Delete __init__.py with huggingface_hub

__init__.py

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-"""
-CNN-BiGRU — Modelo Multimodal Cooperativo Autoaprendível (v3.0)
-
-Implementação completa de um LLM multimodal com arquitetura CNN-BiGRU cooperativa:
-  - Núcleo dual-stream CNN-BiGRU com 3 níveis de pontes (cross-attention, gated cells, fusão)
-  - Encoders multimodais (imagem + áudio) com fusão gated
-  - Gerador + Verificador + Camada anti-alucinação (lógica fuzzy de Łukasiewicz)
-  - Auto-aprendizado: ajuste dinâmico de LR, spectral norm, orthogonal init
-  - Hipóteses ativadas por punições (Gumbel-Softmax diferenciável)
-  - Synergy search (N tentativas de combinação de hiperparâmetros)
-  - Múltiplas funções de perda: L_G, L_V, L_AH, L_reg, L_EWC
-
-v2.0:
-  - EWC (Elastic Weight Consolidation) para aprendizado contínuo
-  - Context Window com cache KV (sliding window + sink)
-  - RoPE (Rotary Position Embeddings)
-  - TransformerBlock (CausalSelfAttention + FFN)
-  - Self-attention final layer verdadeira (CLS-token + multi-head)
-  - Weight tying opcional
-  - Inferência autoregressiva REAL via GeneratorCNNBiGRU
-
-NOVO v3.0:
-  - Cyclic Reasoning (Raciocínio Cíclico) com convergência antecipada
-  - NLG (Natural Language Generation) com Transformer Decoder + Medusa MTP
-  - NLP (Natural Language Processing) com 4 tarefas: SeqCls, TokCls, Span, Embed
-  - W8A8 Quantization via SmoothQuant (alpha=0.5, per-channel)
-  - VQ-VAE-2 Hierárquico (top + bottom codebooks, EMA update)
-  - Multi-Token Prediction (MTP) com Medusa Heads
-  - Multimodal Multi-Head Attention (cross-modal + modality gate)
-  - Long Context Window (1M tokens) via chunked / ring attention
-  - Monitor completo (treino, inferência, evolução, sistema)
-"""
-from __future__ import annotations
-
-__version__ = "3.0.0"
-__author__ = "CNN-BiGRU Project"
-
-# Versão e metadados
-__all__ = [
-    "__version__",
-    "__author__",
-    # Tokenizer
-    "BBPETokenizer",
-    # Utils
-    "MemoryOptimizer",
-    "SemanticEmbedder",
-    "EWCConfig",
-    "EWCState",
-    "W8A8Config",
-    "SmoothQuantizer",
-    "quantize_model_w8a8",
-    "estimate_memory_savings",
-    "VQVAE2Config",
-    "VQVAE2",
-    "Monitor",
-    "get_monitor",
-    # Runtime
-    "optimize_xeon_environment",
-    "get_runtime_info",
-    # Data
-    "MultimodalStreamingDataset",
-    "collate_multimodal",
-    # Models
-    "CooperativeCNNBiGRU",
-    "MultimodalCNNBiGRU",
-    "ImageEncoder",
-    "AudioEncoder",
-    "MultimodalFusion",
-    "GeneratorCNNBiGRU",
-    "VerifierCNNBiGRU",
-    "AntiHallucinationLayer",
-    "RotaryPositionEmbedding",
-    "CausalSelfAttention",
-    "TransformerBlock",
-    "TransformerDecoderStack",
-    "KVCache",
-    "ContextWindowManager",
-    "ContextWindowConfig",
-    "LongContextConfig",
-    "LongContextManager",
-    "make_long_context_window",
-    "CyclicReasoningConfig",
-    "CyclicReasoning",
-    "MedusaConfig",
-    "MedusaMTP",
-    "MedusaHead",
-    "medusa_tree_decode",
-    "NLGConfig",
-    "NLGModule",
-    "NLPConfig",
-    "NLPModule",
-    "SequenceClassificationHead",
-    "TokenClassificationHead",
-    "SpanDetectionHead",
-    "EmbeddingHead",
-    "MultimodalAttentionConfig",
-    "MultimodalMultiHeadAttention",
-    "CrossModalAttention",
-    "ModalityGate",
-    # Losses
-    "LossConfig",
-    "MultiLoss",
-    # Training
-    "TrainerConfig",
-    "CooperativeTrainer",
-    "AutoLearnConfig",
-    "AutoLearner",
-    "HypothesisConfig",
-    "HypothesisController",
-    "SynergySearcher",
-    # Inference
-    "generate_with_sampling",
-    "evaluate_perplexity",
-]
-
-
-# Lazy imports — apenas quando acessado
-def __getattr__(name: str):
-    if name in ("BBPETokenizer",):
-        from .tokenizer.bbpe_tokenizer import BBPETokenizer
-        return BBPETokenizer
-    if name in ("MemoryOptimizer",):
-        from .utils.memory_optimizer import MemoryOptimizer
-        return MemoryOptimizer
-    if name in ("SemanticEmbedder",):
-        from .utils.semantic_embeddings import SemanticEmbedder
