"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import math
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...integrations.deepspeed import is_deepspeed_zero3_enabled
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
from ...modeling_outputs import (MoEModelOutput, MoEModelOutputWithPastAndCrossAttentions, Seq2SeqMoEModelOutput, Seq2SeqMoEOutput)
from ...modeling_utils import PreTrainedModel
from ...utils import (add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings)
from .configuration_nllb_moe import NllbMoeConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "NllbMoeConfig"
_CHECKPOINT_FOR_DOC = "hf-internal-testing/dummy-nllb-moe-2-experts"
_REAL_CHECKPOINT_FOR_DOC = "facebook/nllb-moe-54b"
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
    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.")
    shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
    return shifted_input_ids
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
    mask = input_ids.ne(padding_idx).int()
    incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
    return incremental_indices.long() + padding_idx
def load_balancing_loss_func(router_probs: torch.Tensor, expert_indices: torch.Tensor) -> float:
    if router_probs is None: return 0
    num_experts = router_probs.shape[-1]
    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)
    expert_mask = torch.max(expert_mask, axis=-2).values
    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 NllbMoeScaledWordEmbedding(nn.Embedding):
    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0):
        super().__init__(num_embeddings, embedding_dim, padding_idx)
        self.embed_scale = embed_scale
    def forward(self, input_ids: torch.Tensor): return super().forward(input_ids) * self.embed_scale
class NllbMoeSinusoidalPositionalEmbedding(nn.Module):
    def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
        super().__init__()
        self.offset = 2
        self.embedding_dim = embedding_dim
        self.padding_idx = padding_idx
        self.make_weights(num_positions + self.offset, embedding_dim, padding_idx)
    def make_weights(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
        emb_weights = self.get_embedding(num_embeddings, embedding_dim, padding_idx)
        if hasattr(self, "weights"): emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device)
        self.register_buffer("weights", emb_weights, persistent=False)
    @staticmethod
    def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
        half_dim = embedding_dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb)
        emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0)
        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
        if embedding_dim % 2 == 1: emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
        if padding_idx is not None: emb[padding_idx, :] = 0
        return emb.to(torch.get_default_dtype())
    @torch.no_grad()
    def forward(self, input_ids: torch.Tensor = None, inputs_embeds: torch.Tensor = None, past_key_values_length: int = 0):
        if input_ids is not None:
            bsz, seq_len = input_ids.size()
            position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length).to(input_ids.device)
        else:
            bsz, seq_len = inputs_embeds.size()[:-1]
            position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds, past_key_values_length)
        max_pos = self.padding_idx + 1 + seq_len + past_key_values_length
        if max_pos > self.weights.size(0): self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx)
        return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, self.weights.shape[-1]).detach()
    def create_position_ids_from_inputs_embeds(self, inputs_embeds, past_key_values_length):
        input_shape = inputs_embeds.size()[:-1]
        sequence_length = input_shape[1]
        position_ids = torch.arange(self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device)
        return position_ids.unsqueeze(0).expand(input_shape).contiguous() + past_key_values_length
class NllbMoeTop2Router(nn.Module):
