from typing import Callable, Optional, Tuple, Union

import torch
import torch.nn as nn

from ...cache_utils import Cache, DynamicCache
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_outputs import (
    BaseModelOutputWithPast,
)
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
from ...processing_utils import Unpack
from ...utils import logging
from ..clip.modeling_clip import CLIPMLP
from ..llama.modeling_llama import (
    LlamaAttention,
    LlamaForCausalLM,
    LlamaForSequenceClassification,
    LlamaForTokenClassification,
    LlamaModel,
    apply_rotary_pos_emb,
    eager_attention_forward,  # copied from Llama
)
from .configuration_phi import PhiConfig


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "microsoft/phi-1"
_CONFIG_FOR_DOC = "PhiConfig"


class PhiAttention(LlamaAttention):
    def __init__(self, config: PhiConfig, layer_idx: int):
        super().__init__(config, layer_idx)
        self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True)
        self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True)
        self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True)
        self.dense = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=True)
        del self.o_proj
        self.rotary_ndims = int(self.head_dim * config.partial_rotary_factor)
        self.qk_layernorm = config.qk_layernorm
        if self.qk_layernorm:
            self.q_layernorm = nn.LayerNorm(
                config.hidden_size // config.num_attention_heads, eps=config.layer_norm_eps, elementwise_affine=True
            )
            self.k_layernorm = nn.LayerNorm(
                config.hidden_size // config.num_attention_heads, eps=config.layer_norm_eps, elementwise_affine=True
            )

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[torch.Tensor],
        past_key_value: Optional[Cache] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)

        if self.qk_layernorm:
            query_states = self.q_layernorm(query_states)
            key_states = self.k_layernorm(key_states)

        cos, sin = position_embeddings
        # Partial rotary embedding
        query_rot, query_pass = (
            query_states[..., : self.rotary_ndims],
            query_states[..., self.rotary_ndims :],
        )
        key_rot, key_pass = (
            key_states[..., : self.rotary_ndims],
            key_states[..., self.rotary_ndims :],
        )
        # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
        query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin)

        # [batch_size, seq_length, num_heads, head_dim]
        query_states = torch.cat((query_rot, query_pass), dim=-1)
        key_states = torch.cat((key_rot, key_pass), dim=-1)

        if past_key_value is not None:
            # sin and cos are specific to RoPE models; cache_position needed for the static cache
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        attention_interface: Callable = eager_attention_forward
        if self.config._attn_implementation != "eager":
            if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
                logger.warning_once(
                    "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
                    'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
                )
            else:
                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]

        attn_output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            **kwargs,
        )

        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
        attn_output = self.dense(attn_output)
        return attn_output, attn_weights


class PhiMLP(CLIPMLP):
    pass


class PhiDecoderLayer(nn.Module):
    def __init__(self, config: PhiConfig, layer_idx: int):
        super().__init__()
        self.self_attn = PhiAttention(config, layer_idx=layer_idx)
        self.mlp = PhiMLP(config)
        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.resid_dropout = nn.Dropout(config.resid_pdrop)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
        **kwargs,
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        attn_outputs, self_attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            **kwargs,
        )
        attn_outputs = self.resid_dropout(attn_outputs)

        feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
        hidden_states = attn_outputs + feed_forward_hidden_states + residual
        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        return outputs


class PhiModel(LlamaModel):
    def __init__(self, config: PhiConfig):
        super().__init__(config)
        self.layers = nn.ModuleList(
            [PhiDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.embed_dropout = nn.Dropout(config.embd_pdrop)
        self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        del self.norm

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if self.gradient_checkpointing and self.training and use_cache:
            logger.warning_once(
                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
            )
            use_cache = False

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache()

        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position = torch.arange(
                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
            )

        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        causal_mask = self._update_causal_mask(
            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
        )

        inputs_embeds = self.embed_dropout(inputs_embeds)  # diff with Llama
        hidden_states = inputs_embeds

        # create position embeddings to be shared across the decoder layers
        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None

        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    causal_mask,
                    position_ids,
                    past_key_values,
                    output_attentions,
                    use_cache,
                    cache_position,
                    position_embeddings,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=causal_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_values,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                    cache_position=cache_position,
                    position_embeddings=position_embeddings,
                    **flash_attn_kwargs,
                )

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

        hidden_states = self.final_layernorm(hidden_states)  # diff with Llama

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        output = BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values if use_cache else None,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )
        return output if return_dict else output.to_tuple()


class PhiForCausalLM(LlamaForCausalLM):
    def __init__(self, config):
        super().__init__(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=True)


class PhiForSequenceClassification(LlamaForSequenceClassification):
    pass


class PhiForTokenClassification(LlamaForTokenClassification):
    pass
