"""
    #################################################################################################################################################################################################
    # 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.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from torch.nn import functional as F
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache, StaticCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...modeling_outputs import (MoeCausalLMOutputWithPast, MoeModelOutputWithPast, SequenceClassifierOutputWithPast)
from ...modeling_utils import PreTrainedModel
from ...utils import (add_start_docstrings, add_start_docstrings_to_model_forward, is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10,
is_torchdynamo_compiling, logging, replace_return_docstrings)
from .configuration_jetmoe import JetMoeConfig
if is_flash_attn_2_available(): from ...modeling_flash_attention_utils import _flash_attention_forward
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "jetmoe"
_CONFIG_FOR_DOC = "JetMoeConfig"
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,
min_dtype: float, cache_position: torch.Tensor, batch_size: int):
    if attention_mask is not None and attention_mask.dim() == 4: causal_mask = attention_mask
    else:
        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()
            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
def load_balancing_loss_func(gate_logits: torch.Tensor, num_experts: torch.Tensor = None, top_k=2, attention_mask: Optional[torch.Tensor] = None) -> float:
    if gate_logits is None or not isinstance(gate_logits, tuple): return 0
    if isinstance(gate_logits, tuple):
        compute_device = gate_logits[0].device
        concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
    _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
    expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
    if attention_mask is None:
        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
        router_prob_per_expert = torch.mean(routing_weights, dim=0)
    else:
        batch_size, sequence_length = attention_mask.shape
        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
        expert_attention_mask = (attention_mask[None, :, :, None, None].expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
        .reshape(-1, top_k, num_experts).to(compute_device))
        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(expert_attention_mask, dim=0)
        router_per_expert_attention_mask = (attention_mask[None, :, :, None].expand((num_hidden_layers, batch_size, sequence_length, num_experts))
        .reshape(-1, num_experts).to(compute_device))
        router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(router_per_expert_attention_mask, dim=0)
    overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
    return overall_loss * num_experts
class JetMoeParallelExperts(nn.Module):
    def __init__(self, num_experts: int, input_size: int, output_size: int) -> None:
        super().__init__()
        self.weight = nn.Parameter(torch.empty(num_experts, output_size, input_size))
        self.num_experts = num_experts
        self.input_size = input_size
        self.output_size = output_size
    def forward(self, inputs, expert_size):
        input_list = inputs.split(expert_size, dim=0)
        output_list = []
        for i in range(self.num_experts): output_list.append(F.linear(input_list[i], self.weight[i]))
        results = torch.cat(output_list, dim=0)
        return results
class JetMoeTopKGating(nn.Module):
    def __init__(self, input_size: int, num_experts: int, top_k: int):
        super().__init__()
        self.num_experts = num_experts
        self.input_size = input_size
        self.top_k = top_k
        self.layer = nn.Linear(input_size, num_experts, bias=False)
    def forward(self, hidden_states):
        logits = self.layer(hidden_states).float()
        top_k_logits, top_k_indices = logits.topk(self.top_k, dim=1)
        top_k_gates = torch.softmax(top_k_logits, dim=1).type_as(hidden_states)
        zeros = torch.zeros([top_k_gates.size(0), self.num_experts], dtype=top_k_gates.dtype, device=top_k_gates.device)
        gates = zeros.scatter(1, top_k_indices, 1)
        expert_size = gates.long().sum(0)
        expert_size = expert_size.tolist()
        top_k_experts = top_k_indices.flatten()