    def __init__(self, config: NllbMoeConfig):
        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.router_ignore_padding_tokens = config.router_ignore_padding_tokens
        self.dtype = getattr(torch, config.router_dtype)
        self.second_expert_policy = config.second_expert_policy
        self.normalize_router_prob_before_dropping = config.normalize_router_prob_before_dropping
        self.batch_prioritized_routing = config.batch_prioritized_routing
        self.moe_eval_capacity_token_fraction = config.moe_eval_capacity_token_fraction
    def _cast_classifier(self):
        if not (hasattr(self.classifier, "SCB") or hasattr(self.classifier, "CB")): self.classifier = self.classifier.to(self.dtype)
    def normalize_router_probabilities(self, router_probs, top_1_mask, top_2_mask):
        top_1_max_probs = (router_probs * top_1_mask).sum(dim=1)
        top_2_max_probs = (router_probs * top_2_mask).sum(dim=1)
        denom_s = torch.clamp(top_1_max_probs + top_2_max_probs, min=torch.finfo(router_probs.dtype).eps)
        top_1_max_probs = top_1_max_probs / denom_s
        top_2_max_probs = top_2_max_probs / denom_s
        return top_1_max_probs, top_2_max_probs
    def route_tokens(self, router_logits: torch.Tensor, input_dtype: torch.dtype = torch.float32, padding_mask: Optional[torch.LongTensor] = None) -> Tuple:
        nb_tokens = router_logits.shape[0]
        router_probs = nn.functional.softmax(router_logits, dim=-1, dtype=self.dtype).to(input_dtype)
        top_1_expert_index = torch.argmax(router_probs, dim=-1)
        top_1_mask = torch.nn.functional.one_hot(top_1_expert_index, num_classes=self.num_experts)
        if self.second_expert_policy == "sampling":
            gumbel = torch.distributions.gumbel.Gumbel(0, 1).rsample
            router_logits += gumbel(router_logits.shape).to(router_logits.device)
        logits_except_top_1 = router_logits.masked_fill(top_1_mask.bool(), float("-inf"))
        top_2_expert_index = torch.argmax(logits_except_top_1, dim=-1)
        top_2_mask = torch.nn.functional.one_hot(top_2_expert_index, num_classes=self.num_experts)
        if self.normalize_router_prob_before_dropping: top_1_max_probs, top_2_max_probs = self.normalize_router_probabilities(router_probs, top_1_mask, top_2_mask)
        if self.second_expert_policy == "random":
            top_2_max_probs = (router_probs * top_2_mask).sum(dim=1)
            sampled = (2 * top_2_max_probs) > torch.rand_like(top_2_max_probs.float())
            top_2_mask = top_2_mask * sampled.repeat(self.num_experts, 1).transpose(1, 0)
        if padding_mask is not None and not self.router_ignore_padding_tokens:
            if len(padding_mask.shape) == 4: padding_mask = padding_mask[:, :, -1, :].reshape(-1)[-nb_tokens:]
            non_padding = ~padding_mask.bool()
            top_1_mask = top_1_mask * non_padding.unsqueeze(-1).to(top_1_mask.dtype)
            top_2_mask = top_2_mask * non_padding.unsqueeze(-1).to(top_1_mask.dtype)
        if self.batch_prioritized_routing:
            importance_scores = -1 * router_probs.max(dim=1)[0]
            sorted_top_1_mask = top_1_mask[importance_scores.argsort(dim=0)]
            sorted_cumsum1 = (torch.cumsum(sorted_top_1_mask, dim=0) - 1) * sorted_top_1_mask
            locations1 = sorted_cumsum1[importance_scores.argsort(dim=0).argsort(dim=0)]
            sorted_top_2_mask = top_2_mask[importance_scores.argsort(dim=0)]
            sorted_cumsum2 = (torch.cumsum(sorted_top_2_mask, dim=0) - 1) * sorted_top_2_mask
            locations2 = sorted_cumsum2[importance_scores.argsort(dim=0).argsort(dim=0)]
            locations2 += torch.sum(top_1_mask, dim=0, keepdim=True)
        else:
            locations1 = torch.cumsum(top_1_mask, dim=0) - 1
            locations2 = torch.cumsum(top_2_mask, dim=0) - 1
            locations2 += torch.sum(top_1_mask, dim=0, keepdim=True)
        if not self.training and self.moe_eval_capacity_token_fraction > 0: self.expert_capacity = math.ceil(self.moe_eval_capacity_token_fraction * nb_tokens)
        else:
            capacity = 2 * math.ceil(nb_tokens / self.num_experts)
