Assess the below scripts for errors and compatibility:
`clustered_experts_ST.py`:
```
"""Altered SwitchTransformers model with clustered experts."""

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 transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache, EncoderDecoderCache, StaticCache
from transformers.generation import GenerationMixin
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from transformers.modeling_outputs import (
    MoEModelOutput,
    MoEModelOutputWithPastAndCrossAttentions,
    Seq2SeqMoEModelOutput,
    Seq2SeqMoEOutput,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, find_pruneable_heads_and_indices, prune_linear_layer
from transformers.utils import (
    DUMMY_INPUTS,
    DUMMY_MASK,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_torch_fx_proxy,
    is_torchdynamo_compiling,
    logging,
    replace_return_docstrings,
)
from .model_configuration import SwitchTransformersConfig


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "SwitchTransformersConfig"
_CHECKPOINT_FOR_DOC = "ai1-test/clustered-experts-ST"

####################################################
# This dict contains ids and associated url
# for the pretrained weights provided with the models
####################################################


def router_z_loss_func(cluster_logits: torch.Tensor) -> float:
    """
    Computes the router z-loss for cluster logits to stabilize the cluster selection.
    Args:
        cluster_logits (torch.Tensor): Logits tensor of shape [batch_size, sequence_length, num_clusters].
    Returns:
        Scalar z-loss.
    """
    num_groups, tokens_per_group, _ = cluster_logits.shape
    log_z = torch.logsumexp(cluster_logits, dim=-1)
    z_loss = log_z**2
    return torch.sum(z_loss) / (num_groups * tokens_per_group)

def load_balancing_loss_func(cluster_probs: torch.Tensor, cluster_indices: torch.Tensor, expert_allocation: torch.Tensor) -> float:
    """
    Computes the load balancing loss for clusters of experts.
    Args:
        cluster_probs (torch.Tensor): Probabilities assigned to each cluster per token. Shape: [batch_size, seq_length, num_clusters].
        cluster_indices (torch.Tensor): Indices of the selected cluster for each token. Shape: [batch_size, seq_length].
        expert_allocation (torch.Tensor): Number of experts allocated per cluster. Shape: [num_clusters].
    Returns:
        Scalar auxiliary loss.
    """
    num_clusters = cluster_probs.shape[-1]

    if cluster_indices.dtype != torch.int64:
        cluster_indices = cluster_indices.to(torch.int64)

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

    # One-hot encoding for selected clusters
    cluster_mask = torch.nn.functional.one_hot(cluster_indices, num_clusters)
    cluster_mask = torch.max(cluster_mask, axis=-2).values
    cluster_mask = cluster_mask.to(torch.float32)

    # Calculate tokens per cluster and average probability per cluster
    tokens_per_cluster = torch.mean(cluster_mask, axis=-2)
    cluster_prob_per_token = torch.mean(cluster_probs, axis=-2)

    # Normalize by the number of experts in each cluster
    normalized_tokens_per_cluster = tokens_per_cluster / expert_allocation.float()

    # Compute load balancing loss across clusters
    return torch.mean(normalized_tokens_per_cluster * cluster_prob_per_token) * (num_clusters**2)

class SwitchTransformersTop1Router(nn.Module):
    """
    Router using token-based cluster allocation. This router handles dynamic expert allocation across clusters,
    and routes tokens to all experts within a selected cluster.
    """

    def __init__(self, config: SwitchTransformersConfig):
        super().__init__()
        self.num_experts = config.num_experts
        self.num_clusters = config.num_clusters
        self.expert_capacity = config.expert_capacity
        self.dynamic_expert_allocation = config.dynamic_expert_allocation

        # Classifier to determine routing probabilities
        self.classifier = nn.Linear(config.hidden_size, self.num_clusters, 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)

        # Learnable logits for dynamic expert allocation
        self.allocation_logits = nn.Parameter(torch.zeros(self.num_clusters))

    def _compute_expert_allocation(self) -> torch.Tensor:
        """
        Determines the number of experts per cluster dynamically using a softmax-based allocation.
        Returns:
            A tensor of size (num_clusters,) indicating the number of experts in each cluster.
