# 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
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# 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 flax.linen import compact

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


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "BertConfig"
_TOKENIZER_FOR_DOC = "BertTokenizer"


BERT_START_DOCSTRING = r"""

    This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
    methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
    pruning heads etc.)

    This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__ subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
    usage and behavior.

    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:`torch.LongTensor` 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
            :meth:`transformers.PreTrainedTokenizer.__call__` for details.

            `What are input IDs? <../glossary.html#input-ids>`__
        attention_mask (:obj:`torch.FloatTensor` 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:`torch.LongTensor` 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:`torch.LongTensor` 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]``.

            `What are position IDs? <../glossary.html#position-ids>`_
        head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
            Mask to nullify selected heads of the self-attention modules.
            Mask values selected in ``[0, 1]``:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`({0}, hidden_size)`, `optional`):
            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
            vectors than the model's internal embedding lookup matrix.
        output_attentions (:obj:`bool`, `optional`):
            Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
            tensors for more detail.
        output_hidden_states (:obj:`bool`, `optional`):
            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
            more detail.
        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
    bias: bool = True
    scale: bool = True
    bias_init: jnp.ndarray = nn.initializers.zeros
    scale_init: jnp.ndarray = nn.initializers.ones

    @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
          epsilon: A small float added to variance to avoid dividing by zero.
          dtype: the dtype of the computation (default: float32).
          bias:  If True, bias (beta) is added.
          scale: 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: Initializer for bias, by default, zero.
          scale_init: Initializer for scale, by default, one.
        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)

    @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

    @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

    @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

    @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):
    @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

    @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

    @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

    @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):
    @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

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

        # 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

    def __call__(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=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(token_type_ids, dtype="i4"),
            jnp.array(position_ids, dtype="i4"),
            jnp.array(attention_mask, dtype="i4"),
        )
