# coding=utf-8
# Copyright 2018 The Google Flax Team Authors 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, Dict

import numpy as np

import flax.linen as nn
import jax
import jax.numpy as jnp

from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_flax_utils import FlaxPreTrainedModel, gelu
from ...utils import logging
from .configuration_bert import BertConfig


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "BertConfig"
_TOKENIZER_FOR_DOC = "BertTokenizer"


BERT_START_DOCSTRING = r"""

    This model inherits from :class:`~transformers.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.nn.Module
    <https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html>`__ subclass. Use it as a regular Flax
    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 (:class:`~transformers.BertConfig`): 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 :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
            weights.
"""

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

            Indices can be obtained using :class:`~transformers.BertTokenizer`. See
            :meth:`transformers.PreTrainedTokenizer.encode` and :func:`transformers.PreTrainedTokenizer.__call__` for
            details.

            `What are input IDs? <../glossary.html#input-ids>`__
        attention_mask (:obj:`numpy.ndarray` of shape :obj:`({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.html#attention-mask>`__
        token_type_ids (:obj:`numpy.ndarray` of shape :obj:`({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.html#token-type-ids>`__
        position_ids (:obj:`numpy.ndarray` of shape :obj:`({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 (:obj:`bool`, `optional`):
            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
"""


class FlaxBertLayerNorm(nn.Module):
    """
    Layer normalization (https://arxiv.org/abs/1607.06450). Operates on the last axis of the input data.
    """

    epsilon: float = 1e-6
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    bias: bool = True  # If True, bias (beta) is added.
    scale: bool = True  # If True, multiply by scale (gamma). When the next layer is linear
    # (also e.g. nn.relu), this can be disabled since the scaling will be
    # done by the next layer.
    bias_init: jnp.ndarray = nn.initializers.zeros
    scale_init: jnp.ndarray = nn.initializers.ones

    @nn.compact
    def __call__(self, x):
        """
        Applies layer normalization on the input. It normalizes the activations of the layer for each given example in
        a batch independently, rather than across a batch like Batch Normalization. i.e. applies a transformation that
        maintains the mean activation within each example close to 0 and the activation standard deviation close to 1

        Args:
          x: the inputs

        Returns:
          Normalized inputs (the same shape as inputs).
        """
        features = x.shape[-1]
        mean = jnp.mean(x, axis=-1, keepdims=True)
        mean2 = jnp.mean(jax.lax.square(x), axis=-1, keepdims=True)
        var = mean2 - jax.lax.square(mean)
        mul = jax.lax.rsqrt(var + self.epsilon)
        if self.scale:
            mul = mul * jnp.asarray(self.param("gamma", self.scale_init, (features,)), self.dtype)
        y = (x - mean) * mul
        if self.bias:
            y = y + jnp.asarray(self.param("beta", self.bias_init, (features,)), self.dtype)
        return y


class FlaxBertEmbedding(nn.Module):
    """
    Specify a new class for doing the embedding stuff as Flax's one use 'embedding' for the parameter name and PyTorch
    use 'weight'
    """

    vocab_size: int
    hidden_size: int
    emb_init: Callable[..., np.ndarray] = nn.initializers.normal(stddev=0.1)

    @nn.compact
    def __call__(self, inputs):
        embedding = self.param("weight", self.emb_init, (self.vocab_size, self.hidden_size))
        return jnp.take(embedding, inputs, axis=0)


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

    vocab_size: int
    hidden_size: int
    type_vocab_size: int
    max_length: int

    @nn.compact
    def __call__(self, input_ids, token_type_ids, position_ids, attention_mask):

        # Embed
        w_emb = FlaxBertEmbedding(self.vocab_size, self.hidden_size, name="word_embeddings")(
            jnp.atleast_2d(input_ids.astype("i4"))
        )
        p_emb = FlaxBertEmbedding(self.max_length, self.hidden_size, name="position_embeddings")(
            jnp.atleast_2d(position_ids.astype("i4"))
        )
        t_emb = FlaxBertEmbedding(self.type_vocab_size, self.hidden_size, name="token_type_embeddings")(
            jnp.atleast_2d(token_type_ids.astype("i4"))
        )