        _, index_sorted_experts = top_k_experts.sort(0)
        batch_index = index_sorted_experts.div(self.top_k, rounding_mode="trunc")
        top_k_gates = top_k_gates.flatten()
        batch_gates = top_k_gates[index_sorted_experts]
        return index_sorted_experts, batch_index, batch_gates, expert_size, logits
class JetMoeMoE(nn.Module):
    def __init__(self, config: JetMoeConfig):
        super(JetMoeMoE, self).__init__()
        self.input_size = config.hidden_size
        self.hidden_size = config.intermediate_size
        self.activation = ACT2FN[config.activation_function]
        self.bias = torch.nn.Parameter(torch.empty(self.input_size))
        self.input_linear = JetMoeParallelExperts(config.num_local_experts, self.input_size, self.hidden_size * 2)
        self.output_linear = JetMoeParallelExperts(config.num_local_experts, self.hidden_size, self.input_size)
        self.router = JetMoeTopKGating(input_size=self.input_size, num_experts=config.num_local_experts, top_k=config.num_experts_per_tok)
    def forward(self, layer_input):
        bsz, length, emb_size = layer_input.size()
        layer_input = layer_input.reshape(-1, emb_size)
        _, batch_index, batch_gates, expert_size, router_logits = self.router(layer_input)
        expert_inputs = layer_input[batch_index]
        hidden_states = self.input_linear(expert_inputs, expert_size)
        chunked_hidden_states = hidden_states.chunk(2, dim=-1)
        hidden_states = self.activation(chunked_hidden_states[0]) * chunked_hidden_states[1]
        expert_outputs = self.output_linear(hidden_states, expert_size)
        expert_outputs = expert_outputs * batch_gates[:, None]
        zeros = torch.zeros((bsz * length, self.input_size), dtype=expert_outputs.dtype, device=expert_outputs.device)
        layer_output = zeros.index_add(0, batch_index, expert_outputs)
        layer_output = layer_output.view(bsz, length, self.input_size)
        layer_output = layer_output + self.bias
        return layer_output, router_logits
class JetMoeMoA(nn.Module):
    def __init__(self, config: JetMoeConfig):
        super(JetMoeMoA, self).__init__()
        self.num_experts = config.num_local_experts
        self.input_size = config.hidden_size
        self.hidden_size = config.kv_channels * config.num_key_value_heads
        self.top_k = config.num_experts_per_tok
        self.bias = torch.nn.Parameter(torch.empty(self.input_size))
        self.input_linear = JetMoeParallelExperts(self.num_experts, self.input_size, self.hidden_size)
        self.output_linear = JetMoeParallelExperts(self.num_experts, self.hidden_size, self.input_size)
        self.router = JetMoeTopKGating(input_size=self.input_size, num_experts=self.num_experts, top_k=self.top_k)
    def map(self, layer_input):
        bsz, length, emb_size = layer_input.size()
        layer_input = layer_input.reshape(-1, emb_size)
        index_sorted_experts, batch_index, batch_gates, expert_size, router_logits = self.router(layer_input)
        topo_info = (index_sorted_experts, batch_index, batch_gates, expert_size)
        expert_inputs = layer_input[batch_index]
        expert_outputs = self.input_linear(expert_inputs, expert_size)
        zeros = torch.zeros((bsz * length * self.top_k, self.hidden_size), dtype=expert_outputs.dtype, device=expert_outputs.device)
        layer_output = zeros.index_add(0, index_sorted_experts, expert_outputs)
        layer_output = layer_output.view(bsz, length, self.top_k, -1)
        return layer_output, router_logits, topo_info
    def reduce(self, layer_input, topo_info):
        bsz, length, k, hidden_size = layer_input.size()
        layer_input = layer_input.reshape(-1, hidden_size)
        index_sorted_experts, batch_index, batch_gates, expert_size = topo_info
        expert_inputs = layer_input[index_sorted_experts]
        expert_outputs = self.output_linear(expert_inputs, expert_size)
        expert_outputs = expert_outputs * batch_gates[:, None]
        zeros = torch.zeros((bsz * length, self.input_size), dtype=expert_outputs.dtype, device=expert_outputs.device)
        layer_output = zeros.index_add(0, batch_index, expert_outputs)
        layer_output = layer_output.view(bsz, length, self.input_size)
        layer_output = layer_output + self.bias
        return layer_output
    def forward(self, layer_input): raise NotImplementedError("This module doesn't support call and forward.")
class JetMoeRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps
    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)
    def extra_repr(self): return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
class JetMoeRotaryEmbedding(nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
        super().__init__()
        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float() / self.dim))
        self.register_buffer("inv_freq", tensor=inv_freq, persistent=False)
    @torch.no_grad()
    def forward(self, x, position_ids, seq_len=None):
        self.inv_freq.to(x.device)
        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
        position_ids_expanded = position_ids[:, None, :].float()
        device_type = x.device.type
        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
        with torch.autocast(device_type=device_type, enabled=False):
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos()
            sin = emb.sin()
        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
def rotate_half(x):
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed
class JetMoeAttention(nn.Module):
    def __init__(self, config: JetMoeConfig, layer_idx: Optional[int] = None):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.is_causal = True
        if layer_idx is None: logger.warning_once(f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` when creating this class.")
        self.top_k = config.num_experts_per_tok
        self.attention_dropout = config.attention_dropout
        self.kv_projection_size = config.kv_channels * config.num_key_value_heads
        self.num_key_value_heads = config.num_key_value_heads
        self.num_heads = config.num_attention_heads
        self.head_dim = config.kv_channels
        self.experts = JetMoeMoA(config)
        self.kv_proj = torch.nn.Linear(config.hidden_size, self.kv_projection_size * 2, bias=False)
        self.rotary_emb = JetMoeRotaryEmbedding(config.kv_channels, max_position_embeddings=config.max_position_embeddings, base=config.rope_theta)
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None,
    output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        bsz, q_len, _ = hidden_states.size()
        query_states, router_logits, topo_info = self.experts.map(hidden_states)
        key_states, value_states = self.kv_proj(hidden_states).chunk(2, dim=-1)
        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        cos, sin = self.rotary_emb(value_states, position_ids)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
        if past_key_value is not None:
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
        key_states = key_states.repeat(1, self.top_k, 1, 1)
        value_states = value_states.repeat(1, self.top_k, 1, 1)
        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
        if attention_mask is not None:
            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
            attn_weights = attn_weights + causal_mask
        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
        attn_output = torch.matmul(attn_weights, value_states)
        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): raise ValueError(f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is {attn_output.size()}")
        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.reshape(bsz, q_len, self.top_k, self.kv_projection_size)
        attn_output = self.experts.reduce(attn_output, topo_info)
        attn_output = attn_output.view(bsz, q_len, -1)
        if not output_attentions: attn_weights = None
        return attn_output, attn_weights, past_key_value, router_logits
class JetMoeSdpaAttention(JetMoeAttention):
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None,
    output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]], Optional[torch.Tensor]]:
        if output_attentions:
            logger.warning_once("JetMoeModel is using JetMoeSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "+'but specifying the manual implementation will be required from sapiens_transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.')
            return super().forward(hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position)
        bsz, q_len, _ = hidden_states.size()
        query_states, router_logits, topo_info = self.experts.map(hidden_states)
        key_states, value_states = self.kv_proj(hidden_states).chunk(2, dim=-1)
        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        cos, sin = self.rotary_emb(value_states, position_ids)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
        if past_key_value is not None:
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
        key_states = key_states.repeat(1, self.top_k, 1, 1)
        value_states = value_states.repeat(1, self.top_k, 1, 1)
        causal_mask = attention_mask
        if attention_mask is not None: causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
        if query_states.device.type == "cuda" and causal_mask is not None:
            query_states = query_states.contiguous()
            key_states = key_states.contiguous()
            value_states = value_states.contiguous()
        is_causal = True if causal_mask is None and q_len > 1 else False
        attn_output = torch.nn.functional.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=causal_mask, dropout_p=self.attention_dropout if self.training else 0.0, is_causal=is_causal)
        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.reshape(bsz, q_len, self.top_k, self.kv_projection_size)
        attn_output = self.experts.reduce(attn_output, topo_info)
        attn_output = attn_output.view(bsz, q_len, -1)
        return attn_output, None, past_key_value, router_logits
class JetMoeFlashAttention2(JetMoeAttention):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
    def forward(self, hidden_states: Optional[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None,
    use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None) -> Union[Tuple[torch.Tensor, Tuple[torch.Tensor]],
    Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]]]:
        output_attentions = False
        bsz, q_len, hidden_size = hidden_states.size()
        query_states, router_logits, topo_info = self.experts.map(hidden_states)
        key_states, value_states = self.kv_proj(hidden_states).chunk(2, dim=-1)
        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        cos, sin = self.rotary_emb(value_states, position_ids)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
        if past_key_value is not None:
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
        key_states = key_states.repeat(1, self.top_k, 1, 1)
        value_states = value_states.repeat(1, self.top_k, 1, 1)
        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)
        dropout_rate = self.attention_dropout if self.training else 0.0
        input_dtype = query_states.dtype
        if input_dtype == torch.float32:
            if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype()
            elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype
            else: target_dtype = self.kv_proj.weight.dtype
            logger.warning_once(f"The input hidden states seems to be silently casted in float32, this might be related to the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in {target_dtype}.")
