






'''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    _snake_case   (				snake_case     ,					snake_case					):

					"""simple docstring"""





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

										self.register_buffer('watermark_image'   , torch.zeros((62, 62, 4)     )     )
										a_									=   None




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

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

										a_									=   sample_size or h

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

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

										if self.watermark_image_as_pil is None:
															a_									=   self.watermark_image.to(torch.uinta     ).cpu().numpy()
															a_									=   Image.fromarray(UpperCAmelCase__   , mode='RGBA'     )
															a_									=   watermark_image

										a_									=   self.watermark_image_as_pil.resize(
										    (wm_size, wm_size)   , PIL_INTERPOLATION['bicubic']   , reducing_gap=UpperCAmelCase__     )

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

										return images









'''simple docstring'''
from typing import TYPE_CHECKING

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


__lowerCAmelCase							={"configuration_regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=[
						    "REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "RegNetForImageClassification",
						    "RegNetModel",
						    "RegNetPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=[
						    "TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFRegNetForImageClassification",
						    "TFRegNetModel",
						    "TFRegNetPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=[
						    "FlaxRegNetForImageClassification",
						    "FlaxRegNetModel",
						    "FlaxRegNetPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_regnet import (
												    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RegNetForImageClassification,
												    RegNetModel,
												    RegNetPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_regnet import (
												    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRegNetForImageClassification,
												    TFRegNetModel,
												    TFRegNetPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_regnet import (
												    FlaxRegNetForImageClassification,
												    FlaxRegNetModel,
												    FlaxRegNetPreTrainedModel,
												)


else:
						import sys

						__lowerCAmelCase							=_LazyModule(__name__, globals()["__file__"], _import_structure)

