




'''simple docstring'''







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


_lowerCamelCase							:						Union[str, Any]								=  logging.get_logger(__name__)

_lowerCamelCase							:						Union[str, Any]								=  {
    'dandelin/vilt-b32-mlm': 'https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'
}





class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__    = """vilt"""






					def __init__(    self,  __UpperCamelCase=30_522,  __UpperCamelCase=2,  __UpperCamelCase=2,  __UpperCamelCase=40,  __UpperCamelCase=768,  __UpperCamelCase=12,  __UpperCamelCase=12,  __UpperCamelCase=3_072,  __UpperCamelCase="gelu",  __UpperCamelCase=0.0,  __UpperCamelCase=0.0,  __UpperCamelCase=0.02,  __UpperCamelCase=1E-12,  __UpperCamelCase=384,  __UpperCamelCase=32,  __UpperCamelCase=3,  __UpperCamelCase=True,  __UpperCamelCase=-1,  __UpperCamelCase=False,  __UpperCamelCase=-1,  **__UpperCamelCase,  ):
							super().__init__(tie_word_embeddings=__UpperCamelCase,  **__UpperCamelCase )

							_snake_case	       =       vocab_size
							_snake_case	       =       type_vocab_size
							_snake_case	       =       modality_type_vocab_size
							_snake_case	       =       max_position_embeddings

							_snake_case	       =       hidden_size
							_snake_case	       =       num_hidden_layers
							_snake_case	       =       num_attention_heads
							_snake_case	       =       intermediate_size
							_snake_case	       =       hidden_act
							_snake_case	       =       hidden_dropout_prob
							_snake_case	       =       attention_probs_dropout_prob
							_snake_case	       =       initializer_range
							_snake_case	       =       layer_norm_eps

							_snake_case	       =       image_size
							_snake_case	       =       patch_size
							_snake_case	       =       num_channels
							_snake_case	       =       qkv_bias
							_snake_case	       =       max_image_length
							_snake_case	       =       num_images












"""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

