model.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import hashlib

# --- Future Entropy/State Predictor (FEP V6) --- (No changes from V6.1/V6.2)
class FutureEntropyStatePredictor(nn.Module):
    def __init__(self, ssr_dim, input_scalar_dim=2, hidden_dim=32, name=""):
        super().__init__()
        self.ssr_dim = ssr_dim; self.name = name; self.debug_prints_enabled = False
        fep_input_dim = ssr_dim + input_scalar_dim
        self.fc_ssr1 = nn.Linear(fep_input_dim, hidden_dim * 2); self.fc_ssr2 = nn.Linear(hidden_dim * 2, hidden_dim); self.fc_ssr_out = nn.Linear(hidden_dim, ssr_dim)
        self.fc_ent1 = nn.Linear(fep_input_dim, hidden_dim); self.fc_ent_out = nn.Linear(hidden_dim, 1)
    def forward(self, current_ssr_detached, current_block_entropy_detached, current_static_target_diff_detached):
        if current_ssr_detached.dim() == 1: current_ssr_expanded = current_ssr_detached.unsqueeze(0)
        else: current_ssr_expanded = current_ssr_detached
        current_block_entropy_exp = current_block_entropy_detached.view(current_ssr_expanded.size(0), -1)
        current_static_target_diff_exp = current_static_target_diff_detached.view(current_ssr_expanded.size(0),-1)
        fep_input = torch.cat((current_ssr_expanded, current_block_entropy_exp, current_static_target_diff_exp), dim=1)
        h_ssr = F.relu(self.fc_ssr1(fep_input)); h_ssr = F.relu(self.fc_ssr2(h_ssr)); delta_ssr_proposal = torch.tanh(self.fc_ssr_out(h_ssr))
        h_ent = F.relu(self.fc_ent1(fep_input)); entropy_adj_factor_raw = self.fc_ent_out(h_ent)
        if current_ssr_detached.dim() == 1: delta_ssr_proposal = delta_ssr_proposal.squeeze(0); entropy_adj_factor_raw = entropy_adj_factor_raw.squeeze(0)
        return delta_ssr_proposal, entropy_adj_factor_raw.squeeze(-1)

# --- Entropy Estimator --- (No change from V6.1/V6.2)
class EntropyEstimator(nn.Module):
    def __init__(self, input_dim, hidden_dim=32, name=""):
        super().__init__(); self.fc1 = nn.Linear(input_dim, hidden_dim); self.fc2 = nn.Linear(hidden_dim, 1); self.name = name; self.debug_prints_enabled = False
    def forward(self, x, active_mask=None):
        if x.numel() == 0: return torch.tensor(0.0, device=x.device)
        if active_mask is not None:
            if active_mask.dtype != torch.bool: active_mask = active_mask.bool()
            if x.dim() == 3 and active_mask.dim() == 2 and x.shape[0] == active_mask.shape[0] and x.shape[1] == active_mask.shape[1]: x_masked = x[active_mask]
            elif x.dim() == 2 and active_mask.dim() == 1 and x.shape[0] == active_mask.shape[0]: x_masked = x[active_mask]
            else: x_masked = x.reshape(-1, x.size(-1))
        else: x_masked = x.reshape(-1, x.size(-1))
        if x_masked.numel() == 0: return torch.tensor(0.0, device=x.device)
        h = F.relu(self.fc1(x_masked)); return torch.sigmoid(self.fc2(h)).mean()

