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""

SWITCH_TRANSFORMERS_ENCODER_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. SWITCH_TRANSFORMERS is a model with relative position
            embeddings so you should be able to pad the inputs on both the right and the left.

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

            To know more on how to prepare `input_ids` for pretraining take a look a [SWITCH_TRANSFORMERS
            Training](./switch_transformers#training).
        attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

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

            [What are attention masks?](../glossary#attention-mask)
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        output_router_logits (`bool`, *optional*):
            Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
            should not be returned during inference.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""

# Warning message for FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
__HEAD_MASK_WARNING_MSG = """
The input argument `head_mask` was split into two arguments `head_mask` and `decoder_head_mask`. Currently,
`decoder_head_mask` is set to copy `head_mask`, but this feature is deprecated and will be removed in future versions.
If you do not want to use any `decoder_head_mask` now, please set `decoder_head_mask = torch.ones(num_layers,
num_heads)`.
"""


@add_start_docstrings(
    "The bare SWITCH_TRANSFORMERS Model transformer outputting raw hidden-states without any specific head on top.",
    SWITCH_TRANSFORMERS_START_DOCSTRING,
)
class SwitchTransformersModel(SwitchTransformersPreTrainedModel):
    _keys_to_ignore_on_load_missing = [r"encoder.embed_tokens.weight", r"decoder.embed_tokens.weight"]

    def __init__(self, config: SwitchTransformersConfig):
        super().__init__(config)
        self.shared = nn.Embedding(config.vocab_size, config.d_model)

        encoder_config = copy.deepcopy(config)
        encoder_config.is_decoder = False
        encoder_config.use_cache = False
        encoder_config.is_encoder_decoder = False
        self.encoder = SwitchTransformersStack(encoder_config, self.shared)

        decoder_config = copy.deepcopy(config)
        decoder_config.is_decoder = True
        decoder_config.is_encoder_decoder = False
        self.decoder = SwitchTransformersStack(decoder_config, self.shared)

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

        # Model parallel
        self.device_map = None

    def get_input_embeddings(self):
        return self.shared

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

    def get_encoder(self):
        return self.encoder

    def get_decoder(self):
        return self.decoder

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

    @add_start_docstrings_to_model_forward(SWITCH_TRANSFORMERS_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=Seq2SeqMoEModelOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.BoolTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        decoder_head_mask: Optional[torch.FloatTensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        decoder_inputs_embeds: Optional[torch.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_router_logits: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqMoEModelOutput]:
        r"""
        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, SwitchTransformersModel

        >>> tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
        >>> model = SwitchTransformersModel.from_pretrained("google/switch-base-8")

        >>> input_ids = tokenizer(
        ...     "Studies have been shown that owning a dog is good for you", return_tensors="pt"
        ... ).input_ids  # Batch size 1
        >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1

        >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for SwitchTransformersModel.
        >>> # This is not needed for torch's SwitchTransformersForConditionalGeneration as it does this internally using labels arg.
        >>> decoder_input_ids = model._shift_right(decoder_input_ids)

        >>> # forward pass
        >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
        >>> last_hidden_states = outputs.last_hidden_state
        ```"""
        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

        # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
        if head_mask is not None and decoder_head_mask is None:
            if self.config.num_layers == self.config.num_decoder_layers:
                warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
                decoder_head_mask = head_mask

        if (
            output_router_logits
            and self.config.num_sparse_encoder_layers == 0
            and self.config.num_sparse_encoder_layers == 0
        ):
            raise ValueError(
                "You asked to return `output_router_logits` but the transformer in dense, and does                    "
                "           not contain any sparse MLP Layers. Set `output_router_logits = False` and restart"
            )
        # Encode if needed (training, first prediction pass)
        if encoder_outputs is None:
            encoder_outputs = self.encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                inputs_embeds=inputs_embeds,
                head_mask=head_mask,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                output_router_logits=output_router_logits,
                return_dict=return_dict,
            )
        elif return_dict and not isinstance(encoder_outputs, MoEModelOutput):
            encoder_outputs = MoEModelOutput(
                last_hidden_state=encoder_outputs[0],
                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
                router_probs=encoder_outputs[3] if len(encoder_outputs) > 3 else None,
            )

        hidden_states = encoder_outputs[0]

        # Decode
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            inputs_embeds=decoder_inputs_embeds,
            past_key_values=past_key_values,
            encoder_hidden_states=hidden_states,
            encoder_attention_mask=attention_mask,
            head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            output_router_logits=output_router_logits,
            return_dict=return_dict,
        )

        if not return_dict:
            return decoder_outputs + encoder_outputs

        return Seq2SeqMoEModelOutput(
            last_hidden_state=decoder_outputs.last_hidden_state,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            decoder_router_logits=decoder_outputs.router_probs,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
            encoder_router_logits=encoder_outputs.router_probs,
        )


