from transformers import PretrainedConfig
from transformers import logging
from transformers import CONFIG_MAPPING

logger = logging.get_logger(__name__)

class XGenMMVisionEncoderConfig(PretrainedConfig):
    model_type = "xgenmm_vision_encoder"
    
    def __init__(self, 
                 model_name: str = 'google/siglip-so400m-patch14-384',
                 **kwargs):
        self.model_name = model_name
        super().__init__(**kwargs)
    

class XGenMMVisionTokenizerConfig(PretrainedConfig):
    model_type = "xgenmm_vision_tokenizer"
    
    def __init__(self, 
                 vis_feature_dim: int = 1152,
                 lang_embedding_dim: int = 3072,
                 num_vis_tokens: int = 128,
                 image_aspect_ratio: str = 'none',
                **kwargs):
        self.vis_feature_dim = vis_feature_dim
        self.lang_embedding_dim = lang_embedding_dim
        self.num_vis_tokens = num_vis_tokens
        self.image_aspect_ratio = image_aspect_ratio
        super().__init__(**kwargs)
        
        
class XGenMMConfig(PretrainedConfig):
    model_type = "xgenmm"
    
    def __init__(self, 
                 vision_encoder_config: dict = None,
                 vision_tokenizer_config: dict = None,
                 text_config: dict = None,
                 **kwargs):
        
        if vision_encoder_config is None:
            vision_encoder_config = {'image_aspect_ratio': 'anyres', 'anyres_patch_sampling': True}
            logger.info("vision_encoder_config is None. initializing the XGenMMVisionEncoderConfig with default values.")

        if vision_tokenizer_config is None:
            vision_tokenizer_config = {}
            logger.info("vision_tokenizer_config is None. Initializing the XGenMMVisionTokenizerConfig with default values.")

        if text_config is None:
            text_config = {
                'initial_tokenizer_len':32012,
                'pad_token_id':32011, 
                'bos_token_id':1, 
                'eos_token_id':32000,
                'vocab_size': 32064,
                'hidden_size': 3072,
                'intermediate_size': 8192,
                'num_hidden_layers': 32,
                'num_attention_heads': 32,
                'num_key_value_heads': 32,
                'resid_pdrop': 0.0,
                'embd_pdrop': 0.0,
                'attention_dropout': 0.0,
                'hidden_act': 'silu',
                'max_position_embeddings': 4096,
                'original_max_position_embeddings': 4096,
                'initializer_range': 0.02,
                'rms_norm_eps': 1e-05,
                'use_cache': True,
                'rope_theta': 10000.0,
                'rope_scaling': None,
                'sliding_window': 2047,
                'return_dict': True,
                'output_hidden_states': False,
                'output_attentions': False,
                'torchscript': False,
                'torch_dtype': 'bfloat16',
                'use_bfloat16': False,
                'tf_legacy_loss': False,
                'pruned_heads': {},
                'tie_word_embeddings': False,
                'chunk_size_feed_forward': 0,
                'is_encoder_decoder': False,
                'is_decoder': False,
                'cross_attention_hidden_size': None,
                'add_cross_attention': False,
                'tie_encoder_decoder': False,
                'max_length': 20,
                'min_length': 0,
                'do_sample': False,
                'early_stopping': False,
                'num_beams': 1,
                'num_beam_groups': 1,
                'diversity_penalty': 0.0,
                'temperature': 1.0,
                'top_k': 50,
                'top_p': 1.0,
                'typical_p': 1.0,
                'repetition_penalty': 1.0,
                'length_penalty': 1.0,
                'no_repeat_ngram_size': 0,
                'encoder_no_repeat_ngram_size': 0,
                'bad_words_ids': None,
                'num_return_sequences': 1,
                'output_scores': False,
                'return_dict_in_generate': False,
                'forced_bos_token_id': None,
                'forced_eos_token_id': None,
                'remove_invalid_values': False,
                'exponential_decay_length_penalty': None,
                'suppress_tokens': None,
                'begin_suppress_tokens': None,
                'finetuning_task': None,
                'id2label': {0: 'LABEL_0', 1: 'LABEL_1'},
                'label2id': {'LABEL_0': 0, 'LABEL_1': 1},
                'tokenizer_class': None,
                'prefix': None,
                'bos_token_id': 1,
                'pad_token_id': 32000,
                'eos_token_id': 32000,
                'sep_token_id': None,
                'decoder_start_token_id': None,
                'task_specific_params': None,
                'problem_type': None,
                'model_type': 'phi3'
                }
            logger.info("text_config is None. Initializing the text config with default values (`Phi3Config`).")
        
        self.vision_encoder_config = XGenMMVisionEncoderConfig(**vision_encoder_config)
        
        self.vision_tokenizer_config = XGenMMVisionTokenizerConfig(**vision_tokenizer_config)
        
        text_model_type = text_config["model_type"] if "model_type" in text_config else "phi3"
        self.text_config = CONFIG_MAPPING[text_model_type](**text_config)
        
        for key in ['initial_tokenizer_len', 'pad_token_id']:
            if key not in self.text_config.to_dict():
                raise ValueError(f"The key `{key}` is missing in the text_config.")
            
        super().__init__(**kwargs)
        
    @classmethod
    def from_vision_encoder_vision_tokenizer_text_configs(
        cls,
        vision_encoder_config: XGenMMVisionEncoderConfig,
        vision_tokenizer_config: XGenMMVisionTokenizerConfig,
        text_config: PretrainedConfig,
        **kwargs):
        
        return cls(
            vision_encoder_config=vision_encoder_config.to_dict(),
            vision_tokenizer_config=vision_tokenizer_config.to_dict(),
            text_config=text_config.to_dict(),
            **kwargs,
        )