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"""chameleon model configuration"""

from typing import List

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging

logger = logging.get_logger(__name__)


class ChameleonVQVAEConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`ChameleonVQModel`]. It is used to instantiate a
    `ChameleonVQModel` according to the specified arguments, defining the model architecture.
    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information. Instantiating a
    configuration with the defaults will yield a similar configuration to the VQModel of the
    [meta/chameleon-7B](https://huggingface.co/meta/chameleon-7B).

    Args:
        embed_dim (`int`, *optional*, defaults to 256):
            Dimensionality of each embedding vector.
        num_embeddings (`int`, *optional*, defaults to 8192):
            Number of codebook embeddings.
        double_latent (`bool`, *optional*, defaults to `False`):
            Whether to use double z channels.
        latent_channels (`int`, *optional*, defaults to 256):
            Number of channels for the latent space.
        resolution (`int`, *optional*, defaults to 512):
            Resolution of the input images.
        in_channels (`int`, *optional*, defaults to 3):
            Number of input channels.
        base_channels (`int`, *optional*, defaults to 128):
            Base channel count.
        channel_multiplier (`List[int]`, *optional*, defaults to `[1, 1, 2, 2, 4]`):
            Channel multipliers for each resolution.
        num_res_blocks (`int`, *optional*, defaults to 2):
            Number of residual blocks.
        attn_resolutions (`List[int]`, *optional*):
            Resolutions to apply attention.
        dropout (`float`, *optional*, defaults to 0.0):
            Dropout rate.
        attn_type (`str`, *optional*, defaults to `"vanilla"`):
            Attention type used in VQ-GAN encoder. Can be "vanilla" or None.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
    """

    model_type = "chameleon_vqgan"

    def __init__(
        self,
        embed_dim: int = 256,
        num_embeddings: int = 8192,
        double_latent: bool = False,
        latent_channels: int = 256,
        resolution: int = 512,
        in_channels: int = 3,
        base_channels: int = 128,
        channel_multiplier: List[int] = [1, 1, 2, 2, 4],
        num_res_blocks: int = 2,
        attn_resolutions: List[int] = None,
        dropout: float = 0.0,
        attn_type: str = "vanilla",
        initializer_range=0.02,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.embed_dim = embed_dim
        self.num_embeddings = num_embeddings
        self.double_latent = double_latent
        self.latent_channels = latent_channels
        self.resolution = resolution
        self.in_channels = in_channels
        self.base_channels = base_channels
        self.channel_multiplier = channel_multiplier
        self.num_res_blocks = num_res_blocks
        self.attn_resolutions = attn_resolutions
        self.dropout = dropout
        self.attn_type = attn_type
        self.initializer_range = initializer_range


class ChameleonConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`ChameleonModel`]. It is used to instantiate a
    chameleon model according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the
    [meta/chameleon-7B](https://huggingface.co/meta/chameleon-7B).

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.


    Args:
        vocab_size (`int`, *optional*, defaults to 65536):
            Vocabulary size of the chameleon model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`ChameleonModel`]; this includes text and image tokens.
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 11008):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer decoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer decoder.
        num_key_value_heads (`int`, *optional*, defaults to 32):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
            by meanpooling all the original heads within that group. For more details checkout [this
            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
            `num_attention_heads`.
        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 4096):
            The maximum sequence length that this model might ever be used with. Chameleon supports up to 4096 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.
        bos_token_id (`int`, *optional*, defaults to 1):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 2):
            End of stream token id.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings
        rope_theta (`float`, *optional*, defaults to 10000.0):
            The base period of the RoPE embeddings.
        rope_scaling (`Dict`, *optional*):
            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
            these scaling strategies behave:
            https://www.reddit.com/r/Localchameleon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
            experimental feature, subject to breaking API changes in future versions.
        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        model_parallel_size (`int`, *optional*, defaults to 1):
            Number of shards used when training the model. This will be used in qk layernorm because the original Chameleon inference
            doesn't do reduction in those layers and each rank has its own biases.
        swin_norm (`bool`, *optional*, defaults to `False`):
            Use Swin Transformer normalization.
        vq_config (`dict`, *optional*):
            ChameleonVQConfig instance containing the configuration for the VQ-VAE model.
        vocabulary_map (`dict`, *optional*):
            A dictionary containing the vocabulary map from the tokenizer. Used to obtain tokens from the image inputs.
        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.


    ```python
    >>> from transformers import ChameleonModel, ChameleonConfig

    >>> # Initializing a chameleon chameleon-7b style configuration
    >>> configuration = ChameleonConfig()

    >>> # Initializing a model from the chameleon-7b style configuration
    >>> model = ChameleonModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "chameleon"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=65536,
        hidden_size=4096,
        intermediate_size=11008,
        num_hidden_layers=32,
        num_attention_heads=32,
        num_key_value_heads=32,
        hidden_act="silu",
        max_position_embeddings=4096,
        initializer_range=0.02,
        rms_norm_eps=1e-05,
        use_cache=True,
        pad_token_id=None,
        bos_token_id=1,
        eos_token_id=2,
        tie_word_embeddings=False,
        rope_theta=10000.0,
        rope_scaling=None,
        attention_bias=False,
        attention_dropout=0.0,
        model_parallel_size=1,
        swin_norm=False,
        vq_config=None,
        vocabulary_map=None,
        mlp_bias=False,
        mask_image_logits=True,
        dropout=0.0,
        **kwargs,
    ):
        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
        self.mlp_bias = mlp_bias

        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.use_cache = use_cache
        self.rope_theta = rope_theta
        self.rope_scaling = rope_scaling
        self._rope_scaling_validation()
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        self.model_parallel_size = model_parallel_size
        self.swin_norm = swin_norm
        self.mask_image_logits = mask_image_logits

        if vq_config is None:
            vq_config = {}
            logger.info("vq_config is None. initializing the ChameleonVQConfig with default values.")

        self.vq_config = ChameleonVQVAEConfig(**vq_config)

        self.vocabulary_map = vocabulary_map

        self.dropout = dropout

        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,
        )

    def _rope_scaling_validation(self):
        """
        Validate the `rope_scaling` configuration.
        """
        if self.rope_scaling is None:
            return

        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
            raise ValueError(
                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
                f"got {self.rope_scaling}"
            )
        rope_scaling_type = self.rope_scaling.get("type", None)
        rope_scaling_factor = self.rope_scaling.get("factor", None)
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
            raise ValueError(
                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
            )
        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
