# coding=utf-8
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"""OWL-ViT model configuration"""

from ...configuration_utils import PreTrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)


class OwlViTTextConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of an [`OwlViTTextModel`]. It is used to instantiate an
    OwlViT text encoder according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the OwlViT
    [google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32) architecture.

    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 49408):
            Vocabulary size of the OWL-ViT text model. Defines the number of different tokens that can be represented
            by the `inputs_ids` passed when calling [`OwlViTTextModel`].
        hidden_size (`int`, *optional*, defaults to 512):
            Dimensionality of the encoder layers and the pooler layer.
        intermediate_size (`int`, *optional*, defaults to 2048):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer in the Transformer encoder.
        max_position_embeddings (`int`, *optional*, defaults to 16):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the layer normalization layers.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        initializer_factor (`float`, *optional*, defaults to 1.0):
            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
            testing).
        pad_token_id (`int`, *optional*, defaults to 0):
            The id of the padding token in the input sequences.
        bos_token_id (`int`, *optional*, defaults to 49406):
            The id of the beginning-of-sequence token in the input sequences.
        eos_token_id (`int`, *optional*, defaults to 49407):
            The id of the end-of-sequence token in the input sequences.

    Example:

    ```python
    >>> from transformers import OwlViTTextConfig, OwlViTTextModel

    >>> # Initializing a OwlViTTextModel with google/owlvit-base-patch32 style configuration
    >>> configuration = OwlViTTextConfig()

    >>> # Initializing a OwlViTTextConfig from the google/owlvit-base-patch32 style configuration
    >>> model = OwlViTTextModel(configuration)

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

    model_type = "owlvit_text_model"
    base_config_key = "text_config"

    def __init__(
        self,
        vocab_size=49408,
        hidden_size=512,
        intermediate_size=2048,
        num_hidden_layers=12,
        num_attention_heads=8,
        max_position_embeddings=16,
        hidden_act="quick_gelu",
        layer_norm_eps=1e-5,
        attention_dropout=0.0,
        initializer_range=0.02,
        initializer_factor=1.0,
        pad_token_id=0,
        bos_token_id=49406,
        eos_token_id=49407,
        **kwargs,
    ):
        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)

        self.vocab_size = vocab_size
        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.max_position_embeddings = max_position_embeddings
        self.hidden_act = hidden_act
        self.layer_norm_eps = layer_norm_eps
        self.attention_dropout = attention_dropout
        self.initializer_range = initializer_range
        self.initializer_factor = initializer_factor


class OwlViTVisionConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of an [`OwlViTVisionModel`]. It is used to instantiate
    an OWL-ViT image encoder according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the OWL-ViT
    [google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32) architecture.

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

    Args:
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_channels (`int`, *optional*, defaults to 3):
            Number of channels in the input images.
        image_size (`int`, *optional*, defaults to 768):
            The size (resolution) of each image.
        patch_size (`int`, *optional*, defaults to 32):
            The size (resolution) of each patch.
        hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the layer normalization layers.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        initializer_factor (`float`, *optional*, defaults to 1.0):
            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
            testing).

    Example:

    ```python
    >>> from transformers import OwlViTVisionConfig, OwlViTVisionModel

    >>> # Initializing a OwlViTVisionModel with google/owlvit-base-patch32 style configuration
    >>> configuration = OwlViTVisionConfig()

    >>> # Initializing a OwlViTVisionModel model from the google/owlvit-base-patch32 style configuration
    >>> model = OwlViTVisionModel(configuration)

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

    model_type = "owlvit_vision_model"
    base_config_key = "vision_config"

    def __init__(
        self,
        hidden_size=768,
        intermediate_size=3072,
        num_hidden_layers=12,
        num_attention_heads=12,
        num_channels=3,
        image_size=768,
        patch_size=32,
        hidden_act="quick_gelu",
        layer_norm_eps=1e-5,
        attention_dropout=0.0,
        initializer_range=0.02,
        initializer_factor=1.0,
        **kwargs,
    ):
        super().__init__(**kwargs)

        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.num_channels = num_channels
        self.image_size = image_size
        self.patch_size = patch_size
        self.hidden_act = hidden_act
        self.layer_norm_eps = layer_norm_eps
        self.attention_dropout = attention_dropout
        self.initializer_range = initializer_range
        self.initializer_factor = initializer_factor


class OwlViTConfig(PreTrainedConfig):
    r"""
    [`OwlViTConfig`] is the configuration class to store the configuration of an [`OwlViTModel`]. It is used to
    instantiate an OWL-ViT model according to the specified arguments, defining the text model and vision model
    configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the OWL-ViT
    [google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32) architecture.

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

    Args:
        text_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`OwlViTTextConfig`].
        vision_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`OwlViTVisionConfig`].
        projection_dim (`int`, *optional*, defaults to 512):
            Dimensionality of text and vision projection layers.
        logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
            The initial value of the *logit_scale* parameter. Default is used as per the original OWL-ViT
            implementation.
        return_dict (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return a dictionary. If `False`, returns a tuple.
        kwargs (*optional*):
            Dictionary of keyword arguments.
    """

    model_type = "owlvit"
    sub_configs = {"text_config": OwlViTTextConfig, "vision_config": OwlViTVisionConfig}

    def __init__(
        self,
        text_config=None,
        vision_config=None,
        projection_dim=512,
        logit_scale_init_value=2.6592,
        return_dict=True,
        **kwargs,
    ):
        if text_config is None:
            text_config = OwlViTTextConfig()
            logger.info("`text_config` is `None`. initializing the `OwlViTTextConfig` with default values.")
        elif isinstance(text_config, dict):
            text_config = OwlViTTextConfig(**text_config)

        if vision_config is None:
            vision_config = OwlViTVisionConfig()
            logger.info("`vision_config` is `None`. initializing the `OwlViTVisionConfig` with default values.")
        elif isinstance(vision_config, dict):
            vision_config = OwlViTVisionConfig(**vision_config)

        self.text_config = text_config
        self.vision_config = vision_config

        self.projection_dim = projection_dim
        self.logit_scale_init_value = logit_scale_init_value
        self.return_dict = return_dict
        self.initializer_factor = 1.0
        super().__init__(**kwargs)


__all__ = ["OwlViTConfig", "OwlViTTextConfig", "OwlViTVisionConfig"]
