from typing import Optional
from transformers.modeling_rope_utils import rope_config_validation
from transformers.configuration_utils import layer_type_validation
from transformers.utils import logging
from transformers import PretrainedConfig

import transformers.configuration_utils as configuration_util


logger = logging.get_logger(__name__)


class PMNetConfig(PretrainedConfig):

    model_type = "PMNet"
    keys_to_ignore_at_inference = ["past_key_values"]

    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"]),
    }

    def __init__(
        self,
        vocab_size: Optional[int] = 151936,
        hidden_size: Optional[int] = 4096,
        intermediate_size: Optional[int] = 22016,
        num_hidden_layers: Optional[int] = 32,
        num_attention_heads: Optional[int] = 32,
        num_key_value_heads: Optional[int] = 32,
        head_dim: Optional[int] = 128,
        memory_size: Optional[int] = 64,
        num_memory: Optional[int] = 32,
        num_memory_read_heads: Optional[int] = 8,
        memory_write_period: Optional[int] = 4,
        hidden_act: Optional[str] = "silu",
        max_position_embeddings: Optional[int] = 32768,
        initializer_range: Optional[float] = 0.02,
        rms_norm_eps: Optional[int] = 1e-6,
        use_cache: Optional[bool] = True,
        tie_word_embeddings: Optional[bool] = False,
        rope_theta=10000.0,
        rope_scaling=None,
        attention_bias: Optional[bool] = False,
        use_sliding_window: Optional[bool] = False,
        sliding_window: Optional[int] = 4096,
        max_window_layers: Optional[int] = 28,
        layer_types: Optional[list[str]] = None,
        attention_dropout: Optional[float] = 0.0,
        memory_cumsum: bool = True,
        **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.use_sliding_window = use_sliding_window
        self.sliding_window = sliding_window if self.use_sliding_window else None
        self.max_window_layers = max_window_layers

        self.memory_size = memory_size
        self.num_memory = num_memory
        self.num_memory_read_heads = num_memory_read_heads
        self.memory_write_period = memory_write_period

        # 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.head_dim = head_dim
        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.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        self.memory_cumsum = memory_cumsum

        if self.rope_scaling is not None and "type" in self.rope_scaling:
            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
        rope_config_validation(self)

        self.layer_types = layer_types
        if self.layer_types is None:
            self.layer_types = [
                (
                    "sliding_attention"
                    if self.sliding_window is not None and i >= self.max_window_layers
                    else "full_attention"
                )
                for i in range(self.num_hidden_layers)
            ]
        layer_type_validation(self.layer_types, self.num_hidden_layers)

        super().__init__(
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )


__all__ = ["PMNetConfig"]