            query_states = query_states.to(target_dtype)
            key_states = key_states.to(target_dtype)
            value_states = value_states.to(target_dtype)
        attn_output = _flash_attention_forward(query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate, use_top_left_mask=self._flash_attn_uses_top_left_mask, is_causal=self.is_causal).to(input_dtype)
        attn_output = attn_output.reshape(bsz, q_len, self.top_k, self.kv_projection_size)
        attn_output = self.experts.reduce(attn_output, topo_info)
        attn_output = attn_output.view(bsz, q_len, hidden_size)
        if not output_attentions: attn_weights = None
        return attn_output, attn_weights, past_key_value, router_logits
JETMOE_ATTENTION_CLASSES = {"eager": JetMoeAttention, "flash_attention_2": JetMoeFlashAttention2, "sdpa": JetMoeSdpaAttention}
class JetMoeBlock(nn.Module):
    def __init__(self, config: JetMoeConfig, layer_idx: Optional[int] = None):
        super().__init__()
        self.input_layernorm = JetMoeRMSNorm(config.hidden_size)
        self.self_attention = JETMOE_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
        self.post_attention_layernorm = JetMoeRMSNorm(config.hidden_size)
        self.mlp = JetMoeMoE(config)
    def forward(self, hidden_states: Optional[torch.FloatTensor], position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None,
    attention_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, output_router_logits: Optional[bool] = False, use_cache: Optional[bool] = False,
    cache_position: Optional[torch.LongTensor] = None) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
        attn_output, self_attn_weights, present_key_value, attn_router_logits = self.self_attention(hidden_states=self.input_layernorm(hidden_states), attention_mask=attention_mask,
        position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position)
        hidden_states = hidden_states + attn_output
        x_mlp, mlp_router_logits = self.mlp(self.post_attention_layernorm(hidden_states))
        hidden_states = hidden_states + x_mlp
        outputs = (hidden_states,)
        if output_attentions: outputs += (self_attn_weights,)
        if use_cache: outputs += (present_key_value,)
        if output_router_logits: outputs += attn_router_logits, mlp_router_logits
        return outputs
class JetMoePreTrainedModel(PreTrainedModel):
    config_class = JetMoeConfig
    base_model_prefix = "transformer"
    supports_gradient_checkpointing = False
    _no_split_modules = ["JetMoeBlock"]
    _skip_keys_device_placement = ["past_key_values"]
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_cache_class = True
    def _init_weights(self, module):
        if isinstance(module, (nn.Linear,)):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        elif isinstance(module, JetMoeParallelExperts): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
        elif isinstance(module, JetMoeMoA): module.bias.data.zero_()
        elif isinstance(module, JetMoeMoE): module.bias.data.zero_()
JETMOE_START_DOCSTRING = r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.
    Parameters:
        config ([`JetMoeConfig`]): 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.
"""
JETMOE_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(0)`):
            Indices of input sequence tokens in the vocabulary.
            Indices can be obtained using [`AutoProcenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.FloatTensor` of shape `(0)`, *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)
        position_ids (`torch.LongTensor` of shape `(0)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.n_positions - 1]`.