# --- Seed Parser (V6) --- (No changes from V6.1/V6.2)
class SeedParser:
    def __init__(self, seed_phrase, seed_number_str, d_model, ssr_dim, num_adaptive_blocks, num_sub_modules_per_block):
        self.seed_phrase = seed_phrase; self.seed_number_str = seed_number_str; self.d_model = d_model
        self.ssr_dim = ssr_dim
        self.num_adaptive_blocks = num_adaptive_blocks; self.num_sub_modules_per_block = num_sub_modules_per_block
        self.debug_prints_enabled = True
        if self.debug_prints_enabled: print(f"--- SeedParser Initialization (V6) ---\n  Seed Phrase (start): '{self.seed_phrase[:50]}...'\n  Seed Number: {self.seed_number_str}")
        phrase_hash = hashlib.sha256(seed_phrase.encode()).hexdigest(); self.phrase_base_val = int(phrase_hash[:16], 16)
        if self.debug_prints_enabled: print(f"  Phrase Base Value (from hash): {self.phrase_base_val}")
        self.num_sequence = [int(d) for d in seed_number_str if d.isdigit()]
        if not self.num_sequence: self.num_sequence = [sum(bytearray(seed_number_str.encode())) % 10]
        if self.debug_prints_enabled: print(f"  Numerical Sequence (from seed number): {self.num_sequence}")
        self.init_map = self._generate_init_map()
        if self.debug_prints_enabled:
            print(f"  SeedParser: Generated InitMap:")
            for i, block_config in enumerate(self.init_map["block_configs"]):
                raw_gate_scores_str = [f'{g:.3f}' for g in block_config['raw_gate_scores_for_param_init']]
                initial_ssr_str = [f'{s:.3f}' for s in block_config['initial_ssr_values'][:min(3, self.ssr_dim)]] + (["..."] if self.ssr_dim > 3 else [])
                print(f"    Block {i}: StaticTgtEnt: {block_config['static_target_entropy']:.4f}, RawGateScores: {raw_gate_scores_str}, InitialSSR (sample): {initial_ssr_str}")
        if self.debug_prints_enabled: print(f"--- SeedParser Initialized ---")
    def _get_deterministic_float_list(self, key_name_prefix, num_values, min_val=-1.0, max_val=1.0, sequence_idx_offset=0):
        values = []
        for i in range(num_values): values.append(self._get_deterministic_float(f"{key_name_prefix}_{i}", min_val, max_val, sequence_idx_offset + i))
        return values
    def _get_deterministic_float(self, key_name, min_val=0.0, max_val=1.0, sequence_idx_offset=0):
        key_specific_hash = int(hashlib.sha256(key_name.encode() + self.seed_phrase.encode()).hexdigest()[:8], 16); num_seq_val = 0
        if self.num_sequence:
            for i_digit, digit in enumerate(self.num_sequence): num_seq_val = (num_seq_val * 10 + digit + i_digit) % 1000003
        combined_seed_val = self.phrase_base_val + key_specific_hash + num_seq_val + sequence_idx_offset
        norm_float = (math.sin(float(combined_seed_val) * 0.12345) + 1.0) / 2.0
        return min_val + norm_float * (max_val - min_val)
    def _generate_init_map(self):
        init_map = {"block_configs": []}
        for i in range(self.num_adaptive_blocks):
            gate_raw_scores = self._get_deterministic_float_list(f"block_{i}_gate_raw_score", self.num_sub_modules_per_block, -1.5, 1.5, sequence_idx_offset=i*30)
            initial_ssr_values = self._get_deterministic_float_list(f"block_{i}_initial_ssr", self.ssr_dim, -0.1, 0.1, sequence_idx_offset=i*30 + self.num_sub_modules_per_block)
            static_target_entropy = self._get_deterministic_float(f"block_{i}_static_target_entropy", 0.15, 0.45, sequence_idx_offset=i*30 + self.num_sub_modules_per_block + self.ssr_dim)
            init_map["block_configs"].append({"raw_gate_scores_for_param_init": gate_raw_scores, "initial_ssr_values": initial_ssr_values, "static_target_entropy": static_target_entropy})
        return init_map
    def get_block_config(self, block_idx):
        if 0 <= block_idx < len(self.init_map["block_configs"]): return self.init_map["block_configs"][block_idx]
        return None

# --- Adaptive Block (V6.3) ---
class AdaptiveBlock(nn.Module):
    MAX_DYNAMIC_ENTROPY_ADJUSTMENT_RANGE = 0.05
    INITIAL_HEURISTIC_STRENGTH = 0.025
    FINAL_HEURISTIC_STRENGTH = 0.005
    # V6.3: Increased initial SSR proposal scale
    INITIAL_SSR_PROPOSAL_SCALE = 0.25 # Was 0.2
    FINAL_SSR_PROPOSAL_SCALE = 0.05


    def __init__(self, d_model, ssr_dim, n_heads, d_ff, dropout, seed_parser_config_for_block, block_idx, num_sub_modules=3):
        super().__init__()
        self.d_model = d_model; self.ssr_dim = ssr_dim; self.block_idx = block_idx; self.num_sub_modules = num_sub_modules
        self.config_from_seed = seed_parser_config_for_block; self.debug_prints_enabled = True

