





'''simple docstring'''

from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__lowerCAmelCase				:      int						      =logging.get_logger(__name__)






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""pixel_values"""]
 def __init__(							self :str		,			lowercase_ :bool = True		,			lowercase_ :Union[int, float] = 1 / 2_55		,			lowercase_ :bool = True		,			lowercase_ :int = 8		,			**lowercase_ :List[str]		,			)->				None:
     super().__init__(**lowercase_				)

     A__							=     do_rescale
     A__							=     rescale_factor
     A__							=     do_pad
     A__							=     pad_size
 def 	UpperCAmelCase_				(							self :Tuple		,			lowercase_ :np.ndarray		,			lowercase_ :float		,			lowercase_ :Optional[Union[str, ChannelDimension]] = None		,			**lowercase_ :Union[str, Any]				)->				np.ndarray:
     return rescale(lowercase_		,			scale=lowercase_		,			data_format=lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :np.ndarray		,			lowercase_ :int		,			lowercase_ :Optional[Union[str, ChannelDimension]] = None				)->				List[Any]:
     A__,      A__							=     get_image_size(lowercase_				)
     A__							=     (old_height // size + 1) * size - old_height
     A__							=     (old_width // size + 1) * size - old_width

     return pad(lowercase_		,			((0, pad_height), (0, pad_width))		,			mode="symmetric"		,			data_format=lowercase_				)



 def 	UpperCAmelCase_				(							self :List[str]		,			lowercase_ :ImageInput		,			lowercase_ :Optional[bool] = None		,			lowercase_ :Optional[float] = None		,			lowercase_ :Optional[bool] = None		,			lowercase_ :Optional[int] = None		,			lowercase_ :Optional[Union[str, TensorType]] = None		,			lowercase_ :Union[str, ChannelDimension] = ChannelDimension.FIRST		,			**lowercase_ :int		,			)->				str:
     A__							=     do_rescale if do_rescale is not None else self.do_rescale
     A__							=     rescale_factor if rescale_factor is not None else self.rescale_factor
     A__							=     do_pad if do_pad is not None else self.do_pad
     A__							=     pad_size if pad_size is not None else self.pad_size

     A__							=     make_list_of_images(lowercase_				)

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

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

     # All transformations expect numpy arrays.
     A__							=     [to_numpy_array(lowercase_				) for image in images]

     if do_rescale:
         A__							=     [self.rescale(image=lowercase_		,			scale=lowercase_				) for image in images]

     if do_pad:
         A__							=     [self.pad(lowercase_		,			size=lowercase_				) for image in images]

     A__							=     [to_channel_dimension_format(lowercase_		,			lowercase_				) for image in images]

     A__							=     {"pixel_values": images}
     return BatchFeature(data=lowercase_		,			tensor_type=lowercase_				)










'''simple docstring'''

from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin






class   UpperCAmelCase      (			UpperCamelCase__     ,			UpperCamelCase__							):
 @register_to_config
 def __init__(							self :List[str]		,			lowercase_ :int = 7_68		,			)->				str:
     super().__init__()

     A__							=     nn.Parameter(torch.zeros(1		,			lowercase_				)				)
     A__							=     nn.Parameter(torch.ones(1		,			lowercase_				)				)
 def 	UpperCAmelCase_				(							self :List[Any]		,			lowercase_ :Optional[Union[str, torch.device]] = None		,			lowercase_ :Optional[torch.dtype] = None		,			)->				Tuple:
     A__							=     nn.Parameter(self.mean.to(lowercase_				).to(lowercase_				)				)
     A__							=     nn.Parameter(self.std.to(lowercase_				).to(lowercase_				)				)
     return self
 def 	UpperCAmelCase_				(							self :Optional[int]		,			lowercase_ :str				)->				Union[str, Any]:
     A__							=     (embeds - self.mean) * 1.0 / self.std
     return embeds



 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :List[Any]				)->				Tuple:
     A__							=     (embeds * self.std) + self.mean
     return embeds