            [What are position IDs?](../glossary#position-ids)
        inputs_embeds (`torch.FloatTensor` of shape `(0, hidden_dim)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
            model's internal embedding lookup matrix.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        output_router_logits (`bool`, *optional*):
            Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
            should not be returned during inference.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
            the complete sequence length.
"""
@add_start_docstrings("The bare JetMoe Model outputting raw hidden-states without any specific head on top.", JETMOE_START_DOCSTRING)
class JetMoeModel(JetMoePreTrainedModel):
    def __init__(self, config: JetMoeConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList([JetMoeBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
        self._attn_implementation = config._attn_implementation
        self.norm = JetMoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.gradient_checkpointing = False
        self.post_init()
    def get_input_embeddings(self): return self.embed_tokens
    def set_input_embeddings(self, value): self.embed_tokens = value
    @add_start_docstrings_to_model_forward(JETMOE_INPUTS_DOCSTRING)
    def forward(self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
    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, cache_position: Optional[torch.LongTensor] = None) -> Union[Tuple, MoeModelOutputWithPast]:
        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)
        output_router_logits = (output_router_logits if output_router_logits is not None else self.config.output_router_logits)
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one")
        if self.gradient_checkpointing and self.training and use_cache:
            logger.warning_once("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.")
            use_cache = False
        if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids)
        return_legacy_cache = False
        if use_cache and not isinstance(past_key_values, Cache):
            return_legacy_cache = True
            if past_key_values is None: past_key_values = DynamicCache()
            else:
                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
                logger.warning_once("We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and will be removed in v1.0.")
        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position = torch.arange(past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device)
        if position_ids is None: position_ids = cache_position.unsqueeze(0)
        if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
            batch_size = inputs_embeds.shape[0]
            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
            if is_padding_right: raise ValueError("You are attempting to perform batched generation with padding_side='right' this may lead to unexpected behaviour for Flash Attention version of JetMoe. Make sure to call `tokenizer.padding_side  = 'left'` before tokenizing the input. ")
        causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions)
        hidden_states = inputs_embeds
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_router_logits = () if output_router_logits else None
        next_decoder_cache = None
        for decoder_layer in self.layers:
            if output_hidden_states: all_hidden_states += (hidden_states,)
            if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func(decoder_layer.__call__, hidden_states, position_ids, past_key_values, causal_mask, output_attentions, output_router_logits, use_cache, use_reentrant=False)
            else: layer_outputs = decoder_layer(hidden_states, attention_mask=causal_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, output_router_logits=output_router_logits, use_cache=use_cache)
            hidden_states = layer_outputs[0]
            if use_cache: next_decoder_cache = layer_outputs[2 if output_attentions else 1]
            if output_attentions: all_self_attns += (layer_outputs[1],)
            if output_router_logits: all_router_logits += (layer_outputs[-2], layer_outputs[-1])
        hidden_states = self.norm(hidden_states)
        if output_hidden_states: all_hidden_states += (hidden_states,)
        next_cache = next_decoder_cache if use_cache else None
        if return_legacy_cache: next_cache = next_cache.to_legacy_cache()
        if not return_dict: return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
        return MoeModelOutputWithPast(last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, router_logits=all_router_logits)
    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
        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)
        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
        min_dtype = torch.finfo(dtype).min
        sequence_length = input_tensor.shape[1]
        if using_static_cache: target_length = past_key_values.get_max_length()
        else: target_length = (attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1)
        causal_mask = _prepare_4d_causal_attention_mask_with_cache_position(attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, device=device, min_dtype=min_dtype, 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): causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
        return causal_mask
class JetMoeForCausalLM(JetMoePreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]
    def __init__(self, config):
        super().__init__(config)
        self.model = JetMoeModel(config)
        self.vocab_size = config.vocab_size
        self.aux_loss_coef = config.aux_loss_coef
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.tie_word_embeddings = config.tie_word_embeddings
        self.post_init()