        initial_ssr_vals = self.config_from_seed.get("initial_ssr_values", [0.0] * self.ssr_dim)
        if len(initial_ssr_vals) != self.ssr_dim: initial_ssr_vals = [0.0] * self.ssr_dim
        self.ssr = nn.Parameter(torch.tensor(initial_ssr_vals, dtype=torch.float32))
        self.register_buffer('initial_ssr_buffer', torch.tensor(initial_ssr_vals, dtype=torch.float32))

        raw_gate_param_inits_list = self.config_from_seed.get("raw_gate_scores_for_param_init", [0.0] * self.num_sub_modules)
        if len(raw_gate_param_inits_list) != self.num_sub_modules: raw_gate_param_inits_list = [0.0] * self.num_sub_modules
        self.gates_params = nn.Parameter(torch.tensor(raw_gate_param_inits_list, dtype=torch.float32))
        self.register_buffer('initial_raw_gate_scores_buffer', torch.tensor(raw_gate_param_inits_list, dtype=torch.float32))

        if self.debug_prints_enabled:
            raw_gate_scores_str = [f'{g:.3f}' for g in raw_gate_param_inits_list]
            ssr_sample_str = [f'{s:.3f}' for s in initial_ssr_vals[:min(3, self.ssr_dim)]] + (["..."] if self.ssr_dim > 3 else [])
            print(f"  Initializing AdaptiveBlock {self.block_idx} (V6.3): StaticSeedTgtEnt={self.config_from_seed['static_target_entropy']:.3f}, InitialRawGateScores={raw_gate_scores_str}, InitialSSR (sample): {ssr_sample_str}")

        self.d_model_effective = self.d_model + self.ssr_dim
        self.sub_module_0 = nn.MultiheadAttention(self.d_model_effective, n_heads, dropout=dropout, batch_first=True)
        self.sub_module_1 = nn.Sequential(nn.Linear(self.d_model_effective, d_ff), nn.GELU(), nn.Dropout(dropout), nn.Linear(d_ff, self.d_model_effective))
        self.sub_module_2 = nn.Sequential(nn.Linear(self.d_model_effective, self.d_model_effective), nn.GELU(), nn.Dropout(dropout))
        self.sub_modules = nn.ModuleList([self.sub_module_0, self.sub_module_1, self.sub_module_2])
        if self.num_sub_modules > len(self.sub_modules): self.num_sub_modules = len(self.sub_modules)
        elif self.num_sub_modules <= 0: raise ValueError(f"AdaptiveBlock {self.block_idx} must have at least one sub_module.")

        self.norm_input_x = nn.LayerNorm(self.d_model)
        self.norm_ssr_input = nn.LayerNorm(self.ssr_dim)
        self.norm_after_gates = nn.LayerNorm(self.d_model_effective)
        self.ssr_update_net = nn.Sequential(
            nn.Linear(self.ssr_dim + self.d_model_effective + self.ssr_dim, self.ssr_dim * 2),
            nn.GELU(), nn.Dropout(dropout),
            nn.Linear(self.ssr_dim * 2, self.ssr_dim)
        )
        self.norm_ssr_output = nn.LayerNorm(self.ssr_dim)
        self.dropout_layer = nn.Dropout(dropout)
        self.output_entropy_estimator = EntropyEstimator(self.d_model_effective, name=f"Block{block_idx}_ProcessedOutEntropy")
        self.x_output_entropy_estimator = EntropyEstimator(self.d_model, name=f"Block{block_idx}_X_OutEntropy") # V6.3

        self.fep = FutureEntropyStatePredictor(ssr_dim=self.ssr_dim, input_scalar_dim=2, name=f"Block{block_idx}_FEP")
        self.wiring_phase_active = False
        self.static_seed_target_entropy = self.config_from_seed.get("static_target_entropy", 0.25)
        self.current_epoch_in_wiring = 0
        self.total_wiring_epochs = 1

    def set_wiring_phase(self, active, current_epoch_num=0, total_wiring_epochs=1):
        self.wiring_phase_active = active
        if active: self.current_epoch_in_wiring = current_epoch_num; self.total_wiring_epochs = total_wiring_epochs if total_wiring_epochs > 0 else 1