        # Sum all embeddings
        summed_emb = w_emb + jnp.broadcast_to(p_emb, w_emb.shape) + t_emb

        # Layer Norm
        layer_norm = FlaxBertLayerNorm(name="layer_norm")(summed_emb)

        return layer_norm


class FlaxBertAttention(nn.Module):
    num_heads: int
    head_size: int

    @nn.compact
    def __call__(self, hidden_state, attention_mask):
        self_att = nn.attention.SelfAttention(num_heads=self.num_heads, qkv_features=self.head_size, name="self")(
            hidden_state, attention_mask
        )

        layer_norm = FlaxBertLayerNorm(name="layer_norm")(self_att + hidden_state)
        return layer_norm


class FlaxBertIntermediate(nn.Module):
    output_size: int

    @nn.compact
    def __call__(self, hidden_state):
        # TODO: Add ACT2FN reference to change activation function
        dense = nn.Dense(features=self.output_size, name="dense")(hidden_state)
        return gelu(dense)


class FlaxBertOutput(nn.Module):
    @nn.compact
    def __call__(self, intermediate_output, attention_output):
        hidden_state = nn.Dense(attention_output.shape[-1], name="dense")(intermediate_output)
        hidden_state = FlaxBertLayerNorm(name="layer_norm")(hidden_state + attention_output)
        return hidden_state


class FlaxBertLayer(nn.Module):
    num_heads: int
    head_size: int
    intermediate_size: int

    @nn.compact
    def __call__(self, hidden_state, attention_mask):
        attention = FlaxBertAttention(self.num_heads, self.head_size, name="attention")(hidden_state, attention_mask)
        intermediate = FlaxBertIntermediate(self.intermediate_size, name="intermediate")(attention)
        output = FlaxBertOutput(name="output")(intermediate, attention)

        return output


class FlaxBertLayerCollection(nn.Module):
    """
    Stores N BertLayer(s)
    """

    num_layers: int
    num_heads: int
    head_size: int
    intermediate_size: int

    @nn.compact
    def __call__(self, inputs, attention_mask):
        assert self.num_layers > 0, f"num_layers should be >= 1, got ({self.num_layers})"

        # Initialize input / output
        input_i = inputs

        # Forward over all encoders
        for i in range(self.num_layers):
            layer = FlaxBertLayer(self.num_heads, self.head_size, self.intermediate_size, name=f"{i}")
            input_i = layer(input_i, attention_mask)
        return input_i


class FlaxBertEncoder(nn.Module):
    num_layers: int
    num_heads: int
    head_size: int
    intermediate_size: int

    @nn.compact
    def __call__(self, hidden_state, attention_mask):
        layer = FlaxBertLayerCollection(
            self.num_layers, self.num_heads, self.head_size, self.intermediate_size, name="layer"
        )(hidden_state, attention_mask)
        return layer


class FlaxBertPooler(nn.Module):
    @nn.compact
    def __call__(self, hidden_state):
        cls_token = hidden_state[:, 0]
        out = nn.Dense(hidden_state.shape[-1], name="dense")(cls_token)
        return jax.lax.tanh(out)


class FlaxBertModule(nn.Module):
    vocab_size: int
    hidden_size: int
    type_vocab_size: int
    max_length: int
    num_encoder_layers: int
    num_heads: int
    head_size: int
    intermediate_size: int

    @nn.compact
    def __call__(self, input_ids, attention_mask, token_type_ids, position_ids):

        # Embedding
        embeddings = FlaxBertEmbeddings(
            self.vocab_size, self.hidden_size, self.type_vocab_size, self.max_length, name="embeddings"
        )(input_ids, token_type_ids, position_ids, attention_mask)

