# Copyright (c) 2023, NVIDIA CORPORATION.  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.
from collections import namedtuple
from typing import Optional

from timm.models import VisionTransformer
import torch
from transformers import PretrainedConfig, PreTrainedModel


from .model import create_model_from_args
from .input_conditioner import get_default_conditioner, InputConditioner


resource_map = {
    'radio_v1': 'https://huggingface.co./nvidia/RADIO/raw/main/radio_v1.pth.tar'
}


class RADIOConfig(PretrainedConfig):
    """Pretrained Hugging Face configuration for RADIO models."""

    def __init__(
        self,
        args: Optional[dict] = None,
        version: Optional[str]="v1",
        **kwargs,
    ):
        self.args = args
        self.version = version
        super().__init__(**kwargs)


class RADIOModel(PreTrainedModel):
    """Pretrained Hugging Face model for RADIO."""

    def __init__(self, config):
        super().__init__(config)

        RADIOArgs = namedtuple("RADIOArgs", config.args.keys())
        args = RADIOArgs(**config.args)
        self.model = create_model_from_args(args)

        self.input_conditioner: InputConditioner = get_default_conditioner()

        #return RADIOModel(mod, conditioner, return_summary=return_summary, return_spatial_features=return_spatial_features)

    def forward(self, x: torch.Tensor):
        x = self.input_conditioner(x)

        y = self.model.forward_features(x)

        if isinstance(y, (list, tuple)):
            summary, all_feat = y
        elif isinstance(self.model, VisionTransformer):
            patch_gen = getattr(self.model, 'patch_generator', None)
            if patch_gen is not None:
                summary = y[:, :patch_gen.num_cls_tokens].flatten(1)
                all_feat = y[:, patch_gen.num_skip:]
            elif self.model.global_pool == 'avg':
                summary = y[:, self.model.num_prefix_tokens:].mean(dim=1)
                all_feat = y
            else:
                summary = y[:, 0]
                all_feat = y[:, 1:]
        else:
            raise ValueError("Unsupported model type")

        if self.return_summary and self.return_spatial_features:
            return summary, all_feat
        elif self.return_summary:
            return summary
        return all_feat