    def _get_current_decaying_factor(self, initial_val, final_val):
        if not self.wiring_phase_active or self.total_wiring_epochs <= 1: return initial_val
        progress = min(self.current_epoch_in_wiring / max(1, (self.total_wiring_epochs - 1)), 1.0)
        return initial_val - progress * (initial_val - final_val)

    def _get_current_heuristic_strength(self):
        return self._get_current_decaying_factor(self.INITIAL_HEURISTIC_STRENGTH, self.FINAL_HEURISTIC_STRENGTH)
    def _get_current_ssr_proposal_scale(self): # V6.1
        return self._get_current_decaying_factor(self.INITIAL_SSR_PROPOSAL_SCALE, self.FINAL_SSR_PROPOSAL_SCALE)


    def forward(self, x, key_padding_mask=None, attn_mask=None):
        batch_size, seq_len, _ = x.shape
        ssr_before_update_for_loss = self.ssr.data.clone().detach()

        current_ssr_expanded = self.ssr.unsqueeze(0).unsqueeze(0).expand(batch_size, seq_len, -1).to(x.device)
        normed_x = self.norm_input_x(x)
        normed_ssr_expanded = self.norm_ssr_input(current_ssr_expanded)
        x_conditioned = torch.cat((normed_x, normed_ssr_expanded), dim=-1)
        current_gates_activations = torch.sigmoid(self.gates_params)

        if self.debug_prints_enabled and (self.wiring_phase_active or not self.training):
            ssr_print_val = self.ssr.data.detach().clone()
            ssr_sample_str = [f'{s.item():.3f}' for s in ssr_print_val[:min(3, self.ssr_dim)]] + (["..."] if self.ssr_dim > 3 else [])
            print(f"    AdaptiveBlock {self.block_idx} (Wiring: {'ON' if self.wiring_phase_active else 'OFF'}, Epoch {self.current_epoch_in_wiring+1}/{self.total_wiring_epochs if self.wiring_phase_active else 'N/A'})")
            print(f"      Input x: {x.shape}, CurrentSSR (sample): {ssr_sample_str}, RawG: {[f'{g.item():.3f}' for g in self.gates_params.data]}, SigmoidG: {[f'{s.item():.3f}' for s in current_gates_activations.data]}")

        outputs_from_submodules = []
        for i, module_instance in enumerate(self.sub_modules):
            if i >= self.num_sub_modules: break
            if i == 0: module_out, _ = module_instance(x_conditioned, x_conditioned, x_conditioned, key_padding_mask=key_padding_mask, attn_mask=attn_mask, need_weights=False)
            else: module_out = module_instance(x_conditioned)
            outputs_from_submodules.append(module_out * current_gates_activations[i])

        gated_sum_output = torch.sum(torch.stack(outputs_from_submodules, dim=0), dim=0) if outputs_from_submodules else torch.zeros_like(x_conditioned)
        block_processed_output_unnorm = x_conditioned + self.dropout_layer(gated_sum_output)
        block_processed_output = self.norm_after_gates(block_processed_output_unnorm)
        x_output_for_next_block = block_processed_output[:, :, :self.d_model]

        # V6.2: Get entropy of d_model part for loss
        x_output_part_entropy = self.x_output_entropy_estimator(x_output_for_next_block.detach(), active_mask=~key_padding_mask if key_padding_mask is not None else None)
        block_processed_output_entropy = self.output_entropy_estimator(block_processed_output.detach(), active_mask=~key_padding_mask if key_padding_mask is not None else None)

        current_static_target_diff = block_processed_output_entropy - self.static_seed_target_entropy
        dynamic_target_entropy_for_heuristic = self.static_seed_target_entropy
        fep_delta_ssr_proposal_scaled = torch.zeros_like(self.ssr.data, device=x.device)
        fep_entropy_adj_factor_for_report = torch.tensor(0.0, device=x.device)

