from transformers import PretrainedConfig
import copy

class Florence2VisionConfig(PretrainedConfig):

    model_type = "florence2_vision"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        drop_path_rate=0.1,
        patch_size=[7, 3, 3, 3],
        patch_stride=[4, 2, 2, 2],
        patch_padding=[3, 1, 1, 1],
        patch_prenorm=[False, True, True, True],
        enable_checkpoint=False,
        dim_embed=[256, 512, 1024, 2048],
        num_heads=[8, 16, 32, 64],
        num_groups=[8, 16, 32, 64],
        depths=[1, 1, 9, 1],
        window_size=12,
        projection_dim=1024,
        visual_temporal_embedding=None,
        image_pos_embed=None,
        image_feature_source=["spatial_avg_pool", "temporal_avg_pool"],
        **kwargs,
    ):
        self.drop_path_rate = drop_path_rate
        self.patch_size = patch_size
        self.patch_stride = patch_stride
        self.patch_padding = patch_padding
        self.patch_prenorm = patch_prenorm
        self.enable_checkpoint = enable_checkpoint
        self.dim_embed = dim_embed
        self.num_heads = num_heads
        self.num_groups = num_groups
        self.depths = depths
        self.window_size = window_size
        self.projection_dim = projection_dim
        self.visual_temporal_embedding = visual_temporal_embedding
        self.image_pos_embed = image_pos_embed
        self.image_feature_source = image_feature_source

        super().__init__(**kwargs)



class Gemma2Config(PretrainedConfig):

    model_type = "gemma2"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=256000,
        hidden_size=3072,
        intermediate_size=24576,
        num_hidden_layers=28,
        num_attention_heads=16,
        num_key_value_heads=16,
        head_dim=256,
        hidden_activation="gelu_pytorch_tanh",
        max_position_embeddings=8192,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        pad_token_id=0,
        eos_token_id=1,
        bos_token_id=2,
        tie_word_embeddings=True,
        rope_theta=10000.0,
        attention_bias=False,
        attention_dropout=0.0,
        final_logit_softcapping=30.0,
        attn_logit_softcapping=50.0,
        query_pre_attn_scalar=224,
        sliding_window=4096,
        **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.head_dim = head_dim
        self.num_key_value_heads = num_key_value_heads
        self.hidden_activation = hidden_activation
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        self.attn_logit_softcapping = attn_logit_softcapping

        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,
        )
        self.final_logit_softcapping = final_logit_softcapping
        self.query_pre_attn_scalar = query_pre_attn_scalar
        self.sliding_window = sliding_window
        self.cache_implementation = "hybrid"



class FeynModelConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`FeynModel`]. It is used to instantiate a FeynModel
    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 Gemma2-2B + Florence-2-Base + FeynModel V0.1.0.
    ```python
    >>> from transformers import FeynModel, FeynModelConfig
    >>> # Initializing a FeynModel style configuration
    >>> configuration = FeynModelConfig()
    >>> # Initializing a model 
    >>> model = FeynModel(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    # model_type = "gemma2"
    # is_composition = False
    model_type = "FeynModel"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vision_config=None,
        text_config=None,
        ignore_index=-100,
        vocab_size=256000,
        projection_dim=1024,
        **kwargs,
    ):
        self.ignore_index = ignore_index
        self.vocab_size = vocab_size
        self.projection_dim = projection_dim
        self.vision_config = vision_config
        self.vocab_size = self.vocab_size

        self.text_config = text_config
        # self.sliding_window = text_config.sliding_window
        # Ajout des attributs de text_config à l'instance actuelle de Config
        
        if text_config is not None:
            for attr, value in text_config.items():
                setattr(self, attr, value)


        super().__init__(**kwargs)