-        return SemanticEmbedder
-    if name in ("EWCConfig", "EWCState"):
-        from .utils.ewc import EWCConfig, EWCState
-        if name == "EWCConfig":
-            return EWCConfig
-        return EWCState
-    # W8A8 Quantization
-    if name in ("W8A8Config", "SmoothQuantizer"):
-        from .utils.quantization import W8A8Config, SmoothQuantizer
-        if name == "W8A8Config":
-            return W8A8Config
-        return SmoothQuantizer
-    if name == "quantize_model_w8a8":
-        from .utils.quantization import quantize_model_w8a8
-        return quantize_model_w8a8
-    if name == "estimate_memory_savings":
-        from .utils.quantization import estimate_memory_savings
-        return estimate_memory_savings
-    # VQ-VAE-2
-    if name in ("VQVAE2Config", "VQVAE2"):
-        from .utils.vqvae2 import VQVAE2Config, VQVAE2
-        if name == "VQVAE2Config":
-            return VQVAE2Config
-        return VQVAE2
-    # Monitor
-    if name in ("Monitor", "get_monitor"):
-        from .utils.monitoring import Monitor, get_monitor
-        if name == "Monitor":
-            return Monitor
-        return get_monitor
-    # Runtime
-    if name in ("optimize_xeon_environment", "get_runtime_info"):
-        from .utils import xeon_runtime
-        if name == "optimize_xeon_environment":
-            return xeon_runtime.optimize_xeon_environment
-        return xeon_runtime.get_runtime_info
-    # Data
-    if name in ("MultimodalStreamingDataset", "collate_multimodal"):
-        from .data.streaming_dataset import MultimodalStreamingDataset, collate_multimodal
-        if name == "MultimodalStreamingDataset":
-            return MultimodalStreamingDataset
-        return collate_multimodal
-    # Models
-    if name == "CooperativeCNNBiGRU":
-        from .models.cooperative_bigru import CooperativeCNNBiGRU
-        return CooperativeCNNBiGRU
-    if name == "MultimodalCNNBiGRU":
-        from .models.multimodal_model import MultimodalCNNBiGRU
-        return MultimodalCNNBiGRU
-    if name in ("ImageEncoder", "AudioEncoder", "MultimodalFusion"):
-        from .models.multimodal_encoders import ImageEncoder, AudioEncoder, MultimodalFusion
-        if name == "ImageEncoder":
-            return ImageEncoder
-        if name == "AudioEncoder":
-            return AudioEncoder
-        return MultimodalFusion
-    if name in ("GeneratorCNNBiGRU", "VerifierCNNBiGRU", "AntiHallucinationLayer"):
-        from .models.generator_verifier import (
-            GeneratorCNNBiGRU,
-            VerifierCNNBiGRU,
-            AntiHallucinationLayer,
-        )
-        if name == "GeneratorCNNBiGRU":
-            return GeneratorCNNBiGRU
-        if name == "VerifierCNNBiGRU":
-            return VerifierCNNBiGRU
-        return AntiHallucinationLayer
-    if name == "RotaryPositionEmbedding":
-        from .models.rope import RotaryPositionEmbedding
-        return RotaryPositionEmbedding
-    if name in ("CausalSelfAttention", "TransformerBlock", "TransformerDecoderStack"):
-        from .models.transformer_block import (
-            CausalSelfAttention,
-            TransformerBlock,
-            TransformerDecoderStack,
-        )
-        if name == "CausalSelfAttention":
-            return CausalSelfAttention
-        if name == "TransformerBlock":
-            return TransformerBlock
-        return TransformerDecoderStack
-    # Context window (incl. 1M tokens)
-    if name in ("KVCache", "ContextWindowManager", "ContextWindowConfig"):
-        from .models.context_window import KVCache, ContextWindowManager, ContextWindowConfig
-        if name == "KVCache":
-            return KVCache
-        if name == "ContextWindowManager":
-            return ContextWindowManager
-        return ContextWindowConfig
-    if name in ("LongContextConfig", "LongContextManager", "make_long_context_window"):
-        from .models.context_window import (
-            LongContextConfig,
-            LongContextManager,
-            make_long_context_window,
-        )
-        if name == "LongContextConfig":
-            return LongContextConfig