        if self.wiring_phase_active and self.training:
            fep_delta_ssr_proposal_raw, fep_entropy_adj_factor_raw = self.fep(self.ssr.data.detach(), block_processed_output_entropy.detach(), current_static_target_diff.detach())
            current_ssr_scale = self._get_current_ssr_proposal_scale() # V6.1
            fep_delta_ssr_proposal_scaled = fep_delta_ssr_proposal_raw * current_ssr_scale
            fep_entropy_adj_factor_tanh = torch.tanh(fep_entropy_adj_factor_raw)
            dynamic_adjustment = fep_entropy_adj_factor_tanh * self.MAX_DYNAMIC_ENTROPY_ADJUSTMENT_RANGE
            dynamic_target_entropy_for_heuristic = self.static_seed_target_entropy + dynamic_adjustment.item()
            dynamic_target_entropy_for_heuristic = max(0.01, min(0.99, dynamic_target_entropy_for_heuristic))
            fep_entropy_adj_factor_for_report = fep_entropy_adj_factor_tanh

            with torch.no_grad():
                entropy_diff_for_heuristic = block_processed_output_entropy - dynamic_target_entropy_for_heuristic
                base_adj_strength = self._get_current_heuristic_strength()
                adaptive_strength_factor = min(max(abs(entropy_diff_for_heuristic.item()) * 7.0, 0.3), 2.5)
                adj_strength = base_adj_strength * adaptive_strength_factor
                if self.debug_prints_enabled:
                    print(f"    AdaptiveBlock {self.block_idx} WIRING HEURISTIC: RawG={[f'{g.item():.3f}' for g in self.gates_params.data]}, SigmoidG={[f'{s.item():.3f}' for s in current_gates_activations.data]}")
                    print(f"      BlockProcOutEnt={block_processed_output_entropy.item():.4f}, X_OutEnt={x_output_part_entropy.item():.4f}, StaticTgtEnt={self.static_seed_target_entropy:.4f}, FEP_EntAdjFactor={fep_entropy_adj_factor_tanh.item():.4f}, DynTgtEnt={dynamic_target_entropy_for_heuristic:.4f}, ED_Dyn={entropy_diff_for_heuristic.item():.4f}, BaseHeurStr={base_adj_strength:.4f} AdjStr={adj_strength:.4f}, SSR_PropScale={current_ssr_scale:.4f}")
                if entropy_diff_for_heuristic.item() > 1e-4:
                    self.gates_params.data[0] -= adj_strength; self.gates_params.data[1] += adj_strength * 0.6
                    if self.num_sub_modules > 2: self.gates_params.data[2] += adj_strength * 0.4
                elif entropy_diff_for_heuristic.item() < -1e-4:
                    self.gates_params.data[0] += adj_strength; self.gates_params.data[1] -= adj_strength * 0.6
                    if self.num_sub_modules > 2: self.gates_params.data[2] -= adj_strength * 0.4
                self.gates_params.data.clamp_(-3.5, 3.5)
                if self.debug_prints_enabled: print(f"    AdaptiveBlock {self.block_idx} WIRING HEURISTIC POST: RawG={[f'{g.item():.3f}' for g in self.gates_params.data]}, SigmoidG={[f'{s.item():.3f}' for s in torch.sigmoid(self.gates_params.data)]}")

        block_output_aggregated = torch.mean(block_processed_output, dim=1)
        ssr_update_input_list = []
        for b_idx in range(batch_size):
            current_fep_delta_ssr_for_update = fep_delta_ssr_proposal_scaled[b_idx] if fep_delta_ssr_proposal_scaled.dim() > 1 and fep_delta_ssr_proposal_scaled.size(0) == batch_size else fep_delta_ssr_proposal_scaled

            # V6.2 EXPERIMENT: block_output_aggregated is NOT detached to allow gradients to flow back
            ssr_update_input_list.append(torch.cat((
                self.ssr.data.detach().clone(),      # Previous SSR state (context for update)
                block_output_aggregated[b_idx],       # Current block's processed output (NOT detached)
                current_fep_delta_ssr_for_update.detach() # FEP proposal (context for update)
            )))

        ssr_update_input_batched = torch.stack(ssr_update_input_list, dim=0)
        new_ssr_values_batched = self.ssr_update_net(ssr_update_input_batched)

        if self.training: self.ssr.data = self.norm_ssr_output(torch.mean(new_ssr_values_batched, dim=0))
        elif batch_size == 1: self.ssr.data = self.norm_ssr_output(new_ssr_values_batched.squeeze(0))

        ssr_after_update_for_report = self.ssr.data.clone()

        return x_output_for_next_block, block_processed_output_entropy, x_output_part_entropy, \
               current_gates_activations, self.gates_params.data.clone(), \
               fep_entropy_adj_factor_for_report, torch.tensor(dynamic_target_entropy_for_heuristic, device=x.device), \
               ssr_before_update_for_loss, ssr_after_update_for_report, fep_delta_ssr_proposal_scaled