        """
        # Apply softmax to get a distribution of experts across clusters
        allocation_distribution = nn.functional.softmax(self.allocation_logits, dim=-1)
        # Calculate the number of experts per cluster based on the allocation distribution
        expert_allocation = torch.round(allocation_distribution * self.num_experts).to(torch.int64)

        # Adjust to ensure the total number of experts matches `self.num_experts`
        difference = self.num_experts - expert_allocation.sum()
        if difference != 0:
            # Add or remove experts based on the difference
            expert_allocation[torch.argmax(self.allocation_logits)] += difference

        return expert_allocation

    def _compute_router_probabilities(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Computes router probabilities from input hidden states.
        Args:
            hidden_states (`torch.Tensor`): Input hidden states of shape (batch_size, seq_len, hidden_dim).
        Returns:
            router_probabilities (`torch.Tensor`): Probabilities for each token and cluster.
            router_logits (`torch.Tensor`): Raw logits for each token and cluster.
        """
        self.input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(self.dtype)

        if self.training and self.jitter_noise > 0:
            hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise)

        self._cast_classifier()
        router_logits = self.classifier(hidden_states)

        # Apply softmax to get cluster probabilities
        router_probabilities = nn.functional.softmax(router_logits, dim=-1, dtype=self.dtype).to(self.input_dtype)
        return router_probabilities, router_logits

    def forward(self, hidden_states: torch.Tensor) -> Tuple:
        """
        Main routing function. Determines the cluster for each token and routes the token to all experts in the cluster.
        Args:
            hidden_states (`torch.Tensor`): Input hidden states of shape (batch_size, seq_len, hidden_dim).
        Returns:
            expert_index (`torch.Tensor`): One-hot encoding of selected experts for each token.
            router_probs (`torch.Tensor`): Probabilities for each token's selected cluster.
            router_logits (`torch.Tensor`): Raw logits used for computing the router z-loss.
        """
        # Step 1: Compute router probabilities and logits
        router_probs, router_logits = self._compute_router_probabilities(hidden_states)

        # Step 2: Determine cluster index for each token
        cluster_index = torch.argmax(router_probs, dim=-1)

        # Step 3: Determine expert allocation for each cluster
        expert_allocation = self._compute_expert_allocation()

        # Step 4: Create one-hot encoding for experts in the selected cluster
        expert_index = torch.zeros(hidden_states.size(0), hidden_states.size(1), self.num_experts, device=hidden_states.device)

        # Route tokens to all experts in the selected cluster
        start_idx = 0
        for cluster_id in range(self.num_clusters):
            num_experts_in_cluster = expert_allocation[cluster_id]
            end_idx = start_idx + num_experts_in_cluster

            # Select tokens belonging to this cluster
            mask = (cluster_index == cluster_id).unsqueeze(-1)
            expert_index[:, :, start_idx:end_idx] = mask.expand(-1, -1, num_experts_in_cluster)

            start_idx = end_idx

        # Return the expert index, router probabilities, and logits
        return expert_index, router_probs, router_logits

    def _cast_classifier(self):
        """
        Handles casting for compatibility with certain layers (e.g., Linear8bitLt).
        """
        if not (hasattr(self.classifier, "SCB") or hasattr(self.classifier, "CB")):
            self.classifier = self.classifier.to(self.dtype)

# 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)
        if (
            isinstance(self.wo.weight, torch.Tensor)
            and hidden_states.dtype != self.wo.weight.dtype
            and self.wo.weight.dtype != torch.int8
        ):
            hidden_states = hidden_states.to(self.wo.weight.dtype)
        hidden_states = self.wo(hidden_states)
        return hidden_states

class SwitchTransformersSparseMLP(nn.Module):
    def __init__(self, config: SwitchTransformersConfig, expert_class: nn.Module = SwitchTransformersDenseActDense):
        super().__init__()
        self.router = SwitchTransformersTop1Router(config)
        self.experts = nn.ModuleDict()

        # Initialize experts based on the number of experts allocated per cluster
        cluster_expert_counts = self.router.cluster_expert_counts
        expert_idx = 0
        for cluster_id, num_experts in enumerate(cluster_expert_counts):
            for _ in range(num_experts):
                self.experts[f"cluster_{cluster_id}_expert_{expert_idx}"] = expert_class(config)
                expert_idx += 1

    def forward(self, hidden_states):
        router_mask, router_probs, router_logits = self.router(hidden_states)
        batch_size, seq_len, num_clusters = router_mask.shape

        next_states = hidden_states.clone()

        for cluster_id in range(num_clusters):
            cluster_mask = router_mask[:, :, cluster_id].bool()
            cluster_experts = [
                key for key in self.experts.keys() if key.startswith(f"cluster_{cluster_id}_expert")
            ]
            for expert_key in cluster_experts:
                expert_output = self.experts[expert_key](hidden_states[cluster_mask])
                next_states[cluster_mask] += (router_probs[:, :, cluster_id].unsqueeze(-1).repeat(1, 1, expert_output.size(-1)) * expert_output)

        return next_states, (router_logits, router_mask)

class SwitchTransformersLayerFF(nn.Module):
    def __init__(self, config: SwitchTransformersConfig, is_sparse=False):
        super().__init__()
        self.is_sparse = is_sparse

        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):
        normed_hidden_states = self.layer_norm(hidden_states)
        mlp_output = self.mlp(normed_hidden_states)

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

        hidden_states = hidden_states + self.dropout(mlp_output)
        return hidden_states if router_tuple is None else (hidden_states, router_tuple)

    # 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,
        layer_idx: Optional[int] = None,
    ):
        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
        self.layer_idx = layer_idx
        if layer_idx is None and self.is_decoder:
            logger.warning_once(
                f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
                "will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
                "when creating this class."
