"""GPT Blocks used for the GPT Model."""
import copy
from typing import Any, Optional
import torch
import torch.nn as nn
from .layers_registry import ffns_with_norm
from .layer_builders import build_attention_layer, build_ffn, build_norm
from .config_defaults import attn_config_defaults, fc_type_defaults
try:
    from flash_attn.bert_padding import unpad_input, pad_input
except:
    unpad_input, pad_input = (None, None)

class MPTBlock(nn.Module):

    def __init__(self, d_model: int, n_heads: int, expansion_ratio: int, attn_config: Optional[dict]=None, ffn_config: Optional[dict]=None, resid_pdrop: float=0.0, norm_type: str='low_precision_layernorm', norm_eps: float=1e-05, fc_type: Optional[dict[str, Any]]=None, device: Optional[str]=None, no_bias: bool=False, use_pad_tok_in_ffn: bool=True, **kwargs: Any):
        if attn_config is None:
            attn_config = attn_config_defaults
        if ffn_config is None:
            self.ffn_config: dict[str, Any] = {'ffn_type': 'mptmlp'}
        else:
            self.ffn_config = ffn_config
        if fc_type is None:
            fc_type = copy.deepcopy(fc_type_defaults)
        fc_type['bias'] = not no_bias
        fc_type['device'] = device
        self.ffn_config['fc_type'] = fc_type
        self.fuse_norm_attn_norm = kwargs.get('fuse_norm_attn_norm', False)
        del kwargs
        super().__init__()
        ffn_type = self.ffn_config['ffn_type']
        ffn_has_norm = ffn_type in ffns_with_norm
        if self.fuse_norm_attn_norm:
            self.norm_attn_norm = FusedNormAttentionNorm(d_model=d_model, n_heads=n_heads, args_to_exclude_in_attn_class=self.args_to_exclude_in_attn_class, attn_config=attn_config, ffn_has_norm=ffn_has_norm, fc_type=fc_type, resid_pdrop=resid_pdrop, norm_type=norm_type, norm_eps=norm_eps, device=device, no_bias=no_bias)
        else:
            assert isinstance(attn_config['attn_type'], str)
            attn_config_subset_for_attn_class = {k: v for k, v in attn_config.items() if k not in self.args_to_exclude_in_attn_class}
            self.norm_1 = build_norm(name=norm_type.lower(), normalized_shape=d_model, eps=norm_eps, device=device)
            self.attn = build_attention_layer(name=attn_config['attn_type'], attn_kwargs={'d_model': d_model, 'n_heads': n_heads, 'fc_type': fc_type, 'device': device, 'bias': not no_bias, **attn_config_subset_for_attn_class})
            self.norm_2 = None
            if not ffn_has_norm:
                self.norm_2 = build_norm(name=norm_type.lower(), normalized_shape=d_model, eps=norm_eps, device=device)
        self.ffn = build_ffn(name=ffn_type, d_model=d_model, expansion_ratio=expansion_ratio, device=device, bias=not no_bias, ffn_kwargs=self.ffn_config)
        self.resid_attn_dropout = nn.Dropout(resid_pdrop)
        self.resid_ffn_dropout = nn.Dropout(resid_pdrop)
        self.use_pad_tok_in_ffn = use_pad_tok_in_ffn

    @property
    def args_to_exclude_in_attn_class(self):
        return {'attn_type', 'alibi', 'attn_uses_sequence_id', 'alibi_bias_max', 'rope', 'rope_theta', 'rope_impl', 'rope_dail_config', 'rope_hf_config'}

    def forward(self, x: torch.Tensor, past_key_value: Optional[tuple[torch.Tensor, torch.Tensor]]=None, attn_bias: Optional[torch.Tensor]=None, rotary_emb_w_meta_info: Optional[dict]=None, attention_mask: Optional[torch.ByteTensor]=None, is_causal: bool=True, output_attentions: bool=False, alibi_slopes: Optional[torch.Tensor]=None, flash_attn_padding_info: Optional[dict[str, torch.Tensor]]=None, prev_layer_key_value: Optional[tuple[torch.Tensor, torch.Tensor]]=None) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor, torch.Tensor]]]:
        extra_kwargs = {}
        if prev_layer_key_value is not None:
            extra_kwargs['prev_layer_key_value'] = prev_layer_key_value
        if self.fuse_norm_attn_norm:
            x, m, attn_weights, past_key_value = self.norm_attn_norm(x, past_key_value=past_key_value, attn_bias=attn_bias, rotary_emb_w_meta_info=rotary_emb_w_meta_info, attention_mask=attention_mask, is_causal=is_causal, output_attentions=output_attentions, alibi_slopes=alibi_slopes, flash_attn_padding_info=flash_attn_padding_info, **extra_kwargs)
        else:
            a = self.norm_1(x)
            b, attn_weights, past_key_value = self.attn(a, past_key_value=past_key_value, attn_bias=attn_bias, rotary_emb_w_meta_info=rotary_emb_w_meta_info, attention_mask=attention_mask, is_causal=is_causal, needs_weights=output_attentions, alibi_slopes=alibi_slopes, flash_attn_padding_info=flash_attn_padding_info, **extra_kwargs)
            x = x + self.resid_attn_dropout(b)
            m = x
            if self.norm_2 is not None:
                m = self.norm_2(x)
        n = self.apply_ffn(attention_mask, m)
        x = x.to(device=n.device) + self.resid_ffn_dropout(n).to(device=n.device)
        return (x, attn_weights, past_key_value)

