# coding=utf-8
# Copyright 2010, The T5 Authors and HuggingFace Inc.
#
# 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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""" T5 model configuration """

from __future__ import absolute_import, division, print_function, unicode_literals

import json
import logging
import sys
import six
from io import open

from .configuration_utils import PretrainedConfig

logger = logging.getLogger(__name__)

T5_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    't5-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-uncased-config.json",
    't5-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-uncased-config.json",
}


class T5Config(PretrainedConfig):
    r"""
        :class:`~transformers.T5Config` is the configuration class to store the configuration of a
        `T5Model`.


        Arguments:
            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `T5Model`.
            hidden_size: Size of the encoder layers and the pooler layer.
            num_hidden_layers: Number of hidden layers in the Transformer encoder.
            num_attention_heads: Number of attention heads for each attention layer in
                the Transformer encoder.
            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
                layer in the Transformer encoder.
            hidden_act: The non-linear activation function (function or string) in the
                encoder and pooler. If string, "gelu", "relu", "swish" and "gelu_new" are supported.
            hidden_dropout_prob: The dropout probabilitiy for all fully connected
                layers in the embeddings, encoder, and pooler.
            attention_probs_dropout_prob: The dropout ratio for the attention
                probabilities.
            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).
            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
                `T5Model`.
            initializer_factor: A factor for initializing all weight matrices (should be kept to 1.0, used for initialization testing).
            layer_norm_eps: The epsilon used by LayerNorm.
    """
    pretrained_config_archive_map = T5_PRETRAINED_CONFIG_ARCHIVE_MAP

    def __init__(self,
                 vocab_size_or_config_json_file=32128,
                 n_positions=512,
                 d_model=512,
                 d_kv=64,
                 d_ff=2048,
                 num_layers=6,
                 num_heads=8,
                 relative_attention_num_buckets=32,
                 dropout_rate=0.1,
                 layer_norm_epsilon=1e-6,
                 initializer_factor=1.0,
                 **kwargs):
        super(T5Config, self).__init__(**kwargs)
        self.vocab_size = vocab_size_or_config_json_file if isinstance(vocab_size_or_config_json_file, int) else -1
        self.n_positions = n_positions
        self.d_model = d_model
        self.d_kv = d_kv
        self.d_ff = d_ff
        self.num_layers = num_layers
        self.num_heads = num_heads
        self.relative_attention_num_buckets = relative_attention_num_buckets
        self.dropout_rate = dropout_rate
        self.layer_norm_epsilon = layer_norm_epsilon
        self.initializer_factor = initializer_factor

        if isinstance(vocab_size_or_config_json_file, six.string_types):
            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 not isinstance(vocab_size_or_config_json_file, int):
            raise ValueError(
                "First argument must be either a vocabulary size (int)"
                "or the path to a pretrained model config file (str)"
            )

    @property
    def max_position_embeddings(self):
        return self.n_positions

    @property
    def hidden_size(self):
        return self.d_model

    @property
    def num_attention_heads(self):
        return self.num_heads

    @property
    def num_hidden_layers(self):
        return self.num_layers
