# Copyright (c) 2024 Advanced Micro Devices, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License") and the MIT License (the "License2");

""" FDViT model configuration"""

from collections import OrderedDict
from typing import Mapping

from packaging import version

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


logger = logging.get_logger(__name__)


#from ..deprecated._archive_maps import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP  # noqa: F401, E402


class FDViTConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`FDViTModel`]. It is used to instantiate an FDViT
    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 FDViT
    [amd/fdvit_ti](https://huggingface.co/amd/fdvit_ti) architecture.

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


    Args:
        image_size (`int`, *optional*, defaults to 224):
            The size (resolution) of each image.
        patch_size (`int`, *optional*, defaults to 16):
            The size of the input patch.
        stride (`int`, *optional*, defaults to 16):
            The stride of the input patch.
        base_dims (`list`, *optional*, defaults to `[32, 23, 21, 23, 26]`):
            The basic dimension of each encoder block.
        depth (`list`, *optional*, defaults to `[2, 3, 3, 2, 2]`):
            The depth of each encoder block.
        heads (`list`, *optional*, defaults to `[2, 4, 6, 8, 10]`):
            The depth of each encoder block.
        channels (`list`, *optional*, defaults to `[64, 92, 126, 184, 260]`):
            The depth of each encoder block.
        out_size (`list`, *optional*, defaults to `[27, 19, 14, 10, 7]`):
            The output size of each encoder block.
        mlp_ratio (`float`, *optional*, defaults to 4.0):
            The ratio of the number of channels in the output of the MLP to the number of channels in the input.
        num_classes (`int`, *optional*, defaults to 1000):
            The number of classes of the dataset.
        in_chans (`int`, *optional*, defaults to 3):
            The number of channels in the input image.
        attn_drop_rate (`float`, *optional*, defaults to 0.0):
            The attention drop rate for the attention dropout layers.
        drop_rate (`float`, *optional*, defaults to 0.0):
            The dropout rate for the dropout layers.
        drop_path_rate (`float`, *optional*, defaults to 0.1):
            The droppath rate for the droppath layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The initializer range for the weights.


    Example:

    ```python
    >>> from transformers import FDViTConfig, FDViTModel

    >>> # Initializing a FDViT fdvit_ti style configuration
    >>> configuration = FDViTConfig()

    >>> # Initializing a model (with random weights) from the fdvit_ti style configuration
    >>> model = FDViTModel(configuration)

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

    model_type = "fdvit"

    def __init__(
        self,
        image_size=224,
        patch_size=16,
        stride=8,
        base_dims=[32, 23, 21, 23, 26],
        depth=[2, 3, 3, 2, 2],
        heads=[2, 4, 6, 8, 10],
        channels=[64,92,126,184,260],
        out_size=[27, 19, 14, 10, 7],
        mlp_ratio=4,
        num_classes=1000,
        in_chans=3,
        attn_drop_rate=0.0,
        drop_rate=0.0,
        drop_path_rate=0.1,
        initializer_range=0.02,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.image_size = image_size
        self.patch_size = patch_size
        self.stride = stride
        self.base_dims = base_dims
        self.depth = depth
        self.heads = heads
        self.channels = channels
        self.out_size = out_size
        self.mlp_ratio = mlp_ratio
        self.num_classes = num_classes
        self.in_chans = in_chans
        self.attn_drop_rate = attn_drop_rate
        self.drop_rate = drop_rate
        self.drop_path_rate = drop_path_rate
        self.initializer_range = initializer_range
        


class FDViTOnnxConfig(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: "num_channels", 2: "height", 3: "width"}),
            ]
        )

    @property
    def atol_for_validation(self) -> float:
        return 1e-4