            self.expert_capacity = capacity if self.expert_capacity is None else self.expert_capacity
        top_1_mask = top_1_mask * torch.lt(locations1, self.expert_capacity)
        top_2_mask = top_2_mask * torch.lt(locations2, self.expert_capacity)
        if not self.normalize_router_prob_before_dropping: top_1_max_probs, top_2_max_probs = self.normalize_router_probabilities(router_probs, top_1_mask, top_2_mask)
        gates1 = top_1_max_probs[:, None] * top_1_mask
        gates2 = top_2_max_probs[:, None] * top_2_mask
        router_probs = gates1 + gates2
        return top_1_mask, router_probs
    def forward(self, hidden_states: torch.Tensor, padding_mask: Optional[torch.LongTensor] = None) -> Tuple:
        self.input_dtype = hidden_states.dtype
        batch_size, sequence_length, hidden_dim = hidden_states.shape
        hidden_states = hidden_states.reshape((batch_size * sequence_length), hidden_dim)
        hidden_states = hidden_states.to(self.dtype)
        self._cast_classifier()
        router_logits = self.classifier(hidden_states)
        top_1_mask, router_probs = self.route_tokens(router_logits, self.input_dtype, padding_mask)
        return top_1_mask, router_probs
class NllbMoeDenseActDense(nn.Module):
    def __init__(self, config: NllbMoeConfig, ffn_dim: int):
        super().__init__()
        self.fc1 = nn.Linear(config.d_model, ffn_dim)
        self.fc2 = nn.Linear(ffn_dim, config.d_model)
        self.dropout = nn.Dropout(config.activation_dropout)
        self.act = ACT2FN[config.activation_function]
    def forward(self, hidden_states):
        hidden_states = self.fc1(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.dropout(hidden_states)
        if (isinstance(self.fc2.weight, torch.Tensor) and hidden_states.dtype != self.fc2.weight.dtype and (self.fc2.weight.dtype != torch.int8 and self.fc2.weight.dtype != torch.uint8)): hidden_states = hidden_states.to(self.fc2.weight.dtype)
        hidden_states = self.fc2(hidden_states)
        return hidden_states
class NllbMoeSparseMLP(nn.Module):
    def __init__(self, config: NllbMoeConfig, ffn_dim: int, expert_class: nn.Module = NllbMoeDenseActDense):
        super().__init__()
        self.router = NllbMoeTop2Router(config)
        self.moe_token_dropout = config.moe_token_dropout
        self.token_dropout = nn.Dropout(self.moe_token_dropout)
        self.num_experts = config.num_experts
        self.experts = nn.ModuleDict()
        for idx in range(self.num_experts): self.experts[f"expert_{idx}"] = expert_class(config, ffn_dim)
    def forward(self, hidden_states: torch.Tensor, padding_mask: Optional[torch.Tensor] = False):
        batch_size, sequence_length, hidden_dim = hidden_states.shape
        top_1_mask, router_probs = self.router(hidden_states, padding_mask)
        router_mask = router_probs.bool()
        hidden_states = hidden_states.reshape((batch_size * sequence_length), hidden_dim)
        masked_hidden_states = torch.einsum("bm,be->ebm", hidden_states, router_mask)
        for idx, expert in enumerate(self.experts.values()):
            token_indices = router_mask[:, idx]
            combining_weights = router_probs[token_indices, idx]
            expert_output = expert(masked_hidden_states[idx, token_indices])
            if self.moe_token_dropout > 0:
                if self.training: expert_output = self.token_dropout(expert_output)
                else: expert_output *= 1 - self.moe_token_dropout
            masked_hidden_states[idx, token_indices] = torch.einsum("b,be->be", combining_weights, expert_output)
        hidden_states = masked_hidden_states.sum(dim=0).reshape(batch_size, sequence_length, hidden_dim)
        top_1_expert_index = torch.argmax(top_1_mask, dim=-1)
        return hidden_states, (router_probs, top_1_expert_index)
class NllbMoeAttention(nn.Module):
    def __init__(self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, is_causal: bool = False, config: Optional[NllbMoeConfig] = None):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        self.config = config
        if (self.head_dim * num_heads) != self.embed_dim: raise ValueError(f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {num_heads}).")
