# coding=utf-8
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""" ConvNeXT model configuration"""

from collections import OrderedDict
from typing import Mapping

from packaging import version

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


logger = logging.get_logger(__name__)

CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/convnext-tiny-224": "https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json",
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}


class ConvNextConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`ConvNextModel`]. It is used to instantiate an
    ConvNeXT 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 ConvNeXT
    [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) architecture.

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

    Args:
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        patch_size (`int`, optional, defaults to 4):
            Patch size to use in the patch embedding layer.
        num_stages (`int`, optional, defaults to 4):
            The number of stages in the model.
        hidden_sizes (`List[int]`, *optional*, defaults to [96, 192, 384, 768]):
            Dimensionality (hidden size) at each stage.
        depths (`List[int]`, *optional*, defaults to [3, 3, 9, 3]):
            Depth (number of blocks) for each stage.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`,
            `"selu"` and `"gelu_new"` are supported.
        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.
        layer_scale_init_value (`float`, *optional*, defaults to 1e-6):
            The initial value for the layer scale.
        drop_path_rate (`float`, *optional*, defaults to 0.0):
            The drop rate for stochastic depth.

    Example:
    ```python
    >>> from transformers import ConvNextModel, ConvNextConfig

    >>> # Initializing a ConvNext convnext-tiny-224 style configuration
    >>> configuration = ConvNextConfig()
    >>> # Initializing a model from the convnext-tiny-224 style configuration
    >>> model = ConvNextModel(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "convnext"

    def __init__(
        self,
        num_channels=3,
        patch_size=4,
        num_stages=4,
        hidden_sizes=None,
        depths=None,
        hidden_act="gelu",
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        is_encoder_decoder=False,
        layer_scale_init_value=1e-6,
        drop_path_rate=0.0,
        image_size=224,
        **kwargs
    ):
        super().__init__(**kwargs)

        self.num_channels = num_channels
        self.patch_size = patch_size
        self.num_stages = num_stages
        self.hidden_sizes = [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
        self.depths = [3, 3, 9, 3] if depths is None else depths
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.layer_scale_init_value = layer_scale_init_value
        self.drop_path_rate = drop_path_rate
        self.image_size = image_size


class ConvNextOnnxConfig(OnnxConfig):

    torch_onnx_minimum_version = version.parse("1.11")

    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "sequence"}),
            ]
        )

    @property
    def atol_for_validation(self) -> float:
        return 1e-5