# --- Positional Encoding ---
class PositionalEncoding(nn.Module):
    def __init__(self,d_model,dropout=0.1,max_len=512): super().__init__(); self.dropout=nn.Dropout(p=dropout); pe=torch.zeros(max_len,d_model); pos=torch.arange(0,max_len,dtype=torch.float).unsqueeze(1); div=torch.exp(torch.arange(0,d_model,2).float()*(-math.log(10000.0)/d_model)); pe[:,0::2]=torch.sin(pos*div); pe[:,1::2]=torch.cos(pos*div); self.register_buffer('pe',pe.unsqueeze(0))
    def forward(self,x): x=x+self.pe[:,:x.size(1),:]; return self.dropout(x)

# --- Main SWCK Model (V6.2) ---
class SWCKModel(nn.Module):
    def __init__(self, vocab_size, d_model, ssr_dim, n_heads, d_ff, num_adaptive_blocks,
                 dropout, seed_phrase, seed_number_str, num_sub_modules_per_block=3):
        super().__init__()
        self.d_model = d_model; self.ssr_dim = ssr_dim; self.seed_phrase = seed_phrase; self.seed_number_str = seed_number_str
        self.num_adaptive_blocks = num_adaptive_blocks
        self.debug_prints_enabled = True
        if self.debug_prints_enabled: print(f"--- Initializing SWCKModel (V6.2) ---")
        self.seed_parser = SeedParser(seed_phrase, seed_number_str, d_model, ssr_dim, num_adaptive_blocks, num_sub_modules_per_block)
        self.seed_parser.debug_prints_enabled = self.debug_prints_enabled
        self.embedding = nn.Embedding(vocab_size, d_model)
        self.pos_encoder = PositionalEncoding(d_model, dropout)
        self.adaptive_blocks = nn.ModuleList()
        for i in range(num_adaptive_blocks):
            block_config = self.seed_parser.get_block_config(i)
            if block_config is None: raise ValueError(f"SWCKModel Error: Could not get seed config for block {i}")
            new_block = AdaptiveBlock(d_model, ssr_dim, n_heads, d_ff, dropout, block_config, block_idx=i, num_sub_modules=num_sub_modules_per_block)
            new_block.debug_prints_enabled = self.debug_prints_enabled
            self.adaptive_blocks.append(new_block)
            if self.debug_prints_enabled: print(f"  SWCKModel: Added AdaptiveBlock {i} (V6.2)")
        self.fc_out = nn.Linear(d_model, vocab_size)
        # V6.2: Renamed for clarity
        self.final_d_model_entropy_estimator = EntropyEstimator(d_model, name="Final_DMODEL_OutEntropy")
        self.final_d_model_entropy_estimator.debug_prints_enabled = False
        self._init_weights()
        if self.debug_prints_enabled: print(f"--- SWCKModel V6.2 Initialized (Vocab: {vocab_size}, d_model: {d_model}, SSR_dim: {ssr_dim}, Blocks: {num_adaptive_blocks}x{num_sub_modules_per_block}sub) ---")

    def _init_weights(self):
        initrange = 0.1; self.embedding.weight.data.uniform_(-initrange, initrange)
        self.fc_out.bias.data.zero_(); self.fc_out.weight.data.uniform_(-initrange, initrange)

    def set_wiring_phase(self, active, current_epoch_num=0, total_wiring_epochs=1):
        if self.debug_prints_enabled: print(f"SWCKModel: Setting wiring phase to {active} for all blocks (Epoch {current_epoch_num+1}/{total_wiring_epochs} of wiring if active).")
        for block in self.adaptive_blocks: block.set_wiring_phase(active, current_epoch_num, total_wiring_epochs)

    def forward(self, src_tokens, src_key_padding_mask=None):
        if self.debug_prints_enabled:
            print(f"\n--- SWCKModel V6.2 Forward Pass (Training: {self.training}) ---")
            print(f"  Input src_tokens: {src_tokens.shape}")
        x = self.embedding(src_tokens) * math.sqrt(self.d_model)
        x = self.pos_encoder(x)
        if self.debug_prints_enabled: print(f"  After Embedding & PosEnc, x: {x.shape}")