        self.scaling = self.head_dim**-0.5
        self.is_decoder = is_decoder
        self.is_causal = is_causal
        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
    def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None,
    attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        is_cross_attention = encoder_hidden_states is not None
        bsz, tgt_len, _ = hidden_states.size()
        query_states = self.q_proj(hidden_states) * self.scaling
        if (is_cross_attention and past_key_value is not None and past_key_value[0].shape[2] == encoder_hidden_states.shape[1]):
            key_states = past_key_value[0]
            value_states = past_key_value[1]
        elif is_cross_attention:
            key_states = self._shape(self.k_proj(encoder_hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(encoder_hidden_states), -1, bsz)
        elif past_key_value is not None:
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)
        else:
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
        if self.is_decoder: past_key_value = (key_states, value_states)
        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
        key_states = key_states.reshape(*proj_shape)
        value_states = value_states.reshape(*proj_shape)
        src_len = key_states.size(1)
        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError(f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}")
        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError(f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}")
            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
        if layer_head_mask is not None:
            if layer_head_mask.size() != (self.num_heads,): raise ValueError(f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}")
            attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
        if output_attentions:
            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
        else: attn_weights_reshaped = None
        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
        attn_output = torch.bmm(attn_probs, value_states)
        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError(f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}")
        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
        attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
        attn_output = self.out_proj(attn_output)
        return attn_output, attn_weights_reshaped, past_key_value
class NllbMoeEncoderLayer(nn.Module):
    def __init__(self, config: NllbMoeConfig, is_sparse: bool = False):
        super().__init__()
        self.embed_dim = config.d_model
        self.is_sparse = is_sparse
        self.self_attn = NllbMoeAttention(embed_dim=self.embed_dim, num_heads=config.encoder_attention_heads, dropout=config.attention_dropout)
        self.attn_dropout = nn.Dropout(config.dropout)
        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        if not self.is_sparse: self.ffn = NllbMoeDenseActDense(config, ffn_dim=config.encoder_ffn_dim)
        else: self.ffn = NllbMoeSparseMLP(config, ffn_dim=config.encoder_ffn_dim)
        self.ff_layer_norm = nn.LayerNorm(config.d_model)
        self.ff_dropout = nn.Dropout(config.activation_dropout)
    def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, layer_head_mask: torch.Tensor, output_attentions: bool = False, output_router_logits: bool = False) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)
        hidden_states, attn_weights, _ = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions)
        hidden_states = self.attn_dropout(hidden_states)
        hidden_states = residual + hidden_states
        residual = hidden_states
        hidden_states = self.ff_layer_norm(hidden_states)
        if self.is_sparse: hidden_states, router_states = self.ffn(hidden_states, attention_mask)
        else: hidden_states, router_states = self.ffn(hidden_states), None
        hidden_states = self.ff_dropout(hidden_states)
        hidden_states = residual + hidden_states
        if hidden_states.dtype == torch.float16 and (torch.isinf(hidden_states).any() or torch.isnan(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 output_attentions: outputs += (attn_weights,)
        if output_router_logits: outputs += (router_states,)
        return outputs
class NllbMoeDecoderLayer(nn.Module):
    def __init__(self, config: NllbMoeConfig, is_sparse: bool = False):
        super().__init__()
        self.embed_dim = config.d_model
        self.is_sparse = is_sparse
        self.self_attn = NllbMoeAttention(embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True)
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.attn_dropout = nn.Dropout(config.dropout)
        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.cross_attention = NllbMoeAttention(self.embed_dim, config.decoder_attention_heads, config.attention_dropout, is_decoder=True)
        self.cross_attention_layer_norm = nn.LayerNorm(self.embed_dim)
        if not self.is_sparse: self.ffn = NllbMoeDenseActDense(config, ffn_dim=config.decoder_ffn_dim)
        else: self.ffn = NllbMoeSparseMLP(config, ffn_dim=config.decoder_ffn_dim)
        self.ff_layer_norm = nn.LayerNorm(config.d_model)
        self.ff_dropout = nn.Dropout(config.activation_dropout)
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None,
    encoder_attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
    past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, output_router_logits: Optional[bool] = False,
    use_cache: Optional[bool] = True) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        hidden_states, self_attn_weights, present_key_value = self.self_attn(hidden_states=hidden_states, past_key_value=self_attn_past_key_value, attention_mask=attention_mask,
        layer_head_mask=layer_head_mask, output_attentions=output_attentions)
        hidden_states = self.attn_dropout(hidden_states)
        hidden_states = residual + hidden_states
        cross_attn_present_key_value = None