    def apply_ffn(self, attention_mask: Optional[torch.ByteTensor], m: torch.Tensor) -> torch.Tensor:
        """Apply feed forward layers to the input.

        Args:
            attention_mask (Optional[torch.ByteTensor]): The attention mask.
            m (torch.Tensor): The input.

        Returns:
            n (torch.Tensor): The output.
        """
        batch_size, seq_len = m.size()[:2]
        indices = None
        if not self.use_pad_tok_in_ffn and attention_mask is not None:
            assert unpad_input is not None
            attention_mask = self.slice_attention_mask(attention_mask, seq_len)
            m, indices, _, _ = unpad_input(m, attention_mask)
        n = self.ffn(m)
        if not self.use_pad_tok_in_ffn and attention_mask is not None:
            assert pad_input is not None
            n = pad_input(n, indices, batch_size, seq_len)
        return n

    def slice_attention_mask(self, attention_mask: torch.ByteTensor, seq_len: int) -> torch.ByteTensor:
        """Slice attention mask to the correct size.

        Can be overridden by subclasses to apply different slicing logic.

        Args:
            attention_mask (torch.ByteTensor): The attention mask.
            seq_len (int): The sequence length.

        Returns:
            torch.ByteTensor: The sliced attention mask.
        """
        return attention_mask

class FusedNormAttentionNorm(nn.Module):

    def __init__(self, d_model: int, n_heads: int, args_to_exclude_in_attn_class: set[str], attn_config: Optional[dict]=None, ffn_has_norm: bool=False, fc_type: Optional[dict[str, Any]]=None, resid_pdrop: float=0.0, norm_type: str='low_precision_layernorm', norm_eps: float=1e-05, device: Optional[str]=None, no_bias: bool=False, **kwargs: Any):
        super().__init__()
        assert attn_config is not None
        assert isinstance(attn_config['attn_type'], str)
        if fc_type is None:
            fc_type = copy.deepcopy(fc_type_defaults)
            fc_type['bias'] = not no_bias
            fc_type['device'] = device
        attn_config_subset_for_attn_class = {k: v for k, v in attn_config.items() if k not in args_to_exclude_in_attn_class}
        self.norm_1 = build_norm(name=norm_type.lower(), normalized_shape=d_model, eps=norm_eps, device=device)
        self.attn = build_attention_layer(name=attn_config['attn_type'], attn_kwargs={'d_model': d_model, 'n_heads': n_heads, 'fc_type': fc_type, 'device': device, 'bias': not no_bias, **attn_config_subset_for_attn_class})
        self.norm_2 = None
        if not ffn_has_norm:
            self.norm_2 = build_norm(name=norm_type.lower(), normalized_shape=d_model, eps=norm_eps, device=device)
        self.resid_attn_dropout = nn.Dropout(resid_pdrop)

    def forward(self, x: torch.Tensor, past_key_value: Optional[tuple[torch.Tensor, torch.Tensor]]=None, attn_bias: Optional[torch.Tensor]=None, rotary_emb_w_meta_info: Optional[dict]=None, attention_mask: Optional[torch.ByteTensor]=None, is_causal: bool=True, output_attentions: bool=False, alibi_slopes: Optional[torch.Tensor]=None, flash_attn_padding_info: Optional[dict[str, torch.Tensor]]=None, prev_layer_key_value: Optional[tuple[torch.Tensor, torch.Tensor]]=None) -> tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor, torch.Tensor]]]:
        a = self.norm_1(x)
        extra_kwargs = {}
        if prev_layer_key_value is not None:
            extra_kwargs['prev_layer_key_value'] = prev_layer_key_value
        b, attn_weights, past_key_value = self.attn(a, past_key_value=past_key_value, attn_bias=attn_bias, rotary_emb_w_meta_info=rotary_emb_w_meta_info, attention_mask=attention_mask, is_causal=is_causal, needs_weights=output_attentions, alibi_slopes=alibi_slopes, flash_attn_padding_info=flash_attn_padding_info, **extra_kwargs)
        x = x + self.resid_attn_dropout(b)
        m = x
        if self.norm_2 is not None:
            m = self.norm_2(x)
        return (x, m, attn_weights, past_key_value)