"""
    #################################################################################################################################################################################################
    # 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 Dict, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...modeling_outputs import BaseModelOutputWithNoAttention, CausalLMOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import ALL_LAYERNORM_LAYERS
from ...utils import (add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings)
from ...utils.import_utils import is_torchdynamo_compiling
from .configuration_recurrent_gemma import RecurrentGemmaConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "RecurrentGemmaConfig"
_MAX_SQRT_GRADIENT = 1000.0
class RecurrentGemmaRMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.zeros(dim))
    def _norm(self, x): return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
    def forward(self, x):
        output = self._norm(x.float())
        output = output * (1.0 + self.weight.float())
        return output.type_as(x)
    def extra_repr(self): return f"{tuple(self.weight.shape)}, eps={self.eps}"
ALL_LAYERNORM_LAYERS.append(RecurrentGemmaRMSNorm)
class RecurrentGemmaRotaryEmbedding(nn.Module):
    def __init__(self, dim, base=10000, device=None):
        super().__init__()
        self.dim = dim
        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
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1: return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
class RecurrentGemmaSdpaAttention(nn.Module):
    def __init__(self, config: RecurrentGemmaConfig):
        super().__init__()
        self.config = config
        self.attention_dropout = config.attention_dropout
        self.hidden_size = config.hidden_size
        self.num_attention_heads = config.num_attention_heads
        self.head_dim = config.head_dim
        self.num_key_value_heads = config.num_key_value_heads
        self.num_key_value_groups = self.num_attention_heads // self.num_key_value_heads
        self.partial_rotary_factor = config.partial_rotary_factor
        self.q_proj = nn.Linear(self.hidden_size, self.num_attention_heads * self.head_dim, bias=config.attention_bias)
        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
        self.o_proj = nn.Linear(self.num_attention_heads * self.head_dim, self.hidden_size, bias=True)
        self.rotary_emb = RecurrentGemmaRotaryEmbedding(int(self.partial_rotary_factor * self.head_dim), base=config.rope_theta)
    def forward(self, hidden_states: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, cache_position: Optional[torch.LongTensor] = None,
    use_cache: bool = False) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        bsz, q_len, _ = hidden_states.size()
        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)
        query_states = query_states.view(bsz, q_len, self.num_attention_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, seq_len=None)
        query_rot, query_pass = torch.chunk(query_states, int(1 / self.partial_rotary_factor), dim=-1)
        key_rot, key_pass = torch.chunk(key_states, int(1 / self.partial_rotary_factor), dim=-1)
        query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
        query_states = torch.cat((query_rot, query_pass), dim=-1)
        key_states = torch.cat((key_rot, key_pass), dim=-1)
        if use_cache and hasattr(self, "key_states"):
            cache_kwargs = {"cache_position": cache_position}
            key_states, value_states = self._update_cache(key_states, value_states, **cache_kwargs)
        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)
        causal_mask = attention_mask
        if attention_mask is not None: causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
        attn_output = torch.nn.functional.scaled_dot_product_attention(query_states.contiguous(), key_states.contiguous(), value_states.contiguous(),
        attn_mask=causal_mask, dropout_p=self.attention_dropout if self.training else 0.0, scale=self.head_dim**-0.5)
        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
        attn_output = self.o_proj(attn_output)
        return attn_output
    def _setup_cache(self, batch_size, device, dtype=None):
        if dtype is None and self.config.torch_dtype is not None: dtype = self.config.torch_dtype
        dtype = dtype if dtype is not None else torch.float32
        cache_shape = (batch_size, self.num_key_value_heads, self.config.attention_window_size, self.head_dim)
        self.value_states = torch.zeros(cache_shape, dtype=dtype, device=device)
        self.key_states = torch.zeros(cache_shape, dtype=dtype, device=device)
    @torch.no_grad()
    def _update_cache(self, key_states, value_states, **cache_kwargs):