        cross_attn_weights = None
        if encoder_hidden_states is not None:
            residual = hidden_states
            hidden_states = self.cross_attention_layer_norm(hidden_states)
            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
            hidden_states, cross_attn_weights, cross_attn_present_key_value = self.cross_attention(hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states,
            past_key_value=cross_attn_past_key_value, attention_mask=encoder_attention_mask, layer_head_mask=cross_attn_layer_head_mask, output_attentions=output_attentions)
            hidden_states = self.attn_dropout(hidden_states)
            hidden_states = residual + hidden_states
            present_key_value += cross_attn_present_key_value
        residual = hidden_states
        hidden_states = self.ff_layer_norm(hidden_states)
        if self.is_sparse: hidden_states, router_states = self.ffn(hidden_states, attention_mask)
        else: hidden_states, router_states = self.ffn(hidden_states), None
        hidden_states = self.ff_dropout(hidden_states)
        hidden_states = residual + hidden_states
        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, present_key_value)
        if output_attentions: outputs += (self_attn_weights, cross_attn_weights)
        if output_router_logits: outputs += (router_states,)
        return outputs
class NllbMoePreTrainedModel(PreTrainedModel):
    config_class = NllbMoeConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["NllbMoeEncoderLayer", "NllbMoeDecoderLayer"]
    def _init_weights(self, module):
        std = self.config.init_std
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_()
NLLB_MOE_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, 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 ([`NllbMoeConfig`]):
            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.
"""
model_generate = 'model.generate(**model_inputs, forced_bos_token_id=tokenizer.get_lang_id("eng_Latn"))'
NLLB_MOE_GENERATION_EXAMPLE = r"""
    Translation example:
    ```python
    >>> from sapiens_transformers import AutoTokenizer, NllbMoeForConditionalGeneration
    >>> model = NllbMoeForConditionalGeneration.from_pretrained("facebook/nllb-moe-54b")
    >>> tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-moe-54b")
    >>> text_to_translate = "Life is like a box of chocolates"
    >>> model_inputs = tokenizer(text_to_translate, return_tensors="pt")
    >>> # translate to French
    >>> gen_tokens = """+model_generate+r"""
    >>> print(tokenizer.batch_decode(gen_tokens, skip_special_tokens=True))
    ```
"""
NLLB_MOE_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` 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)
            NllbMoe uses the `eos_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`).
        decoder_attention_mask (`torch.LongTensor` 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.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the 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.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the 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 `(decoder_layers, decoder_attention_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)`, *optional*) 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))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)` and 2 additional tensors of shape
            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used (see `past_key_values` input) to speed up sequential 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.
"""
class NllbMoeEncoder(NllbMoePreTrainedModel):
    def __init__(self, config: NllbMoeConfig, embed_tokens: Optional[nn.Embedding] = None):
        super().__init__(config)
        self.dropout = config.dropout
        self.layerdrop = config.encoder_layerdrop
        embed_dim = config.d_model
        self.padding_idx = config.pad_token_id
        self.max_source_positions = config.max_position_embeddings
        embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
        self.embed_tokens = NllbMoeScaledWordEmbedding(config.vocab_size, embed_dim, self.padding_idx, embed_scale=embed_scale)
        if embed_tokens is not None: self.embed_tokens.weight = embed_tokens.weight
        self.embed_positions = NllbMoeSinusoidalPositionalEmbedding(config.max_position_embeddings, embed_dim, self.padding_idx)
        sparse_step = config.encoder_sparse_step
        self.layers = nn.ModuleList()
        for i in range(config.encoder_layers):
            is_sparse = (i + 1) % sparse_step == 0 if sparse_step > 0 else False
            self.layers.append(NllbMoeEncoderLayer(config, is_sparse))
        self.layer_norm = nn.LayerNorm(config.d_model)
        self.gradient_checkpointing = False
        self.post_init()
    def forward(self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None,
    inputs_embeds: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    output_router_logits: Optional[bool] = None, return_dict: Optional[bool] = None):
        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.return_dict
        if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
        elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1]
        else: raise ValueError("You have to specify either input_ids or inputs_embeds")
        if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids)
        embed_pos = self.embed_positions(input_ids, inputs_embeds)
        embed_pos = embed_pos.to(inputs_embeds.device)
        hidden_states = inputs_embeds + embed_pos
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        if attention_mask is not None: attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
        encoder_states = () if output_hidden_states else None
        all_router_probs = () if output_router_logits else None
        all_attentions = () if output_attentions else None
        if head_mask is not None:
            if head_mask.size()[0] != len(self.layers): raise ValueError(f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}.")
