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#           This file was automatically generated from src/transformers/models/cwm/modular_cwm.py.
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# Copyright 2025
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# limitations under the License.


from ...configuration_utils import PreTrainedConfig, layer_type_validation


class CwmConfig(PreTrainedConfig):
    """
    Configuration for Code World Model (CWM).
    This is an inherited Llama3-compatible configuration with layer-interleaved
    sliding-window attention. Configures a `CwmModel`. Designed to yield a configuration mirroring the model in the
    [facebook/cwm](https://huggingface.co/facebook/cwm) architecture by default. Other models include:
    - [facebook/cwm-sft](https://huggingface.co/facebook/cwm-sft)
    - [facebook/cwm-pretrain](https://huggingface.co/facebook/cwm-pretrain)

    Args:
        vocab_size (`int`, *optional*, defaults to 128256):
            Vocabulary size of the CWM model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`CwmModel`]
        hidden_size (`int`, *optional*, defaults to 6144):
            Dimension of the hidden representations
        intermediate_size (`int`, *optional*, defaults to 21504):
            Dimension of the MLP representations
        num_hidden_layers (`int`, *optional*, defaults to 64):
            Number of hidden layers in the Transformer decoder
        num_attention_heads (`int`, *optional*, defaults to 48):
            Number of attention heads for each attention layer in the Transformer decoder
        num_key_value_heads (`int`, *optional*, defaults to 8):
            This is the number of key_value heads that should be used to implement Grouped Query Attention (GQA).
            If it is not specified, will default to `num_attention_heads`.
        head_dim (`int`, *optional*, defaults to 128):
            The attention head dimension.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to 131072):
            The maximum sequence length that this model might ever be used with. CWM's attention allows sequence
            lengths up to 131072 tokens.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the rms normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        pad_token_id (`int`, *optional*):
            Padding token id.
        eos_token_id (`int` or `list[int]`, *optional*, defaults to `[128001, 128008, 128009]`):
            The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
        bos_token_id (`int`, *optional*, defaults to 128000):
            The id of the *beginning-of-sequence* token.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        pretraining_tp (`int`, *optional*, defaults to 1):
            Tensor parallelism degree used during pretraining. See [this
            document](https://huggingface.co/docs/transformers/parallelism) and [this
            issue](https://github.com/pytorch/pytorch/issues/76232).
        mlp_bias (`bool`, *optional*, defaults to `False`):
            Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
        rope_parameters (`RopeParameters`, *optional*):
            Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
            a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
            with longer `max_position_embeddings`.
        sliding_window (`int`, *optional*, defaults to 8192):
            Sliding window attention window size.
        layer_types (`List[str]`, *optional*):
            List of layer types for each layer. Each element should be either "full_attention" or "sliding_attention".
            If not specified, will default to alternating pattern based on the provided window pattern.
    """

    model_type = "cwm"
    keys_to_ignore_at_inference = ["past_key_values"]
    # Default tensor parallel plan for base model `CwmModel`
    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise",
        "layers.*.self_attn.k_proj": "colwise",
        "layers.*.self_attn.v_proj": "colwise",
        "layers.*.self_attn.o_proj": "rowwise",
        "layers.*.mlp.gate_proj": "colwise",
        "layers.*.mlp.up_proj": "colwise",
        "layers.*.mlp.down_proj": "rowwise",
    }
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }
    default_theta = 1_000_000.0

    def __init__(
        self,
        vocab_size: int = 128256,
        hidden_size: int = 6144,
        intermediate_size: int = 21504,
        num_hidden_layers: int = 64,
        num_attention_heads: int = 48,
        num_key_value_heads: int = 8,
        head_dim: int = 128,
        hidden_act: str = "silu",
        max_position_embeddings: int = 131072,
        initializer_range: float = 0.02,
        rms_norm_eps: float = 1e-5,
        use_cache: bool = True,
        pad_token_id: int | None = None,
        eos_token_id=[128001, 128008, 128009],
        bos_token_id: int = 128000,
        tie_word_embeddings: bool = False,
        attention_dropout: float = 0.0,
        pretraining_tp: int = 1,
        mlp_bias: bool = False,
        rope_parameters: dict | None = None,
        # CWM interleaved sliding window fields
        sliding_window: int = 8192,
        layer_types: list[str] | None = None,  # ["full_attention"|"sliding_attention"] per layer
        **kwargs,
    ):
        if rope_parameters is None:
            rope_parameters = {
                "rope_theta": 1_000_000.0,
                "factor": 16.0,
                "high_freq_factor": 4.0,
                "low_freq_factor": 1.0,
                "original_max_position_embeddings": 8192,
                "rope_type": "llama3",
            }

        if layer_types is None:
            # Default pattern: every 4th layer uses full attention, others use sliding attention
            window_pattern = 4
            layer_types = [
                ("full_attention" if (i % window_pattern == 0) else "sliding_attention")
                for i in range(num_hidden_layers)
            ]
        else:
            layer_type_validation(layer_types, num_hidden_layers)

        self.sliding_window = int(sliding_window) if sliding_window else None
        self.layer_types = list(layer_types)
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads

        # for backward compatibility
        if num_key_value_heads is None:
            num_key_value_heads = num_attention_heads

        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.pretraining_tp = pretraining_tp
        self.use_cache = use_cache
        self.attention_dropout = attention_dropout
        self.mlp_bias = mlp_bias
        self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
        self.rope_parameters = rope_parameters

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )


__all__ = ["CwmConfig"]
