# coding=utf-8
# Copyright 2021 Google AI and The HuggingFace Inc. team. All rights reserved.
#
# 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.
""" SpecT model configuration"""

from collections import OrderedDict
from typing import Mapping

from packaging import version

from transformers.utils import logging
from transformers.onnx import OnnxConfig
from transformers.configuration_utils import PretrainedConfig


logger = logging.get_logger(__name__)

# VIT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
#     "google/vit-base-patch16-224": "https://huggingface.co./vit-base-patch16-224/resolve/main/config.json",
#     # See all ViT models at https://huggingface.co./models?filter=vit
# }


class SpecTConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`SpecTModel`]. It is used to instantiate an SpecT
    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the SpecT architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.


    Args:
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        spectral_length (`int`, *optional*, defaults to 4096):
            The length of each spectral.
        patch_size (`int`, *optional*, defaults to 64):
            The size (resolution) of each patch.
        num_channels (`int`, *optional*, defaults to 1):
            The number of input channels.
        qkv_bias (`bool`, *optional*, defaults to `True`):
            Whether to add a bias to the queries, keys and values.

    Example:

    ```python
    >>> from transformers import ViTConfig, ViTModel

    >>> # Initializing a ViT vit-base-patch16-224 style configuration
    >>> configuration = ViTConfig()

    >>> # Initializing a model (with random weights) from the vit-base-patch16-224 style configuration
    >>> model = ViTModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "spect"

    def __init__(
        self,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        spectral_length=4096,
        patch_size=64,
        num_channels=1,
        qkv_bias=True,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.spectral_length = spectral_length
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.qkv_bias = qkv_bias