            )

        # 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, cache_position=None):
        """Compute binned relative position bias"""
        if device is None:
            device = self.relative_attention_bias.weight.device
        if cache_position is None:
            context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
        else:
            context_position = cache_position[:, None].to(device)
        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,
        cache_position=None,
    ):
        """
        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, 1, 1, key_length) (non-causal encoder) or (batch_size, 1, seq_length, key_length) (causal decoder)
        batch_size, seq_length = hidden_states.shape[:2]

        # if key_value_states are provided this layer is used as a cross-attention layer for the decoder
        is_cross_attention = key_value_states is not None

        query_states = self.q(hidden_states)
        query_states = query_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)

        if past_key_value is not None:
            is_updated = past_key_value.is_updated.get(self.layer_idx)
            if is_cross_attention:
                # after the first generated id, we can subsequently re-use all key/value_states from cache
                curr_past_key_value = past_key_value.cross_attention_cache
            else:
                curr_past_key_value = past_key_value.self_attention_cache

        current_states = key_value_states if is_cross_attention else hidden_states
        if is_cross_attention and past_key_value is not None and is_updated:
            # reuse k,v, cross_attentions
            key_states = curr_past_key_value.key_cache[self.layer_idx]
            value_states = curr_past_key_value.value_cache[self.layer_idx]
        else:
            key_states = self.k(current_states)
            value_states = self.v(current_states)
            key_states = key_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
            value_states = value_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)

            if past_key_value is not None:
                # save all key/value_states to cache to be re-used for fast auto-regressive generation
                cache_position = cache_position if not is_cross_attention else None
                key_states, value_states = curr_past_key_value.update(
                    key_states, value_states, self.layer_idx, {"cache_position": cache_position}
                )
                # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
                if is_cross_attention:
                    past_key_value.is_updated[self.layer_idx] = True

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

        if position_bias is None:
            key_length = key_states.shape[-2]
            # cache position is 0-indexed so we add 1 to get the real length of queries (aka with past)
            real_seq_length = query_length if query_length is not None else cache_position[-1] + 1
            if not self.has_relative_attention_bias:
                position_bias = torch.zeros(
                    (1, self.n_heads, 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, cache_position=cache_position
                )
                position_bias = position_bias[:, :, -seq_length:, :]

            if mask is not None:
                causal_mask = mask[:, :, :, : key_states.shape[-2]]
                position_bias = position_bias + causal_mask

        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

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

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

        attn_output = torch.matmul(attn_weights, value_states)

        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.view(batch_size, -1, self.inner_dim)
        attn_output = self.o(attn_output)

        outputs = (attn_output, past_key_value, 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, layer_idx: Optional[int] = None):
        super().__init__()
        self.SelfAttention = SwitchTransformersAttention(
            config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx
        )
        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,
        cache_position=None,
    ):
        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,
            cache_position=cache_position,
        )
        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, layer_idx: Optional[int] = None):
        super().__init__()
        self.EncDecAttention = SwitchTransformersAttention(
            config, has_relative_attention_bias=False, layer_idx=layer_idx
        )
        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,
        cache_position=None,
    ):
        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,
            cache_position=cache_position,
        )
        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, layer_idx: Optional[int] = None):
        super().__init__()
        self.is_decoder = config.is_decoder
        self.layer = nn.ModuleList()

        self.layer.append(
            SwitchTransformersLayerSelfAttention(
                config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx
            )
        )

        if self.is_decoder:
            self.layer.append(SwitchTransformersLayerCrossAttention(config, layer_idx=layer_idx))

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

    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,
        cache_position=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=past_key_value,
            use_cache=use_cache,
            output_attentions=output_attentions,
            cache_position=cache_position,
        )
        hidden_states, past_key_value = 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:
            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=past_key_value,
                query_length=cache_position[-1] + 1,
                use_cache=use_cache,
                output_attentions=output_attentions,
                cache_position=cache_position,
            )
            hidden_states, past_key_value = cross_attention_outputs[:2]

            # 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)

