


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   lowerCAmelCase__ (       __lowerCamelCase  ,					__lowerCamelCase):




     '''simple docstring'''


     def __init__(		self)    ->       Optional[int]:
            super().__init__()

            self.register_buffer("watermark_image"				,		torch.zeros((6_2, 6_2, 4)))
            _A   : List[Any]       					=				None


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

            _A   : Union[str, Any]       					=				images[0].height
            _A   : Optional[Any]       					=				images[0].width

            _A   : int       					=				sample_size or h

            _A   : str       					=				min(h / sample_size				,		w / sample_size)
            _A   : Union[str, Any]       					=				(int(h / coef), int(w / coef)) if coef < 1 else (h, w)

            _A   : Union[str, Any]       					=				1_0_2_4**2, img_w * img_h
            _A   : int       					=				(Sa / Sa) ** 0.5
            _A   : Optional[Any]       					=				int(K * 6_2), img_w - int(1_4 * K), img_h - int(1_4 * K)

            if self.watermark_image_as_pil is None:
                   _A   : Optional[Any]       					=				self.watermark_image.to(torch.uinta).cpu().numpy()
                   _A   : str       					=				Image.fromarray(_lowerCAmelCase				,		mode="RGBA")
                   _A   : List[Any]       					=				watermark_image

            _A   : Tuple       					=				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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase__   	=				{'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__   	=				['DetrFeatureExtractor']
    lowerCAmelCase__   	=				['DetrImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__   	=				[
        'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
        'DetrForObjectDetection',
        'DetrForSegmentation',
        'DetrModel',
        'DetrPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_detr import DetrFeatureExtractor
        from .image_processing_detr import DetrImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_detr import (
            DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
            DetrForObjectDetection,
            DetrForSegmentation,
            DetrModel,
            DetrPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