@add_start_docstrings(
    """SWITCH_TRANSFORMERS Model with a `language modeling` head on top.""", SWITCH_TRANSFORMERS_START_DOCSTRING
)
class SwitchTransformersForConditionalGeneration(SwitchTransformersPreTrainedModel):
    _keys_to_ignore_on_load_missing = [
        r"encoder.embed_tokens.weight",
        r"decoder.embed_tokens.weight",
        r"lm_head.weight",
    ]

    def __init__(self, config: SwitchTransformersConfig):
        super().__init__(config)
        self.model_dim = config.d_model

        self.shared = nn.Embedding(config.vocab_size, config.d_model)

        encoder_config = copy.deepcopy(config)
        encoder_config.is_decoder = False
        encoder_config.use_cache = False
        encoder_config.is_encoder_decoder = False
        self.encoder = SwitchTransformersStack(encoder_config, self.shared)

        decoder_config = copy.deepcopy(config)
        decoder_config.is_decoder = True
        decoder_config.is_encoder_decoder = False
        decoder_config.num_layers = config.num_decoder_layers
        self.decoder = SwitchTransformersStack(decoder_config, self.shared)

        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)

        self.router_z_loss_coef = config.router_z_loss_coef
        self.router_aux_loss_coef = config.router_aux_loss_coef

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

        # Model parallel
        self.device_map = None

    def get_input_embeddings(self):
        return self.shared

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

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

    def get_output_embeddings(self):
        return self.lm_head

    def get_encoder(self):
        return self.encoder

    def get_decoder(self):
        return self.decoder

    @add_start_docstrings_to_model_forward(SWITCH_TRANSFORMERS_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=Seq2SeqMoEOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.BoolTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        decoder_head_mask: Optional[torch.FloatTensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_router_logits: Optional[bool] = True,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqMoEOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ...,
            config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for
            labels in `[0, ..., config.vocab_size]`

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration

        >>> tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
        >>> model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-base-8")

        >>> # training
        >>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
        >>> labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="pt").input_ids
        >>> outputs = model(input_ids=input_ids, labels=labels)
        >>> loss = outputs.loss
        >>> logits = outputs.logits

        >>> # inference
        >>> input_ids = tokenizer(
        ...     "summarize: studies have shown that owning a dog is good for you", return_tensors="pt"
        ... ).input_ids  # Batch size 1
        >>> outputs = model.generate(input_ids)
        >>> # . To, let’s say you have a dog. To summarize:
        >>> # Since the model has been trained on MLM, this will output gibberish
        ```"""
        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

        # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
        if head_mask is not None and decoder_head_mask is None:
            if self.config.num_layers == self.config.num_decoder_layers:
                warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
                decoder_head_mask = head_mask

        # Encode if needed (training, first prediction pass)
        if encoder_outputs is None:
            # Convert encoder inputs in embeddings if needed
            encoder_outputs = self.encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                inputs_embeds=inputs_embeds,
                head_mask=head_mask,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                output_router_logits=output_router_logits,
                return_dict=return_dict,
            )
        elif return_dict and not isinstance(encoder_outputs, MoEModelOutput):
            encoder_outputs = MoEModelOutput(
                last_hidden_state=encoder_outputs[0],
                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
                router_probs=encoder_outputs[3] if len(encoder_outputs) > 3 else None,
            )

        hidden_states = encoder_outputs[0]

        if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
            # get decoder inputs from shifting lm labels to the right
            decoder_input_ids = self._shift_right(labels)

        # Decode
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            inputs_embeds=decoder_inputs_embeds,
            past_key_values=past_key_values,
            encoder_hidden_states=hidden_states,
            encoder_attention_mask=attention_mask,
            head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            output_router_logits=output_router_logits,
            return_dict=return_dict,
        )

        sequence_output = decoder_outputs[0]

        if self.config.tie_word_embeddings:
            # Rescale output before projecting on vocab
            # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
            sequence_output = sequence_output * (self.model_dim**-0.5)

        lm_logits = self.lm_head(sequence_output)

        loss = None
        encoder_z_loss = None
        encoder_aux_loss = None
        decoder_z_loss = None
        decoder_aux_loss = None

        if labels is not None:
            loss_fct = CrossEntropyLoss(ignore_index=-100)
            # todo check in the config if router loss enables

            if output_router_logits:
                # Compute the router loss (z_loss + auxiliary loss) for each router in the encoder and decoder
                encoder_router_logits, encoder_expert_indexes = self._unpack_router_logits(
                    encoder_outputs.router_probs
                )
                encoder_z_loss = router_z_loss_func(encoder_router_logits)
                encoder_router_probs = nn.Softmax(dim=-1)(encoder_router_logits)
                encoder_aux_loss = load_balancing_loss_func(encoder_router_probs, encoder_expert_indexes)

                decoder_router_logits, decoder_expert_indexes = self._unpack_router_logits(
                    decoder_outputs.router_probs
                )
                decoder_z_loss = router_z_loss_func(decoder_router_logits)
                decoder_router_probs = nn.Softmax(dim=-1)(decoder_router_logits)
                decoder_aux_loss = load_balancing_loss_func(decoder_router_probs, decoder_expert_indexes)

            loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))

            if output_router_logits and labels is not None:
                z_loss = self.router_z_loss_coef * (encoder_z_loss + decoder_z_loss)
                aux_loss = self.router_aux_loss_coef * (encoder_aux_loss + decoder_aux_loss)
                loss = loss + z_loss + aux_loss