            # 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 = (torch.zeros((1,), device=hidden_states.device, dtype=torch.int64),)

        # 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 + (past_key_value,) + attention_outputs + (router_tuple,)
        else:
            outputs = outputs + attention_outputs + (router_tuple,)

        return outputs  # hidden-states, past_key_value, (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
    _supports_cache_class = True
    _supports_static_cache = False
    _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, 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 _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)
        self.is_decoder = config.is_decoder

        sparse_step = config.decoder_sparse_step if self.is_decoder else config.encoder_sparse_step
        self.block = nn.ModuleList()
        for i in range(config.num_layers):
            is_sparse = config.dynamic_expert_allocation and (i % sparse_step == 1 or sparse_step == 1)
            self.block.append(
                SwitchTransformersBlock(
                    config, has_relative_attention_bias=bool(i == 0), is_sparse=is_sparse, layer_idx=i
                )
            )

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

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

        if inputs_embeds is None:
            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

        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")

        # initialize past_key_values
        return_legacy_cache = False
        return_self_attention_cache = False
        if self.is_decoder and (use_cache or past_key_values is not None):
            if isinstance(past_key_values, Cache) and not isinstance(past_key_values, EncoderDecoderCache):
                return_self_attention_cache = True
                past_key_values = EncoderDecoderCache(past_key_values, DynamicCache())
            elif not isinstance(past_key_values, EncoderDecoderCache):
                return_legacy_cache = True
                logger.warning_once(
                    "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.48.0. "
                    "You should pass an instance of `EncoderDecoderCache` instead, e.g. "
                    "`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
                )
                past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)
            elif past_key_values is None:
                past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache())
        elif not self.is_decoder:
            # do not pass cache object down the line for encoder stack
            # it messes indexing later in decoder-stack because cache object is modified in-place
            past_key_values = None

        past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
        if cache_position is None:
            cache_position = torch.arange(
                past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device
            )

        if attention_mask is None and not is_torchdynamo_compiling():
            # required mask seq length can be calculated via length of past cache
            mask_seq_length = past_key_values_length + seq_length
            attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)

        if self.config.is_decoder:
            causal_mask = self._update_causal_mask(
                attention_mask,
                inputs_embeds,
                cache_position,
                past_key_values.self_attention_cache if past_key_values is not None else None,
                output_attentions,
            )
        else:
            causal_mask = attention_mask[:, None, None, :]
            causal_mask = causal_mask.to(dtype=inputs_embeds.dtype)
            causal_mask = (1.0 - causal_mask) * torch.finfo(inputs_embeds.dtype).min

        # 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)
        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 in enumerate(self.block):
            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:
                layer_outputs = self._gradient_checkpointing_func(
                    layer_module.forward,
                    hidden_states,
                    causal_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
                    use_cache,
                    output_attentions,
                    output_router_logits,
                    return_dict,
                    cache_position,
                )
            else:
                layer_outputs = layer_module(
                    hidden_states,
                    attention_mask=causal_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_values,
                    use_cache=use_cache,
                    output_attentions=output_attentions,
                    output_router_logits=output_router_logits,
                    return_dict=return_dict,
                    cache_position=cache_position,
                )

            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, next_decoder_cache = 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]

            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,)

        next_cache = next_decoder_cache if use_cache else None
        if return_self_attention_cache:
            next_cache = past_key_values.self_attention_cache
        if return_legacy_cache:
            next_cache = past_key_values.to_legacy_cache()

        if not return_dict:
            return tuple(
                v
                for v in [
                    hidden_states,
                    next_cache,
                    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=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_attentions,
            cross_attentions=all_cross_attentions,
            router_probs=all_router_probs,
        )

    # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
    def _update_causal_mask(
        self,
        attention_mask: torch.Tensor,
        input_tensor: torch.Tensor,
        cache_position: torch.Tensor,
        past_key_values: Cache,
        output_attentions: bool,
    ):
        if self.config._attn_implementation == "flash_attention_2":
            if attention_mask is not None and 0.0 in attention_mask:
                return attention_mask
            return None