        # N stacked encoding layers
        encoder = FlaxBertEncoder(
            self.num_encoder_layers, self.num_heads, self.head_size, self.intermediate_size, name="encoder"
        )(embeddings, attention_mask)

        pooled = FlaxBertPooler(name="pooler")(encoder)
        return encoder, pooled


@add_start_docstrings(
    "The bare Bert Model transformer outputting raw hidden-states without any specific head on top.",
    BERT_START_DOCSTRING,
)
class FlaxBertModel(FlaxPreTrainedModel):
    """
    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
    """

    model_class = FlaxBertModule
    config_class = BertConfig
    base_model_prefix = "bert"

    @staticmethod
    def convert_from_pytorch(pt_state: Dict, config: BertConfig) -> Dict:
        jax_state = dict(pt_state)

        # Need to change some parameters name to match Flax names so that we don't have to fork any layer
        for key, tensor in pt_state.items():
            # Key parts
            key_parts = set(key.split("."))

            # Every dense layer has "kernel" parameters instead of "weight"
            if "dense.weight" in key:
                del jax_state[key]
                key = key.replace("weight", "kernel")
                jax_state[key] = tensor

            # SelfAttention needs also to replace "weight" by "kernel"
            if {"query", "key", "value"} & key_parts:

                # Flax SelfAttention decomposes the heads (num_head, size // num_heads)
                if "bias" in key:
                    jax_state[key] = tensor.reshape((config.num_attention_heads, -1))
                elif "weight":
                    del jax_state[key]
                    key = key.replace("weight", "kernel")
                    tensor = tensor.reshape((config.num_attention_heads, -1, config.hidden_size)).transpose((2, 0, 1))
                    jax_state[key] = tensor

            # SelfAttention output is not a separate layer, remove one nesting
            if "attention.output.dense" in key:
                del jax_state[key]
                key = key.replace("attention.output.dense", "attention.self.out")
                jax_state[key] = tensor

            # SelfAttention output is not a separate layer, remove nesting on layer norm
            if "attention.output.LayerNorm" in key:
                del jax_state[key]
                key = key.replace("attention.output.LayerNorm", "attention.LayerNorm")
                jax_state[key] = tensor

            # There are some transposed parameters w.r.t their PyTorch counterpart
            if "intermediate.dense.kernel" in key or "output.dense.kernel" in key:
                jax_state[key] = tensor.T

            # Self Attention output projection needs to be transposed
            if "out.kernel" in key:
                jax_state[key] = tensor.reshape((config.hidden_size, config.num_attention_heads, -1)).transpose(
                    1, 2, 0
                )

            # Pooler needs to transpose its kernel
            if "pooler.dense.kernel" in key:
                jax_state[key] = tensor.T

            # Handle LayerNorm conversion
            if "LayerNorm" in key:
                del jax_state[key]

                # Replace LayerNorm by layer_norm
                new_key = key.replace("LayerNorm", "layer_norm")

                if "weight" in key:
                    new_key = new_key.replace("weight", "gamma")
                elif "bias" in key:
                    new_key = new_key.replace("bias", "beta")

                jax_state[new_key] = tensor

        return jax_state

    def __init__(self, config: BertConfig, state: dict, seed: int = 0, **kwargs):
        model = FlaxBertModule(
            vocab_size=config.vocab_size,
            hidden_size=config.hidden_size,
            type_vocab_size=config.type_vocab_size,
            max_length=config.max_position_embeddings,
            num_encoder_layers=config.num_hidden_layers,
            num_heads=config.num_attention_heads,
            head_size=config.hidden_size,
            intermediate_size=config.intermediate_size,
        )

        super().__init__(config, model, state, seed)

    @property
    def module(self) -> nn.Module:
        return self._module

    @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def __call__(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None):
        if token_type_ids is None:
            token_type_ids = jnp.ones_like(input_ids)

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

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

        return self.model.apply(
            {"params": 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"),
        )
