



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


UpperCAmelCase_			      =  logging.get_logger(__name__)





class 		__SCREAMING_SNAKE_CASE      (				lowerCAmelCase_      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  ['pixel_values']







				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = True						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = PIL.Image.NEAREST						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = 1 / 2_55						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = True						,	SCREAMING_SNAKE_CASE__ = True						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = True						,	**SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					super().__init__(**lowerCamelCase_      )
					_snake_case					:							List[Any]					     =   size if size is not None else {"""height""": 3_46, """width""": 3_46}
					_snake_case					:							Tuple					     =   get_size_dict(lowerCamelCase_      )
					_snake_case					:							Union[str, Any]					     =   crop_size if crop_size is not None else {"""height""": 2_89, """width""": 2_89}
					_snake_case					:							int					     =   get_size_dict(lowerCamelCase_						,	param_name="""crop_size"""      )

					_snake_case					:							Any					     =   do_resize
					_snake_case					:							Union[str, Any]					     =   size
					_snake_case					:							Tuple					     =   resample
					_snake_case					:							str					     =   do_center_crop
					_snake_case					:							int					     =   crop_size
					_snake_case					:							Optional[int]					     =   do_rescale
					_snake_case					:							Union[str, Any]					     =   rescale_factor
					_snake_case					:							int					     =   rescale_offset
					_snake_case					:							Optional[int]					     =   do_normalize
					_snake_case					:							Union[str, Any]					     =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					_snake_case					:							Any					     =   image_std if image_std is not None else IMAGENET_STANDARD_STD
					_snake_case					:							Any					     =   include_top







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = PIL.Image.NEAREST						,	SCREAMING_SNAKE_CASE__ = None						,	**SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					_snake_case					:							List[Any]					     =   get_size_dict(lowerCamelCase_      )
					if "height" not in size or "width" not in size:
						raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''      )
					return resize(
					    lowerCamelCase_						,	size=(size["""height"""], size["""width"""])						,	resample=lowerCamelCase_						,	data_format=lowerCamelCase_						,	**lowerCamelCase_      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	**SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					_snake_case					:							Dict					     =   get_size_dict(lowerCamelCase_      )
					if "height" not in size or "width" not in size:
						raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''      )
					return center_crop(lowerCamelCase_						,	size=(size["""height"""], size["""width"""])						,	data_format=lowerCamelCase_						,	**lowerCamelCase_      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = True						,	SCREAMING_SNAKE_CASE__ = None						,	**SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					if offset:
						_snake_case					:							List[str]					     =   (image - 1_27.5) * scale
						if data_format is not None:
							_snake_case					:							Dict					     =   to_channel_dimension_format(lowerCamelCase_						,	lowerCamelCase_      )
						_snake_case					:							Optional[Any]					     =   rescaled_image.astype(np.floataa      )
					else:
						_snake_case					:							Tuple					     =   rescale(lowerCamelCase_						,	scale=lowerCamelCase_						,	data_format=lowerCamelCase_						,	**lowerCamelCase_      )
					return rescaled_image







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	**SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					return normalize(lowerCamelCase_						,	mean=lowerCamelCase_						,	std=lowerCamelCase_						,	data_format=lowerCamelCase_						,	**lowerCamelCase_      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST						,	**SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					_snake_case					:							List[str]					     =   do_resize if do_resize is not None else self.do_resize
					_snake_case					:							List[Any]					     =   resample if resample is not None else self.resample
					_snake_case					:							List[str]					     =   do_center_crop if do_center_crop is not None else self.do_center_crop
					_snake_case					:							Union[str, Any]					     =   do_rescale if do_rescale is not None else self.do_rescale
					_snake_case					:							Any					     =   rescale_factor if rescale_factor is not None else self.rescale_factor
					_snake_case					:							Union[str, Any]					     =   rescale_offset if rescale_offset is not None else self.rescale_offset
					_snake_case					:							Optional[Any]					     =   do_normalize if do_normalize is not None else self.do_normalize
					_snake_case					:							List[Any]					     =   image_mean if image_mean is not None else self.image_mean
					_snake_case					:							int					     =   image_std if image_std is not None else self.image_std
					_snake_case					:							str					     =   include_top if include_top is not None else self.include_top

					_snake_case					:							List[str]					     =   size if size is not None else self.size
					_snake_case					:							Optional[Any]					     =   get_size_dict(lowerCamelCase_      )
					_snake_case					:							Optional[int]					     =   crop_size if crop_size is not None else self.crop_size
					_snake_case					:							Optional[int]					     =   get_size_dict(lowerCamelCase_						,	param_name="""crop_size"""      )

					_snake_case					:							Any					     =   make_list_of_images(lowerCamelCase_      )

					if not valid_images(lowerCamelCase_      ):
						raise ValueError(
						    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
						    """torch.Tensor, tf.Tensor or jax.ndarray."""      )

					if do_resize and size is None or resample is None:
						raise ValueError("""Size and resample must be specified if do_resize is True."""      )

					if do_center_crop and crop_size is None:
						raise ValueError("""Crop size must be specified if do_center_crop is True."""      )

					if do_rescale and rescale_factor is None:
						raise ValueError("""Rescale factor must be specified if do_rescale is True."""      )

					if do_normalize and (image_mean is None or image_std is None):
						raise ValueError("""Image mean and std must be specified if do_normalize is True."""      )

					# All transformations expect numpy arrays.
					_snake_case					:							Optional[Any]					     =   [to_numpy_array(lowerCamelCase_      ) for image in images]

					if do_resize:
						_snake_case					:							Any					     =   [self.resize(image=lowerCamelCase_						,	size=lowerCamelCase_						,	resample=lowerCamelCase_      ) for image in images]

					if do_center_crop:
						_snake_case					:							Tuple					     =   [self.center_crop(image=lowerCamelCase_						,	size=lowerCamelCase_      ) for image in images]

					if do_rescale:
						_snake_case					:							str					     =   [self.rescale(image=lowerCamelCase_						,	scale=lowerCamelCase_						,	offset=lowerCamelCase_      ) for image in images]

					if do_normalize:
						_snake_case					:							Optional[int]					     =   [self.normalize(image=lowerCamelCase_						,	mean=lowerCamelCase_						,	std=lowerCamelCase_      ) for image in images]

					if include_top:
						_snake_case					:							Union[str, Any]					     =   [self.normalize(image=lowerCamelCase_						,	mean=[0, 0, 0]						,	std=lowerCamelCase_      ) for image in images]

					_snake_case					:							List[Any]					     =   [to_channel_dimension_format(lowerCamelCase_						,	lowerCamelCase_      ) for image in images]

					_snake_case					:							Tuple					     =   {"""pixel_values""": images}
					return BatchFeature(data=lowerCamelCase_						,	tensor_type=lowerCamelCase_      )







"""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

