from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION






class 				_SCREAMING_SNAKE_CASE( snake_case__							,   snake_case__						):
   def __init__(     self     )						->Union[str, Any]:
        super().__init__()

        self.register_buffer('''watermark_image'''				,  torch.zeros((62, 62, 4)     )     )
        a	:     List[Any]						      =    None


   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_=None     )						->int:
        # copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

        a	:     Optional[Any]						      =    images[0].height
        a	:     str						      =    images[0].width

        a	:     Dict						      =    sample_size or h

        a	:     List[Any]						      =    min(h / sample_size				,  w / sample_size     )
        a      ,				a	:     int						      =    (int(h / coef     ), int(w / coef     )) if coef < 1 else (h, w)

        a      ,				a	:     Tuple						      =    10_24**2, img_w * img_h
        a	:     Dict						      =    (Sa / Sa) ** 0.5
        a      ,				a      ,				a	:     Any						      =    int(K * 62     ), img_w - int(14 * K     ), img_h - int(14 * K     )

        if self.watermark_image_as_pil is None:
             a	:     Dict						      =    self.watermark_image.to(torch.uinta     ).cpu().numpy()
             a	:     int						      =    Image.fromarray(lowerCAmelCase_				,  mode='''RGBA'''     )
             a	:     Tuple						      =    watermark_image

        a	:     Any						      =    self.watermark_image_as_pil.resize(
            (wm_size, wm_size)				,  PIL_INTERPOLATION['''bicubic''']				,  reducing_gap=lowerCAmelCase_     )

        for pil_img in images:
             pil_img.paste(lowerCAmelCase_				,  box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)				,  mask=wm_img.split()[-1]     )

        return images
from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


UpperCAmelCase				 =   datasets.utils.logging.get_logger(__name__)






class 				_SCREAMING_SNAKE_CASE( folder_based_builder.FolderBasedBuilderConfig						):
   UpperCamelCase_					:    bool			      = None
   UpperCamelCase_					:    bool			      = None






class 				_SCREAMING_SNAKE_CASE( folder_based_builder.FolderBasedBuilder						):
   UpperCamelCase_					:    List[str]			      = datasets.Image()
   UpperCamelCase_					:    Any			      = 'image'
   UpperCamelCase_					:    str			      = ImageFolderConfig
   UpperCamelCase_					:    List[str]  # definition at the bottom of the script
   UpperCamelCase_					:    Union[str, Any]			      = ImageClassification(image_column='image'							,   label_column='label'						)

UpperCAmelCase				 =   [
    """.blp""",
    """.bmp""",
    """.dib""",
    """.bufr""",
    """.cur""",
    """.pcx""",
    """.dcx""",
    """.dds""",
    """.ps""",
    """.eps""",
    """.fit""",
    """.fits""",
    """.fli""",
    """.flc""",
    """.ftc""",
    """.ftu""",
    """.gbr""",
    """.gif""",
    """.grib""",
    """.h5""",
    """.hdf""",
    """.png""",
    """.apng""",
    """.jp2""",
    """.j2k""",
    """.jpc""",
    """.jpf""",
    """.jpx""",
    """.j2c""",
    """.icns""",
    """.ico""",
    """.im""",
    """.iim""",
    """.tif""",
    """.tiff""",
    """.jfif""",
    """.jpe""",
    """.jpg""",
    """.jpeg""",
    """.mpg""",
    """.mpeg""",
    """.msp""",
    """.pcd""",
    """.pxr""",
    """.pbm""",
    """.pgm""",
    """.ppm""",
    """.pnm""",
    """.psd""",
    """.bw""",
    """.rgb""",
    """.rgba""",
    """.sgi""",
    """.ras""",
    """.tga""",
    """.icb""",
    """.vda""",
    """.vst""",
    """.webp""",
    """.wmf""",
    """.emf""",
    """.xbm""",
    """.xpm""",
]
UpperCAmelCase				 =   IMAGE_EXTENSIONS