        for idx, encoder_layer in enumerate(self.layers):
            if output_hidden_states: encoder_states = encoder_states + (hidden_states,)
            dropout_probability = torch.rand([])
            if self.training and (dropout_probability < self.layerdrop): layer_outputs = (None, None, None)
            else:
                if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func(encoder_layer.__call__, hidden_states, attention_mask, (head_mask[idx] if head_mask is not None else None), output_attentions)
                else:
                    layer_outputs = encoder_layer(hidden_states, attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), output_attentions=output_attentions,
                    output_router_logits=output_router_logits)
                hidden_states = layer_outputs[0]
            if output_attentions: all_attentions += (layer_outputs[1],)
            if output_router_logits: all_router_probs += (layer_outputs[-1],)
        last_hidden_state = self.layer_norm(hidden_states)
        if output_hidden_states: encoder_states += (last_hidden_state,)
        if not return_dict: return tuple(v for v in [last_hidden_state, encoder_states, all_attentions, all_router_probs] if v is not None)
        return MoEModelOutput(last_hidden_state=last_hidden_state, hidden_states=encoder_states, attentions=all_attentions, router_probs=all_router_probs)
class NllbMoeDecoder(NllbMoePreTrainedModel):
    def __init__(self, config: NllbMoeConfig, embed_tokens: Optional[nn.Embedding] = None):
        super().__init__(config)
        self.dropout = config.dropout
        self.layerdrop = config.decoder_layerdrop
        self.padding_idx = config.pad_token_id
        self.max_target_positions = config.max_position_embeddings
        embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
        self.embed_tokens = NllbMoeScaledWordEmbedding(config.vocab_size, config.d_model, self.padding_idx, embed_scale=embed_scale)
        if embed_tokens is not None: self.embed_tokens.weight = embed_tokens.weight
        self.embed_positions = NllbMoeSinusoidalPositionalEmbedding(config.max_position_embeddings, config.d_model, self.padding_idx)
        sparse_step = config.decoder_sparse_step
        self.layers = nn.ModuleList()
        for i in range(config.decoder_layers):
            is_sparse = (i + 1) % sparse_step == 0 if sparse_step > 0 else False
            self.layers.append(NllbMoeDecoderLayer(config, is_sparse))
        self.layer_norm = nn.LayerNorm(config.d_model)
        self.gradient_checkpointing = False
        self.post_init()
    def forward(self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None,
    encoder_attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None,
    past_key_values: Optional[List[torch.FloatTensor]] = None, 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):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.return_dict
        if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both decoder_input_ids and decoder_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: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
        if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids)
        combined_attention_mask = _prepare_4d_causal_attention_mask(attention_mask, input_shape, inputs_embeds, past_key_values_length)
        if encoder_hidden_states is not None and encoder_attention_mask is not None: encoder_attention_mask = _prepare_4d_attention_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
        positions = self.embed_positions(input_ids, inputs_embeds, past_key_values_length)
        positions = positions.to(inputs_embeds.device)
        hidden_states = inputs_embeds + positions
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        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
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_router_probs = () if output_router_logits else None
        all_cross_attentions = () if output_attentions else None
        present_key_value_states = () if use_cache else None
        for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
            if attn_mask is not None:
                if attn_mask.size()[0] != len(self.layers): raise ValueError(f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}.")
        deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled()
        for idx, decoder_layer in enumerate(self.layers):
            if output_hidden_states: all_hidden_states += (hidden_states,)
            dropout_probability = torch.rand([])
            skip_the_layer = True if self.training and (dropout_probability < self.layerdrop) else False
            if not skip_the_layer or deepspeed_zero3_is_enabled:
                layer_head_mask = head_mask[idx] if head_mask is not None else None
                cross_attn_layer_head_mask = cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
                past_key_value = past_key_values[idx] if past_key_values is not None else None
                if self.gradient_checkpointing and self.training:
                    if use_cache:
                        logger.warning_once("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
                        use_cache = False
                    layer_outputs = self._gradient_checkpointing_func(decoder_layer.forward, hidden_states, combined_attention_mask, encoder_hidden_states,
                    encoder_attention_mask, layer_head_mask, cross_attn_layer_head_mask, None, use_cache, output_attentions)
                else:
                    layer_outputs = decoder_layer(hidden_states, attention_mask=combined_attention_mask, encoder_hidden_states=encoder_hidden_states,
                    encoder_attention_mask=encoder_attention_mask, 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)
                hidden_states = layer_outputs[0]
            if skip_the_layer: continue
            if use_cache: present_key_value_states += (layer_outputs[1],)
            if output_attentions:
                all_self_attns += (layer_outputs[2],)
                all_cross_attentions += (layer_outputs[3],)
            if output_router_logits: all_router_probs += (layer_outputs[-1],)
        hidden_states = self.layer_norm(hidden_states)
        if output_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_self_attns, 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_self_attns, cross_attentions=all_cross_attentions, router_probs=all_router_probs)
@add_start_docstrings("The bare NllbMoe Model outputting raw hidden-states without any specific head on top.", NLLB_MOE_START_DOCSTRING)
class NllbMoeModel(NllbMoePreTrainedModel):
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
    def __init__(self, config: NllbMoeConfig):
        super().__init__(config)
        padding_idx, vocab_size = config.pad_token_id, config.vocab_size
        embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
        self.shared = NllbMoeScaledWordEmbedding(vocab_size, config.d_model, padding_idx, embed_scale=embed_scale)
        self.encoder = NllbMoeEncoder(config, self.shared)
        self.decoder = NllbMoeDecoder(config, self.shared)
        self.post_init()
    def get_input_embeddings(self): return self.shared
    def set_input_embeddings(self, value):
        self.shared = value
        self.encoder.embed_tokens = self.shared
        self.decoder.embed_tokens = self.shared
    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
    @add_start_docstrings_to_model_forward(NLLB_MOE_INPUTS_DOCSTRING)
    @add_start_docstrings_to_model_forward(NLLB_MOE_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None,
    decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = 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.FloatTensor] = None, decoder_inputs_embeds: Optional[torch.FloatTensor] = 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.Tensor], Seq2SeqMoEModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.return_dict
        if encoder_outputs is None:
            encoder_outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, 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)
        decoder_outputs = self.decoder(input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, encoder_hidden_states=encoder_outputs[0],
        encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values,
        inputs_embeds=decoder_inputs_embeds, 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(past_key_values=decoder_outputs.past_key_values, cross_attentions=decoder_outputs.cross_attentions, last_hidden_state=decoder_outputs.last_hidden_state,
        encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, decoder_hidden_states=decoder_outputs.hidden_states,
        encoder_attentions=encoder_outputs.attentions, decoder_attentions=decoder_outputs.attentions, encoder_router_logits=encoder_outputs.router_probs,
        decoder_router_logits=decoder_outputs.router_probs)
@add_start_docstrings("The NllbMoe Model with a language modeling head. Can be used for summarization.", NLLB_MOE_START_DOCSTRING)
class NllbMoeForConditionalGeneration(NllbMoePreTrainedModel, GenerationMixin):
    base_model_prefix = "model"
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
    def __init__(self, config: NllbMoeConfig):
        super().__init__(config)
