






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_				(		UpperCAmelCase__			,			UpperCAmelCase__	):


       '''simple docstring'''






       def __init__(	self     )      ->      Optional[int]:
              '''simple docstring'''

              super().__init__()

              self.register_buffer('watermark_image'    ,    torch.zeros((6_2, 6_2, 4)     )     )
              lowerCAmelCase_       		=  None






       def   _lowercase (	self    ,    lowercase_    ,    lowercase_=None     )      ->      List[Any]:
              '''simple docstring'''

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

              lowerCAmelCase_       		=  sample_size or h

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

              lowerCAmelCase_       		=  1_0_2_4**2, img_w * img_h
              lowerCAmelCase_       		=  (Sa / Sa) ** 0.5
              lowerCAmelCase_       		=  int(K * 6_2     ), img_w - int(1_4 * K     ), img_h - int(1_4 * K     )

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

              lowerCAmelCase_       		=  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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_		  =						{
    """configuration_prophetnet""": ["""PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ProphetNetConfig"""],
    """tokenization_prophetnet""": ["""ProphetNetTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase_		  =						[
		    """PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """ProphetNetDecoder""",
		    """ProphetNetEncoder""",
		    """ProphetNetForCausalLM""",
		    """ProphetNetForConditionalGeneration""",
		    """ProphetNetModel""",
		    """ProphetNetPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
		from .tokenization_prophetnet import ProphetNetTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_prophetnet import (
				    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ProphetNetDecoder,
				    ProphetNetEncoder,
				    ProphetNetForCausalLM,
				    ProphetNetForConditionalGeneration,
				    ProphetNetModel,
				    ProphetNetPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase_		  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