        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
        # to infer the attention mask.
        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
        using_static_cache = isinstance(past_key_values, StaticCache)

        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
        if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
            if AttentionMaskConverter._ignore_causal_mask_sdpa(
                attention_mask,
                inputs_embeds=input_tensor,
                past_key_values_length=past_seen_tokens,
                is_training=self.training,
            ):
                return None

        dtype, device = input_tensor.dtype, input_tensor.device
        sequence_length = input_tensor.shape[1]
        if using_static_cache:
            target_length = past_key_values.get_max_cache_shape()
        else:
            target_length = (
                attention_mask.shape[-1]
                if isinstance(attention_mask, torch.Tensor)
                else past_seen_tokens + sequence_length + 1
            )

        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
            attention_mask,
            sequence_length=sequence_length,
            target_length=target_length,
            dtype=dtype,
            device=device,
            cache_position=cache_position,
            batch_size=input_tensor.shape[0],
        )

        if (
            self.config._attn_implementation == "sdpa"
            and attention_mask is not None
            and attention_mask.device.type == "cuda"
            and not output_attentions
        ):
            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
            # Details: https://github.com/pytorch/pytorch/issues/110213
            min_dtype = torch.finfo(dtype).min
            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)

        return causal_mask

    @staticmethod
    # Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel._prepare_4d_causal_attention_mask_with_cache_position
    def _prepare_4d_causal_attention_mask_with_cache_position(
        attention_mask: torch.Tensor,
        sequence_length: int,
        target_length: int,
        dtype: torch.dtype,
        device: torch.device,
        cache_position: torch.Tensor,
        batch_size: int,
        **kwargs,
    ):
        """
        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.

        Args:
            attention_mask (`torch.Tensor`):
                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
                `(batch_size, 1, query_length, key_value_length)`.
            sequence_length (`int`):
                The sequence length being processed.
            target_length (`int`):
                The target length: when generating with static cache, the mask should be as long as the static cache,
                to account for the 0 padding, the part of the cache that is not filled yet.
            dtype (`torch.dtype`):
                The dtype to use for the 4D attention mask.
            device (`torch.device`):
                The device to plcae the 4D attention mask on.
            cache_position (`torch.Tensor`):
                Indices depicting the position of the input sequence tokens in the sequence.
            batch_size (`torch.Tensor`):
                Batch size.
        """
        if attention_mask is not None and attention_mask.dim() == 4:
            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
            causal_mask = attention_mask
        else:
            min_dtype = torch.finfo(dtype).min
            causal_mask = torch.full(
                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
            )
            if sequence_length != 1:
                causal_mask = torch.triu(causal_mask, diagonal=1)
            causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
            if attention_mask is not None:
                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
                mask_length = attention_mask.shape[-1]
                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
                padding_mask = padding_mask == 0
                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
                    padding_mask, min_dtype
                )

        return causal_mask

class SwitchTransformersModel(SwitchTransformersPreTrainedModel):
    _tied_weights_keys = ["encoder.embed_tokens.weight", "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 _tie_weights(self):
        if self.config.tie_word_embeddings:
            self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
            self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared)

    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,
        cache_position: Optional[torch.LongTensor] = 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,
            cache_position=cache_position,
        )

        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,
        )

class SwitchTransformersForConditionalGeneration(SwitchTransformersPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "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 _tie_weights(self):
        if self.config.tie_word_embeddings:
            self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
            self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared)

    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,
        cache_position: Optional[torch.LongTensor] = None,
    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqMoEOutput]:

    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

    # Handling head_mask and decoder_head_mask warnings
    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

    # Step 1: Encoder 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]

    # Step 2: Prepare Decoder Input
    if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
        decoder_input_ids = self._shift_right(labels)

    # Step 3: Decoder Pass
    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,
        cache_position=cache_position,
    )

    sequence_output = decoder_outputs[0]

    # Step 4: Rescale Output if Tied Word Embeddings
    if self.config.tie_word_embeddings:
        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

    # Step 5: Clustered Expert Handling and Loss Calculation
    if output_router_logits:
        # Aggregating router logits across clusters instead of individual experts
        if self.encoder.config.encoder_sparse_step > 1:
            encoder_router_logits, encoder_expert_indexes = self._unpack_clustered_router_logits(encoder_outputs[-1])
            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)
        else:
            encoder_z_loss = 0
            encoder_aux_loss = 0

        if self.decoder.config.decoder_sparse_step > 1:
            decoder_router_logits, decoder_expert_indexes = self._unpack_clustered_router_logits(decoder_outputs[-1])
            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)
        else:
            decoder_z_loss = 0
            decoder_aux_loss = 0

    # Step 6: Compute Final Loss
    if labels is not None:
        loss_fct = CrossEntropyLoss(ignore_index=-100)
        labels = labels.to(lm_logits.device)
        loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))

        if output_router_logits:
            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 += z_loss + aux_loss

    # Step 7: Return Outputs
    if not return_dict:
        output = (lm_logits,)
        if output_router_logits:
            output += (encoder_z_loss, encoder_aux_loss, decoder_z_loss, decoder_aux_loss)
        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,
        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,
    )

    def _unpack_clustered_router_logits(self, router_outputs):
    """
    Unpacks clustered router logits and expert indexes from the router outputs.
