# coding=utf-8

# Copyright 2019-present, Facebook, Inc 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

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""" XLM configuration """

from __future__ import absolute_import, division, print_function, unicode_literals



import json

import logging

import sys

from io import open



from .configuration_utils import PretrainedConfig



logger = logging.getLogger(__name__)



XLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {

    'xlm-mlm-en-2048': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-en-2048-config.json",

    'xlm-mlm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-ende-1024-config.json",

    'xlm-mlm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enfr-1024-config.json",

    'xlm-mlm-enro-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enro-1024-config.json",

    'xlm-mlm-tlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-tlm-xnli15-1024-config.json",

    'xlm-mlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-xnli15-1024-config.json",

    'xlm-clm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-enfr-1024-config.json",

    'xlm-clm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-ende-1024-config.json",

    'xlm-mlm-17-1280': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-17-1280-config.json",

    'xlm-mlm-100-1280': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-100-1280-config.json",

}





class XLMConfig(PretrainedConfig):

    """Configuration class to store the configuration of a `XLMModel`.



    Args:

        vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `XLMModel`.

        d_model: Size of the encoder layers and the pooler layer.

        n_layer: Number of hidden layers in the Transformer encoder.

        n_head: Number of attention heads for each attention layer in

            the Transformer encoder.

        d_inner: The size of the "intermediate" (i.e., feed-forward)

            layer in the Transformer encoder.

        ff_activation: The non-linear activation function (function or string) in the

            encoder and pooler. If string, "gelu", "relu" and "swish" are supported.

        untie_r: untie relative position biases

        attn_type: 'bi' for XLM, 'uni' for Transformer-XL



        dropout: The dropout probabilitiy for all fully connected

            layers in the embeddings, encoder, and pooler.

        max_position_embeddings: The maximum sequence length that this model might

            ever be used with. Typically set this to something large just in case

            (e.g., 512 or 1024 or 2048).

        initializer_range: The sttdev of the truncated_normal_initializer for

            initializing all weight matrices.

        layer_norm_eps: The epsilon used by LayerNorm.



        dropout: float, dropout rate.

        init: str, the initialization scheme, either "normal" or "uniform".

        init_range: float, initialize the parameters with a uniform distribution

            in [-init_range, init_range]. Only effective when init="uniform".

        init_std: float, initialize the parameters with a normal distribution

            with mean 0 and stddev init_std. Only effective when init="normal".

        mem_len: int, the number of tokens to cache.

        reuse_len: int, the number of tokens in the currect batch to be cached

            and reused in the future.

        bi_data: bool, whether to use bidirectional input pipeline.

            Usually set to True during pretraining and False during finetuning.

        clamp_len: int, clamp all relative distances larger than clamp_len.

            -1 means no clamping.

        same_length: bool, whether to use the same attention length for each token.

    """

    pretrained_config_archive_map = XLM_PRETRAINED_CONFIG_ARCHIVE_MAP



    def __init__(self,

                 vocab_size_or_config_json_file=30145,

                 emb_dim=2048,

                 n_layers=12,

                 n_heads=16,

                 dropout=0.1,

                 attention_dropout=0.1,

                 gelu_activation=True,

                 sinusoidal_embeddings=False,

                 causal=False,

                 asm=False,

                 n_langs=1,

                 use_lang_emb=True,

                 max_position_embeddings=512,

                 embed_init_std=2048 ** -0.5,

                 layer_norm_eps=1e-12,

                 init_std=0.02,

                 bos_index=0,

                 eos_index=1,

                 pad_index=2,

                 unk_index=3,

                 mask_index=5,

                 is_encoder=True,



                 finetuning_task=None,

                 num_labels=2,

                 summary_type='first',

                 summary_use_proj=True,

                 summary_activation=None,

                 summary_proj_to_labels=True,

                 summary_first_dropout=0.1,

                 start_n_top=5,

                 end_n_top=5,

                 **kwargs):

        """Constructs XLMConfig.

        """

        super(XLMConfig, self).__init__(**kwargs)



        if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2

                        and isinstance(vocab_size_or_config_json_file, unicode)):

            with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:

                json_config = json.loads(reader.read())

            for key, value in json_config.items():

                self.__dict__[key] = value

        elif isinstance(vocab_size_or_config_json_file, int):

            self.n_words = vocab_size_or_config_json_file

            self.emb_dim = emb_dim

            self.n_layers = n_layers

            self.n_heads = n_heads

            self.dropout = dropout

            self.attention_dropout = attention_dropout

            self.gelu_activation = gelu_activation

            self.sinusoidal_embeddings = sinusoidal_embeddings

            self.causal = causal

            self.asm = asm

            self.n_langs = n_langs

            self.use_lang_emb = use_lang_emb

            self.layer_norm_eps = layer_norm_eps

            self.bos_index = bos_index

            self.eos_index = eos_index

            self.pad_index = pad_index

            self.unk_index = unk_index

            self.mask_index = mask_index

            self.is_encoder = is_encoder

            self.max_position_embeddings = max_position_embeddings

            self.embed_init_std = embed_init_std

            self.init_std = init_std

            self.finetuning_task = finetuning_task

            self.num_labels = num_labels

            self.summary_type = summary_type

            self.summary_use_proj = summary_use_proj

            self.summary_activation = summary_activation

            self.summary_proj_to_labels = summary_proj_to_labels

            self.summary_first_dropout = summary_first_dropout

            self.start_n_top = start_n_top

            self.end_n_top = end_n_top

        else:

            raise ValueError("First argument must be either a vocabulary size (int)"

                             " or the path to a pretrained model config file (str)")



    @property

    def vocab_size(self):

        return self.n_words



    @vocab_size.setter

    def vocab_size(self, value):

        self.n_words = value



    @property

    def hidden_size(self):

        return self.emb_dim



    @property

    def num_attention_heads(self):

        return self.n_heads



    @property

    def num_hidden_layers(self):

        return self.n_layers