    def get_input_embeddings(self): return self.model.embed_tokens
    def set_input_embeddings(self, value): self.model.embed_tokens = value
    def get_output_embeddings(self): return self.lm_head
    def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings
    def set_decoder(self, decoder): self.model = decoder
    def get_decoder(self): return self.model
    @add_start_docstrings_to_model_forward(JETMOE_INPUTS_DOCSTRING)
    def forward(self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None,
    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, cache_position: Optional[torch.LongTensor] = None,
    num_logits_to_keep: int = 0) -> Union[Tuple, MoeCausalLMOutputWithPast]:
        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
        outputs = self.model(input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds,
        use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position)
        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()
        loss = None
        if labels is not None:
            logits = logits.float()
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            shift_labels = shift_labels.to(shift_logits.device)
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(shift_logits, shift_labels)
        aux_loss = None
        if output_router_logits:
            aux_loss = load_balancing_loss_func(outputs.router_logits if return_dict else outputs[-1], self.num_experts, self.num_experts_per_tok, attention_mask)
            if labels is not None: loss += self.aux_loss_coef * aux_loss.to(loss.device)
        if not return_dict:
            output = (logits,) + outputs[1:]
            if output_router_logits: output = (aux_loss,) + output
            return (loss,) + output if loss is not None else output
        return MoeCausalLMOutputWithPast(loss=loss, aux_loss=aux_loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, router_logits=outputs.router_logits)
    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, cache_position=None, output_router_logits=False,
    position_ids=None, use_cache=True, num_logits_to_keep=None, **kwargs):
        if past_key_values is not None:
            if inputs_embeds is not None: input_ids = input_ids[:, -cache_position.shape[0] :]
            elif input_ids.shape[1] != cache_position.shape[0]: input_ids = input_ids[:, cache_position]
        if attention_mask is not None and position_ids is None:
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values: position_ids = position_ids[:, -input_ids.shape[1] :]
        if inputs_embeds is not None and cache_position[0] == 0: model_inputs = {"inputs_embeds": inputs_embeds}
        else: model_inputs = {"input_ids": input_ids.contiguous()}
        if num_logits_to_keep is not None: model_inputs["num_logits_to_keep"] = num_logits_to_keep
        model_inputs.update({"position_ids": position_ids, "cache_position": cache_position, "past_key_values": past_key_values, "use_cache": use_cache,
        "attention_mask": attention_mask, "output_router_logits": output_router_logits})
        return model_inputs
@add_start_docstrings("""
    The JetMoe Model transformer with a sequence classification head on top (linear layer).
    [`JetMoeForSequenceClassification`] uses the last token in order to do the classification, as other causal models
    (e.g. GPT-2) do.
    Since it does classification on the last token, it requires to know the position of the last token. If a
    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
    each row of the batch).
    """, JETMOE_START_DOCSTRING)
class JetMoeForSequenceClassification(JetMoePreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.model = JetMoeModel(config)
        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
        self.post_init()
    def get_input_embeddings(self): return self.model.embed_tokens
    def set_input_embeddings(self, value): self.model.embed_tokens = value
    @add_start_docstrings_to_model_forward(JETMOE_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
    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, return_dict: Optional[bool] = None) -> Union[Tuple, SequenceClassifierOutputWithPast]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        transformer_outputs = self.model(input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds,
        use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = transformer_outputs[0]
        logits = self.score(hidden_states)
        if input_ids is not None: batch_size = input_ids.shape[0]
        else: batch_size = inputs_embeds.shape[0]
        if self.config.pad_token_id is None and batch_size != 1: raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
        if self.config.pad_token_id is None: sequence_lengths = -1
        else:
            if input_ids is not None:
                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
                sequence_lengths = sequence_lengths % input_ids.shape[-1]
                sequence_lengths = sequence_lengths.to(logits.device)
            else: sequence_lengths = -1
        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
        loss = None
        if labels is not None:
            labels = labels.to(logits.device)
            if self.config.problem_type is None:
                if self.num_labels == 1: self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification"
                else: self.config.problem_type = "multi_label_classification"
            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1: loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
                else: loss = loss_fct(pooled_logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(pooled_logits, labels)
        if not return_dict:
            output = (pooled_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return SequenceClassifierOutputWithPast(loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