    This method assumes router outputs are structured with separate router logits 
    and expert indexes for each cluster.
    
    Args:
        router_outputs (list of tuples): Each tuple contains (router_logits, expert_indexes)
                                         for a specific cluster.

    Returns:
        torch.Tensor: Concatenated router logits across clusters.
        torch.Tensor: Concatenated expert indexes across clusters.
    """
    total_router_logits = []
    total_expert_indexes = []
    
    for router_output in router_outputs:
        # Check if the output has the expected tuple structure
        if isinstance(router_output, tuple) and len(router_output) == 2:
            router_logits, expert_indexes = router_output
            
            # Ensure that router_logits and expert_indexes are 2D
            if len(router_logits.shape) > 1 and len(expert_indexes.shape) > 1:
                total_router_logits.append(router_logits)
                total_expert_indexes.append(expert_indexes)
            else:
                raise ValueError("router_logits and expert_indexes should be at least 2-dimensional.")
        else:
            raise ValueError("Each router_output should be a tuple of (router_logits, expert_indexes).")

    # Concatenate all router logits and expert indexes across clusters
    concatenated_router_logits = torch.cat(total_router_logits, dim=1)
    concatenated_expert_indexes = torch.cat(total_expert_indexes, dim=1)
    
    return concatenated_router_logits, concatenated_expert_indexes

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

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

        reordered_decoder_past = ()
        for layer_past_states in past_key_values:
            # 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"but 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
```
`convert_model_checkpoint_to_pytorch.py`:
```
"""Convert SwitchTransformersX checkpoints from the original repository to PyTorch models."""

!pip install git+https://github.com/google-research/t5x.git

import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from transformers.t5x import checkpoints

from .model_configuration import SwitchTransformersConfig
from .clustered_experts_ST import SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
MOE_LAYER_NAME_MAPPING = {
    "/attention/": "/0/SelfAttention/",
    "/self_attention/": "/0/SelfAttention/",
    "/encoder_decoder_attention/": "/1/EncDecAttention/",
    "value": "v",
    "query": "q",
    "key": "k",
    "out": "o",
    "pre_self_attention_layer_norm": "0/layer_norm",
    "pre_cross_attention_layer_norm": "1/layer_norm",
    "pre_attention_layer_norm": "0/layer_norm",  # previously 1, but seems wrong
    "token_embedder": "shared",
    "encoder_norm": "final_layer_norm",
    "decoder_norm": "final_layer_norm",
    "relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
    "router/router_weights/w/": "router/classifier/",
    "roer/roer_weights/w/": "router/classifier/",
    "logits_dense": "lm_head",
    "experts/cluster_": "experts/cluster_",
    "experts/expert_": "experts/cluster_",
}

def rename_keys(s_dict):
    keys = list(s_dict.keys())
    for key in keys:
        layer_to_block_of_layer = r".*/layers_(\d+)"
        new_key = key
        if re.match(layer_to_block_of_layer, key):
            new_key = re.sub(r"layers_(\d+)", r"block/\1/layer", new_key)

        layer_to_block_of_layer = r"(encoder|decoder)\/"
        if re.match(layer_to_block_of_layer, key):
            groups = re.match(layer_to_block_of_layer, new_key).groups()
            if groups[0] == "encoder":
                new_key = re.sub(r"/mlp/", r"/1/mlp/", new_key)
                new_key = re.sub(r"/pre_mlp_layer_norm/", r"/1/layer_norm/", new_key)
            elif groups[0] == "decoder":
                new_key = re.sub(r"/mlp/", r"/2/mlp/", new_key)
                new_key = re.sub(r"/pre_mlp_layer_norm/", r"/2/layer_norm/", new_key)

