from typing import Callable, Optional, Tuple
from copy import deepcopy

import numpy as np

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax

from transformers import AlbertConfig
from transformers.models.albert.modeling_flax_albert import FlaxAlbertOnlyMLMHead, FlaxAlbertEmbeddings, FlaxAlbertPreTrainedModel
from transformers.modeling_flax_outputs import (
    FlaxBaseModelOutput,
    FlaxBaseModelOutputWithPooling,
    FlaxMaskedLMOutput,
    FlaxMultipleChoiceModelOutput,
    FlaxQuestionAnsweringModelOutput,
    FlaxSequenceClassifierOutput,
    FlaxTokenClassifierOutput,
)
from transformers.utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging

from transformers.modeling_flax_utils import (
    ACT2FN,
    FlaxPreTrainedModel,
    append_call_sample_docstring,
    append_replace_return_docstrings,
    overwrite_call_docstring,
)

class CustomFlaxAlbertSelfAttention(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        if self.config.hidden_size % self.config.num_attention_heads != 0:
            raise ValueError(
                "`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads` "
                "                   : {self.config.num_attention_heads}"
            )

        self.query = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.key = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.value = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.dense = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)

    def __call__(
        self,
        hidden_states,
        attention_mask,
        deterministic=True,
        output_attentions: bool = False,
        layer_id: int = None,
        interv_type: str = "swap",
        interv_dict: dict = {},
    ):
        head_dim = self.config.hidden_size // self.config.num_attention_heads

        query_states = self.query(hidden_states).reshape(
            hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim)
        )
        value_states = self.value(hidden_states).reshape(
            hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim)
        )
        key_states = self.key(hidden_states).reshape(
            hidden_states.shape[:2] + (self.config.num_attention_heads, head_dim)
        )

        reps = {
                'lay': hidden_states,
                'qry': query_states,
                'key': key_states,
                'val': value_states,
                }
        if layer_id in interv_dict:
            interv = interv_dict[layer_id]
            for rep_name in ['lay','qry','key','val']:
                if rep_name in interv:
                    new_state = deepcopy(reps[rep_name])
                    for head_id, pos, swap_ids in interv[rep_name]:
                        new_state[swap_ids[0],pos,head_id] = reps[rep_name][swap_ids[1],pos,head_id]
                        new_state[swap_ids[1],pos,head_id] = reps[rep_name][swap_ids[0],pos,head_id]
                    reps[rep_name] = deepcopy(new_state)

        hidden_states = deepcopy(reps['lay'])
        query_states = deepcopy(reps['qry'])
        key_states = deepcopy(reps['key'])
        value_states = deepcopy(reps['val'])

        # Convert the boolean attention mask to an attention bias.
        if attention_mask is not None:
            # attention mask in the form of attention bias
            attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
            attention_bias = lax.select(
                attention_mask > 0,
                jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
                jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
            )
        else:
            attention_bias = None

        dropout_rng = None
        if not deterministic and self.config.attention_probs_dropout_prob > 0.0:
            dropout_rng = self.make_rng("dropout")

        attn_weights = dot_product_attention_weights(
            query_states,
            key_states,
            bias=attention_bias,
            dropout_rng=dropout_rng,
            dropout_rate=self.config.attention_probs_dropout_prob,
            broadcast_dropout=True,
            deterministic=deterministic,
            dtype=self.dtype,
            precision=None,
        )

        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
        attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,))

        projected_attn_output = self.dense(attn_output)
        projected_attn_output = self.dropout(projected_attn_output, deterministic=deterministic)
        layernormed_attn_output = self.LayerNorm(projected_attn_output + hidden_states)
        outputs = (layernormed_attn_output, attn_weights) if output_attentions else (layernormed_attn_output,)
        return outputs

class CustomFlaxAlbertLayer(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.attention = CustomFlaxAlbertSelfAttention(self.config, dtype=self.dtype)
        self.ffn = nn.Dense(
            self.config.intermediate_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )
        self.activation = ACT2FN[self.config.hidden_act]
        self.ffn_output = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )
        self.full_layer_layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)

    def __call__(
        self,
        hidden_states,
        attention_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        layer_id: int = None,
        interv_type: str = "swap",
        interv_dict: dict = {},
    ):
        attention_outputs = self.attention(
            hidden_states,
            attention_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            layer_id=layer_id,
            interv_type=interv_type,
            interv_dict=interv_dict,
        )
        attention_output = attention_outputs[0]
        ffn_output = self.ffn(attention_output)
        ffn_output = self.activation(ffn_output)
        ffn_output = self.ffn_output(ffn_output)
        ffn_output = self.dropout(ffn_output, deterministic=deterministic)
        hidden_states = self.full_layer_layer_norm(ffn_output + attention_output)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attention_outputs[1],)
        return outputs

class CustomFlaxAlbertLayerCollection(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.layers = [
            CustomFlaxAlbertLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.inner_group_num)
        ]

    def __call__(
        self,
        hidden_states,
        attention_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        layer_id: int = None,
        interv_type: str = "swap",
        interv_dict: dict = {},
    ):
        layer_hidden_states = ()
        layer_attentions = ()

        for layer_index, albert_layer in enumerate(self.layers):
            layer_output = albert_layer(
                hidden_states,
                attention_mask,
                deterministic=deterministic,
                output_attentions=output_attentions,
                layer_id=layer_id,
                interv_type=interv_type,
                interv_dict=interv_dict,
            )
            hidden_states = layer_output[0]

