# coding=utf-8
# Copyright 2021 Google AI, Google Brain and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import Callable, Optional, Tuple

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 ...modeling_flax_outputs import (
    FlaxBaseModelOutput,
    FlaxBaseModelOutputWithPooling,
    FlaxMaskedLMOutput,
    FlaxMultipleChoiceModelOutput,
    FlaxQuestionAnsweringModelOutput,
    FlaxSequenceClassifierOutput,
    FlaxTokenClassifierOutput,
)
from ...modeling_flax_utils import (
    ACT2FN,
    FlaxPreTrainedModel,
    append_call_sample_docstring,
    append_replace_return_docstrings,
    overwrite_call_docstring,
)
from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_albert import AlbertConfig


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "albert-base-v2"
_CONFIG_FOR_DOC = "AlbertConfig"


@flax.struct.dataclass
class FlaxAlbertForPreTrainingOutput(ModelOutput):
    """
    Output type of [`FlaxAlbertForPreTraining`].

    Args:
        prediction_logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        sop_logits (`jnp.ndarray` of shape `(batch_size, 2)`):
            Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
            before SoftMax).
        hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    prediction_logits: jnp.ndarray = None
    sop_logits: jnp.ndarray = None
    hidden_states: Optional[Tuple[jnp.ndarray]] = None
    attentions: Optional[Tuple[jnp.ndarray]] = None


ALBERT_START_DOCSTRING = r"""

    This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

    This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
    subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
    general usage and behavior.

    Finally, this model supports inherent JAX features such as:

    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

    Parameters:
        config ([`AlbertConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
        dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
            The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
            `jax.numpy.bfloat16` (on TPUs).

            This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
            specified all the computation will be performed with the given `dtype`.

            **Note that this only specifies the dtype of the computation and does not influence the dtype of model
            parameters.**

            If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
            [`~FlaxPreTrainedModel.to_bf16`].
"""

ALBERT_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`numpy.ndarray` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`numpy.ndarray` of shape `({0})`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`numpy.ndarray` of shape `({0})`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:

            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.

            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`numpy.ndarray` of shape `({0})`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.

"""


class FlaxAlbertEmbeddings(nn.Module):
    """Construct the embeddings from word, position and token_type embeddings."""

    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.word_embeddings = nn.Embed(
            self.config.vocab_size,
            self.config.embedding_size,
            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
        )
        self.position_embeddings = nn.Embed(
            self.config.max_position_embeddings,
            self.config.embedding_size,
            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
        )
        self.token_type_embeddings = nn.Embed(
            self.config.type_vocab_size,
            self.config.embedding_size,
            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
        )
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)

    # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEmbeddings.__call__
    def __call__(self, input_ids, token_type_ids, position_ids, deterministic: bool = True):
        # Embed
        inputs_embeds = self.word_embeddings(input_ids.astype("i4"))
        position_embeds = self.position_embeddings(position_ids.astype("i4"))
        token_type_embeddings = self.token_type_embeddings(token_type_ids.astype("i4"))

        # Sum all embeddings
        hidden_states = inputs_embeds + token_type_embeddings + position_embeds

        # Layer Norm
        hidden_states = self.LayerNorm(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        return hidden_states


class FlaxAlbertSelfAttention(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):
        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)
        )

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

    def setup(self):
        self.attention = FlaxAlbertSelfAttention(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,
    ):
        attention_outputs = self.attention(
            hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions
        )
        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 FlaxAlbertLayerCollection(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.layers = [
            FlaxAlbertLayer(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_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,
            )
            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 FlaxAlbertLayerCollections(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 = FlaxAlbertLayerCollection(self.config, dtype=self.dtype)

    def __call__(
        self,
        hidden_states,
        attention_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
    ):
        outputs = self.albert_layers(
            hidden_states,
            attention_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
        )
        return outputs


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

    def setup(self):
        self.layers = [
            FlaxAlbertLayerCollections(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,
    ):
        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,
            )
            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 FlaxAlbertEncoder(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 = FlaxAlbertLayerGroups(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,
    ):
        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,
        )


class FlaxAlbertOnlyMLMHead(nn.Module):
    config: AlbertConfig
    dtype: jnp.dtype = jnp.float32
    bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros

    def setup(self):
        self.dense = nn.Dense(self.config.embedding_size, dtype=self.dtype)
        self.activation = ACT2FN[self.config.hidden_act]
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.decoder = nn.Dense(self.config.vocab_size, dtype=self.dtype, use_bias=False)
        self.bias = self.param("bias", self.bias_init, (self.config.vocab_size,))

    def __call__(self, hidden_states, shared_embedding=None):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.activation(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)

        if shared_embedding is not None:
            hidden_states = self.decoder.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
        else:
            hidden_states = self.decoder(hidden_states)

        hidden_states += self.bias
        return hidden_states


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

    def setup(self):
        self.dropout = nn.Dropout(self.config.classifier_dropout_prob)
        self.classifier = nn.Dense(2, dtype=self.dtype)