        cache_position = cache_kwargs.get("cache_position")
        if cache_position.shape[0] > self.config.attention_window_size:
            k_out = key_states[:, :, -self.config.attention_window_size :, :]
            v_out = value_states[:, :, -self.config.attention_window_size :, :]
        else:
            slicing = torch.ones(self.config.attention_window_size, dtype=torch.long, device=value_states.device).cumsum(0)
            cache_position = cache_position.clamp(0, self.config.attention_window_size - 1)
            to_shift = cache_position >= self.config.attention_window_size - 1
            indices = (slicing + to_shift[-1].int() - 1) % self.config.attention_window_size
            k_out, v_out = self.key_states.to(key_states.device), self.value_states.to(value_states.device)
            k_out = k_out[:, :, indices]
            v_out = v_out[:, :, indices]
            k_out[:, :, cache_position] = key_states.to(k_out.dtype)
            v_out[:, :, cache_position] = value_states.to(v_out.dtype)
        self.key_states, self.value_states = k_out, v_out
        return k_out, v_out
class SqrtBoundDerivative(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x: torch.Tensor) -> torch.Tensor:
        ctx.save_for_backward(x)
        return torch.sqrt(x)
    @staticmethod
    def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor:
        (x,) = ctx.saved_tensors
        clipped_x_times_4 = torch.clip(4.0 * x, min=1 / (_MAX_SQRT_GRADIENT**2))
        return grad_output / torch.sqrt(clipped_x_times_4)
class RecurrentGemmaRglru(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.num_attention_heads = config.num_attention_heads
        self.block_width = config.lru_width // self.num_attention_heads
        self.recurrent_param = nn.Parameter(torch.empty([config.lru_width]))
        self.input_gate_weight = nn.Parameter(torch.empty([self.num_attention_heads, self.block_width, self.block_width]))
        self.input_gate_bias = nn.Parameter(torch.empty([self.num_attention_heads, self.block_width]))
        self.recurrent_gate_weight = nn.Parameter(torch.empty([self.num_attention_heads, self.block_width, self.block_width]))
        self.recurrent_gate_bias = nn.Parameter(torch.empty([self.num_attention_heads, self.block_width]))
        self.recurrent_states = None
    def forward(self, activations: torch.Tensor, position_ids: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        batch_size, seq_len, lru_width = activations.shape
        reset = position_ids[:, :, None] == 0
        reshape_act = activations.reshape(batch_size * seq_len, self.num_attention_heads, self.block_width)
        reshape_act = reshape_act.permute(1, 0, 2)
        res = torch.baddbmm(self.input_gate_bias[:, None, :], reshape_act, self.input_gate_weight)
        input_gate = torch.sigmoid(res.transpose(0, 1).reshape(batch_size, seq_len, lru_width))
        res = torch.baddbmm(self.recurrent_gate_bias[:, None, :], reshape_act, self.recurrent_gate_weight)
        recurrent_gate = torch.sigmoid(res.transpose(0, 1).reshape(batch_size, seq_len, lru_width))
        log_recurrent_gate = -8.0 * recurrent_gate * nn.functional.softplus(self.recurrent_param)
        recurrent_gate = torch.exp(log_recurrent_gate)
        a_square = torch.exp(2 * log_recurrent_gate)
        gated_inputs = activations * input_gate
        multiplier = 1
        tracing = isinstance(activations, torch.fx.Proxy) or is_torchdynamo_compiling()
        if not torch.jit.is_tracing() and not tracing: multiplier = SqrtBoundDerivative.apply(1 - a_square)
        multiplier = reset + ~reset * multiplier
        normalized_x = gated_inputs * multiplier.type(activations.dtype)
        hidden_states, recurrent_states = self._rnn_scan(hidden_states=normalized_x, recurrent_gate=recurrent_gate, reset=reset, recurrent_states=self.recurrent_states)
        self.recurrent_states = recurrent_states
        return hidden_states
    def _rnn_scan(self, hidden_states: torch.Tensor, recurrent_gate: torch.Tensor, reset: torch.Tensor, recurrent_states: Union[torch.Tensor, None],
    acc_dtype: torch.dtype = torch.float32) -> Tuple[torch.Tensor, torch.Tensor]:
        recurrent_gate = recurrent_gate * ~reset
        if hidden_states.shape[1] == 1:
            if recurrent_states is None: return hidden_states, hidden_states[:, 0].type(acc_dtype)
            else:
                contextualized_states = recurrent_gate.type(acc_dtype) * recurrent_states[:, None].to(recurrent_gate.device)
                contextualized_states += hidden_states.type(acc_dtype)
                return contextualized_states.type(hidden_states.dtype), contextualized_states[:, -1]
        else:
            if recurrent_states is None: recurrent_states = torch.zeros(hidden_states[:, 0].shape, dtype=acc_dtype, device=hidden_states.device)
            contextualized_states = torch.zeros_like(hidden_states)
            for t in range(hidden_states.shape[1]):
                recurrent_states = recurrent_gate[:, t].type(acc_dtype) * recurrent_states.to(recurrent_gate.device)
                recurrent_states = recurrent_states + hidden_states[:, t].type(acc_dtype)
                contextualized_states[:, t] = recurrent_states.type(hidden_states.dtype)
        return contextualized_states, recurrent_states
class RecurrentGemmaRecurrentBlock(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.lru_width = config.lru_width
        self.hidden_size = config.hidden_size
        self.linear_y = nn.Linear(in_features=config.hidden_size, out_features=config.lru_width)
        self.linear_x = nn.Linear(in_features=config.hidden_size, out_features=config.lru_width)
        self.linear_out = nn.Linear(in_features=config.lru_width, out_features=config.hidden_size)
        self.conv1d_width = config.conv1d_width
        self.conv_1d = nn.Conv1d(config.lru_width, config.lru_width, kernel_size=config.conv1d_width, groups=config.lru_width, padding=config.conv1d_width - 1)
        self.rg_lru = RecurrentGemmaRglru(config)
        self.act_fn = ACT2FN[config.hidden_activation]
        self.conv1d_state = None
    def forward(self, input_states: torch.Tensor, position_ids: torch.Tensor, attention_mask: torch.Tensor, cache_position: torch.Tensor, use_cache: bool = True) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
        _, seq_len, _ = input_states.shape
        y_branch = self.linear_y(input_states)
        y_branch = self.act_fn(y_branch)
        x_branch = self.linear_x(input_states)
        x_branch = x_branch.transpose(1, 2)
        if use_cache:
            if cache_position.shape[0] != 1:
                self.conv1d_state = nn.functional.pad(x_branch, (self.conv1d_width - x_branch.shape[-1] - 1, 0))
                x_branch = self.conv_1d(x_branch)[..., :seq_len]
            else:
                conv_state = torch.cat((self.conv1d_state, x_branch), -1)
                x_branch = torch.sum(conv_state * self.conv_1d.weight[:, 0, :], dim=-1) + self.conv_1d.bias
                x_branch = x_branch.unsqueeze(-1)
                self.conv1d_state = conv_state[:, :, 1:]
        else: x_branch = self.conv_1d(x_branch)[..., :seq_len]
        x_branch = self.rg_lru(x_branch.transpose(1, 2), position_ids)
        hidden_states = x_branch * y_branch
        hidden_states = self.linear_out(hidden_states)
        return hidden_states
    def _setup_cache(self, batch, device, dtype):
        self.rg_lru.recurrent_states = torch.zeros((batch, self.lru_width), device=device, dtype=torch.float32)
        self.conv1d_state = torch.zeros((batch, self.hidden_size, self.conv1d_width - 1), device=device, dtype=dtype)
TEMPORAL_BLOCK_CLASSES = {"recurrent": RecurrentGemmaRecurrentBlock, "attention": RecurrentGemmaSdpaAttention}
class RecurrentGemmaMlp(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size // 2
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=True)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=True)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=True)
        self.act_fn = ACT2FN[config.hidden_activation]
    def forward(self, hidden_states):
        gate = self.act_fn(self.gate_proj(hidden_states))
        return self.down_proj(gate * self.up_proj(hidden_states))
class RecurrentGemmaDecoderLayer(nn.Module):
    def __init__(self, config, layer_idx):
        super().__init__()
        self.temporal_pre_norm = RecurrentGemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.temporal_block = TEMPORAL_BLOCK_CLASSES[config.layers_block_type[layer_idx]](config)
        self.channel_pre_norm = RecurrentGemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.mlp_block = RecurrentGemmaMlp(config)
    def forward(self, activations: torch.Tensor, position_ids: torch.Tensor, attention_mask: torch.Tensor, cache_position: torch.Tensor = None,
    use_cache: bool = None) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
        raw_activations = activations
        inputs_normalized = self.temporal_pre_norm(raw_activations)
        hidden_states = self.temporal_block(inputs_normalized, position_ids, attention_mask, cache_position=cache_position, use_cache=use_cache)
        residual = hidden_states + raw_activations
        hidden_states = self.channel_pre_norm(residual)
        hidden_states = self.mlp_block(hidden_states)
        hidden_states = hidden_states + residual
        return hidden_states
RECURRENTGEMMA_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 ([`RecurrentGemmaConfig`]):
            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.