        # Handle clustered expert keys
        if "experts/cluster_" in key:
            new_key = key.replace("experts/cluster_", "experts/cluster_").replace("/expert_", "_expert_")

        # Apply standard mappings
        for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
            if old_key in new_key:
                new_key = new_key.replace(old_key, temp_key)

        s_dict[new_key] = s_dict.pop(key)

    return s_dict

GIN_TO_CONFIG_MAPPING = {
    "NUM_ENCODER_LAYERS": "num_layers",
    "NUM_DECODER_LAYERS": "num_decoder_layers",
    "NUM_HEADS": "num_heads",
    "HEAD_DIM": "d_kv",
    "EMBED_DIM": "d_model",
    "MLP_DIM": "d_ff",
    "NUM_SELECTED_EXPERTS": "num_selected_experts",
    "NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
    "NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
    "dense.MlpBlock.activations": "feed_forward_proj",
}

def convert_gin_to_config(gin_file, num_experts):
    import regex as re

    with open(gin_file, "r") as f:
        raw_gin = f.read()

    regex_match = re.findall(r"(.*) = ([0-9.]*)", raw_gin)
    args = {}
    for param, value in regex_match:
        if param in GIN_TO_CONFIG_MAPPING and value != "":
            args[GIN_TO_CONFIG_MAPPING[param]] = float(value) if "." in value else int(value)

    activation = re.findall(r"(.*activations) = \(\'(.*)\',\)", raw_gin)[0]
    args[GIN_TO_CONFIG_MAPPING[activation[0]]] = str(activation[1])

    # Add clustered expert parameters
    args["num_experts"] = num_experts
    args["num_clusters"] = 4  # Default value if not specified
    args["dynamic_allocation"] = True
    args["expert_allocation_strategy"] = "dynamic_expert_allocation"
    args["cluster_specialization"] = ["general"] * args["num_clusters"]

    config = SwitchTransformersConfig(**args)
    return config

def convert_flax_checkpoint_to_pytorch(
    flax_checkpoint_path, config_file, gin_file=None, pytorch_dump_path="./", num_experts=8
):
    # Initialise PyTorch model

    print(f"Loading flax weights from : {flax_checkpoint_path}")
    flax_params = checkpoints.load_t5x_checkpoint(flax_checkpoint_path)

    if gin_file is not None:
        config = convert_gin_to_config(gin_file, num_experts)
    else:
        config = SwitchTransformersConfig.from_pretrained(config_file)

    pt_model = SwitchTransformersForConditionalGeneration(config)

    flax_params = flax_params["target"]
    flax_params = flatten_dict(flax_params, sep="/")
    flax_params = rename_keys(flax_params)
    flax_params = unflatten_dict(flax_params, sep="/")

    # Load the flax params in the PT model
    load_flax_weights_in_pytorch_model(pt_model, flax_params)

    print(f"Save PyTorch model to {pytorch_dump_path}")
    pt_model.save_pretrained(pytorch_dump_path)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
    "--switch_t5x_checkpoint_path",
    default=None,
    type=str,
    required=True,
    help="The path to the SwitchTransformersX checkpoint to be converted.",
    )
    parser.add_argument(
        "--gin_file",
        default=None,
        type=str,
        required=False,
        help="Path to the gin config file. If not provided, a `config_file` has to be passed   ",
    )
    parser.add_argument(
        "--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
    )
    parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
    args = parser.parse_args()
    convert_flax_checkpoint_to_pytorch(
        args.switch_t5x_checkpoint_path,
        args.config_name,
        args.gin_file,
        args.pytorch_dump_folder_path,
        args.num_experts,
    )
```
`model_configuration.py`:
```
"""Clustered Experts Switch Transformers model configuration"""

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging

logger = logging.get_logger(__name__)

class SwitchTransformersConfig(PretrainedConfig):
    r"""
    Configuration class for the SwitchTransformers model with clustered experts (BOST).
    Includes additional parameters for managing clusters and dynamic expert allocation.
