
"""simple docstring"""







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


SCREAMING_SNAKE_CASE_  					= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_  					= {
    'dandelin/vilt-b32-mlm': 'https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'
}






class      __a      (       lowerCAmelCase_      ):
       _lowerCamelCase				:				Optional[int]   			=      'vilt'







       def __init__(       self				,lowerCamelCase_=3_0522				,lowerCamelCase_=2				,lowerCamelCase_=2				,lowerCamelCase_=40				,lowerCamelCase_=768				,lowerCamelCase_=12				,lowerCamelCase_=12				,lowerCamelCase_=3072				,lowerCamelCase_="gelu"				,lowerCamelCase_=0.0				,lowerCamelCase_=0.0				,lowerCamelCase_=0.0_2				,lowerCamelCase_=1e-12				,lowerCamelCase_=384				,lowerCamelCase_=32				,lowerCamelCase_=3				,lowerCamelCase_=True				,lowerCamelCase_=-1				,lowerCamelCase_=False				,lowerCamelCase_=-1				,**lowerCamelCase_				,):



          '''simple docstring'''

          super().__init__(tie_word_embeddings=lowerCamelCase_				,**lowerCamelCase_		)

          _UpperCAmelCase 						=       vocab_size
          _UpperCAmelCase 						=       type_vocab_size
          _UpperCAmelCase 						=       modality_type_vocab_size
          _UpperCAmelCase 						=       max_position_embeddings

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

          _UpperCAmelCase 						=       image_size
          _UpperCAmelCase 						=       patch_size
          _UpperCAmelCase 						=       num_channels
          _UpperCAmelCase 						=       qkv_bias
          _UpperCAmelCase 						=       max_image_length
          _UpperCAmelCase 						=       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      __a      (       lowerCAmelCase_					,						lowerCAmelCase_      ):







       def __init__(       self		):



          '''simple docstring'''

          super().__init__()

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







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_=None		):



          '''simple docstring'''

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

          _UpperCAmelCase 						=       sample_size or h

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

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

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

          _UpperCAmelCase 						=       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

