





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_lowerCamelCase					:				Union[str, Any]  							=					{
    """configuration_sam""": [
        """SAM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SamConfig""",
        """SamMaskDecoderConfig""",
        """SamPromptEncoderConfig""",
        """SamVisionConfig""",
    ],
    """processing_sam""": ["""SamProcessor"""],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase					:				Tuple  							=					[
		    """SAM_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """SamModel""",
		    """SamPreTrainedModel""",
		]
try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase					:				Tuple  							=					[
		    """TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFSamModel""",
		    """TFSamPreTrainedModel""",
		]
try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase					:				List[str]  							=					["""SamImageProcessor"""]


if TYPE_CHECKING:
		from .configuration_sam import (
		    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    SamConfig,
		    SamMaskDecoderConfig,
		    SamPromptEncoderConfig,
		    SamVisionConfig,
		)
		from .processing_sam import SamProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_sam import SamImageProcessor

else:
		import sys

		_lowerCamelCase					:				Union[str, Any]  							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)













"""simple docstring"""


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    lowercase_ (   __lowerCAmelCase ,__lowerCAmelCase							):

       def __init__(	self   ):






        '''simple docstring'''

        super().__init__()

        self.register_buffer("watermark_image"      ,    torch.zeros((62, 62, 4)   )   )
        snake_case__										= None

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=None   ):






        '''simple docstring'''

        snake_case__										= images[0].height
        snake_case__										= images[0].width

        snake_case__										= sample_size or h

        snake_case__										= min(h / sample_size      ,    w / sample_size   )
        snake_case__							,      snake_case__										= (int(h / coef   ), int(w / coef   )) if coef < 1 else (h, w)

        snake_case__							,      snake_case__										= 1024**2, img_w * img_h
        snake_case__										= (Sa / Sa) ** 0.5
        snake_case__							,      snake_case__							,      snake_case__										= int(K * 62   ), img_w - int(14 * K   ), img_h - int(14 * K   )

        if self.watermark_image_as_pil is None:
         snake_case__										= self.watermark_image.to(torch.uinta   ).cpu().numpy()
         snake_case__										= Image.fromarray(__SCREAMING_SNAKE_CASE      ,    mode="RGBA"   )
         snake_case__										= watermark_image

        snake_case__										= self.watermark_image_as_pil.resize(
            (wm_size, wm_size)      ,    PIL_INTERPOLATION["bicubic"]      ,    reducing_gap=__SCREAMING_SNAKE_CASE   )

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

        return images