-        if name == "LongContextManager":
-            return LongContextManager
-        return make_long_context_window
-    # Cyclic Reasoning
-    if name in ("CyclicReasoningConfig", "CyclicReasoning"):
-        from .models.cyclic_reasoning import CyclicReasoningConfig, CyclicReasoning
-        if name == "CyclicReasoningConfig":
-            return CyclicReasoningConfig
-        return CyclicReasoning
-    # Medusa MTP
-    if name in ("MedusaConfig", "MedusaMTP", "MedusaHead", "medusa_tree_decode"):
-        from .models.medusa_heads import (
-            MedusaConfig,
-            MedusaMTP,
-            MedusaHead,
-            medusa_tree_decode,
-        )
-        if name == "MedusaConfig":
-            return MedusaConfig
-        if name == "MedusaMTP":
-            return MedusaMTP
-        if name == "MedusaHead":
-            return MedusaHead
-        return medusa_tree_decode
-    # NLG
-    if name in ("NLGConfig", "NLGModule"):
-        from .models.nlg import NLGConfig, NLGModule
-        if name == "NLGConfig":
-            return NLGConfig
-        return NLGModule
-    # NLP
-    if name in ("NLPConfig", "NLPModule",
-                "SequenceClassificationHead", "TokenClassificationHead",
-                "SpanDetectionHead", "EmbeddingHead"):
-        from .models.nlp import (
-            NLPConfig,
-            NLPModule,
-            SequenceClassificationHead,
-            TokenClassificationHead,
-            SpanDetectionHead,
-            EmbeddingHead,
-        )
-        if name == "NLPConfig":
-            return NLPConfig
-        if name == "NLPModule":
-            return NLPModule
-        if name == "SequenceClassificationHead":
-            return SequenceClassificationHead
-        if name == "TokenClassificationHead":
-            return TokenClassificationHead
-        if name == "SpanDetectionHead":
-            return SpanDetectionHead
-        return EmbeddingHead
-    # Multimodal Attention
-    if name in ("MultimodalAttentionConfig", "MultimodalMultiHeadAttention",
-                "CrossModalAttention", "ModalityGate"):
-        from .models.multimodal_attention import (
-            MultimodalAttentionConfig,
-            MultimodalMultiHeadAttention,
-            CrossModalAttention,
-            ModalityGate,
-        )
-        if name == "MultimodalAttentionConfig":
-            return MultimodalAttentionConfig
-        if name == "MultimodalMultiHeadAttention":
-            return MultimodalMultiHeadAttention
-        if name == "CrossModalAttention":
-            return CrossModalAttention
-        return ModalityGate
-    # Losses
-    if name in ("LossConfig", "MultiLoss"):
-        from .losses.losses import LossConfig, MultiLoss
-        if name == "LossConfig":
-            return LossConfig
-        return MultiLoss
-    # Training
-    if name in ("TrainerConfig", "CooperativeTrainer"):
-        from .training.trainer import TrainerConfig, CooperativeTrainer
-        if name == "TrainerConfig":
-            return TrainerConfig
-        return CooperativeTrainer
-    if name in ("AutoLearnConfig", "AutoLearner"):
-        from .training.auto_learner import AutoLearnConfig, AutoLearner
-        if name == "AutoLearnConfig":
-            return AutoLearnConfig
-        return AutoLearner
-    if name in ("HypothesisConfig", "HypothesisController", "SynergySearcher"):
-        from .training.hypothesis_controller import (
-            HypothesisConfig,
-            HypothesisController,
-            SynergySearcher,
-        )
-        if name == "HypothesisConfig":
-            return HypothesisConfig
-        if name == "HypothesisController":
-            return HypothesisController
-        return SynergySearcher
-    # Inference
-    if name in ("generate_with_sampling", "evaluate_perplexity"):
-        from .inference.inference import generate_with_sampling, evaluate_perplexity
-        if name == "generate_with_sampling":
-            return generate_with_sampling
-        return evaluate_perplexity
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")