    """

    model_type = "switch_transformers"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}

    def __init__(
        self,
        vocab_size=32128,
        d_model=768,
        d_kv=64,
        d_ff=2048,
        expert_capacity=64,
        num_layers=12,
        num_sparse_encoder_layers=3,
        num_decoder_layers=12,
        num_sparse_decoder_layers=3,
        num_heads=12,
        num_experts=8,
        num_clusters=4,
        dynamic_expert_allocation=True,
        cluster_specialization=None,
        expert_allocation_strategy="dynamic_expert_allocation",
        cluster_allocation_weights=None,
        router_bias=False,
        router_jitter_noise=0.01,
        router_dtype="float32",
        router_ignore_padding_tokens=False,
        relative_attention_num_buckets=32,
        relative_attention_max_distance=128,
        dropout_rate=0.1,
        layer_norm_epsilon=1e-6,
        router_z_loss_coef=0.001,
        router_aux_loss_coef=0.001,
        initializer_factor=1.0,
        dense_act_fn="relu",
        is_encoder_decoder=True,
        add_router_probs=False,
        use_cache=True,
        pad_token_id=0,
        eos_token_id=1,
        **kwargs,
    ):
        # Core model configurations
        self.vocab_size = vocab_size
        self.d_model = d_model
        self.d_kv = d_kv
        self.d_ff = d_ff
        self.num_layers = num_layers
        self.num_decoder_layers = num_decoder_layers if num_decoder_layers is not None else self.num_layers
        self.num_heads = num_heads
        self.num_experts = num_experts
        self.expert_capacity = expert_capacity

        # Sparse layer configuration
        self.num_sparse_encoder_layers = num_sparse_encoder_layers
        self.num_sparse_decoder_layers = num_sparse_decoder_layers
        self.encoder_sparse_step = self.num_layers // max(1, num_sparse_encoder_layers)
        self.decoder_sparse_step = self.num_decoder_layers // max(1, num_sparse_decoder_layers)

        # Cluster and expert allocation configuration
        self.num_clusters = num_clusters
        self.dynamic_expert_allocation = dynamic_expert_allocation
        self.cluster_specialization = (
            cluster_specialization if cluster_specialization else ["general"] * num_clusters
        )
        self.expert_allocation_strategy = expert_allocation_strategy
        self.cluster_allocation_weights = (
            cluster_allocation_weights if cluster_allocation_weights else [1.0 / num_clusters] * num_clusters
        )

        if len(self.cluster_allocation_weights) != num_clusters:
            raise ValueError(
                "The length of `cluster_allocation_weights` must match `num_clusters`. "
                f"Expected {num_clusters}, got {len(self.cluster_allocation_weights)}."
            )

        # Router configurations
        self.router_bias = router_bias
        self.router_jitter_noise = router_jitter_noise
        if router_dtype not in ["float32", "float16", "bfloat16"]:
            raise ValueError(f"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}")
        self.router_dtype = router_dtype
        self.router_ignore_padding_tokens = router_ignore_padding_tokens

        # Attention configurations
        self.relative_attention_num_buckets = relative_attention_num_buckets
        self.relative_attention_max_distance = relative_attention_max_distance

        # Regularization and initialization
        self.dropout_rate = dropout_rate
        self.layer_norm_epsilon = layer_norm_epsilon
        self.router_z_loss_coef = router_z_loss_coef
        self.router_aux_loss_coef = router_aux_loss_coef
        self.initializer_factor = initializer_factor
        self.dense_act_fn = dense_act_fn

        # Model behavior settings
        self.use_cache = use_cache
        self.add_router_probs = add_router_probs

        super().__init__(
            pad_token_id=pad_token_id,
            eos_token_id=eos_token_id,
            is_encoder_decoder=is_encoder_decoder,
            **kwargs,
        )
```
`__init__.py`:
```
rom typing import TYPE_CHECKING

from transformers.utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_import_structure = {
    "model_configuration": [
        "SwitchTransformersConfig",
    ]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _import_structure["clustered_experts_ST"] = [
        "SwitchTransformersForConditionalGeneration",
        "SwitchTransformersModel",
        "SwitchTransformersPreTrainedModel",
        "SwitchTransformersTop1Router",
        "SwitchTransformersSparseMLP",
    ]


if TYPE_CHECKING:
    from .model_configuration import (
        SwitchTransformersConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .clustered_experts_ST import (
            SwitchTransformersForConditionalGeneration,
            SwitchTransformersModel,
            SwitchTransformersPreTrainedModel,
            SwitchTransformersSparseMLP,
            SwitchTransformersTop1Router,
        )


else:
    import sys
    
    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
```