        self.model = NllbMoeModel(config)
        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
        self.post_init()
    def get_encoder(self): return self.model.get_encoder()
    def get_decoder(self): return self.model.get_decoder()
    def get_output_embeddings(self): return self.lm_head
    def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings
    @add_start_docstrings_to_model_forward(NLLB_MOE_INPUTS_DOCSTRING)
    @add_end_docstrings(NLLB_MOE_GENERATION_EXAMPLE)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None,
    decoder_attention_mask: Optional[torch.LongTensor] = None, head_mask: Optional[torch.Tensor] = None, decoder_head_mask: Optional[torch.Tensor] = 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.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] = None,
    return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], Seq2SeqMoEOutput]:
        return_dict = return_dict if return_dict is not None else self.config.return_dict
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_router_logits = (output_router_logits if output_router_logits is not None else self.config.output_router_logits)
        if labels is not None:
            if decoder_input_ids is None: decoder_input_ids = shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
        outputs = self.model(input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, encoder_outputs=encoder_outputs, decoder_attention_mask=decoder_attention_mask,
        head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds,
        decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states,
        output_router_logits=output_router_logits, return_dict=return_dict)
        lm_logits = self.lm_head(outputs[0])
        loss = None
        encoder_aux_loss = None
        decoder_aux_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss(ignore_index=-100)
            if output_router_logits:
                encoder_router_logits = outputs[-1]
                decoder_router_logits = outputs[3 if output_attentions else 4]
                encoder_router_logits, encoder_expert_indexes = self._unpack_router_logits(encoder_router_logits)
                encoder_aux_loss = load_balancing_loss_func(encoder_router_logits, encoder_expert_indexes)
                decoder_router_logits, decoder_expert_indexes = self._unpack_router_logits(decoder_router_logits)
                decoder_aux_loss = load_balancing_loss_func(decoder_router_logits, 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:
                aux_loss = self.router_aux_loss_coef * (encoder_aux_loss + decoder_aux_loss)
                loss = loss + aux_loss
        output = (loss,) if loss is not None else ()
        if not return_dict:
            output += (lm_logits,)
            if output_router_logits: output += (encoder_aux_loss, decoder_aux_loss, *outputs[1:])
            else: output += outputs[1:]
            return output
        return Seq2SeqMoEOutput(loss=loss, logits=lm_logits, past_key_values=outputs.past_key_values, cross_attentions=outputs.cross_attentions, encoder_aux_loss=encoder_aux_loss,
        decoder_aux_loss=decoder_aux_loss, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states,
        decoder_hidden_states=outputs.decoder_hidden_states, encoder_attentions=outputs.encoder_attentions, decoder_attentions=outputs.decoder_attentions,
        encoder_router_logits=outputs.encoder_router_logits, decoder_router_logits=outputs.decoder_router_logits)
    def _unpack_router_logits(self, router_outputs):
        total_router_logits = []
        total_expert_indexes = []
        for router_output in router_outputs:
            if router_output is not None:
                router_logits, expert_indexes = router_output
                total_router_logits.append(router_logits)
                total_expert_indexes.append(expert_indexes)
        total_router_logits = torch.cat(total_router_logits, dim=1) if len(total_router_logits) > 0 else None
        total_expert_indexes = torch.stack(total_expert_indexes, dim=1) if len(total_expert_indexes) > 0 else None
        return total_router_logits, total_expert_indexes
    def prepare_inputs_for_generation(self, decoder_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):
        if past_key_values is not None:
            past_length = past_key_values[0][0].shape[2]
            if decoder_input_ids.shape[1] > past_length: remove_prefix_length = past_length
            else: remove_prefix_length = decoder_input_ids.shape[1] - 1
            decoder_input_ids = decoder_input_ids[:, remove_prefix_length:]
        return {"input_ids": None, "encoder_outputs": encoder_outputs, "past_key_values": past_key_values, "decoder_input_ids": decoder_input_ids, "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}
    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values: reordered_past += (tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),)
        return reordered_past
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
