






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 					a					(				__lowerCamelCase							, __lowerCamelCase     ):



      """simple docstring"""


      def __init__(   self      :    List[Any]      )							->   List[Any]:
        super().__init__()

        self.register_buffer("""watermark_image"""	,   torch.zeros((62, 62, 4)      )      )
        __UpperCAmelCase							:			Dict     =						None




      def 			UpperCAmelCase   (   self      :    Optional[Any]	,   __lowercase      :    List[PIL.Image.Image]	,   __lowercase      :    Optional[int]=None      )							->   Optional[int]:
        __UpperCAmelCase							:			Any     =						images[0].height
        __UpperCAmelCase							:			str     =						images[0].width

        __UpperCAmelCase							:			int     =						sample_size or h

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

        __UpperCAmelCase   ,				__UpperCAmelCase							:			Union[str, Any]     =						1024**2, img_w * img_h
        __UpperCAmelCase							:			int     =						(Sa / Sa) ** 0.5
        __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase							:			int     =						int(K * 62      ), img_w - int(14 * K      ), img_h - int(14 * K      )

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

        __UpperCAmelCase							:			Tuple     =						self.watermark_image_as_pil.resize(
            (wm_size, wm_size)	,   PIL_INTERPOLATION["""bicubic"""]	,   reducing_gap=__lowercase      )

        for pil_img in images:
          pil_img.paste(__lowercase	,   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


a	: Optional[int]      =				{
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: Optional[int]      =				["DeformableDetrFeatureExtractor"]
       a	: List[str]      =				["DeformableDetrImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[Any]      =				[
           "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DeformableDetrForObjectDetection",
           "DeformableDetrModel",
           "DeformableDetrPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
              from .image_processing_deformable_detr import DeformableDetrImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deformable_detr import (
                  DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DeformableDetrForObjectDetection,
                  DeformableDetrModel,
                  DeformableDetrPreTrainedModel,
              )

else:
       import sys

       a	: List[Any]      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