        if not return_dict:
            output = (lm_logits,)
            if output_router_logits:  # only return the loss if they are not None
                output += (
                    encoder_z_loss,
                    encoder_aux_loss,
                    decoder_z_loss,
                    decoder_aux_loss,
                    *decoder_outputs[1:],
                    *encoder_outputs,
                )
            else:
                output += (*decoder_outputs[1:], *encoder_outputs)

            return ((loss,) + output) if loss is not None else output
        return Seq2SeqMoEOutput(
            loss=loss,
            logits=lm_logits,
            encoder_z_loss=encoder_z_loss,
            encoder_aux_loss=encoder_aux_loss,
            decoder_z_loss=decoder_z_loss,
            decoder_aux_loss=decoder_aux_loss,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
            encoder_router_logits=encoder_outputs.router_probs,
            decoder_router_logits=decoder_outputs.router_probs,
        )

    def _unpack_router_logits(self, router_outputs):
        total_router_logits = []
        total_expert_indexes = []
        for router_output in router_outputs:
            if router_output[0] is not None:
                router_logits, expert_indexes = router_output
                total_router_logits.append(router_logits)
                total_expert_indexes.append(expert_indexes)
        return torch.cat(total_router_logits, dim=1), torch.cat(total_expert_indexes, dim=1)

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        attention_mask=None,
        head_mask=None,
        decoder_head_mask=None,
        cross_attn_head_mask=None,
        use_cache=None,
        encoder_outputs=None,
        **kwargs
    ):

        # cut decoder_input_ids if past is used
        if past_key_values is not None:
            input_ids = input_ids[:, -1:]

        return {
            "decoder_input_ids": input_ids,
            "past_key_values": past_key_values,
            "encoder_outputs": encoder_outputs,
            "attention_mask": attention_mask,
            "head_mask": head_mask,
            "decoder_head_mask": decoder_head_mask,
            "cross_attn_head_mask": cross_attn_head_mask,
            "use_cache": use_cache,
        }

    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
        return self._shift_right(labels)

    def _reorder_cache(self, past, beam_idx):
        # if decoder past is not included in output
        # speedy decoding is disabled and no need to reorder
        if past is None:
            logger.warning("You might want to consider setting `use_cache=True` to speed up decoding")
            return past

        reordered_decoder_past = ()
        for layer_past_states in past:
            # get the correct batch idx from layer past batch dim
            # batch dim of `past` is at 2nd position
            reordered_layer_past_states = ()
            for layer_past_state in layer_past_states:
                # need to set correct `past` for each of the four key / value states
                reordered_layer_past_states = reordered_layer_past_states + (
                    layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)),
                )

            if reordered_layer_past_states[0].shape != layer_past_states[0].shape:
                raise ValueError(
                    "expected reordered_layer_past_states to have the same shape than layer_past_states"
                    f"but got {reordered_layer_past_states[0].shape} and {layer_past_states[0].shape}"
                )
            if len(reordered_layer_past_states) != len(layer_past_states):
                raise ValueError(
                    "expected layer_past_states to have the same length as reordered_layer_past_states"
                    f"got {len(layer_past_states)} and {len(reordered_layer_past_states)}"
                )

            reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,)
        return reordered_decoder_past


@add_start_docstrings(
    "The bare SWITCH_TRANSFORMERS Model transformer outputting encoder's raw hidden-states without any specific head"
    " on top.",
    SWITCH_TRANSFORMERS_START_DOCSTRING,
)
class SwitchTransformersEncoderModel(SwitchTransformersPreTrainedModel):
    _keys_to_ignore_on_load_missing = [r"encoder.embed_tokens.weight"]

    def __init__(self, config: SwitchTransformersConfig):
        super().__init__(config)
        self.shared = nn.Embedding(config.vocab_size, config.d_model)

        encoder_config = copy.deepcopy(config)
        encoder_config.use_cache = False
        encoder_config.is_encoder_decoder = False
        self.encoder = SwitchTransformersStack(encoder_config, self.shared)

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

        # Model parallel
        self.device_map = None

    def get_input_embeddings(self):
        return self.shared

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

    def get_encoder(self):
        return self.encoder

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.block[layer].layer[0].SelfAttention.prune_heads(heads)

    @add_start_docstrings_to_model_forward(SWITCH_TRANSFORMERS_ENCODER_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=MoEModelOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_router_logits: Optional[bool] = True,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.FloatTensor], MoEModelOutput]:
        r"""
        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, SwitchTransformersEncoderModel

        >>> tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
        >>> model = SwitchTransformersEncoderModel.from_pretrained("google/switch-base-8")
        >>> input_ids = tokenizer(
        ...     "Studies have been shown that owning a dog is good for you", return_tensors="pt"
        ... ).input_ids  # Batch size 1
        >>> outputs = model(input_ids=input_ids)
        >>> last_hidden_states = outputs.last_hidden_state
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        encoder_outputs = self.encoder(
            input_ids=input_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            output_router_logits=output_router_logits,
            return_dict=return_dict,
        )

        return encoder_outputs
