




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					):


     """simple docstring"""


     def __init__(     self      :			List[Any]						)		->       Union[str, Any]:


         '''simple docstring'''





         super().__init__()

         self.register_buffer("""watermark_image"""    ,torch.zeros((6_2, 6_2, 4)						)						)
         SCREAMING_SNAKE_CASE__       =None




     def   __A    (     self      :			Optional[int]    ,_UpperCamelCase      :			List[PIL.Image.Image]    ,_UpperCamelCase      :			int=None						)		->       Optional[Any]:


         '''simple docstring'''





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

         SCREAMING_SNAKE_CASE__       =sample_size or h

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

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

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

         SCREAMING_SNAKE_CASE__       =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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_  						=       {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_  						=       ["MarianTokenizer"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_  						=       [
        "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MarianForCausalLM",
        "MarianModel",
        "MarianMTModel",
        "MarianPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_  						=       ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_  						=       ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
    from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_marian import MarianTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_marian import (
            MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
            MarianForCausalLM,
            MarianModel,
            MarianMTModel,
            MarianPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
    import sys

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






