




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 				__UpperCamelCase      (			__UpperCAmelCase  ,       __UpperCAmelCase	):





				'''simple docstring'''



				def __init__(			self    ):
											super().__init__()

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






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

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

											lowerCAmelCase =		sample_size or h

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

											lowerCAmelCase ,  lowerCAmelCase =		10_24**2, img_w * img_h
											lowerCAmelCase =		(Sa / Sa) ** 0.5
											lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase =		int(K * 62    ), img_w - int(14 * K    ), img_h - int(14 * K    )

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

											lowerCAmelCase =		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









from typing import TYPE_CHECKING

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


UpperCAmelCase_    ={"""configuration_vilt""": ["""VILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViltConfig"""]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_    =["""ViltFeatureExtractor"""]
		UpperCAmelCase_    =["""ViltImageProcessor"""]
		UpperCAmelCase_    =["""ViltProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_    =[
		    """VILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """ViltForImageAndTextRetrieval""",
		    """ViltForImagesAndTextClassification""",
		    """ViltForTokenClassification""",
		    """ViltForMaskedLM""",
		    """ViltForQuestionAnswering""",
		    """ViltLayer""",
		    """ViltModel""",
		    """ViltPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_vilt import ViltFeatureExtractor
				from .image_processing_vilt import ViltImageProcessor
				from .processing_vilt import ViltProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vilt import (
				    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViltForImageAndTextRetrieval,
				    ViltForImagesAndTextClassification,
				    ViltForMaskedLM,
				    ViltForQuestionAnswering,
				    ViltForTokenClassification,
				    ViltLayer,
				    ViltModel,
				    ViltPreTrainedModel,
				)


else:
		import sys

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



