





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase:  Optional[Any]   =   logging.get_logger(__name__)

__UpperCamelCase:  str   =   {
    """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
}


class 			__lowerCAmelCase				(					_UpperCamelCase			):


	'''simple docstring'''





	_A       	=							"vilt"

	def __init__(	self:	str,				lowerCamelCase_:	List[str]=30522,				lowerCamelCase_:	str=2,				lowerCamelCase_:	Optional[int]=2,				lowerCamelCase_:	Tuple=40,				lowerCamelCase_:	Optional[Any]=768,				lowerCamelCase_:	List[str]=12,				lowerCamelCase_:	Optional[Any]=12,				lowerCamelCase_:	Dict=3072,				lowerCamelCase_:	Union[str, Any]="gelu",				lowerCamelCase_:	Any=0.0,				lowerCamelCase_:	List[str]=0.0,				lowerCamelCase_:	Tuple=0.0_2,				lowerCamelCase_:	Union[str, Any]=1E-12,				lowerCamelCase_:	Optional[int]=384,				lowerCamelCase_:	Any=32,				lowerCamelCase_:	str=3,				lowerCamelCase_:	Union[str, Any]=True,				lowerCamelCase_:	Any=-1,				lowerCamelCase_:	Tuple=False,				lowerCamelCase_:	Dict=-1,				**lowerCamelCase_:	Optional[Any],				):
			super().__init__(tie_word_embeddings=lowerCamelCase_,				**lowerCamelCase_ )

			lowercase__				:			int            =		vocab_size
			lowercase__				:			Tuple            =		type_vocab_size
			lowercase__				:			Optional[Any]            =		modality_type_vocab_size
			lowercase__				:			str            =		max_position_embeddings

			lowercase__				:			Any            =		hidden_size
			lowercase__				:			List[Any]            =		num_hidden_layers
			lowercase__				:			List[Any]            =		num_attention_heads
			lowercase__				:			str            =		intermediate_size
			lowercase__				:			str            =		hidden_act
			lowercase__				:			List[Any]            =		hidden_dropout_prob
			lowercase__				:			Dict            =		attention_probs_dropout_prob
			lowercase__				:			Dict            =		initializer_range
			lowercase__				:			Tuple            =		layer_norm_eps

			lowercase__				:			Any            =		image_size
			lowercase__				:			Tuple            =		patch_size
			lowercase__				:			List[Any]            =		num_channels
			lowercase__				:			Tuple            =		qkv_bias
			lowercase__				:			str            =		max_image_length
			lowercase__				:			int            =		num_images









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				(					_UpperCamelCase      ,						_UpperCamelCase			):


	'''simple docstring'''





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

			self.register_buffer('watermark_image',				torch.zeros((62, 62, 4) ) )
			lowercase__				:			int            =		None





	def       snake_case__(	self:	Optional[int],				lowerCamelCase_:	List[PIL.Image.Image],				lowerCamelCase_:	Dict=None ):
			# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

			lowercase__				:			str            =		images[0].height
			lowercase__				:			Optional[Any]            =		images[0].width

			lowercase__				:			List[Any]            =		sample_size or h

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

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

			if self.watermark_image_as_pil is None:
					lowercase__				:			Tuple            =		self.watermark_image.to(torch.uinta ).cpu().numpy()
					lowercase__				:			str            =		Image.fromarray(lowerCamelCase_,				mode='RGBA' )
					lowercase__				:			Tuple            =		watermark_image

			lowercase__				:			Optional[int]            =		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