"""
@add_start_docstrings("The bare RecurrentGemma Model outputting raw hidden-states without any specific head on top.", RECURRENTGEMMA_START_DOCSTRING)
class RecurrentGemmaPreTrainedModel(PreTrainedModel):
    config_class = RecurrentGemmaConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["RecurrentGemmaDecoderLayer"]
    _skip_keys_device_placement = ["cache"]
    _supports_flash_attn_2 = False
    _supports_sdpa = False
    _supports_cache_class = True
    _supports_quantized_cache = True
    def _init_weights(self, module):
        std = math.sqrt(self.config.w_init_variance_scale / self.config.conv1d_width)
        if isinstance(module, nn.Conv1d):
            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
            torch.nn.init.zeros_(module.bias)
        elif isinstance(module, RecurrentGemmaSdpaAttention):
            torch.nn.init.normal_(module.q_proj.weight, mean=0.0, std=math.sqrt(1.0 / self.config.hidden_size))
            torch.nn.init.normal_(module.k_proj.weight, mean=0.0, std=math.sqrt(1.0 / self.config.hidden_size))
            torch.nn.init.normal_(module.v_proj.weight, mean=0.0, std=math.sqrt(1.0 / self.config.hidden_size))
            std = math.sqrt(self.config.final_w_init_variance_scale / self.config.hidden_size)
            torch.nn.init.normal_(module.o_proj.weight, mean=0.0, std=std)
        elif isinstance(module, RecurrentGemmaRecurrentBlock):
            torch.nn.init.zeros_(module.linear_x.bias)
            torch.nn.init.normal_(module.linear_x.weight, mean=0.0, std=math.sqrt(1.0 / self.config.hidden_size))
            torch.nn.init.zeros_(module.linear_y.bias)
            torch.nn.init.normal_(module.linear_y.weight, mean=0.0, std=math.sqrt(1.0 / self.config.hidden_size))
            std = math.sqrt(self.config.final_w_init_variance_scale / self.config.lru_width)
            torch.nn.init.normal_(module.linear_out.weight, mean=0.0, std=std)
            torch.nn.init.zeros_(module.linear_out.bias)
        elif isinstance(module, RecurrentGemmaRglru):
            std = math.sqrt(self.config.w_init_variance_scale / (self.config.lru_width // self.config.num_attention_heads))
            torch.nn.init.normal_(module.input_gate_weight, mean=0.0, std=std)
            torch.nn.init.normal_(module.recurrent_gate_weight, mean=0.0, std=std)
            torch.nn.init.zeros_(module.input_gate_bias)
            torch.nn.init.zeros_(module.recurrent_gate_bias)
            module.recurrent_param.data.uniform_(0.9**2 + 1e-8, 0.999**2 + 1e-8)
            module.recurrent_param.data.log_().mul_(0.5)
            module.recurrent_param.data.neg_().exp_().sub_(1.0).log_()
        elif isinstance(module, nn.Linear):
            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
            if getattr(module, "bias", None) is not None: torch.nn.init.zeros_(module.bias)
    def _setup_cache(self, config, batch, device, dtype):
        layers = getattr(self, "model", self).layers
        for layer in layers: layer.temporal_block._setup_cache(batch, device, dtype)
    def reset_cache(self, batch, device, dtype): pass
RECURRENTGEMMA_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)
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *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 `(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.
        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_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all  See `hidden_states` under returned tensors for
            more detail.
