# ============================================================================================= #
#  Author:       Pavel Iakubovskii, ZFTurbo, ashawkey, Dominik Müller,                          #
#                Samuel Šuľan, Lucia Hradecká, Filip Lux                                        #
#  Copyright:    albumentations:    : https://github.com/albumentations-team                    #
#                Pavel Iakubovskii  : https://github.com/qubvel                                 #
#                ZFTurbo            : https://github.com/ZFTurbo                                #
#                ashawkey           : https://github.com/ashawkey                               #
#                Dominik Müller     : https://github.com/muellerdo                              #
#                Lucia Hradecká     : lucia.d.hradecka@gmail.com                                #
#                Filip Lux          : lux.filip@gmail.com                                       #
#                                                                                               #
#  Volumentations History:                                                                      #
#       - Original:                 https://github.com/albumentations-team/albumentations       #
#       - 3D Conversion:            https://github.com/ashawkey/volumentations                  #
#       - Continued Development:    https://github.com/ZFTurbo/volumentations                   #
#       - Enhancements:             https://github.com/qubvel/volumentations                    #
#       - Further Enhancements:     https://github.com/muellerdo/volumentations                 #
#       - Biomedical Enhancements:  https://gitlab.fi.muni.cz/cbia/bio-volumentations           #
#                                                                                               #
#  MIT License.                                                                                 #
#                                                                                               #
#  Permission is hereby granted, free of charge, to any person obtaining a copy                 #
#  of this software and associated documentation files (the "Software"), to deal                #
#  in the Software without restriction, including without limitation the rights                 #
#  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell                    #
#  copies of the Software, and to permit persons to whom the Software is                        #
#  furnished to do so, subject to the following conditions:                                     #
#                                                                                               #
#  The above copyright notice and this permission notice shall be included in all               #
#  copies or substantial portions of the Software.                                              #
#                                                                                               #
#  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR                   #
#  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,                     #
#  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE                  #
#  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER                       #
#  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,                #
#  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE                #
#  SOFTWARE.                                                                                    #
# ============================================================================================= #


import random
from ..core.transforms_interface import DualTransform
from ..conversion import functional as FCT
from warnings import warn


class ConversionToFormat(DualTransform):
    def __init__(self, always_apply: bool = False, p: float = 1):
        """Adds channel dimension to the 3D images without it"""
        super().__init__(always_apply,p)

    def __call__(self, force_apply, targets, **data):
        if force_apply or self.always_apply or random.random() < self.p:
            params = self.get_params(**data)

            img_shape = []
            mask_shape = []
            float_shape = []
            for k, v in data.items():
                if k in targets[0]:
                    img_shape.append(v.shape) 
                elif k in targets[1]:
                    mask_shape.append(v.shape) 
                elif k in targets[2]:
                    float_shape.append(v.shape) 
            
            if FCT.check_dimensions(img_shape):
                warn(f"Input images shapes do not have same length,", UserWarning)
            elif FCT.check_dimensions(mask_shape):
                warn(f"Input masks shapes do not have same length,", UserWarning)
            elif FCT.check_dimensions(float_shape):
                warn(f"Float masks shapes do not have same length,", UserWarning)

            for k, v in data.items():
                if k in targets[0]:
                    if len(v.shape) == 3:
                        warn(f"Adding channel dimension to the image", UserWarning)
                        data[k] = v[None, ...]

        return data

    def apply(self, volume, **params):
        return volume

    def apply_to_mask(self, mask, **params):
        return mask

    def __repr__(self):
        return f'ConversionToFormat()'


class NoConversion(DualTransform):
    def __init__(self):
        super().__init__()

    def apply(self, volume, **params):
        return volume

    def apply_to_mask(self, mask, **params):
        return mask

    def __repr__(self):
        return f'NoConversion()'


'''
class ToTensor(DualTransform):
    """Convert image and masks to `torch.Tensor` with standard pytorch format: `CDHW(T)` for images, 'DHW(T)' for masks.
    Args:
        always_apply (bool): Indicates whether this transformation should be always applied. Default: True.
        p (float): Probability of applying the transform. Default: 1.0.
    """

    def __init__(self, always_apply=True, p=1.0):
        super(ToTensor, self).__init__(always_apply=always_apply, p=p)

    def apply(self, img, **params):
        return torch.from_numpy(img)

    def apply_to_mask(self, mask, **params):
        return torch.from_numpy(mask)

    def __repr__(self):
        return f'ToTensor({self.always_apply}, {self.p})'
'''