    def __call__(self, pooled_output, deterministic=True):
        pooled_output = self.dropout(pooled_output, deterministic=deterministic)
        logits = self.classifier(pooled_output)
        return logits


class FlaxAlbertPreTrainedModel(FlaxPreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = AlbertConfig
    base_model_prefix = "albert"
    module_class: nn.Module = None

    def __init__(
        self,
        config: AlbertConfig,
        input_shape: Tuple = (1, 1),
        seed: int = 0,
        dtype: jnp.dtype = jnp.float32,
        _do_init: bool = True,
        **kwargs
    ):
        module = self.module_class(config=config, dtype=dtype, **kwargs)
        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)

    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
        # init input tensors
        input_ids = jnp.zeros(input_shape, dtype="i4")
        token_type_ids = jnp.zeros_like(input_ids)
        position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape)
        attention_mask = jnp.ones_like(input_ids)

        params_rng, dropout_rng = jax.random.split(rng)
        rngs = {"params": params_rng, "dropout": dropout_rng}

        random_params = self.module.init(
            rngs, input_ids, attention_mask, token_type_ids, position_ids, return_dict=False
        )["params"]

        if params is not None:
            random_params = flatten_dict(unfreeze(random_params))
            params = flatten_dict(unfreeze(params))
            for missing_key in self._missing_keys:
                params[missing_key] = random_params[missing_key]
            self._missing_keys = set()
            return freeze(unflatten_dict(params))
        else:
            return random_params

    @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def __call__(
        self,
        input_ids,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        params: dict = None,
        dropout_rng: jax.random.PRNGKey = None,
        train: bool = False,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        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
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        # init input tensors if not passed
        if token_type_ids is None:
            token_type_ids = jnp.zeros_like(input_ids)

        if position_ids is None:
            position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)

        if attention_mask is None:
            attention_mask = jnp.ones_like(input_ids)

        # Handle any PRNG if needed
        rngs = {}
        if dropout_rng is not None:
            rngs["dropout"] = dropout_rng

        return self.module.apply(
            {"params": params or self.params},
            jnp.array(input_ids, dtype="i4"),
            jnp.array(attention_mask, dtype="i4"),
            jnp.array(token_type_ids, dtype="i4"),
            jnp.array(position_ids, dtype="i4"),
            not train,
            output_attentions,
            output_hidden_states,
            return_dict,
            rngs=rngs,
        )


class FlaxAlbertModule(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 = FlaxAlbertEncoder(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,
    ):
        # 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,
        )
        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,
        )


@add_start_docstrings(
    "The bare Albert Model transformer outputting raw hidden-states without any specific head on top.",
    ALBERT_START_DOCSTRING,
)
class FlaxAlbertModel(FlaxAlbertPreTrainedModel):
    module_class = FlaxAlbertModule


append_call_sample_docstring(FlaxAlbertModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPooling, _CONFIG_FOR_DOC)


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

    def setup(self):
        self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype)
        self.predictions = FlaxAlbertOnlyMLMHead(config=self.config, dtype=self.dtype)
        self.sop_classifier = FlaxAlbertSOPHead(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,
    ):

        # 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,
        )

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

        hidden_states = outputs[0]
        pooled_output = outputs[1]

        prediction_scores = self.predictions(hidden_states, shared_embedding=shared_embedding)
        sop_scores = self.sop_classifier(pooled_output, deterministic=deterministic)

        if not return_dict:
            return (prediction_scores, sop_scores) + outputs[2:]

        return FlaxAlbertForPreTrainingOutput(
            prediction_logits=prediction_scores,
            sop_logits=sop_scores,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    Albert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
    `sentence order prediction (classification)` head.
    """,
    ALBERT_START_DOCSTRING,
)
class FlaxAlbertForPreTraining(FlaxAlbertPreTrainedModel):
    module_class = FlaxAlbertForPreTrainingModule


FLAX_ALBERT_FOR_PRETRAINING_DOCSTRING = """
    Returns:

    Example:

    ```python
    >>> from transformers import AutoTokenizer, FlaxAlbertForPreTraining

    >>> tokenizer = AutoTokenizer.from_pretrained("albert-base-v2")
    >>> model = FlaxAlbertForPreTraining.from_pretrained("albert-base-v2")

    >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
    >>> outputs = model(**inputs)

    >>> prediction_logits = outputs.prediction_logits
    >>> seq_relationship_logits = outputs.sop_logits
    ```
"""

overwrite_call_docstring(
    FlaxAlbertForPreTraining,
    ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length") + FLAX_ALBERT_FOR_PRETRAINING_DOCSTRING,
)
append_replace_return_docstrings(
    FlaxAlbertForPreTraining, output_type=FlaxAlbertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC
)


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

    def setup(self):
        self.albert = FlaxAlbertModule(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,
    ):
        # 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,
        )