        block_processed_output_entropies = []
        block_x_output_entropies = [] # V6.2
        current_block_gate_activations = []; current_block_gate_raw_params = []
        fep_entropy_adj_factors = []; dynamic_target_entropies_used = []
        ssr_befores_for_loss = []; ssr_afters_for_report = []; fep_delta_ssr_proposals_report = []

        for i, block in enumerate(self.adaptive_blocks):
            if self.debug_prints_enabled: print(f"  Processing AdaptiveBlock {i}...")
            x, blk_proc_out_ent, x_out_ent, current_gate_acts, raw_gate_params, fep_ent_adj_factor, dyn_target_ent, ssr_before, ssr_after, fep_delta_ssr = block(x, key_padding_mask=src_key_padding_mask, attn_mask=None)

            block_processed_output_entropies.append(blk_proc_out_ent)
            block_x_output_entropies.append(x_out_ent)
            current_block_gate_activations.append(current_gate_acts)
            current_block_gate_raw_params.append(raw_gate_params); fep_entropy_adj_factors.append(fep_ent_adj_factor)
            dynamic_target_entropies_used.append(dyn_target_ent)
            ssr_befores_for_loss.append(ssr_before)
            ssr_afters_for_report.append(ssr_after)
            fep_delta_ssr_proposals_report.append(fep_delta_ssr)

            if self.debug_prints_enabled:
                acts_str = [f'{act.item():.3f}' for act in current_gate_acts]
                raw_str = [f'{rp.item():.3f}' for rp in raw_gate_params]
                ssr_after_str = [f'{srp.item():.3f}' for srp in ssr_after[:min(3, self.ssr_dim)]] + (["..."] if self.ssr_dim > 3 else [])
                fep_ds_str_report_inner = "N/A"
                if torch.is_tensor(fep_delta_ssr) and fep_delta_ssr.numel() > 0 : fep_ds_str_report_inner = [f'{ds.item():.3f}' for ds in fep_delta_ssr[:min(3, self.ssr_dim)]] + (["..."] if self.ssr_dim > 3 else [])
                fep_ent_adj_factor_str = f"{fep_ent_adj_factor.item():.3f}" if torch.is_tensor(fep_ent_adj_factor) else "N/A_Scalar"
                dyn_target_str = f"{dyn_target_ent.item():.3f}" if torch.is_tensor(dyn_target_ent) else "N/A_Scalar"
                print(f"  Output x from Block {i}: {x.shape}, BlkProcOutEnt: {blk_proc_out_ent.item():.4f}, X_OutEnt: {x_out_ent.item():.4f}, SigmoidG: {acts_str}, RawG: {raw_str}")
                print(f"    Block {i} SSR_After (sample): {ssr_after_str}, FEP_DeltaSSR_Proposal (sample): {fep_ds_str_report_inner}, FEP_EntAdjFactor: {fep_ent_adj_factor_str}, DynTgtEnt: {dyn_target_str}")

        logits = self.fc_out(x)
        if self.debug_prints_enabled: print(f"  Output logits: {logits.shape}")
        final_active_mask = ~src_key_padding_mask if src_key_padding_mask is not None else None

        overall_d_model_output_entropy = self.final_d_model_entropy_estimator(x.detach(), active_mask=final_active_mask) # Use renamed estimator
        if self.debug_prints_enabled: print(f"  Overall Final d_model Output Entropy (before fc_out): {overall_d_model_output_entropy.item():.4f}")

        entropy_report = {
            "block_processed_output_entropies": block_processed_output_entropies,
            "block_x_output_entropies": block_x_output_entropies, # V6.2
            "overall_d_model_output_entropy": overall_d_model_output_entropy, # V6.2
            "current_block_gate_activations": current_block_gate_activations, "current_block_gate_params": current_block_gate_raw_params,
            "fep_entropy_adj_factors": fep_entropy_adj_factors, "dynamic_target_entropies_used": dynamic_target_entropies_used,
            "ssr_befores_for_loss": ssr_befores_for_loss,
            "ssr_afters_for_report": ssr_afters_for_report,
            "fep_delta_ssr_proposals": fep_delta_ssr_proposals_report
        }
        if self.debug_prints_enabled: print(f"--- SWCKModel V6.2 Forward Pass Complete ---")
        return logits, entropy_report