        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 RecurrentGemma Model outputting raw hidden-states without any specific head on top.", RECURRENTGEMMA_START_DOCSTRING)
class RecurrentGemmaModel(RecurrentGemmaPreTrainedModel):
    def __init__(self, config: RecurrentGemmaConfig):
        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([RecurrentGemmaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
        self.final_norm = RecurrentGemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.gradient_checkpointing = False
        self.register_buffer("normalizer", torch.tensor(self.config.hidden_size**0.5, dtype=torch.bfloat16), persistent=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(RECURRENTGEMMA_INPUTS_DOCSTRING)
    def forward(self, input_ids: torch.LongTensor = None, position_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None,
    cache_position: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, BaseModelOutputWithNoAttention]:
        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.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)
        hidden_states = inputs_embeds
        if use_cache and inputs_embeds.shape[1] != 1: self._setup_cache(self.config, hidden_states.shape[0], hidden_states.device, hidden_states.dtype)
        if cache_position is None: cache_position = torch.arange(hidden_states.shape[1], device=hidden_states.device)
        if position_ids is None: position_ids = cache_position.unsqueeze(0)
        causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position)
        hidden_states = hidden_states * self.normalizer.type(hidden_states.dtype)
        all_hidden_states = () if output_hidden_states else None
        for i, residual_block in enumerate(self.layers):
            if output_hidden_states: all_hidden_states += (hidden_states,)
            if self.gradient_checkpointing and self.training: hidden_states = self._gradient_checkpointing_func(residual_block.__call__, hidden_states, position_ids, causal_mask, cache_position, use_cache)
            else: hidden_states = residual_block(hidden_states, position_ids, causal_mask, cache_position, use_cache)
        hidden_states = self.final_norm(hidden_states)
        if output_hidden_states: all_hidden_states += (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
        return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
    def _update_causal_mask(self, attention_mask, input_tensor, cache_position):
        dtype, device = input_tensor.dtype, input_tensor.device
        min_dtype = torch.finfo(dtype).min
        sequence_length = input_tensor.shape[1]
        target_length = max(self.config.attention_window_size, sequence_length)
        diagonal = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
        causal_mask = diagonal
        if sequence_length != 1: causal_mask = torch.triu(diagonal, diagonal=-1)
        causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
        causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
        if attention_mask is not None:
            causal_mask = causal_mask.clone()
            if attention_mask.dim() == 2:
                mask_length = attention_mask.shape[-1]
                padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
                causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
        if attention_mask is not None and attention_mask.device.type == "cuda": causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
        return causal_mask
class RecurrentGemmaForCausalLM(RecurrentGemmaPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]
    def __init__(self, config):
        super().__init__(config)
        self.model = RecurrentGemmaModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, 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
    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(RECURRENTGEMMA_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, cache_position: Optional[torch.LongTensor] = None,
    attention_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None, use_cache: Optional[bool] = None) -> Union[Tuple, CausalLMOutput]:
        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
        output_hidden_states = True
        outputs = self.model(input_ids=input_ids, position_ids=position_ids, cache_position=cache_position, attention_mask=attention_mask, inputs_embeds=inputs_embeds,
        use_cache=use_cache, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)
        cap = self.config.logits_soft_cap
        logits = nn.functional.tanh(logits / cap) * cap
        logits = logits.float()
        loss = None
        if labels is not None:
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            loss_fct = CrossEntropyLoss()
            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 = loss_fct(shift_logits, shift_labels)
        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output
        return CausalLMOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
    def prepare_inputs_for_generation(self, input_ids, attention_mask=None, inputs_embeds=None, cache_position=None, use_cache=None, **kwargs):
        position_ids = kwargs.get("position_ids", None)
        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)
        attention_mask = attention_mask[:, -self.config.attention_window_size :]
        past_length = cache_position[0]
        if past_length > 0: position_ids = position_ids[:, past_length:]
        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 inputs_embeds is not None and cache_position[0] == 0: model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
        else: model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None}
        model_inputs.update({"position_ids": position_ids, "attention_mask": attention_mask, "cache_position": cache_position, "use_cache": use_cache})
        return model_inputs
    def _reorder_cache(self, past_key_values, beam_idx):
        for layer in self.layers:
            if hasattr(layer.temporal_block, "key_states"):
                k_state = layer.temporal_block.key_states
                v_state = layer.temporal_block.value_states
                k_state = k_state.index_select(0, beam_idx.to(k_state.device))
                v_state = v_state.index_select(0, beam_idx.to(v_state.device))
        return None
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
