# Copyright 2024 EPFL and Apple 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
# limitations under the License.
import hashlib
import torch
import collections.abc
from itertools import repeat
import torchvision.transforms.functional as TF

from fourm.utils.data_constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD

def image_mask(tensor: torch.Tensor, GT_tokens: int, input_budget: int, target_budget: int):
    """Applies input and target masking to an image tensor sequentially

    Args:
        tensor: Image tensor
        GT_tokens: Number of tokens in the tensor
        input_budget: Token budget for the input
        target_budget: Token budget for the target

    Returns:
        Dictionary containing the masked image tensor, the input mask, the target mask, and the decoder attention mask
    """

    # Input mask: First `input_budget` tokens are not masked (0), rest are masked (1)
    input_mask = torch.ones(GT_tokens, dtype=torch.bool)
    input_mask[:input_budget] = 0  # First `input_budget` positions are not masked

    # Target mask: The next `target_budget` tokens are not masked (0), rest are masked (1)
    target_mask = torch.ones(GT_tokens, dtype=torch.bool)
    if target_budget is not None:
        target_mask[input_budget:input_budget + target_budget] = 0  # Next `target_budget` positions are not masked
    else:
        target_mask = ~input_mask  # If target_budget is None, complement input_mask

    # Compute decoder attention mask
    decoder_attention_mask = torch.zeros(GT_tokens, dtype=torch.int)
    first_mask_token = torch.argmin(target_mask + torch.arange(target_mask.shape[0], device=target_mask.device) * 1e-6)
    decoder_attention_mask[first_mask_token] = (~target_mask).sum()  # Equivalent to target budget

    return {
        "tensor": torch.tensor(tensor).long().cuda(),
        "input_mask": input_mask.unsqueeze(0).cuda(),
        "target_mask": target_mask.unsqueeze(0).cuda(),
        "decoder_attention_mask": decoder_attention_mask.unsqueeze(0).cuda(),
    }

def denormalize(img, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD):
    """
    Denormalizes an image.

    Args:
        img (torch.Tensor): Image to denormalize.
        mean (tuple): Mean to use for denormalization.
        std (tuple): Standard deviation to use for denormalization.
    """
    return TF.normalize(
        img.clone(),
        mean= [-m/s for m, s in zip(mean, std)],
        std= [1/s for s in std]
    )


def generate_uint15_hash(seed_str):
    """Generates a hash of the seed string as an unsigned int15 integer"""
    return int(hashlib.sha256(seed_str.encode('utf-8')).hexdigest(), 16) % (2**15)


# From PyTorch internals
def _ntuple(n):
    def parse(x):
        if isinstance(x, collections.abc.Iterable):
            return x
        return tuple(repeat(x, n))

    return parse


to_1tuple = _ntuple(1)
to_2tuple = _ntuple(2)
to_3tuple = _ntuple(3)
to_4tuple = _ntuple(4)
to_ntuple = _ntuple