            if output_attentions:
                layer_attentions = layer_attentions + (layer_output[1],)

            if output_hidden_states:
                layer_hidden_states = layer_hidden_states + (hidden_states,)

        outputs = (hidden_states,)
        if output_hidden_states:
            outputs = outputs + (layer_hidden_states,)
        if output_attentions:
            outputs = outputs + (layer_attentions,)
        return outputs  # last-layer hidden state, (layer hidden states), (layer attentions)

class CustomFlaxAlbertLayerCollections(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    layer_index: Optional[str] = None

    def setup(self):
        self.albert_layers = CustomFlaxAlbertLayerCollection(self.config, dtype=self.dtype)

    def __call__(
        self,
        hidden_states,
        attention_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        layer_id: int = None,
        interv_type: str = "swap",
        interv_dict: dict = {},
    ):
        outputs = self.albert_layers(
            hidden_states,
            attention_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            layer_id=layer_id,
            interv_type=interv_type,
            interv_dict=interv_dict,
        )
        return outputs

class CustomFlaxAlbertLayerGroups(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.layers = [
            CustomFlaxAlbertLayerCollections(self.config, name=str(i), layer_index=str(i), dtype=self.dtype)
            for i in range(self.config.num_hidden_groups)
        ]

    def __call__(
        self,
        hidden_states,
        attention_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
        interv_type: str = "swap",
        interv_dict: dict = {},
    ):
        all_attentions = () if output_attentions else None
        all_hidden_states = (hidden_states,) if output_hidden_states else None

        for i in range(self.config.num_hidden_layers):
            # Index of the hidden group
            group_idx = int(i / (self.config.num_hidden_layers / self.config.num_hidden_groups))
            layer_group_output = self.layers[group_idx](
                hidden_states,
                attention_mask,
                deterministic=deterministic,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                layer_id=i,
                interv_type=interv_type,
                interv_dict=interv_dict,
            )
            hidden_states = layer_group_output[0]

            if output_attentions:
                all_attentions = all_attentions + layer_group_output[-1]

            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
        return FlaxBaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
        )

class CustomFlaxAlbertEncoder(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.embedding_hidden_mapping_in = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )
        self.albert_layer_groups = CustomFlaxAlbertLayerGroups(self.config, dtype=self.dtype)

    def __call__(
        self,
        hidden_states,
        attention_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
        interv_type: str = "swap",
        interv_dict: dict = {},
    ):
        hidden_states = self.embedding_hidden_mapping_in(hidden_states)
        return self.albert_layer_groups(
            hidden_states,
            attention_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            interv_type=interv_type,
            interv_dict=interv_dict,
        )

class CustomFlaxAlbertModule(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    add_pooling_layer: bool = True

    def setup(self):
        self.embeddings = FlaxAlbertEmbeddings(self.config, dtype=self.dtype)
        self.encoder = CustomFlaxAlbertEncoder(self.config, dtype=self.dtype)
        if self.add_pooling_layer:
            self.pooler = nn.Dense(
                self.config.hidden_size,
                kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
                dtype=self.dtype,
                name="pooler",
            )
            self.pooler_activation = nn.tanh
        else:
            self.pooler = None
            self.pooler_activation = None

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids: Optional[np.ndarray] = None,
        position_ids: Optional[np.ndarray] = None,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
        interv_type: str = "swap",
        interv_dict: dict = {},
    ):
        # make sure `token_type_ids` is correctly initialized when not passed
        if token_type_ids is None:
            token_type_ids = jnp.zeros_like(input_ids)

        # make sure `position_ids` is correctly initialized when not passed
        if position_ids is None:
            position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)

        hidden_states = self.embeddings(input_ids, token_type_ids, position_ids, deterministic=deterministic)

        outputs = self.encoder(
            hidden_states,
            attention_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            interv_type=interv_type,
            interv_dict=interv_dict,
        )
        hidden_states = outputs[0]
        if self.add_pooling_layer:
            pooled = self.pooler(hidden_states[:, 0])
            pooled = self.pooler_activation(pooled)
        else:
            pooled = None

        if not return_dict:
            # if pooled is None, don't return it
            if pooled is None:
                return (hidden_states,) + outputs[1:]
            return (hidden_states, pooled) + outputs[1:]

        return FlaxBaseModelOutputWithPooling(
            last_hidden_state=hidden_states,
            pooler_output=pooled,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

class CustomFlaxAlbertForMaskedLMModule(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.albert = CustomFlaxAlbertModule(config=self.config, add_pooling_layer=False, dtype=self.dtype)
        self.predictions = FlaxAlbertOnlyMLMHead(config=self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
        interv_type: str = "swap",
        interv_dict: dict = {},
    ):
        # Model
        outputs = self.albert(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            interv_type=interv_type,
            interv_dict=interv_dict,
        )

        hidden_states = outputs[0]
        if self.config.tie_word_embeddings:
            shared_embedding = self.albert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
        else:
            shared_embedding = None

        # Compute the prediction scores
        logits = self.predictions(hidden_states, shared_embedding=shared_embedding)

        if not return_dict:
            return (logits,) + outputs[1:]

        return FlaxMaskedLMOutput(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

class CustomFlaxAlbertForMaskedLM(FlaxAlbertPreTrainedModel):
    module_class = CustomFlaxAlbertForMaskedLMModule