        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,
        )


@add_start_docstrings("""Albert Model with a `language modeling` head on top.""", ALBERT_START_DOCSTRING)
class FlaxAlbertForMaskedLM(FlaxAlbertPreTrainedModel):
    module_class = FlaxAlbertForMaskedLMModule


append_call_sample_docstring(FlaxAlbertForMaskedLM, _CHECKPOINT_FOR_DOC, FlaxMaskedLMOutput, _CONFIG_FOR_DOC)


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

    def setup(self):
        self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype)
        classifier_dropout = (
            self.config.classifier_dropout_prob
            if self.config.classifier_dropout_prob is not None
            else self.config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(rate=classifier_dropout)
        self.classifier = nn.Dense(
            self.config.num_labels,
            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,
    ):
        # 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,
        )

        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output, deterministic=deterministic)
        logits = self.classifier(pooled_output)

        if not return_dict:
            return (logits,) + outputs[2:]

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


@add_start_docstrings(
    """
    Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
    output) e.g. for GLUE tasks.
    """,
    ALBERT_START_DOCSTRING,
)
class FlaxAlbertForSequenceClassification(FlaxAlbertPreTrainedModel):
    module_class = FlaxAlbertForSequenceClassificationModule


append_call_sample_docstring(
    FlaxAlbertForSequenceClassification,
    _CHECKPOINT_FOR_DOC,
    FlaxSequenceClassifierOutput,
    _CONFIG_FOR_DOC,
)


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

    def setup(self):
        self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
        self.classifier = nn.Dense(1, 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,
    ):
        num_choices = input_ids.shape[1]
        input_ids = input_ids.reshape(-1, input_ids.shape[-1]) if input_ids is not None else None
        attention_mask = attention_mask.reshape(-1, attention_mask.shape[-1]) if attention_mask is not None else None
        token_type_ids = token_type_ids.reshape(-1, token_type_ids.shape[-1]) if token_type_ids is not None else None
        position_ids = position_ids.reshape(-1, position_ids.shape[-1]) if position_ids is not None else None

        # 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,
        )

        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output, deterministic=deterministic)
        logits = self.classifier(pooled_output)

        reshaped_logits = logits.reshape(-1, num_choices)

        if not return_dict:
            return (reshaped_logits,) + outputs[2:]

        return FlaxMultipleChoiceModelOutput(
            logits=reshaped_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    Albert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
    softmax) e.g. for RocStories/SWAG tasks.
    """,
    ALBERT_START_DOCSTRING,
)
class FlaxAlbertForMultipleChoice(FlaxAlbertPreTrainedModel):
    module_class = FlaxAlbertForMultipleChoiceModule


overwrite_call_docstring(
    FlaxAlbertForMultipleChoice, ALBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
append_call_sample_docstring(
    FlaxAlbertForMultipleChoice,
    _CHECKPOINT_FOR_DOC,
    FlaxMultipleChoiceModelOutput,
    _CONFIG_FOR_DOC,
)


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

    def setup(self):
        self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype, add_pooling_layer=False)
        classifier_dropout = (
            self.config.classifier_dropout_prob
            if self.config.classifier_dropout_prob is not None
            else self.config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(rate=classifier_dropout)
        self.classifier = nn.Dense(self.config.num_labels, 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,
    ):
        # 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,
        )

        hidden_states = outputs[0]
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        logits = self.classifier(hidden_states)

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

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


@add_start_docstrings(
    """
    Albert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
    Named-Entity-Recognition (NER) tasks.
    """,
    ALBERT_START_DOCSTRING,
)
class FlaxAlbertForTokenClassification(FlaxAlbertPreTrainedModel):
    module_class = FlaxAlbertForTokenClassificationModule


append_call_sample_docstring(
    FlaxAlbertForTokenClassification,
    _CHECKPOINT_FOR_DOC,
    FlaxTokenClassifierOutput,
    _CONFIG_FOR_DOC,
)


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

    def setup(self):
        self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype, add_pooling_layer=False)
        self.qa_outputs = nn.Dense(self.config.num_labels, 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,
    ):
        # 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,
        )

        hidden_states = outputs[0]

        logits = self.qa_outputs(hidden_states)
        start_logits, end_logits = logits.split(self.config.num_labels, axis=-1)
        start_logits = start_logits.squeeze(-1)
        end_logits = end_logits.squeeze(-1)

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

        return FlaxQuestionAnsweringModelOutput(
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    Albert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
    """,
    ALBERT_START_DOCSTRING,
)
class FlaxAlbertForQuestionAnswering(FlaxAlbertPreTrainedModel):
    module_class = FlaxAlbertForQuestionAnsweringModule


append_call_sample_docstring(
    FlaxAlbertForQuestionAnswering,
    _CHECKPOINT_FOR_DOC,
    FlaxQuestionAnsweringModelOutput,
    _CONFIG_FOR_DOC,
)
